Class: Experian::DataDictionary

Inherits:
Object
  • Object
show all
Defined in:
lib/experian.rb,
lib/element_numbers/en_0000.rb,
lib/element_numbers/en_0100.rb,
lib/element_numbers/en_0103.rb,
lib/element_numbers/en_0107.rb,
lib/element_numbers/en_0108.rb,
lib/element_numbers/en_0110.rb,
lib/element_numbers/en_0112.rb,
lib/element_numbers/en_0113.rb,
lib/element_numbers/en_0118.rb,
lib/element_numbers/en_0119.rb,
lib/element_numbers/en_0130.rb,
lib/element_numbers/en_0131.rb,
lib/element_numbers/en_0132.rb,
lib/element_numbers/en_0133.rb,
lib/element_numbers/en_0134.rb,
lib/element_numbers/en_0135.rb,
lib/element_numbers/en_0136.rb,
lib/element_numbers/en_0137.rb,
lib/element_numbers/en_0138.rb,
lib/element_numbers/en_0139.rb,
lib/element_numbers/en_0140.rb,
lib/element_numbers/en_0141.rb,
lib/element_numbers/en_0155.rb,
lib/element_numbers/en_0156.rb,
lib/element_numbers/en_0160.rb,
lib/element_numbers/en_0161.rb,
lib/element_numbers/en_0162.rb,
lib/element_numbers/en_0164.rb,
lib/element_numbers/en_0174.rb,
lib/element_numbers/en_0177.rb,
lib/element_numbers/en_0601.rb,
lib/element_numbers/en_0711.rb,
lib/element_numbers/en_0715.rb,
lib/element_numbers/en_0716.rb,
lib/element_numbers/en_0717.rb,
lib/element_numbers/en_310M.rb,
lib/element_numbers/en_8102.rb,
lib/element_numbers/en_8103.rb,
lib/element_numbers/en_8109.rb,
lib/element_numbers/en_8123.rb,
lib/element_numbers/en_8128.rb,
lib/element_numbers/en_8133.rb,
lib/element_numbers/en_8136.rb,
lib/element_numbers/en_8137.rb,
lib/element_numbers/en_8180.rb,
lib/element_numbers/en_8181.rb,
lib/element_numbers/en_8502.rb,
lib/element_numbers/en_8503.rb,
lib/element_numbers/en_8504.rb,
lib/element_numbers/en_8505.rb,
lib/element_numbers/en_8509.rb,
lib/element_numbers/en_8519.rb,
lib/element_numbers/en_8523.rb,
lib/element_numbers/en_8525.rb,
lib/element_numbers/en_8526.rb,
lib/element_numbers/en_8528.rb,
lib/element_numbers/en_8531.rb,
lib/element_numbers/en_8532.rb,
lib/element_numbers/en_8533.rb,
lib/element_numbers/en_8536.rb,
lib/element_numbers/en_8537.rb,
lib/element_numbers/en_8538.rb,
lib/element_numbers/en_8574.rb,
lib/element_numbers/en_8580.rb,
lib/element_numbers/en_8581.rb,
lib/element_numbers/en_A107.rb,
lib/element_numbers/en_B000.rb,
lib/element_numbers/en_D200.rb,
lib/element_numbers/en_F031.rb,
lib/element_numbers/en_GEO6.rb,
lib/element_numbers/en_L000.rb,
lib/element_numbers/en_P400.rb,
lib/element_numbers/en_V000.rb,
lib/element_numbers/en_Y000.rb,
lib/element_numbers/en_313HH.rb,
lib/element_numbers/en_B2185.rb,
lib/element_numbers/en_B3010.rb,
lib/element_numbers/en_B3076.rb,
lib/element_numbers/en_B3080.rb,
lib/element_numbers/en_B3081.rb,
lib/element_numbers/en_B3200.rb,
lib/element_numbers/en_B3225.rb,
lib/element_numbers/en_B3226.rb,
lib/element_numbers/en_B3227.rb,
lib/element_numbers/en_B3251.rb,
lib/element_numbers/en_B5011.rb,
lib/element_numbers/en_B5013.rb,
lib/element_numbers/en_B5014.rb,
lib/element_numbers/en_B5016.rb,
lib/element_numbers/en_D105N.rb,
lib/element_numbers/en_D105S.rb,
lib/element_numbers/en_G2001.rb,
lib/element_numbers/en_G2514.rb,
lib/element_numbers/en_G2516.rb,
lib/element_numbers/en_G2601.rb,
lib/element_numbers/en_G2602.rb,
lib/element_numbers/en_G2603.rb,
lib/element_numbers/en_P213E.rb,
lib/element_numbers/en_P213H.rb,
lib/element_numbers/en_P213W.rb,
lib/element_numbers/en_T2000.rb

Class Method Summary collapse

Class Method Details

.column_name(column) ⇒ Object 



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# File 'lib/experian.rb', line 12

def self.column_name(column)
  column = column.to_s.downcase.gsub(/\s+/, '')
  self.send("en_#{column}_column_name")
end

.description(column) ⇒ Object 



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# File 'lib/experian.rb', line 22

def self.description(column)
  column = column.to_s.downcase.gsub(/\s+/, '')
  self.send("en_#{column}_description")
end

.en_0000(key) ⇒ Object 



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# File 'lib/element_numbers/en_0000.rb', line 14

def self.en_0000(key)
  values = {
    'H' => 'Household Match',
    'F' => 'Household Match',
    'P' => 'Person Match',
    'I' => 'Person Match',
    'G' => 'Geographic or Area Level Match',
    'E' => 'Geographic or Area Level Match',
    'N' => 'Non-match'
  }
  key.empty? ? 'Non-match' : values[key]
end

.en_0000_column_name ⇒ Object 



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# File 'lib/element_numbers/en_0000.rb', line 4

def self.en_0000_column_name
  'Enhancement Mandatory Append'
end

.en_0000_description ⇒ Object 



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# File 'lib/element_numbers/en_0000.rb', line 8

def self.en_0000_description
  'The enrichment mandatory append is required when any data from the enrichment master file is appended. It consists of a match type.'
end

.en_0000_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_0000.rb', line 12

def self.en_0000_value_description(key); ''; end

.en_0100(key) ⇒ Object

CCYYMM



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# File 'lib/element_numbers/en_0100.rb', line 25

def self.en_0100(key)
  return nil if key.empty?
  year = key[0..3]
  month = key.length > 4 ? key[4..5] : nil
  [month, year].compact.join('/')
end

.en_0100_column_name ⇒ Object 



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# File 'lib/element_numbers/en_0100.rb', line 4

def self.en_0100_column_name
  'Date of Birth'
end

.en_0100_description ⇒ Object 



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# File 'lib/element_numbers/en_0100.rb', line 8

def self.en_0100_description
  'Date of Birth is acquired from public and proprietary files. These sources provide, at a minimum, the year of birth. The birth month is provided where available. Estimated ages are acquired from proprietary data sources and Experian models which estimate the adult age.'
end

.en_0100_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_0100.rb', line 20

def self.en_0100_value_description(key); ''; end

.en_0100c(key) ⇒ Object 



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# File 'lib/element_numbers/en_0100.rb', line 33

def self.en_0100c(key)
  if key[0].nil?
    return 'Unknown age'
  else
    return nil unless %w(e i u).include?(key[0].to_s.downcase) # I noticed another 0100c column that uses M and F which is probably for Male and Female
    text = case key[0].downcase
      when 'e' then 'Exact age'
      when 'i' then 'Estimated age'
      when 'u' then 'Unknown age'
      else nil
    end
    return [key[1..key.length-1], text].compact.join(' - ')
  end
end

.en_0100c_column_name ⇒ Object 



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# File 'lib/element_numbers/en_0100.rb', line 12

def self.en_0100c_column_name
  'Combined Adult Age'
end

.en_0100c_description ⇒ Object 



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# File 'lib/element_numbers/en_0100.rb', line 16

def self.en_0100c_description
  self.en_0100_description
end

.en_0100c_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_0100.rb', line 21

def self.en_0100c_value_description(key); ''; end

.en_0103(key) ⇒ Object 



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# File 'lib/element_numbers/en_0103.rb', line 14

def self.en_0103(key)
  values = {
    'M' => 'Male',
    'F' => 'Female',
    'B' => 'Both',
    'U' => 'Unknown'
  }
  key.empty? ? 'Unknown' : values[key]
end

.en_0103_column_name ⇒ Object 



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# File 'lib/element_numbers/en_0103.rb', line 4

def self.en_0103_column_name
  'Gender Code'
end

.en_0103_description ⇒ Object 



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# File 'lib/element_numbers/en_0103.rb', line 8

def self.en_0103_description
  'Gender information is applied during the convert prior to enhancement. Approximately five to eight percent of the client records are coded as gender unknown because of ambiguous or unisex names. Records coded as gender both include those with prefixes of Mr. & Mrs. and/or first names like John & Mary.'
end

.en_0103_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_0103.rb', line 12

def self.en_0103_value_description(key); ''; end

.en_0107a(key) ⇒ Object 



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# File 'lib/element_numbers/en_0107.rb', line 14

def self.en_0107a(key)
  likely = {
    '1' => 'Extremely Likely',
    '5' => 'Likely',
    '0' => 'Unknown'
  }
  marital_status = {
    'M' => 'Married',
    'S' => 'Single',
    'U' => 'Unknown Status'
  }
  return 'Unknown' if key.size != 2 or marital_status[key[1]].nil? or likely[key[0]].nil?
  [marital_status[key[1]], likely[key[0]]].join(' - ')
end

.en_0107a_column_name ⇒ Object 



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# File 'lib/element_numbers/en_0107.rb', line 4

def self.en_0107a_column_name
  'Marital Status'
end

.en_0107a_description ⇒ Object 



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# File 'lib/element_numbers/en_0107.rb', line 8

def self.en_0107a_description
  'Marital status is determined based on the composition of the Living unit or through the application of a predictive model. Note: Single refers to an individual that has never married and not single because of divorce or spouse\'s death.'
end

.en_0107a_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_0107.rb', line 12

def self.en_0107a_value_description(key); ''; end

.en_0108c(key) ⇒ Object 



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# File 'lib/element_numbers/en_0108.rb', line 82

def self.en_0108c(key)
  values = {
    '00' => 'Unknown',
    '01' => 'Mexico',
    '02' => 'Cuba',
    '03' => 'Puerto Rico',
    '04' => 'Nicaragua',
    '05' => 'Dominican Republic',
    '06' => 'Colombia',
    '07' => 'Honduras',
    '08' => 'Guatemala',
    '09' => 'El Salvador',
    '10' => 'Costa Rica',
    '11' => 'Panama',
    '12' => 'Ecuador',
    '13' => 'Venezuela',
    '14' => 'Argentina',
    '15' => 'Chile',
    '16' => 'Peru',
    '17' => 'Bolivia',
    '18' => 'Uruguay',
    '19' => 'Paraguay',
    '20' => 'Spain',
    '22' => 'Brazil'
  }
  key.empty? ? 'Unknown' : values[key]
end

.en_0108c_column_name ⇒ Object

Country of Origin



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# File 'lib/element_numbers/en_0108.rb', line 5

def self.en_0108c_column_name
  'Country of Origin'
end

.en_0108c_description ⇒ Object 



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# File 'lib/element_numbers/en_0108.rb', line 12

def self.en_0108c_description
  'Indicates Country of Origin using a sophisticated program that analyzes first name, last name, expert system rules and geography.'
end

.en_0108c_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_0108.rb', line 9

def self.en_0108c_value_description(key); ''; end

.en_0108d(key) ⇒ Object 



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# File 'lib/element_numbers/en_0108.rb', line 110

def self.en_0108d(key)
  values = {
    '00' => 'Unknown',
    '01' => 'English',
    '02' => 'Scottish',
    '03' => 'Danish',
    '04' => 'Swedish',
    '05' => 'Norwegian',
    '06' => 'Finnish',
    '07' => 'Icelandic',
    '08' => 'Dutch',
    '09' => 'Belgian',
    '10' => 'German',
    '11' => 'Austrian',
    '12' => 'Hungarian',
    '13' => 'Czech',
    '14' => 'Slovak',
    '15' => 'Irish',
    '16' => 'Welsh',
    '17' => 'French',
    '18' => 'Swiss',
    '19' => 'Italian',
    '20' => 'Hispanic',
    '21' => 'Portuguese',
    '22' => 'Polish',
    '23' => 'Estonian',
    '24' => 'Latvian',
    '25' => 'Lithuanian',
    '26' => 'Ukrainian',
    '27' => 'Georgian',
    '28' => 'Byelorussian',
    '29' => 'Armenian',
    '30' => 'Russian',
    '31' => 'Turkish',
    '32' => 'Kurdish',
    '33' => 'Greek',
    '34' => 'Persian',

    '35' => 'Moldovan',
    '36' => 'Bulgarian',
    '37' => 'Romanian',
    '38' => 'Albanian',
    '39' => 'Native American',
    '40' => 'Slovenian',
    '41' => 'Croatian',
    '42' => 'Serbian',
    '43' => 'Bosniak',
    '44' => 'Azerbaijani',
    '45' => 'Kazakh',
    '46' => 'Afghan',
    '47' => 'Pakistani',
    '48' => 'Bangladeshi',
    '49' => 'Indonesian',
    '50' => 'Indian',
    '51' => 'Burmese (Myanmar)',
    '52' => 'Mongolian',
    '53' => 'Chinese',
    '56' => 'Korean',
    '57' => 'Japanese',
    '58' => 'Thai',
    '59' => 'Malay',
    '60' => 'Laotian',
    '61' => 'Khmer',
    '62' => 'Vietnamese',
    '63' => 'Sri Lankan',
    '64' => 'Uzbek',
    '65' => 'Other Asian',
    '66' => 'Jewish',
    '67' => 'Aleut',
    '68' => 'Hebrew',
    '70' => 'Arab',
    '72' => 'Turkmen',
    '73' => 'Tajik',
    '74' => 'Kirghiz',
    '75' => 'Saudi',
    '76' => 'Iraqi',
    '77' => 'Libyan',
    '78' => 'Egyptian',
    '79' => 'Rwandan',
    '80' => 'Tongan',
    '81' => 'Senegalese',
    '82' => 'Malawian',
    '83' => 'Sudanese',
    '84' => 'Moroccan',
    '85' => 'African American',
    '86' => 'Kenyan',
    '87' => 'Nigerian (Nigeria)',
    '88' => 'Ghanaian',
    '89' => 'Zambian',
    '90' => 'Congolese (DRG)',
    '91' => 'Surinamese',
    '92' => 'Mozambican (Mozambique)',
    '93' => 'Ivorian (Ivory Coast)',
    '94' => 'Bhutanese',
    '95' => 'Ethiopian',
    '96' => 'Ugandan',
    '97' => 'Batswana (Botswana)',
    '98' => 'Cameroonian',
    '99' => 'Zimbabwean',
    '7A' => 'Hindu',
    '7B' => 'Djiboutian',
    '7C' => 'Manx',
    '7D' => 'Telugu',
    '7E' => 'Nepalese',
    '7F' => 'Samoan',
    '7G' => 'Mauritanian',
    '7H' => 'Native American Inuit',
    '8A' => 'Congolese',
    '8B' => 'Central African (Central African Republic)',
    '8C' => 'Togolese',
    '8D' => 'Bahraini',
    '8E' => 'Qatari',
    '8F' => 'Guyanese',
    '8G' => 'Tibetan',
    '8H' => 'Fijian',
    '8I' => 'Swazi',
    '8J' => 'Zulu',
    '8K' => 'Xhosa',
    '8L' => 'Basotho',
    '8M' => 'South African',
    '8N' => 'Liberian',
    '8O' => 'Comoran',
    '8P' => 'Beninese',
    '8Q' => 'Burkinabe (Burkina Faso)',
    '8R' => 'Nigerien (Niger)',
    '8S' => 'Ashanti',
    '8T' => 'Swahili',
    '8U' => 'Haitian',
    '8V' => 'Malian',
    '8W' => 'Jamaican',
    '8X' => 'Hausa',
    '8Y' => 'Pili',
    '9A' => 'Namibian',
    '9B' => 'Burundi',
    '9C' => 'Tanzanian',
    '9D' => 'Gambian',
    '9E' => 'Somali',
    '9F' => 'Macedonian',
    '9G' => 'Chadian',
    '9H' => 'Gabonese',
    '9I' => 'Angolan',
    '9J' => 'Chechen',
    '9K' => 'Igbo',
    '9L' => 'Yoruba',
    '9M' => 'Algerian',
    '9N' => 'Filipino',
    '9O' => 'Sotho (Lesotho)',
    '9P' => 'Tunisian',
    '9Q' => 'Hawaiian',
    '9R' => 'Malagasy (Madagascar)',
    '9S' => 'Basque',
    '9T' => 'Siere Leonean',
    '9U' => 'Kuwaiti',
    '9V' => 'Yemeni',
    '9W' => 'Guinean (Guinea-Bissau)',
    '9X' => 'Papua New Guinean',
    '9Y' => 'Equatorial Guinean',
    '9Z' => 'Syrian',
    'A1' => 'African American (Arabic Surname), 80%+',
    'A2' => 'African American (Arabic Surname), 70-79%',
    'A3' => 'African American (Arabic Surname), 60-69%',
    'A4' => 'African American (Arabic Surname), 50-59%',
    'A5' => 'African American (Arabic Surname), 30-49%',
    'D1' => 'African American (Dutch Surname), 80%+',
    'D2' => 'African American (Dutch Surname), 70-79%',
    'D3' => 'African American (Dutch Surname), 60-69%',
    'D4' => 'African American (Dutch Surname), 50-59%',
    'D5' => 'African American (Dutch Surname), 30-49%',
    'E1' => 'African American (English Surname), 80%+',
    'E2' => 'African American (English Surname), 70-79%',
    'E3' => 'African American (English Surname), 60-69%',
    'E4' => 'African American (English Surname), 50-59%',
    'E5' => 'African American (English Surname), 30-49%',
    'F1' => 'African American (French Surname), 80%+',
    'F2' => 'African American (French Surname), 70-79%',
    'F3' => 'African American (French Surname), 60-69%',
    'F4' => 'African American (French Surname), 50-59%',
    'F5' => 'African American (French Surname), 30-49%',
    'I1' => 'African American (Irish Surname), 80%+',
    'I2' => 'African American (Irish Surname), 70-79%',
    'I3' => 'African American (Irish Surname), 60-69%',
    'I4' => 'African American (Irish Surname), 50-59%',
    'I5' => 'African American (Irish Surname), 30-49%',
    'S1' => 'African American (Scotch Surname), 80%+',
    'S2' => 'African American (Scotch Surname), 70-79%',
    'S3' => 'African American (Scotch Surname), 60-69%',
    'S4' => 'African American (Scotch Surname), 50-59%',
    'S5' => 'African American (Scotch Surname), 30-49%',
    'U1' => 'African American (Unknown Surname), 80%+',
    'U2' => 'African American (Unknown Surname), 70-79%',
    'U3' => 'African American (Unknown Surname), 60-69%',
    'U4' => 'African American (Unknown Surname), 50-59%',
    'U5' => 'African American (Unknown Surname), 30-49%',
    'W1' => 'African American (Welsh Surname), 80%+',
    'W2' => 'African American (Welsh Surname), 70-79%',
    'W3' => 'African American (Welsh Surname), 60-69%',
    'W4' => 'African American (Welsh Surname), 50-59%',
    'W5' => 'African American (Welsh Surname), 30-49%',
    'ZZ' => 'Multi-Ethnic'
  }
  key.empty? ? 'Unknown' : values[key]
end

.en_0108d_column_name ⇒ Object

Ethnicity Detail



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# File 'lib/element_numbers/en_0108.rb', line 17

def self.en_0108d_column_name
  'Ethnicity Detail'
end

.en_0108d_description ⇒ Object 



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# File 'lib/element_numbers/en_0108.rb', line 23

def self.en_0108d_description
  'Indicates the likely ethnicity using a sophisticated program that analyzes first name, last name, expert system rules and geography.'
end

.en_0108d_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_0108.rb', line 21

def self.en_0108d_value_description(key); ''; end

.en_0108e(key) ⇒ Object 



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# File 'lib/element_numbers/en_0108.rb', line 313

def self.en_0108e(key)
  values = {
    'Y' => 'Matched to Ethnic Insight'
  }
  key.empty? ? 'Unknown' : values[key]
end

.en_0108e_column_name ⇒ Object

Ethnicity Insight



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# File 'lib/element_numbers/en_0108.rb', line 28

def self.en_0108e_column_name
  'Ethnic Insight'
end

.en_0108e_description ⇒ Object 



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# File 'lib/element_numbers/en_0108.rb', line 34

def self.en_0108e_description
  'Ethnic Insight Elements. Ethnic Insight is a comprehensive predictive name analysis process which identifies ethnic origin, probable religion, and the language preference of individuals. Experian can now code and identify 177 ethnicities, 33 groups, 10 religions, 79 language groups and 22 Country of Origin codes.The Ethnic Insight software utilizes over 550,000 surnames by ethnicity, over 75,000 first names unique to a given ethnicity, and over 3000 expert system rules. Also utilized are a group of geographic reference tables for determining the proper codes to apply to an individual. Each record passed through the process uses the above files as references while examining the individuals first name, last names, and the zip code of the record. Note: Element 0108E calls elements 0108C, 0108D, 0108G, 0108L, 0108R, 0108T'
end

.en_0108e_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_0108.rb', line 32

def self.en_0108e_value_description(key); ''; end

.en_0108g(key) ⇒ Object 



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# File 'lib/element_numbers/en_0108.rb', line 320

def self.en_0108g(key)
  values = {
    '01' => 'African',
    '02' => 'African American',
    '03' => 'Asian Other',
    '04' => 'Chinese',
    '05' => 'Czech',
    '06' => 'Dutch',
    '07' => 'Eastern European',
    '08' => 'English',
    '09' => 'French',
    '10' => 'German',
    '11' => 'Greek',
    '12' => 'Hawaiian',
    '13' => 'Hispanic',
    '14' => 'Indonesian',
    '15' => 'Irish',
    '16' => 'Italian',
    '17' => 'Japanese',
    '18' => 'Jewish',
    '19' => 'Korean',
    '20' => 'Middle Eastern',
    '21' => 'Miscellaneous Other',
    '22' => 'Native American',
    '23' => 'Polish',
    '24' => 'Polynesian',
    '25' => 'Portuguese',
    '26' => 'Russian',
    '27' => 'Scandinavian',
    '28' => 'Scotch',
    '29' => 'Swiss',
    '30' => 'Ukrainian',
    '31' => 'Uncoded (no group)',
    '32' => 'Vietnamese',
    '33' => 'Western European'
  }
  key.empty? ? 'Unknown' : values[key]
end

.en_0108g_column_name ⇒ Object

Ethnic Group Code



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# File 'lib/element_numbers/en_0108.rb', line 39

def self.en_0108g_column_name
  'Ethnic Group Code'
end

.en_0108g_description ⇒ Object 



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# File 'lib/element_numbers/en_0108.rb', line 45

def self.en_0108g_description
  'Experian derived code that groups ethnicities in general categories.'
end

.en_0108g_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_0108.rb', line 43

def self.en_0108g_value_description(key); ''; end

.en_0108l(key) ⇒ Object 



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# File 'lib/element_numbers/en_0108.rb', line 359

def self.en_0108l(key)
  values = {
    '00' => 'Unknown',
    '01' => 'English',
    '03' => 'Danish',
    '04' => 'Swedish',
    '05' => 'Norwegian',
    '06' => 'Finnish',
    '07' => 'Icelandic',
    '08' => 'Dutch',
    '09' => 'Flemish/Walloon',
    '10' => 'German',
    '12' => 'Hungarian',
    '13' => 'Czech',
    '14' => 'Slovak',
    '17' => 'French',
    '19' => 'Italian',
    '20' => 'Spanish',
    '21' => 'Portuguese',
    '22' => 'Polish',
    '23' => 'Estonian',
    '24' => 'Latvian',
    '25' => 'Lithuanian',
    '27' => 'Georgian',
    '29' => 'Armenian',
    '30' => 'Russian',
    '31' => 'Turkish',
    '32' => 'Kurdish',
    '33' => 'Greek',
    '34' => 'Farsi',
    '35' => 'Moldovan',
    '36' => 'Bulgarian',
    '37' => 'Romanian',
    '38' => 'Albanian',
    '40' => 'Slovenian',
    '41' => 'Serbo-Croatian',
    '44' => 'Azeri',
    '45' => 'Kazakh',
    '46' => 'Pashtu/Pashto',
    '47' => 'Urdu',
    '48' => 'Bengali/Bangla',
    '49' => 'Bahasa Indonesia',
    '51' => 'Burmese',
    '52' => 'Mongolian',
    '53' => 'Chinese (Mandarin, Cantonese and other dialects',
    '56' => 'Korean',
    '57' => 'Japanese',
    '58' => 'Thai',
    '59' => 'Bahasa Melayu',
    '60' => 'Lao',
    '61' => 'Khmer',
    '62' => 'Vietnamese',
    '63' => 'Sinhala',
    '64' => 'Uzbek',
    '68' => 'Hebrew',
    '70' => 'Arabic',
    '72' => 'Turkmen',
    '73' => 'Tajik',
    '74' => 'Kirghiz',
    '7A' => 'Hindi',
    '7E' => 'Nepali',
    '7F' => 'Samoan',
    '80' => 'Tongan',
    '86' => 'Oromo',
    '88' => 'Ga',
    '8G' => 'Tibetan',
    '8I' => 'Siswati',
    '8J' => 'Zulu',
    '8K' => 'Xhosa',
    '8M' => 'Afrikaans',
    '8O' => 'Comorian/Shikomoro',
    '8S' => 'Twi (Of Ashanti Ethnicity)',
    '8T' => 'Swahili',
    '8X' => 'Hausa',
    '92' => 'Bantu',
    '94' => 'Dzongkha',
    '95' => 'Amharic',
    '97' => 'Tswana',
    '9E' => 'Somali',
    '9F' => 'Macedonian',
    '9K' => 'Ibo/Igbo',
    '9L' => 'Yoruba',
    '9N' => 'Tagalog',
    '9O' => 'Sotho',
    '9R' => 'Malagasy',
    '9S' => 'Basque'
  }
  key.empty? ? 'Unknown' : values[key]
end

.en_0108l_column_name ⇒ Object

Language



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# File 'lib/element_numbers/en_0108.rb', line 50

def self.en_0108l_column_name
  'Language'
end

.en_0108l_description ⇒ Object 



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# File 'lib/element_numbers/en_0108.rb', line 56

def self.en_0108l_description
  'Ethnic Insight Language indicates the language preference.'
end

.en_0108l_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_0108.rb', line 54

def self.en_0108l_value_description(key); ''; end

.en_0108r(key) ⇒ Object 



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# File 'lib/element_numbers/en_0108.rb', line 449

def self.en_0108r(key)
  values = {
    'B' => 'Buddhist',
    'C' => 'Catholic',
    'E' => 'Ethiopian Orthodox',
    'G' => 'Greek Orthodox',
    'H' => 'Hindu',
    'I' => 'Muslim',
    'J' => 'Jewish',
    'K' => 'Sikh',
    'L' => 'Lutheran',
    'M' => 'Mormon',
    'O' => 'Eastern Orthodox',
    'P' => 'Protestant',
    'S' => 'Shinto',
    'X' => 'Not Known or Unmatched',
  }
  key.empty? ? 'Unknown' : values[key]
end

.en_0108r_column_name ⇒ Object

Religion



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# File 'lib/element_numbers/en_0108.rb', line 61

def self.en_0108r_column_name
  'Religion'
end

.en_0108r_description ⇒ Object 



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# File 'lib/element_numbers/en_0108.rb', line 67

def self.en_0108r_description
  'Ethnic Insights Religion indicates the likely religion.'
end

.en_0108r_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_0108.rb', line 65

def self.en_0108r_value_description(key); ''; end

.en_0108t(key) ⇒ Object 



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# File 'lib/element_numbers/en_0108.rb', line 469

def self.en_0108t(key)
  values = {
    'A' => 'African American',
    'B' => 'Southeast Asian',
    'C' => 'South Asian',
    'D' => 'Central Asian',
    'E' => 'Mediterranean',
    'F' => 'Native American',
    'G' => 'Scandinavian',
    'H' => 'Polynesian',
    'I' => 'Middle Eastern',
    'J' => 'Jewish',
    'K' => 'Western European',
    'L' => 'Eastern European',
    'M' => 'Caribbean Non-Hispanic',
    'N' => 'East Asian',
    'O' => 'Hispanic',
    'Z' => 'Uncoded'
  }
  key.empty? ? 'Unknown' : values[key]
end

.en_0108t_column_name ⇒ Object

e-Tech Group



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# File 'lib/element_numbers/en_0108.rb', line 72

def self.en_0108t_column_name
  'e-Tech Group'
end

.en_0108t_description ⇒ Object 



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# File 'lib/element_numbers/en_0108.rb', line 78

def self.en_0108t_description
  'e-Tech derived code that groups ethnicities in general categories.'
end

.en_0108t_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_0108.rb', line 76

def self.en_0108t_value_description(key); ''; end

.en_0110p(key) ⇒ Object 



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# File 'lib/element_numbers/en_0110.rb', line 14

def self.en_0110p(key)
  values = {
    'P' => 'Primary Decision Maker',
    'E' => 'Elderly Parent',
    'D' => 'Deceased',
    'Y' => 'Young Adult',
    'O' => 'Other or blank'
  }
  key.empty? ? 'Other or blank' : values[key]
end

.en_0110p_column_name ⇒ Object 



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# File 'lib/element_numbers/en_0110.rb', line 4

def self.en_0110p_column_name
  'Person Type'
end

.en_0110p_description ⇒ Object 



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# File 'lib/element_numbers/en_0110.rb', line 10

def self.en_0110p_description
  'Assignment of person type in the living unit based on age and activity.'
end

.en_0110p_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_0110.rb', line 8

def self.en_0110p_value_description(key); ''; end

.en_0112(key) ⇒ Object 



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# File 'lib/element_numbers/en_0112.rb', line 14

def self.en_0112(key)
  values = {
    'D' => 'Deceased Indicator',
    'blank' => 'Unknown'
  }
  key.empty? ? 'Unknown' : values[key]
end

.en_0112_column_name ⇒ Object 



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# File 'lib/element_numbers/en_0112.rb', line 4

def self.en_0112_column_name
  'Deceased Indicator'
end

.en_0112_description ⇒ Object 



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# File 'lib/element_numbers/en_0112.rb', line 10

def self.en_0112_description
  'Deceased Indicator identifies client input records that Experian believes are deceased individuals. This information is sourced primarily from Social Security records and other proprietary sources, as well as consumers who may have contacted Experian directly. The deceased indicator communicates that we have reason to believe that the individual is deceased based on this source information.'
end

.en_0112_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_0112.rb', line 8

def self.en_0112_value_description(key); ''; end

.en_0113a(key) ⇒ Object 



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# File 'lib/element_numbers/en_0113.rb', line 14

def self.en_0113a(key)
  values = {
    'H' => 'Homeowner',
    '9' => 'Extremely Likely',
    '8' => 'Highly Likely',
    '7' => 'Likely',
    'R' => 'Renter',
    'T' => 'Probable Renter',
    'U' => 'Unknown'
  }
  key.empty? ? 'Unknown' : values[key]
end

.en_0113a_column_name ⇒ Object 



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# File 'lib/element_numbers/en_0113.rb', line 4

def self.en_0113a_column_name
  'Combined Homeowner'
end

.en_0113a_description ⇒ Object 



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# File 'lib/element_numbers/en_0113.rb', line 10

def self.en_0113a_description
  'Combined homeowner is a mixture of several data elements / fields. This element provides these separate data components in a single position. Homeowner information indicates the likelihood of a consumer owning a home, and is received from tax assessor and deed information. For records where exact Homeownership information is not available, homeownership propensity is calculated using a proprietary statistical model which predicts the likelihood of homeownership. Renter status is derived from self reported data. Unit numbers are not used to infer rented status because units may be owner condominium/coop. Probable Renter is calculated using an algorithm based on lack of Homeowner, the Address Type, and Census Percent Renter.'
end

.en_0113a_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_0113.rb', line 8

def self.en_0113a_value_description(key); ''; end

.en_0118(key) ⇒ Object 



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# File 'lib/element_numbers/en_0118.rb', line 15

def self.en_0118(key)
  values = {
    'S' => 'Single Family',
    'A' => 'Multi-Family & Condominiums',
    'M' => 'Marginal Multi-Family',
    'P' => 'Post Office Box',
    'U' => 'Unknown'
  }
  key.empty? ? 'Unknown' : values[key]
end

.en_0118_column_name ⇒ Object

Dwelling Type



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# File 'lib/element_numbers/en_0118.rb', line 5

def self.en_0118_column_name
  'Dwelling Type'
end

.en_0118_description ⇒ Object 



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# File 'lib/element_numbers/en_0118.rb', line 11

def self.en_0118_description
  'Each household is assigned a dwelling type code based on United States Postal Service (USPS) information. Single Family Dwelling Units are residences for one family or living unit (S). If the address contains an apartment number or has a small dwelling size (5 units or less), the code is set to Multi-Family (A). Marginal Multi Family Dwelling Units lack an apartment number and are considered of questionable deliverability (M). Values also include P.O. Boxes (P) and Unknown dwelling types (U).'
end

.en_0118_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_0118.rb', line 9

def self.en_0118_value_description(key); ''; end

.en_0119(key) ⇒ Object 



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# File 'lib/element_numbers/en_0119.rb', line 15

def self.en_0119(key)
  return nil unless /[0-9]+/ =~ key
  key.to_i == 0  ? 'Less than a year' : [key.to_i, 'years'].join(' ')
end

.en_0119_column_name ⇒ Object

Length Of Residence



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# File 'lib/element_numbers/en_0119.rb', line 5

def self.en_0119_column_name
  'Length Of Residence'
end

.en_0119_description ⇒ Object 



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# File 'lib/element_numbers/en_0119.rb', line 11

def self.en_0119_description
  'Length of Residence (LOR) is the length of time a Living Unit has resided at their current address. A primary source of LOR is public source white page compilation initiating a counter showing the first time a name and number appear in the directory. LOR information is also received from proprietary sources and through updates of our source files against the USPS National Change of Address* file. For all sources, LOR data is aged over time.\n\nWhen there is insufficient data to match a customer\'s record to our Enrichment master for Length of Residence, a median LOR based on the LOR\'s assigned to other living units in the same ZIP+4 area is used. In the rare case that the ZIP+4 is not on the record, median LOR is based on the LOR\'s assigned to other records in that ZIP region.\n\nThe median level data applied to records for this element can be identified through the Enrichment Mandatory Append - Total Enrichment Match Type indicator (E).'
end

.en_0119_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_0119.rb', line 9

def self.en_0119_value_description(key); ''; end

.en_0130(key) ⇒ Object 



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# File 'lib/element_numbers/en_0130.rb', line 14

def self.en_0130(key)
  values = {
    'Y' => 'Yes',
    'U' => 'Unknown'
  }
  key.empty? ? 'Unknown' : values[key]
end

.en_0130_column_name ⇒ Object 



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# File 'lib/element_numbers/en_0130.rb', line 4

def self.en_0130_column_name
  'Direct Mail Responder - Individual Level'
end

.en_0130_description ⇒ Object 



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# File 'lib/element_numbers/en_0130.rb', line 10

def self.en_0130_description
  'Direct Mail Responder information identifies consumers who have purchased by direct mail. Data is carried at the individual level. If one member of a household is a direct mail responder, that member will be coded as a direct mail responder at the individual level.'
end

.en_0130_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_0130.rb', line 8

def self.en_0130_value_description(key); ''; end

.en_0131(key) ⇒ Object 



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# File 'lib/element_numbers/en_0131.rb', line 14

def self.en_0131(key)
  values = {
    'Y' => 'Yes',
    'U' => 'Unknown',
    'M' => 'Multi-Buyer (purchased from multiple mail order companies)'
  }
  key.empty? ? 'Unknown' : values[key]
end

.en_0131_column_name ⇒ Object 



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# File 'lib/element_numbers/en_0131.rb', line 4

def self.en_0131_column_name
  'Multi-Company Direct Mail Responder - Living Units Level'
end

.en_0131_description ⇒ Object 



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# File 'lib/element_numbers/en_0131.rb', line 10

def self.en_0131_description
  'Direct Mail Responder information identifies consumers who have purchased by direct mail. Data is carried at both the individual and household level. If one member of a household is a direct mail responder at the individual level, the remaining members of the household will be coded as a direct mail responder at the household level.'
end

.en_0131_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_0131.rb', line 8

def self.en_0131_value_description(key); ''; end

.en_0132a(key) ⇒ Object 



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# File 'lib/element_numbers/en_0132.rb', line 15

def self.en_0132a(key)
  return 'Unknown' if key.size != 3 or key.to_i > 999
  key
end

.en_0132a_column_name ⇒ Object

MOR Bank- Deduped Hit Count



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# File 'lib/element_numbers/en_0132.rb', line 5

def self.en_0132a_column_name
  'MOR Bank- Deduped Hit Count'
end

.en_0132a_description ⇒ Object 



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# File 'lib/element_numbers/en_0132.rb', line 11

def self.en_0132a_description
  'The total number of categories in which the living unit has at least one hit, but with any specific participant counted in only one category.'
end

.en_0132a_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_0132.rb', line 9

def self.en_0132a_value_description(key); ''; end

.en_0132b(key) ⇒ Object 



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# File 'lib/element_numbers/en_0132.rb', line 31

def self.en_0132b(key)
  return 'Unknown' if key.size != 3 or key.to_i > 999
  key
end

.en_0132b_column_name ⇒ Object

MOR Bank- Non-Deduped Hit Count



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# File 'lib/element_numbers/en_0132.rb', line 21

def self.en_0132b_column_name
  'MOR Bank- Non-Deduped Hit Count'
end

.en_0132b_description ⇒ Object 



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# File 'lib/element_numbers/en_0132.rb', line 27

def self.en_0132b_description
  'The total number of categories in which the living unit has at least one hit, with participant hits counted in all applicable categories.'
end

.en_0132b_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_0132.rb', line 25

def self.en_0132b_value_description(key); ''; end

.en_0133(key) ⇒ Object 



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# File 'lib/element_numbers/en_0133.rb', line 14

def self.en_0133(key)
  values = {
    'Y' => 'Yes'
  }
  key.empty? ? 'Unknown' : values[key]
end

.en_0133_column_name ⇒ Object 



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# File 'lib/element_numbers/en_0133.rb', line 4

def self.en_0133_column_name
  'Multi-Category Buyer'
end

.en_0133_description ⇒ Object 



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# File 'lib/element_numbers/en_0133.rb', line 10

def self.en_0133_description
  'Multi-category buyer information identifies consumers who have made purchases by direct mail in multiple product categories. Data is carried at the household level.'
end

.en_0133_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_0133.rb', line 8

def self.en_0133_value_description(key); ''; end

.en_0134(key) ⇒ Object 



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# File 'lib/element_numbers/en_0134.rb', line 15

def self.en_0134(key)
  return 'Unknown' if key.empty? or key.to_i > 9 or key.size > 1

  if key.to_i == 1
    return key.to_i.to_s + ' Response'
  elsif key.to_i > 1 and key.to_i < 10 or key.to_i == 0
    return key.to_i.to_s + ' Responses'
  else
    return nil
  end
end

.en_0134_column_name ⇒ Object

Direct Mail Merchandise Buyer Categories



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# File 'lib/element_numbers/en_0134.rb', line 5

def self.en_0134_column_name
  'Direct Mail Merchandise Buyer Categories'
end

.en_0134_description ⇒ Object 



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# File 'lib/element_numbers/en_0134.rb', line 11

def self.en_0134_description
  'The Direct Mail Merchandise Buyer Categories information is compiled via mail order responsiveness by categorized offers/media. Information is collected from 300+ proprietary participants who give us mail order responders and we subsequently categorize them into 1 of 25 categories. No single participant\'s information may exceed 25% of any category.'
end

.en_0134_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_0134.rb', line 9

def self.en_0134_value_description(key); ''; end

.en_0135(key) ⇒ Object 



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# File 'lib/element_numbers/en_0135.rb', line 14

def self.en_0135(key)
  return 'Unknown' if key.empty? or key.to_i > 9 or key.size > 1

  if key.to_i == 1
    return key.to_i.to_s + ' Response'
  elsif key.to_i > 1 and key.to_i < 10 or key.to_i == 0
    return key.to_i.to_s + ' Responses'
  else
    return nil
  end
end

.en_0135_column_name ⇒ Object 



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# File 'lib/element_numbers/en_0135.rb', line 4

def self.en_0135_column_name
  'Magazine Buyer Categories'
end

.en_0135_description ⇒ Object 



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# File 'lib/element_numbers/en_0135.rb', line 10

def self.en_0135_description
  'The Direct Mail Magazine Buyer Categories information is compiled via mail order responsiveness by categorized offers/media. Information is collected from 300+ proprietary participants who give us mail order responders and we subsequently categorize them into 1 of 25 categories. No single participant\'s information may exceed 25% of any category.'
end

.en_0135_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_0135.rb', line 8

def self.en_0135_value_description(key); ''; end

.en_0136(key) ⇒ Object 



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# File 'lib/element_numbers/en_0136.rb', line 14

def self.en_0136(key)
  return 'Unknown' if key.empty? or key.to_i > 9 or key.size > 1

  if key.to_i == 1
    return key.to_i.to_s + ' Response'
  elsif key.to_i > 1 and key.to_i < 10 or key.to_i == 0
    return key.to_i.to_s + ' Responses'
  else
    return nil
  end
end

.en_0136_column_name ⇒ Object 



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# File 'lib/element_numbers/en_0136.rb', line 4

def self.en_0136_column_name
  'Contributors Categories'
end

.en_0136_description ⇒ Object 



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# File 'lib/element_numbers/en_0136.rb', line 10

def self.en_0136_description
  'The Contributors Categories information is compiled via mail order responsiveness by categorized offers/media. Information is collected from 300+ proprietary participants who give us mail order responders and we subsequently categorize them into 1 of 25 categories. No single participant\'s information may exceed 25% of any category.'
end

.en_0136_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_0136.rb', line 8

def self.en_0136_value_description(key); ''; end

.en_0137(key) ⇒ Object 



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# File 'lib/element_numbers/en_0137.rb', line 14

def self.en_0137(key)
  return 'Unknown' if key.empty? or key.to_i > 9 or key.size > 1

  if key.to_i == 1
    return key.to_i.to_s + ' Response'
  elsif key.to_i > 1 and key.to_i < 10 or key.to_i == 0
    return key.to_i.to_s + ' Responses'
  else
    return nil
  end
end

.en_0137_column_name ⇒ Object 



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# File 'lib/element_numbers/en_0137.rb', line 4

def self.en_0137_column_name
  'Sweepstakes / Contests'
end

.en_0137_description ⇒ Object 



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# File 'lib/element_numbers/en_0137.rb', line 10

def self.en_0137_description
  'The Sweepstakes/Contests Categories information is compiled via mail order responsiveness by categorized offers/media. Information is collected from 300+ proprietary participants who give us mail order responders and we subsequently categorize them into 1 of 25 categories. No single participant\'s information may exceed 25% of any category.'
end

.en_0137_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_0137.rb', line 8

def self.en_0137_value_description(key); ''; end

.en_0138(key) ⇒ Object 



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# File 'lib/element_numbers/en_0138.rb', line 14

def self.en_0138(key)
  return 'Unknown' if key.empty? or key.to_i > 9 or key.size > 1

  if key.to_i == 1
    return key.to_i.to_s + ' Response'
  elsif key.to_i > 1 and key.to_i < 10 or key.to_i == 0
    return key.to_i.to_s + ' Responses'
  else
    return nil
  end
end

.en_0138_column_name ⇒ Object 



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# File 'lib/element_numbers/en_0138.rb', line 4

def self.en_0138_column_name
  'Do-It-Yourself'
end

.en_0138_description ⇒ Object 



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# File 'lib/element_numbers/en_0138.rb', line 10

def self.en_0138_description
  'The Do-It-Yourself Categories information is compiled via mail order responsiveness by categorized offers/media. Information is collected from 300+ proprietary participants who give us mail order responders and we subsequently categorize them into 1 of 25 categories. No single participant\'s information may exceed 25% of any category.'
end

.en_0138_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_0138.rb', line 8

def self.en_0138_value_description(key); ''; end

.en_0139(key) ⇒ Object 



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# File 'lib/element_numbers/en_0139.rb', line 14

def self.en_0139(key)
  return 'Unknown' if key.empty? or key.to_i > 9 or key.size > 1

  if key.to_i == 1
    return key.to_i.to_s + ' Response'
  elsif key.to_i > 1 and key.to_i < 10 or key.to_i == 0
    return key.to_i.to_s + ' Responses'
  else
    return nil
  end
end

.en_0139_column_name ⇒ Object 



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# File 'lib/element_numbers/en_0139.rb', line 4

def self.en_0139_column_name
  'News & Financial'
end

.en_0139_description ⇒ Object 



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# File 'lib/element_numbers/en_0139.rb', line 10

def self.en_0139_description
  'The News & Financial Categories information is compiled via mail order responsiveness by categorized offers/media. Information is collected from 300+ proprietary participants who give us mail order responders and we subsequently categorize them into 1 of 25 categories. No single participant\'s information may exceed 25% of any category.'
end

.en_0139_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_0139.rb', line 8

def self.en_0139_value_description(key); ''; end

.en_0140(key) ⇒ Object 



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# File 'lib/element_numbers/en_0140.rb', line 15

def self.en_0140(key)
  return 'Unknown' if key.empty? or key.to_i > 9 or key.size > 1

  if key.to_i == 1
    return key.to_i.to_s + ' Response'
  elsif key.to_i > 1 and key.to_i < 10 or key.to_i == 0
    return key.to_i.to_s + ' Responses'
  else
    return nil
  end
end

.en_0140_column_name ⇒ Object

Photography



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# File 'lib/element_numbers/en_0140.rb', line 5

def self.en_0140_column_name
  'Photography'
end

.en_0140_description ⇒ Object 



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# File 'lib/element_numbers/en_0140.rb', line 11

def self.en_0140_description
  'The Photography Categories information is compiled via mail order responsiveness by categorized offers/media. Information is collected from 300+ proprietary participants who give us mail order responders and we subsequently categorize them into 1 of 25 categories. No single participant\'s information may exceed 25% of any category.'
end

.en_0140_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_0140.rb', line 9

def self.en_0140_value_description(key); ''; end

.en_0141(key) ⇒ Object 



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# File 'lib/element_numbers/en_0141.rb', line 15

def self.en_0141(key)
  return 'Unknown' if key.empty? or key.to_i > 9 or key.size > 1

  if key.to_i == 1
    return key.to_i.to_s + ' Response'
  elsif key.to_i > 1 and key.to_i < 10 or key.to_i == 0
    return key.to_i.to_s + ' Responses'
  else
    return nil
  end
end

.en_0141_column_name ⇒ Object

Mail Responders - Odds & Ends / Miscellaneous



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# File 'lib/element_numbers/en_0141.rb', line 5

def self.en_0141_column_name
  'Mail Responders - Odds & Ends / Miscellaneous'
end

.en_0141_description ⇒ Object 



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# File 'lib/element_numbers/en_0141.rb', line 11

def self.en_0141_description
  'The Miscellaneous/Odds & Ends Categories information is compiled via mail order responsiveness by categorized offers/media. Information is collected from 300+ proprietary participants who give us mail order responders and we subsequently categorize them into 1 of 25 categories. No single participant\'s information may exceed 25% of any category.'
end

.en_0141_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_0141.rb', line 9

def self.en_0141_value_description(key); ''; end

.en_0155(key) ⇒ Object 



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# File 'lib/element_numbers/en_0155.rb', line 15

def self.en_0155(key)
  values = {
    'Y' => 'Yes',
    'U' => 'Unknown'
  }
  key.empty? ? 'Unknown' : values[key]
end

.en_0155_column_name ⇒ Object

Presence Of Young Adult



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# File 'lib/element_numbers/en_0155.rb', line 5

def self.en_0155_column_name
  'Presence Of Young Adult'
end

.en_0155_description ⇒ Object 



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# File 'lib/element_numbers/en_0155.rb', line 11

def self.en_0155_description
  'Presence of Young Adult indicates if there is a young adult in the household.'
end

.en_0155_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_0155.rb', line 9

def self.en_0155_value_description(key); ''; end

.en_0156(key) ⇒ Object 



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# File 'lib/element_numbers/en_0156.rb', line 15

def self.en_0156(key)
  values = {
    'Y' => 'Yes',
    'U' => 'Unknown'
  }
  key.empty? ? 'Unknown' : values[key]
end

.en_0156_column_name ⇒ Object

Presence Of Elderly Parent



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# File 'lib/element_numbers/en_0156.rb', line 5

def self.en_0156_column_name
  'Presence Of Elderly Parent'
end

.en_0156_description ⇒ Object 



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# File 'lib/element_numbers/en_0156.rb', line 11

def self.en_0156_description
  'Presence of Elderly Parent indicates if there is an elderly parent in the household.'
end

.en_0156_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_0156.rb', line 9

def self.en_0156_value_description(key); ''; end

.en_0160a(key) ⇒ Object 



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# File 'lib/element_numbers/en_0160.rb', line 15

def self.en_0160a(key)
  type = {
    'K' => 'Known',
    'I' => 'Inferred'
  }
  values = {
    '01' => 'Professional/Technical',
    '02' => 'Sales/Service',
    '03' => 'Farm Related',
    '04' => 'Blue Collar',
    '05' => 'Other',
    '06' => 'Retired'
  }
  return 'Unknown' if key.size != 3 or values[key[1..2]].nil? or type[key[0]].nil? or key.eql?("U00")
  [values[key[1..2]], type[key[0]]].join(' - ')

end

.en_0160a_column_name ⇒ Object

Occupation Group



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# File 'lib/element_numbers/en_0160.rb', line 5

def self.en_0160a_column_name
  'Occupation Group'
end

.en_0160a_description ⇒ Object 



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# File 'lib/element_numbers/en_0160.rb', line 11

def self.en_0160a_description
  'Information is compiled from self-reported surveys, derived from state licensing agencies, or calculated through the application of predictive models. When there is insufficient data to match a customer\'s record in our enrichment master for occupation group, a median occupation group based on the Experian modeled occupation assigned to other living units in the same zip+4 area is used. In the rare case that zip+4 is not on the record, occupation group is based on the occupation assigned to other records in that zip region. Note: the median level data applied to records for this element can be identified through the Enrichment Mandatory Append û Total Enrichment Match Type indicator (E).'
end

.en_0160a_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_0160.rb', line 9

def self.en_0160a_value_description(key); ''; end

.en_0161(key) ⇒ Object 



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# File 'lib/element_numbers/en_0161.rb', line 15

def self.en_0161(key)
  type = {
    'K' => 'Known',
    'I' => 'Inferred'
  }
  values = {
    '1' => 'Management/Business & Financial Operations',
    '2' => 'Technical: Computers/Math & Architect/Engineering',
    '3' => 'Professional: Legal/Education & Health Practitioner/Tech/Support',
    '4' => 'Sales',
    '5' => 'Office & Administrative Support',
    '6' => 'Blue Collar',
    '7' => 'Farming/Fish/Forestry',
    '8' => 'Other',
    '9' => 'Retired',
    '0' => 'Unknown'
  }
  return 'Unknown' if key.size != 2 or values[key[1]].nil? or type[key[0]].nil? or key.eql?("U00")
  [values[key[1]], type[key[0]]].join(' - ')

end

.en_0161_column_name ⇒ Object

Occupation Group



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# File 'lib/element_numbers/en_0161.rb', line 5

def self.en_0161_column_name
  'Occupation Group'
end

.en_0161_description ⇒ Object 



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# File 'lib/element_numbers/en_0161.rb', line 11

def self.en_0161_description
  'Occupation Group V2 information is compiled from self-reported surveys, derived from state licensing agencies, or calculated through the application of predicted models.'
end

.en_0161_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_0161.rb', line 9

def self.en_0161_value_description(key); ''; end

.en_0162(key) ⇒ Object 



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# File 'lib/element_numbers/en_0162.rb', line 14

def self.en_0162(key)
  likely = {
    '1' => 'Likely',
    '5' => 'Extremely Likely'
  }
  education = {
    '1' => 'High School Diploma',
    '2' => 'Some College',
    '3' => 'Bachelor Degree',
    '4' => 'Graduate Degree',
    '5' => 'Less Than High School Diploma'
  }
  return 'Unknown' if key.size != 2 or education[key[1]].nil? or likely[key[0]].nil?
  [education[key[1]], likely[key[0]]].join(' - ')
end

.en_0162_column_name ⇒ Object 



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# File 'lib/element_numbers/en_0162.rb', line 4

def self.en_0162_column_name
  'Individual Education'
end

.en_0162_description ⇒ Object 



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# File 'lib/element_numbers/en_0162.rb', line 10

def self.en_0162_description
  'Information is compiled from self-reported surveys, derived based on occupational information, or calculated through the application of predictive models.'
end

.en_0162_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_0162.rb', line 8

def self.en_0162_value_description(key); ''; end

.en_0164(key) ⇒ Object 



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# File 'lib/element_numbers/en_0164.rb', line 14

def self.en_0164(key)
  political = [
    'Unknown',
    'Unconnected & Unregistered',
    'Informed But Unregistered',
    'Super Democrats',
    'Left Out Democrats',
    'Conservative Democrats',
    'On-the-Fence Liberals',
    'Green Traditionalists',
    'Mild Republicans',
    'Uninvolved Conservatives',
    'Ultra Conservatives'
  ]
  return 'Unknown' unless (0..10) === key.to_i
  political[key.to_i]
end

.en_0164_column_name ⇒ Object 



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# File 'lib/element_numbers/en_0164.rb', line 4

def self.en_0164_column_name
  'Political Persona'
end

.en_0164_description ⇒ Object 



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# File 'lib/element_numbers/en_0164.rb', line 10

def self.en_0164_description
  'PoliticalPersona Segments provide a detailed understanding of key voter segments, demographics, responsiveness to various media platforms, and attitudes and opinions on important political issues and social trends. Gain in-depth information to the demographics and insights on voter attitudes by segment and detailed analysis of media preferences by segment. PoliticalPersona provide actionable data on voter attitudes and opinions in key areas such as healthcare, employment and social security, education, and more.'
end

.en_0164_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_0164.rb', line 8

def self.en_0164_value_description(key); ''; end

.en_0174(key) ⇒ Object 



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# File 'lib/element_numbers/en_0174.rb', line 14

def self.en_0174(key)
  values = {
    '1Y' => 'Confirmed Presence of Children',
    '5Y' => 'Inferred Presence of Children',
    '5N' => 'Inferred No Children Present',
    '5U' => 'Unknown',
    '00' => 'Deceased and Child Only - primary decision maker is coded as deceased or no adult (18+) in household.'
  }
  key.empty? ? 'Unknown' : values[key]
end

.en_0174_column_name ⇒ Object 



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# File 'lib/element_numbers/en_0174.rb', line 4

def self.en_0174_column_name
  'Presence of Children Age Ranges'
end

.en_0174_description ⇒ Object 



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# File 'lib/element_numbers/en_0174.rb', line 10

def self.en_0174_description
  'Known & Inferred Children\'s Age Ranges are a combination of known children\'s age ranges, and where not available, an Experian model which predicts the likelihood that a child within the age group is present in the living unit. When there is insufficient data to match a customer record to our Enrichment master for Inferred Children\'s Age Range, a median Inferred Children\'s Age Range based on the Known & Inferred Children\'s Age Ranges assigned to other living units in the same ZIP+4 area is used. In the rare case that the ZIP+4 is not on the record, median Children\'s Age Range is based on the Known & Inferred children\'s age ranges assigned to other records in that ZIP region. The median level data applied to records for this element can be identified through the Enrichment Mandatory Append - Total Enrichment Match Type indicator (G).'
end

.en_0174_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_0174.rb', line 8

def self.en_0174_value_description(key); ''; end

.en_0177(key) ⇒ Object 



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# File 'lib/element_numbers/en_0177.rb', line 14

def self.en_0177(key)
  values = {
    '1Y' => 'Confirmed Presence of Children',
    '5Y' => 'Inferred Presence of Children',
    '5N' => 'Inferred No Children Present',
    '5U' => 'Unknown'
  }
  key.empty? ? 'Unknown' : values[key]
end

.en_0177_column_name ⇒ Object 



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# File 'lib/element_numbers/en_0177.rb', line 4

def self.en_0177_column_name
  'Presence of Children 0-18'
end

.en_0177_description ⇒ Object 



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# File 'lib/element_numbers/en_0177.rb', line 10

def self.en_0177_description
  'Presence of Children in Household identifies households with children. A child is 0 to 18 years old. This field is sourced from birth records, newspaper birth announcements and proprietary sources. This field is comprised of known and inferred data. When there is insufficient data to match a customer\'s record to our Enrichment master for Presence of Children, a median Presence of Children based on the Presence of Children scores assigned to other living units in the same ZIP+4 area is used. In the rare case that the ZIP+4 is not on the record, median Presence of Children is based on the Presence of Children scores assigned to other records in that ZIP region. The median level data applied to records for this element can be identified through the Enrichment Mandatory Append - Total Enrichment Match Type indicator (4).'
end

.en_0177_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_0177.rb', line 8

def self.en_0177_value_description(key); ''; end

.en_0601(key) ⇒ Object 



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# File 'lib/element_numbers/en_0601.rb', line 14

def self.en_0601(key)
  return nil unless /[0-9]+/ =~ key
  key.gsub(/[^0-9]/,'').to_i
end

.en_0601_column_name ⇒ Object 



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# File 'lib/element_numbers/en_0601.rb', line 4

def self.en_0601_column_name
  'Estimated Current Home Value'
end

.en_0601_description ⇒ Object 



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# File 'lib/element_numbers/en_0601.rb', line 10

def self.en_0601_description
  'Estimated Current Home Value is Experian\'s proprietary model that predicts the current home value. The ECHV model uses county, state and national trends in home sale prices and assessed home values to determine property appreciation rates and current home values. Data was examined at county level and the model inputs, such as sale price, sale year or assessed price of the property that were acquired from realty deed records and county tax assessor records.'
end

.en_0601_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_0601.rb', line 8

def self.en_0601_value_description(key); ''; end

.en_0711(key) ⇒ Object 



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# File 'lib/element_numbers/en_0711.rb', line 14

def self.en_0711(key)
  confidence = {
    '1' => 'Extremely Likely',
    '2' => 'Highly Likely',
    '3' => 'Likely'
  }

  values = {
    'A' => '1,000 - 9,999',
    'B' => '10,000 - 24,999',
    'C' => '25,000 - 39,999',
    'D' => '40,000 - 59,999',
    'E' => '60,000 - 79,999',
    'F' => '80,000 - 99,999',
    'G' => '100,000 - 119,999',
    'H' => '120,000 - 139,999',
    'I' => '140,000 - 159,999',
    'J' => '160,000 - 199,999',
    'K' => '200,000 - 249,999',
    'L' => '250,000 - 249,999',
    'M' => '350,000 - 449,999',
    'N' => '450,000 - 749,999',
    'O' => '750,000 - 999,999',
    'P' => '1,000,000 - +',
    'U' => 'Unknown'
  }
  return 'Unknown' if key.size != 2 or confidence[key[0]].nil? or values[key[1]].nil?
  [values[key[1]], confidence[key[0]]].join(' - ')
end

.en_0711_column_name ⇒ Object 



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# File 'lib/element_numbers/en_0711.rb', line 4

def self.en_0711_column_name
  'Estimated Available Equity Ranges'
end

.en_0711_description ⇒ Object 



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# File 'lib/element_numbers/en_0711.rb', line 10

def self.en_0711_description
  'The Estimated Available Equity model is Experian\'s proprietary model designed to accurately predict equity in the home for Homeowner records. The value of equity equals the estimated current home value minus the remaining principal of the mortgage loan. The model inputs, such as sale price, sale date and mortgage amount of the property were acquired from realty deed records from the county court house.'
end

.en_0711_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_0711.rb', line 8

def self.en_0711_value_description(key); ''; end

.en_0715(key) ⇒ Object 



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# File 'lib/element_numbers/en_0715.rb', line 14

def self.en_0715(key)
  confidence = {
    '1' => 'Extremely Likely',
    '2' => 'Highly Likely',
    '3' => 'Likely'
  }


  return 'Unknown' if key.size != 4 or confidence[key[0]].nil? or key[1...4].to_i.nil? or key[1...4].to_i > 125
  [key[1...4].to_i, confidence[key[0]]].join(' - ')
end

.en_0715_column_name ⇒ Object 



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# File 'lib/element_numbers/en_0715.rb', line 4

def self.en_0715_column_name
  'Estimated Current Loan-to-Value Ratio Ranges'
end

.en_0715_description ⇒ Object 



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# File 'lib/element_numbers/en_0715.rb', line 10

def self.en_0715_description
  'Estimated Current Loan to Value Ratio is a percentage of the current estimated mortgage amount divided by the estimated current market value of the home. The higher the ratio, the less equity the homeowner has on the house. This ratio is available on wherever the loan amount and estimated current home value are available.'
end

.en_0715_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_0715.rb', line 8

def self.en_0715_value_description(key); ''; end

.en_0716(key) ⇒ Object 



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# File 'lib/element_numbers/en_0716.rb', line 14

def self.en_0716(key)
  confidence = {
    '1' => 'Extremely Likely',
    '2' => 'Highly Likely',
    '3' => 'Likely'
  }
  values = {
    'A' => '1 - 199',
    'B' => '200 - 299',
    'C' => '300 - 399',
    'D' => '400 - 499',
    'E' => '500 - 599',
    'F' => '600 - 699',
    'G' => '700 - 799',
    'H' => '800 - 899',
    'I' => '900 - 999',
    'J' => '1000 - 1199',
    'K' => '1200 - 1399',
    'L' => '1400 - 1699',
    'M' => '1700 - 1999',
    'N' => '2000+',
    'U' => 'Unknown'
  }
  return 'Unknown' if key.size != 2 or confidence[key[0]].nil? or key[1].nil? or values[key[1]] == 'Unknown'
  [values[key[1]], confidence[key[0]]].join(' - ')
end

.en_0716_column_name ⇒ Object 



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# File 'lib/element_numbers/en_0716.rb', line 4

def self.en_0716_column_name
  'Estimated Current Monthly Mortgage Payment Ranges'
end

.en_0716_description ⇒ Object 



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# File 'lib/element_numbers/en_0716.rb', line 10

def self.en_0716_description
  'The Estimated Current Monthly Mortgage Payment model is a subset of the Experian Equity model and predicts the monthly home payment. Input variables; such as Sale Date, Mortgage Amount and Interest Rate acquired from realty deed records from the county court house are utilized.'
end

.en_0716_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_0716.rb', line 8

def self.en_0716_value_description(key); ''; end

.en_0717(key) ⇒ Object 



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# File 'lib/element_numbers/en_0717.rb', line 14

def self.en_0717(key)
  confidence = {
    '1' => 'Extremely Likely',
    '2' => 'Highly Likely',
    '3' => 'Likely'
  }

  values = {
    'A' => '1,000 - 9,999',
    'B' => '10,000 - 24,999',
    'C' => '25,000 - 39,999',
    'D' => '40,000 - 59,999',
    'E' => '60,000 - 79,999',
    'F' => '80,000 - 99,999',
    'G' => '100,000 - 119,999',
    'H' => '120,000 - 139,999',
    'I' => '140,000 - 159,999',
    'J' => '160,000 - 199,999',
    'K' => '200,000 - 249,999',
    'L' => '250,000 - 249,999',
    'M' => '350,000 - 449,999',
    'N' => '450,000 - 749,999',
    'O' => '750,000 - 999,999',
    'P' => '1,000,000 - +',
    'U' => 'Unknown'
  }

  return 'Unknown' if key.size != 2 or confidence[key[0]].nil? or values[key[1]].nil?
  [values[key[1]], confidence[key[0]]].join(' - ')
end

.en_0717_column_name ⇒ Object 



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# File 'lib/element_numbers/en_0717.rb', line 4

def self.en_0717_column_name
  'Estimated Current Mortgage Amount Ranges'
end

.en_0717_description ⇒ Object 



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# File 'lib/element_numbers/en_0717.rb', line 10

def self.en_0717_description
  'Estimated Current Mortgage Amount model predicts the amount currently remaining on the primary mortgage. It projects the amount owed on the mortgage by using the original mortgage amount or refinance amount; even where known mortgage data is not available. When data is not available from deed records, ECMA is calculated based on a combination of real estate data including estimated current home value and other ConsumerView variables such as demographics, summarized credit and auto data, and census. A confidence level of Likely is applied when the known data is not available.'
end

.en_0717_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_0717.rb', line 8

def self.en_0717_value_description(key); ''; end

.en_310m(key) ⇒ Object 



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# File 'lib/element_numbers/en_310M.rb', line 14

def self.en_310m(key)
  values = {
    'A01' => 'Americas Wealthiest',
    'A02' => 'Dream Weavers',
    'A03' => 'White-collar Suburbia',
    'A04' => 'Upscale Suburbanites',
    'A05' => 'Enterprising Couples',
    'A06' => 'Small-town Success',
    'A07' => 'New Suburbia Families',
    'B01' => 'Status-conscious Consumers',
    'B02' => 'Affluent Urban Professionals',
    'B03' => 'Urban Commuter Families',
    'B04' => 'Solid Suburban Life',
    'B05' => 'Second-generation Success',
    'B06' => 'Successful Suburbia',
    'C01' => 'Second City Homebodies',
    'C02' => 'Prime Middle America',
    'C03' => 'Suburban Optimists',
    'C04' => 'Family Convenience',
    'C05' => 'Mid-market Enterprise',
    'D01' => 'Nuevo Hispanic Families',
    'D02' => 'Working Rural Communities',
    'D03' => 'Lower-income Essentials',
    'D04' => 'Small-city Endeavors',
    'E01' => 'Ethnic Urban Mix',
    'E02' => 'Urban Blues',
    'E03' => 'Professional Urbanites',
    'E04' => 'Suburban Advantage',
    'E05' => 'American Great Outdoors',
    'E06' => 'Mature America',
    'F01' => 'Steadfast Conservative',
    'F02' => 'Moderate Conventionalists',
    'F03' => 'Southern Blues',
    'F04' => 'Urban Grit',
    'F05' => 'Grass-roots Living',
    'G01' => 'Hardy Rural Families',
    'G02' => 'Rural Southern Living',
    'G03' => 'Coal and Crops',
    'G04' => 'Native Americana',
    'H01' => 'Young Cosmopolitans',
    'H02' => 'Minority Metro Communities',
    'H03' => 'Stable Careers',
    'H04' => 'Aspiring Hispania',
    'I01' => 'Industrious Country Living',
    'I02' => 'Americas Farmlands',
    'I03' => 'Comfy Country Living',
    'I04' => 'Small-town Connections',
    'I05' => 'Hinterland Families',
    'J01' => 'Rugged Rural Style',
    'J02' => 'Latino Nuevo',
    'J03' => 'Struggling City Centers',
    'J04' => 'College Town Communities',
    'J05' => 'Metro Beginnings',
    'K01' => 'Unattached Multi-cultures',
    'K02' => 'Academic Influences',
    'K03' => 'African-American Neighborhoods',
    'K04' => 'Urban Diversity',
    'K05' => 'New Generation Activists',
    'K06' => 'Getting By',
    'L01' => 'Military Family Life',
    'L02' => 'Major University Towns',
    'L03' => 'Gray Perspectives'
  }
  key.empty? ? 'Unknown' : values[key]
end

.en_310m_column_name ⇒ Object 



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# File 'lib/element_numbers/en_310M.rb', line 4

def self.en_310m_column_name
  'MOSAIC Z4'
end

.en_310m_description ⇒ Object 



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# File 'lib/element_numbers/en_310M.rb', line 10

def self.en_310m_description
  'Mosaic Z4 is a geodemographic level classification system developed by Experian. Mosaic Types identify 60 Types and 12 Groups based on consumer behaviors, lifestyles and attitudes. Detailed definitions available upon request.'
end

.en_310m_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_310M.rb', line 8

def self.en_310m_value_description(key); ''; end

.en_313hh(key) ⇒ Object 



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# File 'lib/element_numbers/en_313HH.rb', line 12

def self.en_313hh(key)
  values = {
    'A01' => 'American Royalty',
    'A02' => 'Platinum Prosperity',
    'A03' => 'Kids and Cabernet',
    'A04' => 'Picture Perfect Families',
    'A05' => 'Couples with Clout',
    'A06' => 'Jet Set Urbanites',
    'B07' => 'Generational Soup',
    'B08' => 'Babies and Bliss',
    'B09' => 'Family Fun-tastic',
    'B10' => 'Asian Achievers',
    'C11' => 'Aging of Aquarius',
    'C12' => 'Golf Carts and Gourmets',
    'C13' => 'Silver Sophisticates',
    'C14' => 'Boomers and Boomerangs',
    'D15' => 'Sports Utility Families',
    'D16' => 'Settled in Suburbia',
    'D17' => 'Cul de Sac Diversity',
    'D18' => 'Soulful Spenders',
    'E19' => 'Full Pockets, Empty Nests',
    'E20' => 'No Place Like Home',
    'F22' => 'Fast Track Couples',
    'F23' => 'Families Matter Most',
    'G24' => 'Status Seeking Singles',
    'G25' => 'Urban Edge',
    'H26' => 'Progressive Potpourri',
    'H27' => 'Birkenstocks and Beemers',
    'H28' => 'Everyday Moderates',
    'H29' => 'Destination Recreation',
    'I30' => 'Stockcars and State Parks',
    'I31' => 'Blue Collar Comfort',
    'I32' => 'Latin Flair',
    'I33' => 'Hispanic Harmony',
    'J34' => 'Aging in Place',
    'J35' => 'Rural Escape',
    'J36' => 'Settled and Sensible',
    'K37' => 'Wired for Success',
    'K38' => 'Gotham Blend',
    'K39' => 'Metro Fusion',
    'L41' => 'Booming and Consuming',
    'L42' => 'Rooted Flower Power',
    'L43' => 'Homemade Happiness',
    'M44' => 'Red, White and Bluegrass',
    'M45' => 'Diapers and Debit Cards',
    'N46' => 'True Grit Americans',
    'N47' => 'Countrified Pragmatics',
    'N48' => 'Gospel and Grits',
    'N49' => 'Work Hard, Pray Hard',
    'O50' => 'Full Steam Ahead',
    'O51' => 'Digital Dependents',
    'O52' => 'Urban Ambition',
    'O53' => 'Colleges and Cafes',
    'O54' => 'Striving Single Scene',
    'O55' => 'Family Troopers',
    'P56' => 'Rolling the Dice',
    'P57' => 'Meager Metro Means',
    'P58' => 'Fragile Families',
    'P59' => 'Nuevo Horizons',
    'P60' => 'Ciudad Strivers',
    'P61' => 'Humble Beginnings',
    'Q62' => 'Reaping Rewards',
    'Q63' => 'Footloose and Family Free',
    'Q64' => 'Town Elders',
    'Q65' => 'Senior Discounts',
    'R66' => 'Dare to Dream',
    'R67' => 'Hope for Tomorrow',
    'S68' => 'Small Town Shallow Pockets',
    'S69' => 'Soul Survivors',
    'S70' => 'Enduring Hardships'
  }
  key.empty? ? 'Unknown' : values[key]
end

.en_313hh_column_name ⇒ Object 



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# File 'lib/element_numbers/en_313HH.rb', line 4

def self.en_313hh_column_name
  'Mosiac Household'
end

.en_313hh_description ⇒ Object 



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# File 'lib/element_numbers/en_313HH.rb', line 8

def self.en_313hh_description
  ''
end

.en_313hh_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_313HH.rb', line 86

def self.en_313hh_value_description(key)
  values = {
    'A01' => 'Wealthy, influential and successful couples and families living in prestigious suburbs',
    'A02' => 'PWealthy and established empty-nesting couples residing in suburban and in-town homes',
    'A03' => 'Prosperous, middle-aged married couples with children living child-focused lives in affluent suburbs',
    'A04' => 'Established families of child-rearing households living in wealthy suburbs',
    'A05' => 'Middle-aged, childless couples living in affluent metro areas',
    'A06' => 'Mix of affluent singles and couples living high-rise, fashionable lives in urban neighborhoods',
    'B07' => 'Affluent couples and multi-generational families living a wide range of lifestyles in suburbia',
    'B08' => 'Middle-aged couples with large families and active lives in affluent suburbia',
    'B09' => 'Upscale, middle-aged families with older children pursuing busy kid-centered lives in satellite cities',
    'B10' => 'Affluent, mainly Asian couples and families enjoying dynamic lifestyles in metro areas',
    'C11' => 'Upscale boomer-aged couples living in city and close-in suburbs',
    'C12' => 'Upscale retirees and empty-nesters in comfortable communities',
    'C13' => 'Mature, upscale couples and singles in suburban homes',
    'C14' => 'Baby boomer adults and their teenage/young adult children sharing suburban homes',
    'D15' => 'Upscale, middle-aged couples with school-aged children living active family lifestyles in outlying suburbs',
    'D16' => 'Upper middle-class diverse family units and empty nesters living in established suburbs',
    'D17' => 'Ethnically-diverse, middle-aged families settled in new suburban neighborhoods',
    'D18' => 'Upper middle-class African-American couples and families living in the expanding suburbs',
    'E19' => 'Empty-nesting, upper middle-class households with discretionary income living sophisticated lifestyles',
    'E20' => 'No Place Like Home',
    'F22' => 'Active, young, upper middle-class suburban couples and families living upwardly-mobile lifestyles',
    'F23' => 'Young, middle-class families in scenic suburbs leading active, family-focused lives',
    'G24' => 'Younger, upwardly-mobile singles living in mid-scale metro areas leading leisure-intensive lifestyles',
    'G25' => 'Younger, up-and-coming singles living big city lifestyles located within top CBSA markets',
    'H26' => 'Mature, multi-ethnic couples with comfortable and active lives in middle-class suburbs',
    'H27' => 'Upper middle-class, established couples living leisure lifestyles in small towns and cities',
    'H28' => 'Mid-scale, multi-cultural couples and families living in mid-tier metro suburban settings',
    'H29' => 'Middle-aged, midscale couples in rural towns and fringe suburbs working to enjoy their active lifestyles',
    'I30' => 'Middle-class couples and families living in more remote rural communities',
    'I31' => 'Middle-class families in smaller cities and towns with solid blue-collar jobs',
    'I32' => 'Conventional Hispanic Generation X families located in selected coastal city homes',
    'I33' => 'Middle-class Hispanic families living lively lifestyles in city-centric neighborhoods',
    'J34' => 'Middle-class seniors living solid, suburban lifestyles',
    'J35' => 'Older, middle-class couples and singles living comfortable lives in rural towns',
    'J36' => 'Older, middle-class and empty-nesting couples and singles in city neighborhoods',
    'K37' => 'Young, mid-scale singles and couples living socially-active city lives',
    'K38' => 'Mix of middle-aged and middle-class singles and couples living urban New York City-area lifestyles',
    'K39' => 'Ethnically-diverse, middle-aged singles living urban active lifestyles',
    'L41' => 'Older empty-nesting couples and singles enjoying relaxed lives in small towns',
    'L42' => 'Mid-scale baby boomer singles and couples rooted in established suburban communities and approaching retirement',
    'L43' => 'Lower middle-class baby boomer households living in remote town and country homes',
    'M44' => 'Lower middle-income rural families with diverse adult and children household dynamics',
    'M45' => 'Young, working-class families and single parent households living in small established, city residences',
    'N46' => 'Older, middle-class households in town and country communities located in the nation\'s midsection',
    'N47' => 'Lower middle-income couples and singles living rural, casual lives',
    'N48' => 'Lower middle-income African-American multi-generational families living in small towns',
    'N49' => 'Working-class, middle-aged couples and singles living in rural homes',
    'O50' => 'Younger and middle-aged singles gravitating to second-tier cities',
    'O51' => 'Mix of Generation Y and X singles who live digital-driven, urban lifestyles',
    'O52' => 'Mainly Generation Y African-American singles and single families established in mid-market cities',
    'O53' => 'Young singles and recent college graduates living in college communities',
    'O54' => 'Young, multi-ethnic singles living in Midwest and Southern city centers',
    'O55' => 'Families and single-parent households living near military bases',
    'P56' => 'Middle-aged, mid-scale income singles and divorced individuals in secondary cities',
    'P57' => 'Mid-scale African-American singles established in inner-city communities',
    'P58' => 'Multi-cultural singles and families with mid and low incomes living settled lives in urban apartments',
    'P59' => 'Middle-aged, mid-scale income Hispanic families living mainly within US border cities',
    'P60' => 'Mid-scale Hispanic families and single parents in gateway communities',
    'P61' => 'Multi-ethnic singles and single-parent households with mid-scale incomes in city apartments',
    'Q62' => 'Relaxed, retired couples and widowed individuals in suburban homes living quiet lives',
    'Q63' => 'Elderly couples and widowed individuals living active and comfortable lifestyles',
    'Q64' => 'Stable, minimalist seniors living in older residences and leading sedentary lifestyles',
    'Q65' => 'Downscale, settled retirees in metro apartment communities',
    'R66' => 'Young singles, couples and single parents with lower incomes starting out in city apartments',
    'R67' => 'Young, lower-income African-American single parents in second-city apartments',
    'S68' => 'Older, down-scale singles and empty-nesters living in modest ex-urban small towns',
    'S69' => 'Older, down-scale African-Americans singles and single parents established in modest urban neighborhoods',
    'S70' => 'Middle-aged, down-scale singles and divorced individuals in transitional small town and ex-urban apartments'
  }
  key.empty? ? 'Unknown' : values[key]
end

.en_8102(key) ⇒ Object 



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# File 'lib/element_numbers/en_8102.rb', line 14

def self.en_8102(key)
  values = {
    'Y' => 'Household has indicated they contribute to charities',
    'U' => 'Unknown'
  }
  key.empty? ? 'Unknown' : values[key]
end

.en_8102_column_name ⇒ Object 



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# File 'lib/element_numbers/en_8102.rb', line 4

def self.en_8102_column_name
  'ZDonor'
end

.en_8102_description ⇒ Object 



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# File 'lib/element_numbers/en_8102.rb', line 10

def self.en_8102_description
  'ZDonor indicates a household self reported they contribute to charities. BehaviorBank® Household Indicators groups similar self-reported elements into slightly broader categories'
end

.en_8102_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_8102.rb', line 8

def self.en_8102_value_description(key); ''; end

.en_8103(key) ⇒ Object 



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# File 'lib/element_numbers/en_8103.rb', line 14

def self.en_8103(key)
  values = {
    'Y' => 'Household has indicated being a pet enthusiast',
    'U' => 'Unknown'
  }
  key.empty? ? 'Unknown' : values[key]
end

.en_8103_column_name ⇒ Object 



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# File 'lib/element_numbers/en_8103.rb', line 4

def self.en_8103_column_name
  'ZPets'
end

.en_8103_description ⇒ Object 



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# File 'lib/element_numbers/en_8103.rb', line 10

def self.en_8103_description
  'ZPets indicates a household has self reported being a pet enthusiast. BehaviorBank® Household Indicators groups similar self-reported elements into slightly broader categories.'
end

.en_8103_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_8103.rb', line 8

def self.en_8103_value_description(key); ''; end

.en_8109(key) ⇒ Object 



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# File 'lib/element_numbers/en_8109.rb', line 14

def self.en_8109(key)
  values = {
    'Y' => 'Household has indicated an interest in investing',
    'U' => 'Unknown'
  }
  key.empty? ? 'Unknown' : values[key]
end

.en_8109_column_name ⇒ Object 



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# File 'lib/element_numbers/en_8109.rb', line 4

def self.en_8109_column_name
  'ZInvestor'
end

.en_8109_description ⇒ Object 



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# File 'lib/element_numbers/en_8109.rb', line 10

def self.en_8109_description
  'Zinvestor indicates a household\'s has self reported an interest in investing. BehaviorBank® Household Indicators groups similar self-reported elements into slightly broader categories.'
end

.en_8109_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_8109.rb', line 8

def self.en_8109_value_description(key); ''; end

.en_8123(key) ⇒ Object 



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# File 'lib/element_numbers/en_8123.rb', line 14

def self.en_8123(key)
  values = {
    'Y' => 'Household has indicated a do-it-yourselfer',
    'U' => 'Unknown'
  }
  key.empty? ? 'Unknown' : values[key]
end

.en_8123_column_name ⇒ Object 



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# File 'lib/element_numbers/en_8123.rb', line 4

def self.en_8123_column_name
  'ZDo-It-Yourselfer'
end

.en_8123_description ⇒ Object 



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# File 'lib/element_numbers/en_8123.rb', line 10

def self.en_8123_description
  'ZDo-it-yourselfer indicates a household is a self reported do-it-yourselfer. BehaviorBank® Household Indicators groups similar self-reported elements into slightly broader categories.'
end

.en_8123_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_8123.rb', line 8

def self.en_8123_value_description(key); ''; end

.en_8128(key) ⇒ Object 



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# File 'lib/element_numbers/en_8128.rb', line 14

def self.en_8128(key)
  values = {
    'Y' => 'Household has indicated donating to environmental causes',
    'U' => 'Unknown'
  }
  key.empty? ? 'Unknown' : values[key]
end

.en_8128_column_name ⇒ Object 



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# File 'lib/element_numbers/en_8128.rb', line 4

def self.en_8128_column_name
  'ZEnvironmental Donor'
end

.en_8128_description ⇒ Object 



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# File 'lib/element_numbers/en_8128.rb', line 10

def self.en_8128_description
  'ZEnvironmental donor indicates a household\'s has self reported donating to environmental causes. BehaviorBank® Household Indicators groups similar self-reported elements into slightly broader categories.'
end

.en_8128_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_8128.rb', line 8

def self.en_8128_value_description(key); ''; end

.en_8133(key) ⇒ Object 



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# File 'lib/element_numbers/en_8133.rb', line 14

def self.en_8133(key)
  values = {
    'Y' => 'Household has indicated being a mail order multibuyer',
    'U' => 'Unknown'
  }
  key.empty? ? 'Unknown' : values[key]
end

.en_8133_column_name ⇒ Object 



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# File 'lib/element_numbers/en_8133.rb', line 4

def self.en_8133_column_name
  'ZMail Order Multi-Buyer'
end

.en_8133_description ⇒ Object 



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# File 'lib/element_numbers/en_8133.rb', line 10

def self.en_8133_description
  'ZMail order multi-buyer indicates a household is a self reported mail order multibuyer. BehaviorBank® Household Indicators groups similar self-reported elements into slightly broader categories.'
end

.en_8133_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_8133.rb', line 8

def self.en_8133_value_description(key); ''; end

.en_8136(key) ⇒ Object 



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# File 'lib/element_numbers/en_8136.rb', line 14

def self.en_8136(key)
  values = {
    'Y' => 'Household has indicated being dog enthusiasts',
    'U' => 'Unknown'
  }
  key.empty? ? 'Unknown' : values[key]
end

.en_8136_column_name ⇒ Object 



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# File 'lib/element_numbers/en_8136.rb', line 4

def self.en_8136_column_name
  'ZDogs'
end

.en_8136_description ⇒ Object 



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# File 'lib/element_numbers/en_8136.rb', line 10

def self.en_8136_description
  'ZDogs indicates a household has self reported being dog enthusiasts. BehaviorBank® Household Indicators groups similar self-reported elements into slightly broader categories.'
end

.en_8136_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_8136.rb', line 8

def self.en_8136_value_description(key); ''; end

.en_8137(key) ⇒ Object 



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# File 'lib/element_numbers/en_8137.rb', line 14

def self.en_8137(key)
  values = {
    'Y' => 'Household has indicated being cat enthusiasts',
    'U' => 'Unknown'
  }
  key.empty? ? 'Unknown' : values[key]
end

.en_8137_column_name ⇒ Object 



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# File 'lib/element_numbers/en_8137.rb', line 4

def self.en_8137_column_name
  'ZCats'
end

.en_8137_description ⇒ Object 



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# File 'lib/element_numbers/en_8137.rb', line 10

def self.en_8137_description
  'ZCats indicates a household has self reported being cat enthusiasts. BehaviorBank® Household Indicators groups similar self-reported elements into slightly broader categories.'
end

.en_8137_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_8137.rb', line 8

def self.en_8137_value_description(key); ''; end

.en_8180(key) ⇒ Object 



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# File 'lib/element_numbers/en_8180.rb', line 14

def self.en_8180(key)
  values = {
    'Y' => 'Household has self reported being active military',
    'U' => 'Unknown'
  }
  key.empty? ? 'Unknown' : values[key]
end

.en_8180_column_name ⇒ Object 



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# File 'lib/element_numbers/en_8180.rb', line 4

def self.en_8180_column_name
  'Zactive Military'
end

.en_8180_description ⇒ Object 



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# File 'lib/element_numbers/en_8180.rb', line 10

def self.en_8180_description
  'ZActive Military indicates a household has self reported as being active military. BehaviorBank® Household Indicators groups similar self-reported elements into slightly broader categories.'
end

.en_8180_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_8180.rb', line 8

def self.en_8180_value_description(key); ''; end

.en_8181(key) ⇒ Object 



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# File 'lib/element_numbers/en_8181.rb', line 14

def self.en_8181(key)
  values = {
    'Y' => 'Household has self reported being inactive military',
    'U' => 'Unknown'
  }
  key.empty? ? 'Unknown' : values[key]
end

.en_8181_column_name ⇒ Object 



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# File 'lib/element_numbers/en_8181.rb', line 4

def self.en_8181_column_name
  'Zinactive Military'
end

.en_8181_description ⇒ Object 



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# File 'lib/element_numbers/en_8181.rb', line 10

def self.en_8181_description
  'ZInactive Military indicates a household has self reported as being inactive military. BehaviorBank® Household Indicators groups similar self-reported elements into slightly broader categories.'
end

.en_8181_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_8181.rb', line 8

def self.en_8181_value_description(key); ''; end

.en_8502(key) ⇒ Object 



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# File 'lib/element_numbers/en_8502.rb', line 24

def self.en_8502(key)
  charity = {
    'Y' => 'Living unit has a known to contribute to charities',
    'U' => 'Unknown'
  }
  code = {
    '1' => 'Extremely Likely to contribute to charities',
    '2' => 'Highly Likely to contribute to charities',
    '3' => 'Very Likely to contribute to charities',
    '4' => 'Somewhat Likely to contribute to charities',
    '5' => 'Likely to contribute to charitie',
    '6' => 'Somewhat Unlikely to contribute to charities',
    '7' => 'Very Unlikely to contribute to charities',
    '8' => 'Highly Unlikely to contribute to charities',
    '9' => 'Extremely Unlikely to contribute to charities',
    '0' => 'Unknown / Not Attempted',
  }
  return 'Unknown' if key.size != 2 or code[key[1]].nil? or charity[key[0]].nil?
  [charity[key[0]], code[key[1]]].join(' - ')
end

.en_8502_column_name ⇒ Object 



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# File 'lib/element_numbers/en_8502.rb', line 4

def self.en_8502_column_name
  'Donor & Donor Propensity, Self reported data'
end

.en_8502_description ⇒ Object 



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# File 'lib/element_numbers/en_8502.rb', line 10

def self.en_8502_description
  'Z donor & donor propensities indicates a household\'s self reported to contribute to charities. BehaviorBank Household Indicators groups similar self-reported elements into slightly broader categories. Propensities are created with sophisticated analytical models based solely on self-reported data to predict households that are likely to exhibit the same behaviors. We supplement Behavior Bank Indicators with propensities for those records where self-reported data is not available to maximize coverage and response. The propensity codes rank living units from 1 to 9 using individual, living unit and area level demographics. Field values beginning with 1 indicate the strongest probability'
end

.en_8502_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_8502.rb', line 8

def self.en_8502_value_description(key); ''; end

.en_8502p(key) ⇒ Object 



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# File 'lib/element_numbers/en_8502.rb', line 45

def self.en_8502p(key)
  Experian::DataDictionary.en_8502(key)
end

.en_8502p_column_name ⇒ Object 



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# File 'lib/element_numbers/en_8502.rb', line 14

def self.en_8502p_column_name
  'Donor & Donor Propensity, Quick Predict Code'
end

.en_8502p_description ⇒ Object 



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# File 'lib/element_numbers/en_8502.rb', line 20

def self.en_8502p_description
  'Z donor & donor propensities indicates a household\'s self reported to contribute to charities. BehaviorBank Household Indicators groups similar self-reported elements into slightly broader categories. Propensities are created with sophisticated analytical models based solely on self-reported data to predict households that are likely to exhibit the same behaviors. We supplement Behavior Bank Indicators with propensities for those records where self-reported data is not available to maximize coverage and response. The propensity codes rank living units from 1 to 9 using individual, living unit and area level demographics. Field values beginning with 1 indicate the strongest probability.'
end

.en_8502p_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_8502.rb', line 18

def self.en_8502p_value_description(key); ''; end

.en_8503(key) ⇒ Object 



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# File 'lib/element_numbers/en_8503.rb', line 24

def self.en_8503(key)
  charity = {
    'Y' => 'Living unit has a known pet enthusiast',
    'U' => 'Unknown'
  }
  code = {
    '1' => 'Extremely Likely pet enthusiast',
    '2' => 'Highly Likely pet enthusiast',
    '3' => 'Very Likely pet enthusiast',
    '4' => 'Somewhat Likely pet enthusiast',
    '5' => 'Likely pet enthusiast',
    '6' => 'Somewhat Unlikely pet enthusiast',
    '7' => 'Very Unlikely pet enthusiast',
    '8' => 'Highly Unlikely pet enthusiast',
    '9' => 'Extremely Unlikely pet enthusiast',
    '0' => 'Unknown / Not Attempted',
  }
  return 'Unknown' if key.size != 2 or code[key[1]].nil? or charity[key[0]].nil?
  [charity[key[0]], code[key[1]]].join(' - ')
end

.en_8503_column_name ⇒ Object 



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# File 'lib/element_numbers/en_8503.rb', line 4

def self.en_8503_column_name
  'Pets & Pets Propensity, Self reported data'
end

.en_8503_description ⇒ Object 



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# File 'lib/element_numbers/en_8503.rb', line 10

def self.en_8503_description
  'Z pets & pets propensities indicates a household\'s self reported pet enthusiast. BehaviorBank Household Indicators groups similar self-reported elements inslightly broader categories. Propensities are created with sophisticated analytical models based solely on self-reported data to predict households that are likely to exhibit the same behaviors. We supplement Behavior Bank Indicators with propensities for those records where self-reported data is not available to maximize coverage and response. The propensity codes rank living units from 1 to 9 using individual, living unit and area level demographics. Field values beginning with 1 indicate the strongest probability.'
end

.en_8503_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_8503.rb', line 8

def self.en_8503_value_description(key); ''; end

.en_8503p(key) ⇒ Object 



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# File 'lib/element_numbers/en_8503.rb', line 45

def self.en_8503p(key)
  Experian::DataDictionary.en_8503(key)
end

.en_8503p_column_name ⇒ Object 



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# File 'lib/element_numbers/en_8503.rb', line 14

def self.en_8503p_column_name
  'Pets & Pets Propensity, Quick Predict Code'
end

.en_8503p_description ⇒ Object 



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# File 'lib/element_numbers/en_8503.rb', line 20

def self.en_8503p_description
  'Z pets & pets propensities indicates a household\'s self reported pet enthusiast. BehaviorBank Household Indicators groups similar self-reported elements into slightly broader categories. Propensities are created with sophisticated analytical models based solely on self-reported data to predict households that are likely to exhibit the same behaviors. We supplement Behavior Bank Indicators with propensities for those records where self-reported data is not available to maximize coverage and response. The propensity codes rank living units from 1 to 9 using individual, living unit and area level demographics. Field values beginning with 1 indicate the strongest probability.'
end

.en_8503p_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_8503.rb', line 18

def self.en_8503p_value_description(key); ''; end

.en_8504(key) ⇒ Object 



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# File 'lib/element_numbers/en_8504.rb', line 24

def self.en_8504(key)
  charity = {
    'Y' => 'Living unit has a known interest in cultural arts',
    'U' => 'Unknown'
  }
  code = {
    '1' => 'Extremely Likely interest in cultural arts',
    '2' => 'Highly Likely interest in cultural arts',
    '3' => 'Very Likely interest in cultural arts',
    '4' => 'Somewhat Likely interest in cultural arts',
    '5' => 'Likely interest in cultural arts',
    '6' => 'Somewhat Unlikely interest in cultural arts',
    '7' => 'Very Unlikely interest in cultural arts',
    '8' => 'Highly Unlikely interest in cultural arts',
    '9' => 'Extremely Unlikely interest in cultural arts',
    '0' => 'Unknown / Not Attempted',
  }
  return 'Unknown' if key.size != 2 or code[key[1]].nil? or charity[key[0]].nil?
  [charity[key[0]], code[key[1]]].join(' - ')
end

.en_8504_column_name ⇒ Object 



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# File 'lib/element_numbers/en_8504.rb', line 4

def self.en_8504_column_name
  'Arts & Arts Propensity, Self reported data'
end

.en_8504_description ⇒ Object 



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# File 'lib/element_numbers/en_8504.rb', line 10

def self.en_8504_description
  'Z arts & arts propensities indicates a household\'s self reported interest in cultural arts. BehaviorBank Household Indicators groups similar self-reported elements into slightly broader categories. Propensities are created with sophisticated analytical models based solely on self-reported data to predict households that are likely to exhibit the same behaviors. We supplement Behavior Bank Indicators with propensities for those records where self-reported data is not available to maximize coverage and response.The propensity codes rank living units from 1 to 9 using individual, living unit and area level demographics. Field values beginning with 1 indicate the strongest probability.'
end

.en_8504_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_8504.rb', line 8

def self.en_8504_value_description(key); ''; end

.en_8504p(key) ⇒ Object 



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# File 'lib/element_numbers/en_8504.rb', line 45

def self.en_8504p(key)
  Experian::DataDictionary.en_8504(key)
end

.en_8504p_column_name ⇒ Object 



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# File 'lib/element_numbers/en_8504.rb', line 14

def self.en_8504p_column_name
  'Arts & Arts Propensity, Quick Predict code'
end

.en_8504p_description ⇒ Object 



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# File 'lib/element_numbers/en_8504.rb', line 20

def self.en_8504p_description
  'Z arts & arts propensities indicates a household\'s self reported interest in cultural arts. BehaviorBank Household Indicators groups similar self-reported elements into slightly broader categories. Propensities are created with sophisticated analytical models based solely on self-reported data to predict households that are likely to exhibit the same behaviors. We supplement Behavior Bank Indicators with propensities for those records where self-reported data is not available to maximize coverage and response. The propensity codes rank living units from 1 to 9 using individual, living unit and area level demographics. Field values beginning with 1 indicate the strongest probability.'
end

.en_8504p_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_8504.rb', line 18

def self.en_8504p_value_description(key); ''; end

.en_8505(key) ⇒ Object 



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# File 'lib/element_numbers/en_8505.rb', line 26

def self.en_8505(key)
  charity = {
    'Y' => 'Living unit has purchased through the mail',
    'U' => 'Unknown'
  }
  code = {
    '1' => 'Extremely Likely purchased through the mail',
    '2' => 'Highly Likely purchased through the mail',
    '3' => 'Very Likely purchased through the mail',
    '4' => 'Somewhat Likely purchased through the mail',
    '5' => 'Likely purchased through the mail',
    '6' => 'Somewhat Unlikely purchased through the mail',
    '7' => 'Very Unlikely purchased through the mail',
    '8' => 'Highly Unlikely purchased through the mail',
    '9' => 'Extremely Unlikely purchased through the mail',
    '0' => 'Unknown / Not Attempted',
  }
  return 'Unknown' if key.size != 2 or code[key[1]].nil? or charity[key[0]].nil?
  [charity[key[0]], code[key[1]]].join(' - ')
end

.en_8505_column_name ⇒ Object

Mailorder Buyer & Mailorder Buyer Prop, Self Reported data



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# File 'lib/element_numbers/en_8505.rb', line 5

def self.en_8505_column_name
  'Mailorder Buyer & Mailorder Buyer Prop, Self Reported data'
end

.en_8505_description ⇒ Object 



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# File 'lib/element_numbers/en_8505.rb', line 11

def self.en_8505_description
  'Z mailorder buyer & mailorder buyer propensities indicates a household\'s self reported purchased through the mail. BehaviorBank Household Indicators groups similar self-reported elements into slightly broader categories. Propensities are created with sophisticated analytical models based solely on self-reported data to predict households that are likely to exhibit the same behaviors. We supplement Behavior Bank Indicators with propensities for those records where self-reported data is not available to maximize coverage and response. The propensity codes rank living units from 1 to 9 using individual, living unit and area level demographics. Field values beginning with 1 indicate the strongest probability.'
end

.en_8505_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_8505.rb', line 9

def self.en_8505_value_description(key); ''; end

.en_8505p(key) ⇒ Object 



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# File 'lib/element_numbers/en_8505.rb', line 47

def self.en_8505p(key)
  Experian::DataDictionary.en_8505(key)
end

.en_8505p_column_name ⇒ Object

Mailorder Buyer & Mailorder Buyer Prop, Quick Predict Code



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# File 'lib/element_numbers/en_8505.rb', line 16

def self.en_8505p_column_name
  'Mailorder Buyer & Mailorder Buyer Prop, Quick Predict Code'
end

.en_8505p_description ⇒ Object 



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# File 'lib/element_numbers/en_8505.rb', line 22

def self.en_8505p_description
  'Z mailorder buyer & mailorder buyer propensities indicates a household\'s self reported purchased through the mail. BehaviorBank Household Indicators groups similar self-reported elements into slightly broader categories. Propensities are created with sophisticated analytical models based solely on self-reported data to predict households that are likely to exhibit the same behaviors. We supplement Behavior Bank Indicators with propensities for those records where self-reported data is not available to maximize coverage and response. The propensity codes rank living units from 1 to 9 using individual, living unit and area level demographics. Field values beginning with 1 indicate the strongest probability.'
end

.en_8505p_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_8505.rb', line 20

def self.en_8505p_value_description(key); ''; end

.en_8509(key) ⇒ Object 



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# File 'lib/element_numbers/en_8509.rb', line 26

def self.en_8509(key)
  charity = {
    'Y' => 'Living unit has a known investor',
    'U' => 'Unknown'
  }
  code = {
    '1' => 'Extremely Likely investor',
    '2' => 'Highly Likely investor',
    '3' => 'Very Likely investor',
    '4' => 'Somewhat Likely investor',
    '5' => 'Likely investor',
    '6' => 'Somewhat Unlikely investor',
    '7' => 'Very Unlikely investor',
    '8' => 'Highly Unlikely investor',
    '9' => 'Extremely Unlikely investor',
    '0' => 'Unknown / Not Attempted',
  }
  return 'Unknown' if key.size != 2 or code[key[1]].nil? or charity[key[0]].nil?
  [charity[key[0]], code[key[1]]].join(' - ')
end

.en_8509_column_name ⇒ Object

Investor & Investor Propensity, Self reported data



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# File 'lib/element_numbers/en_8509.rb', line 5

def self.en_8509_column_name
  'Investor & Investor Propensity, Self reported data'
end

.en_8509_description ⇒ Object 



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# File 'lib/element_numbers/en_8509.rb', line 11

def self.en_8509_description
  'Z investor & investor propensities indicates a household\'s self reported as an investor. BehaviorBank Household Indicators groups similar self-reported elements into slightly broader categories. Propensities are created with sophisticated analytical models based solely on self-reported data to predict households that are likely to exhibit the same behaviors. We supplement Behavior Bank Indicators with propensities for those records where self- reported data is not available to maximize coverage and response. The propensity codes rank living units from 1 to 9 using individual, living unit and area level demographics. Field values beginning with 1 indicate the strongest probability.'
end

.en_8509_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_8509.rb', line 9

def self.en_8509_value_description(key); ''; end

.en_8509p(key) ⇒ Object 



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# File 'lib/element_numbers/en_8509.rb', line 47

def self.en_8509p(key)
  Experian::DataDictionary.en_8509(key)
end

.en_8509p_column_name ⇒ Object

Investor & Investor Propensity, Quick Predict Code



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# File 'lib/element_numbers/en_8509.rb', line 16

def self.en_8509p_column_name
  'Investor & Investor Propensity, Quick Predict Code'
end

.en_8509p_description ⇒ Object 



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# File 'lib/element_numbers/en_8509.rb', line 22

def self.en_8509p_description
  'Z investor & investor propensities indicates a household\'s self reported as an investor. BehaviorBank Household Indicators groups similar self-reported elements into slightly broader categories. Propensities are created with sophisticated analytical models based solely on self-reported data to predict households that are likely to exhibit the same behaviors. We supplement Behavior Bank Indicators with propensities for those records where self- reported data is not available to maximize coverage and response. The propensity codes rank living units from 1 to 9 using individual, living unit and area level demographics. Field values beginning with 1 indicate the strongest probability.'
end

.en_8509p_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_8509.rb', line 20

def self.en_8509p_value_description(key); ''; end

.en_8519(key) ⇒ Object 



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# File 'lib/element_numbers/en_8519.rb', line 26

def self.en_8519(key)
  charity = {
    'Y' => 'Living unit has a known interest in politics',
    'U' => 'Unknown'
  }
  code = {
    '1' => 'Extremely Likely interest in politics',
    '2' => 'Highly Likely interest in politics',
    '3' => 'Very Likely interest in politics',
    '4' => 'Somewhat Likely interest in politics',
    '5' => 'Likely interest in politics',
    '6' => 'Somewhat Unlikely interest in politics',
    '7' => 'Very Unlikely interest in politics',
    '8' => 'Highly Unlikely interest in politics',
    '9' => 'Extremely Unlikely interest in politics',
    '0' => 'Unknown / Not Attempted',
  }
  return 'Unknown' if key.size != 2 or code[key[1]].nil? or charity[key[0]].nil?
  [charity[key[0]], code[key[1]]].join(' - ')
end

.en_8519_column_name ⇒ Object

Politics & Politics Propensity, Self reported data



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# File 'lib/element_numbers/en_8519.rb', line 5

def self.en_8519_column_name
  'Politics & Politics Propensity, Self reported data'
end

.en_8519_description ⇒ Object 



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# File 'lib/element_numbers/en_8519.rb', line 11

def self.en_8519_description
  'Z politics & politics propensities indicates a household\'s self reported interest in politics. BehaviorBank Household Indicators groups similar self-reported elements into slightly broader categories. Propensities are created with sophisticated analytical models based solely on self-reported data to predict households that are likely to exhibit the same behaviors. We supplement Behavior Bank Indicators with propensities for those records where self-reported data is not available to maximize coverage and response. The propensity codes rank living units from 1 to 9 using individual, living unit and area level demographics. Field values beginning with 1 indicate the strongest probability.'
end

.en_8519_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_8519.rb', line 9

def self.en_8519_value_description(key); ''; end

.en_8519p(key) ⇒ Object 



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# File 'lib/element_numbers/en_8519.rb', line 47

def self.en_8519p(key)
  Experian::DataDictionary.en_8519(key)
end

.en_8519p_column_name ⇒ Object

Politics & Politics Propensity, Quick Predict Code



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# File 'lib/element_numbers/en_8519.rb', line 16

def self.en_8519p_column_name
  'Politics & Politics Propensity, Quick Predict Code'
end

.en_8519p_description ⇒ Object 



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# File 'lib/element_numbers/en_8519.rb', line 22

def self.en_8519p_description
  'Z politics & politics propensities indicates a household\'s self reported interest in politics. BehaviorBank Household Indicators groups similar self-reported elements into slightly broader categories. Propensities are created with sophisticated analytical models based solely on self-reported data to predict households that are likely to exhibit the same behaviors. We supplement Behavior Bank Indicators with propensities for those records where self-reported data is not available to maximize coverage and response. The propensity codes rank living units from 1 to 9 using individual, living unit and area level demographics. Field values beginning with 1 indicate the strongest probability.'
end

.en_8519p_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_8519.rb', line 20

def self.en_8519p_value_description(key); ''; end

.en_8523(key) ⇒ Object 



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# File 'lib/element_numbers/en_8523.rb', line 26

def self.en_8523(key)
  charity = {
    'Y' => 'Living unit has a known do-it-yourselfer',
    'U' => 'Unknown'
  }
  code = {
    '1' => 'Extremely Likely do-it-yourselfer',
    '2' => 'Highly Likely do-it-yourselfer',
    '3' => 'Very Likely do-it-yourselfer',
    '4' => 'Somewhat Likely do-it-yourselfer',
    '5' => 'Likely do-it-yourselfer',
    '6' => 'Somewhat Unlikely do-it-yourselfer',
    '7' => 'Very Unlikely do-it-yourselfer',
    '8' => 'Highly Unlikely do-it-yourselfer',
    '9' => 'Extremely Unlikely do-it-yourselfer',
    '0' => 'Unknown / Not Attempted',
  }
  return 'Unknown' if key.size != 2 or code[key[1]].nil? or charity[key[0]].nil?
  [charity[key[0]], code[key[1]]].join(' - ')
end

.en_8523_column_name ⇒ Object

‘Doityourselfer & Doityourselfer Prop, Self reported data’



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# File 'lib/element_numbers/en_8523.rb', line 5

def self.en_8523_column_name
  'Doityourselfer & Doityourselfer Prop, Self reported data'
end

.en_8523_description ⇒ Object 



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# File 'lib/element_numbers/en_8523.rb', line 11

def self.en_8523_description
  'Z doityourselfer & doityourselfer propensities indicates a household\'s self reported do-it-yourselfer. BehaviorBank Household Indicators groups similar self-reported elements into slightly broader categories. Propensities are created with sophisticated analytical models based solely on self-reported data to predict households that are likely to exhibit the same behaviors. We supplement Behavior Bank Indicators with propensities for those records where self-reported data is not available to maximize coverage and response. The propensity codes rank living units from 1 to 9 using individual, living unit and area level demographics. Field values beginning with 1 indicate the strongest probability.'
end

.en_8523_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_8523.rb', line 9

def self.en_8523_value_description(key); ''; end

.en_8523p(key) ⇒ Object 



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# File 'lib/element_numbers/en_8523.rb', line 47

def self.en_8523p(key)
  Experian::DataDictionary.en_8523(key)
end

.en_8523p_column_name ⇒ Object

Doityourselfer & Doityourselfer Prop, Quick Predict Code



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# File 'lib/element_numbers/en_8523.rb', line 16

def self.en_8523p_column_name
  'Doityourselfer & Doityourselfer Prop, Quick Predict Code'
end

.en_8523p_description ⇒ Object 



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# File 'lib/element_numbers/en_8523.rb', line 22

def self.en_8523p_description
  'Z doityourselfer & doityourselfer propensities indicates a household\'s self reported do-it-yourselfer. BehaviorBank Household Indicators groups similar self-reported elements into slightly broader categories. Propensities are created with sophisticated analytical models based solely on self-reported data to predict households that are likely to exhibit the same behaviors. We supplement Behavior Bank Indicators with propensities for those records where self-reported data is not available to maximize coverage and response. The propensity codes rank living units from 1 to 9 using individual, living unit and area level demographics. Field values beginning with 1 indicate the strongest probability.'
end

.en_8523p_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_8523.rb', line 20

def self.en_8523p_value_description(key); ''; end

.en_8525(key) ⇒ Object 



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# File 'lib/element_numbers/en_8525.rb', line 26

def self.en_8525(key)
  charity = {
    'Y' => 'Living unit has a known interest in religion',
    'U' => 'Unknown'
  }
  code = {
    '1' => 'Extremely Likely interest in religion',
    '2' => 'Highly Likely interest in religion',
    '3' => 'Very Likely interest in religion',
    '4' => 'Somewhat Likely interest in religion',
    '5' => 'Likely interest in religion',
    '6' => 'Somewhat Unlikely interest in religion',
    '7' => 'Very Unlikely interest in religion',
    '8' => 'Highly Unlikely interest in religion',
    '9' => 'Extremely Unlikely interest in religion',
    '0' => 'Unknown / Not Attempted',
  }
  return 'Unknown' if key.size != 2 or code[key[1]].nil? or charity[key[0]].nil?
  [charity[key[0]], code[key[1]]].join(' - ')
end

.en_8525_column_name ⇒ Object

Religion & Religion Propensity, Self reported data



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# File 'lib/element_numbers/en_8525.rb', line 5

def self.en_8525_column_name
  'Religion & Religion Propensity, Self reported data'
end

.en_8525_description ⇒ Object 



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# File 'lib/element_numbers/en_8525.rb', line 11

def self.en_8525_description
  'Z religion & religion propensities indicates a household\'s self reported interest in religion. BehaviorBank Household Indicators groups similar self-reported elements into slightly broader categories. Propensities are created with sophisticated analytical models based solely on self-reported data to predict households that are likely to exhibit the same behaviors. We supplement Behavior Bank Indicators with propensities for those records where self-reported data is not available to maximize coverage and response. The propensity codes rank living units from 1 to 9 using individual, living unit and area level demographics. Field values beginning with 1 indicate the strongest probability.'
end

.en_8525_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_8525.rb', line 9

def self.en_8525_value_description(key); ''; end

.en_8525p(key) ⇒ Object 



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# File 'lib/element_numbers/en_8525.rb', line 47

def self.en_8525p(key)
  Experian::DataDictionary.en_8525(key)
end

.en_8525p_column_name ⇒ Object

Religion & Religion Propensity, Quick Predict Code



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# File 'lib/element_numbers/en_8525.rb', line 16

def self.en_8525p_column_name
  'Religion & Religion Propensity, Quick Predict Code'
end

.en_8525p_description ⇒ Object 



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# File 'lib/element_numbers/en_8525.rb', line 22

def self.en_8525p_description
  'Z religion & religion propensities indicates a household\'s self reported interest in religion. BehaviorBank Household Indicators groups similar self-reported elements into slightly broader categories. Propensities are created with sophisticated analytical models based solely on self-reported data to predict households that are likely to exhibit the same behaviors. We supplement Behavior Bank Indicators with propensities for those records where self-reported data is not available to maximize coverage and response. The propensity codes rank living units from 1 to 9 using individual, living unit and area level demographics. Field values beginning with 1 indicate the strongest probability.'
end

.en_8525p_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_8525.rb', line 20

def self.en_8525p_value_description(key); ''; end

.en_8526(key) ⇒ Object 



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# File 'lib/element_numbers/en_8526.rb', line 26

def self.en_8526(key)
  charity = {
    'Y' => 'Living unit has a known grandparent',
    'U' => 'Unknown'
  }
  code = {
    '1' => 'Extremely Likely grandparent',
    '2' => 'Highly Likely grandparent',
    '3' => 'Very Likely grandparent',
    '4' => 'Somewhat Likely grandparent',
    '5' => 'Likely grandparent',
    '6' => 'Somewhat Unlikely grandparent',
    '7' => 'Very Unlikely grandparent',
    '8' => 'Highly Unlikely grandparent',
    '9' => 'Extremely Unlikely grandparent',
    '0' => 'Unknown / Not Attempted',
  }
  return 'Unknown' if key.size != 2 or code[key[1]].nil? or charity[key[0]].nil?
  [charity[key[0]], code[key[1]]].join(' - ')
end

.en_8526_column_name ⇒ Object

Grandparent & Grandparent Propensity, Self reported data



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# File 'lib/element_numbers/en_8526.rb', line 5

def self.en_8526_column_name
  'Grandparent & Grandparent Propensity, Self reported data'
end

.en_8526_description ⇒ Object 



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# File 'lib/element_numbers/en_8526.rb', line 11

def self.en_8526_description
  'Z grandparent & grandparent propensities indicates a household\'s self reported grandparent. BehaviorBank Household Indicators groups similar self-reported elements into slightly broader categories. Propensities are created with sophisticated analytical models based solely on self-reported data to predict households that are likely to exhibit the same behaviors. We supplement Behavior Bank Indicators with propensities for those records where self-reported data is not available to maximize coverage and response. The propensity codes rank living units from 1 to 9 using individual, living unit and area level demographics. Field values beginning with 1 indicate the strongest probability.'
end

.en_8526_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_8526.rb', line 9

def self.en_8526_value_description(key); ''; end

.en_8526p(key) ⇒ Object 



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# File 'lib/element_numbers/en_8526.rb', line 47

def self.en_8526p(key)
  Experian::DataDictionary.en_8526(key)
end

.en_8526p_column_name ⇒ Object

Grandparent & Grandparent Propensity, Quick Predict Code



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# File 'lib/element_numbers/en_8526.rb', line 16

def self.en_8526p_column_name
  'Grandparent & Grandparent Propensity, Quick Predict Code'
end

.en_8526p_description ⇒ Object 



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# File 'lib/element_numbers/en_8526.rb', line 22

def self.en_8526p_description
  'Z grandparent & grandparent propensities indicates a household\'s self reported grandparent. BehaviorBank Household Indicators groups similar self-reported elements into slightly broader categories. Propensities are created with sophisticated analytical models based solely on self-reported data to predict households that are likely to exhibit the same behaviors. We supplement Behavior Bank Indicators with propensities for those records where self-reported data is not available to maximize coverage and response.The propensity codes rank living units from 1 to 9 using individual, living unit and area level demographics. Field values beginning with 1 indicate the strongest probability.'
end

.en_8526p_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_8526.rb', line 20

def self.en_8526p_value_description(key); ''; end

.en_8528(key) ⇒ Object 



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# File 'lib/element_numbers/en_8528.rb', line 26

def self.en_8528(key)
  charity = {
    'Y' => 'Living unit has known to donate to environmental causes',
    'U' => 'Unknown'
  }
  code = {
    '1' => 'Extremely Likely donates to environmental causes',
    '2' => 'Highly Likely donates to environmental causes',
    '3' => 'Very Likely donates to environmental causes',
    '4' => 'Somewhat Likely donates to environmental causes',
    '5' => 'Likely donates to environmental causes',
    '6' => 'Somewhat Unlikely donates to environmental causes',
    '7' => 'Very Unlikely donates to environmental causes',
    '8' => 'Highly Unlikely donates to environmental causes',
    '9' => 'Extremely Unlikely donates to environmental causes',
    '0' => 'Unknown / Not Attempted',
  }
  return 'Unknown' if key.size != 2 or code[key[1]].nil? or charity[key[0]].nil?
  [code[key[1]], charity[key[0]]].join(' - ')
end

.en_8528_column_name ⇒ Object

Environmental Donor & Envir Donor Prop, Self reported data



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# File 'lib/element_numbers/en_8528.rb', line 5

def self.en_8528_column_name
  'Environmental Donor & Envir Donor Prop, Self reported data'
end

.en_8528_description ⇒ Object 



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# File 'lib/element_numbers/en_8528.rb', line 11

def self.en_8528_description
  'Z environmental donor & environmental donor propensities indicates a household\'s self reported as donating to environmental causes. BehaviorBank Household Indicators groups similar self-reported elements into slightly broader categories. Propensities are created with sophisticated analytical models based solely on self-reported data to predict households that are likely to exhibit the same behaviors. We supplement Behavior Bank Indicators with propensities for those records where self-reported data is not available to maximize coverage and response. The propensity codes rank living units from 1 to 9 using individual, living unit and area level demographics. Field values beginning with 1 indicate the strongest probability.'
end

.en_8528_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_8528.rb', line 9

def self.en_8528_value_description(key); ''; end

.en_8528p(key) ⇒ Object 



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# File 'lib/element_numbers/en_8528.rb', line 47

def self.en_8528p(key)
  Experian::DataDictionary.en_8528(key)
end

.en_8528p_column_name ⇒ Object

Environmental Donor & Environ Donor Prop, Quick Predict Code



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# File 'lib/element_numbers/en_8528.rb', line 16

def self.en_8528p_column_name
  'Environmental Donor & Envir Donor Prop, Quick Predict Code'
end

.en_8528p_description ⇒ Object 



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# File 'lib/element_numbers/en_8528.rb', line 22

def self.en_8528p_description
  'Z environmental donor & environmental donor propensities indicates a household\'s self reported as donating to environmental causes. Valid Values :BehaviorBank Household Indicators groups similar self-reported elements into slightly broader categories. Propensities are created with sophisticated analytical models based solely on self-reported data to predict households that are likely to exhibit the same behaviors. We supplement Behavior Bank Indicators with propensities for those records where self-reported data is not available to maximize coverage and response. The propensity codes rank living units from 1 to 9 using individual, living unit and area level demographics. Field values beginning with 1 indicate the strongest probability.'
end

.en_8528p_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_8528.rb', line 20

def self.en_8528p_value_description(key); ''; end

.en_8531(key) ⇒ Object 



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# File 'lib/element_numbers/en_8531.rb', line 26

def self.en_8531(key)
  charity = {
    'Y' => 'Living unit has known to purchase via phone',
    'U' => 'Unknown'
  }
  code = {
    '1' => 'Extremely Likely to purchase via phone',
    '2' => 'Highly Likely to purchase via phone',
    '3' => 'Very Likely to purchase via phone',
    '4' => 'Somewhat Likely to purchase via phone',
    '5' => 'Likely to purchase via phone',
    '6' => 'Somewhat Unlikely to purchase via phone',
    '7' => 'Very Unlikely to purchase via phone',
    '8' => 'Highly Unlikely to purchase via phone',
    '9' => 'Extremely Unlikely to purchase via phone',
    '0' => 'Unknown / Not Attempted',
  }
  return 'Unknown' if key.size != 2 or code[key[1]].nil? or charity[key[0]].nil?
  [code[key[1]], charity[key[0]]].join(' - ')
end

.en_8531_column_name ⇒ Object

Buys By Phone & Buys By Phone Propensity, Self reported data



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# File 'lib/element_numbers/en_8531.rb', line 5

def self.en_8531_column_name
  'Buys By Phone & Buys By Phone Propensity, Self reported data'
end

.en_8531_description ⇒ Object 



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# File 'lib/element_numbers/en_8531.rb', line 11

def self.en_8531_description
  'Z buys by phone & buys by phone propensities indicates a household has self reported to purchase via phone. BehaviorBank Household Indicators groups similar self-reported elements into slightly broader categories. Propensities are created with sophisticated analytical models based solely on self-reported data to predict households that are likely to exhibit the same behaviors. We supplement Behavior Bank Indicators with propensities for those records where self-reported data is not available to maximize coverage and response. The propensity codes rank living units from 1 to 9 using individual, living unit and area level demographics. Field values beginning with 1 indicate the strongest probability.'
end

.en_8531_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_8531.rb', line 9

def self.en_8531_value_description(key); ''; end

.en_8531p(key) ⇒ Object 



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# File 'lib/element_numbers/en_8531.rb', line 47

def self.en_8531p(key)
  Experian::DataDictionary.en_8531(key)
end

.en_8531p_column_name ⇒ Object

Buys By Phone & Buys By Phone Propensity, Quick Predict Code



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# File 'lib/element_numbers/en_8531.rb', line 16

def self.en_8531p_column_name
  'Buys By Phone & Buys By Phone Propensity, Quick Predict Code'
end

.en_8531p_description ⇒ Object 



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# File 'lib/element_numbers/en_8531.rb', line 22

def self.en_8531p_description
  'Z buys by phone & buys by phone propensities indicates a household has self reported to purchase via phone. BehaviorBank Household Indicators groups similar self-reported elements into slightly broader categories. Propensities are created with sophisticated analytical models based solely on self-reported data to predict households that are likely to exhibit the same behaviors. We supplement Behavior Bank Indicators with propensities for those records where self-reported data is not available to maximize coverage and response. The propensity codes rank living units from 1 to 9 using individual, living unit and area level demographics. Field values beginning with 1 indicate the strongest probability.'
end

.en_8531p_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_8531.rb', line 20

def self.en_8531p_value_description(key); ''; end

.en_8532(key) ⇒ Object 



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# File 'lib/element_numbers/en_8532.rb', line 26

def self.en_8532(key)
  charity = {
    'Y' => 'Living unit has known to purchase via television',
    'U' => 'Unknown'
  }
  code = {
    '1' => 'Extremely Likely to purchase via television',
    '2' => 'Highly Likely to purchase via television',
    '3' => 'Very Likely to purchase via television',
    '4' => 'Somewhat Likely to purchase via television',
    '5' => 'Likely to purchase via television',
    '6' => 'Somewhat Unlikely to purchase via television',
    '7' => 'Very Unlikely to purchase via television',
    '8' => 'Highly Unlikely to purchase via television',
    '9' => 'Extremely Unlikely to purchase via television',
    '0' => 'Unknown / Not Attempted',
  }
  return 'Unknown' if key.size != 2 or code[key[1]].nil? or charity[key[0]].nil?
  [code[key[1]], charity[key[0]]].join(' - ')
end

.en_8532_column_name ⇒ Object

Buys By TV & Buys By TV Propensity, Self reported data



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# File 'lib/element_numbers/en_8532.rb', line 5

def self.en_8532_column_name
  'Buys By TV & Buys By TV Propensity, Self reported data'
end

.en_8532_description ⇒ Object 



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# File 'lib/element_numbers/en_8532.rb', line 11

def self.en_8532_description
  'Z buys by TV & buys by TV propensities indicates a household has self reported to purchase via television. BehaviorBank Household Indicators groups similar self-reported elements into slightly broader categories. Propensities are created with sophisticated analytical models based solely on self-reported data to predict households that are likely to exhibit the same behaviors. We supplement Behavior Bank Indicators with propensities for those records where self-reported data is not available to maximize coverage and response. The propensity codes rank living units from 1 to 9 using individual, living unit and area level demographics. Field values beginning with 1 indicate the strongest probability.'
end

.en_8532_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_8532.rb', line 9

def self.en_8532_value_description(key); ''; end

.en_8532p(key) ⇒ Object 



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# File 'lib/element_numbers/en_8532.rb', line 47

def self.en_8532p(key)
  Experian::DataDictionary.en_8532(key)
end

.en_8532p_column_name ⇒ Object

Buys By TV & Buys By TV Propensity, Quick Predict Code



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# File 'lib/element_numbers/en_8532.rb', line 16

def self.en_8532p_column_name
  'Buys By TV & Buys By TV Propensity, Quick Predict Code'
end

.en_8532p_description ⇒ Object 



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# File 'lib/element_numbers/en_8532.rb', line 22

def self.en_8532p_description
  'Z buys by TV & buys by TV propensities indicates a household has self reported to purchase via television. BehaviorBank Household Indicators groups similar self-reported elements into slightly broader categories. Propensities are created with sophisticated analytical models based solely on self-reported data to predict households that are likely to exhibit the same behaviors. We supplement Behavior Bank Indicators with propensities for those records where self-reported data is not available to maximize coverage and response. The propensity codes rank living units from 1 to 9 using individual, living unit and area level demographics. Field values beginning with 1 indicate the strongest probability.'
end

.en_8532p_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_8532.rb', line 20

def self.en_8532p_value_description(key); ''; end

.en_8533(key) ⇒ Object

CCYYMM



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# File 'lib/element_numbers/en_8533.rb', line 25

def self.en_8533(key)
  values = {
    'Y' => 'Living unit has a known mail order multibuyer',
    'U' => 'Unknown',
    '1' => 'Extremely Likely mail order multibuyer',
    '2' => 'Highly Likely mail order multibuyer',
    '3' => 'Very Likely mail order multibuyer',
    '4' => 'Somewhat Likely mail order multibuyer',
    '5' => 'Likely mail order multibuyer',
    '6' => 'Somewhat Unlikely mail order multibuyer',
    '7' => 'Very Unlikely mail order multibuyer',
    '8' => 'Highly Unlikely mail order multibuyer',
    '9' => 'Extremely Unlikely mail order multibuyer',
    '0' => 'Unknown / Not Attempted',
  }
  return nil if key.empty? or key.length != 2
  key.split('').collect { |k| values[k] }.compact.join(' - ')
end

.en_8533_column_name ⇒ Object 



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# File 'lib/element_numbers/en_8533.rb', line 4

def self.en_8533_column_name
  'Mailorder Multibuyer & MO Multi Prop, Self reported data'
end

.en_8533_description ⇒ Object 



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# File 'lib/element_numbers/en_8533.rb', line 10

def self.en_8533_description
  'Mailorder Multibuyer & MO Multi Prop, Self reported data'
end

.en_8533_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_8533.rb', line 8

def self.en_8533_value_description(key); ''; end

.en_8533p(key) ⇒ Object 



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# File 'lib/element_numbers/en_8533.rb', line 45

def self.en_8533p(key)
  return nil unless %w(E I U M F).include?(key[0]) # I noticed another 0100c column that uses M and F which is probably for Male and Female
  return key[1..key.length-1]
end

.en_8533p_column_name ⇒ Object 



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# File 'lib/element_numbers/en_8533.rb', line 14

def self.en_8533p_column_name
  'Mailorder Multibuyer & MO Multi Prop, Quick Predict Code'
end

.en_8533p_description ⇒ Object 



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# File 'lib/element_numbers/en_8533.rb', line 20

def self.en_8533p_description
  self.en_8533_description
end

.en_8533p_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_8533.rb', line 18

def self.en_8533p_value_description(key); ''; end

.en_8536(key) ⇒ Object 



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# File 'lib/element_numbers/en_8536.rb', line 26

def self.en_8536(key)
  charity = {
    'Y' => 'Living unit has a known dog enthusiast',
    'U' => 'Unknown'
  }
  code = {
    '1' => 'Extremely Likely dog enthusiast',
    '2' => 'Highly Likely dog enthusiast',
    '3' => 'Very Likely dog enthusiast',
    '4' => 'Somewhat Likely dog enthusiast',
    '5' => 'Likely dog enthusiast',
    '6' => 'Somewhat Unlikely dog enthusiast',
    '7' => 'Very Unlikely dog enthusiast',
    '8' => 'Highly Unlikely dog enthusiast',
    '9' => 'Extremely Unlikely dog enthusiast',
    '0' => 'Unknown / Not Attempted',
  }
  return 'Unknown' if key.size != 2 or code[key[1]].nil? or charity[key[0]].nil?
  [charity[key[0]], code[key[1]]].join(' - ')
end

.en_8536_column_name ⇒ Object

Dogs & Dogs Propensity, Self reported data



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# File 'lib/element_numbers/en_8536.rb', line 5

def self.en_8536_column_name
  'Dogs & Dogs Propensity, Self reported data'
end

.en_8536_description ⇒ Object 



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# File 'lib/element_numbers/en_8536.rb', line 11

def self.en_8536_description
  'Z dogs & dogs propensities indicates a household has self reported as dog enthusiasts. BehaviorBank Household Indicators groups similar self-reported elements into slightly broader categories. Propensities are created with sophisticated analytical models based solely on self-reported data to predict households that are likely to exhibit the same behaviors. We supplement Behavior Bank Indicators with propensities for those records where self- reported data is not available to maximize coverage and response. The propensity codes rank living units from 1 to 9 using individual, living unit and area level demographics. Field values beginning with 1 indicate the strongest probability.'
end

.en_8536_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_8536.rb', line 9

def self.en_8536_value_description(key); ''; end

.en_8536p(key) ⇒ Object 



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# File 'lib/element_numbers/en_8536.rb', line 47

def self.en_8536p(key)
  Experian::DataDictionary.en_8536(key)
end

.en_8536p_column_name ⇒ Object

Dogs & Dogs Propensity, Quick Predict Code



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# File 'lib/element_numbers/en_8536.rb', line 16

def self.en_8536p_column_name
  'Dogs & Dogs Propensity, Quick Predict Code'
end

.en_8536p_description ⇒ Object 



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# File 'lib/element_numbers/en_8536.rb', line 22

def self.en_8536p_description
  'Z dogs & dogs propensities indicates a household has self reported as dog enthusiasts. BehaviorBank Household Indicators groups similar self-reported elements into slightly broader categories. Propensities are created with sophisticated analytical models based solely on self-reported data to predict households that are likely to exhibit the same behaviors. We supplement Behavior Bank Indicators with propensities for those records where self- reported data is not available to maximize coverage and response. The propensity codes rank living units from 1 to 9 using individual, living unit and area level demographics. Field values beginning with 1 indicate the strongest probability.'
end

.en_8536p_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_8536.rb', line 20

def self.en_8536p_value_description(key); ''; end

.en_8537(key) ⇒ Object 



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# File 'lib/element_numbers/en_8537.rb', line 26

def self.en_8537(key)
  charity = {
    'Y' => 'Living unit has a known cat enthusiast',
    'U' => 'Unknown'
  }
  code = {
    '1' => 'Extremely Likely cat enthusiast',
    '2' => 'Highly Likely cat enthusiast',
    '3' => 'Very Likely cat enthusiast',
    '4' => 'Somewhat Likely cat enthusiast',
    '5' => 'Likely cat enthusiast',
    '6' => 'Somewhat Unlikely cat enthusiast',
    '7' => 'Very Unlikely cat enthusiast',
    '8' => 'Highly Unlikely cat enthusiast',
    '9' => 'Extremely Unlikely cat enthusiast',
    '0' => 'Unknown / Not Attempted',
  }
  return 'Unknown' if key.size != 2 or code[key[1]].nil? or charity[key[0]].nil?
  [charity[key[0]], code[key[1]]].join(' - ')
end

.en_8537_column_name ⇒ Object

Cats & Cats Propensity, Self reported data



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# File 'lib/element_numbers/en_8537.rb', line 5

def self.en_8537_column_name
  'Cats & Cats Propensity, Self reported data'
end

.en_8537_description ⇒ Object 



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# File 'lib/element_numbers/en_8537.rb', line 11

def self.en_8537_description
  'Z cats & cats propensities indicates a household has self reported as cat enthusiasts. BehaviorBank Household Indicators groups similar self-reported elements into slightly broader categories. Propensities are created with sophisticated analytical models based solely on self-reported data to predict households that are likely to exhibit the same behaviors. We supplement Behavior Bank Indicators with propensities for those records where self- reported data is not available to maximize coverage and response. The propensity codes rank living units from 1 to 9 using individual, living unit and area level demographics. Field values beginning with 1 indicate the strongest probability.'
end

.en_8537_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_8537.rb', line 9

def self.en_8537_value_description(key); ''; end

.en_8537p(key) ⇒ Object 



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# File 'lib/element_numbers/en_8537.rb', line 47

def self.en_8537p(key)
  Experian::DataDictionary.en_8537(key)
end

.en_8537p_column_name ⇒ Object

Cats & Cats Propensity, Quick Predict Code



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# File 'lib/element_numbers/en_8537.rb', line 16

def self.en_8537p_column_name
  'Cats & Cats Propensity, Quick Predict Code'
end

.en_8537p_description ⇒ Object 



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# File 'lib/element_numbers/en_8537.rb', line 22

def self.en_8537p_description
  'Z cats & cats propensities indicates a household has self reported as cat enthusiasts. BehaviorBank Household Indicators groups similar self-reported elements into slightly broader categories. Propensities are created with sophisticated analytical models based solely on self-reported data to predict households that are likely to exhibit the same behaviors. We supplement Behavior Bank Indicators with propensities for those records where self- reported data is not available to maximize coverage and response. The propensity codes rank living units from 1 to 9 using individual, living unit and area level demographics. Field values beginning with 1 indicate the strongest probability.'
end

.en_8537p_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_8537.rb', line 20

def self.en_8537p_value_description(key); ''; end

.en_8538(key) ⇒ Object 



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# File 'lib/element_numbers/en_8538.rb', line 26

def self.en_8538(key)
  charity = {
    'Y' => 'Living unit has known interest in healthy living',
    'U' => 'Unknown'
  }
  code = {
    '1' => 'Extremely Likely interest in healthy living',
    '2' => 'Highly Likely interest in healthy living',
    '3' => 'Very Likely interest in healthy living',
    '4' => 'Somewhat Likely interest in healthy living',
    '5' => 'Likely interest in healthy living',
    '6' => 'Somewhat Unlikely interest in healthy living',
    '7' => 'Very Unlikely interest in healthy living',
    '8' => 'Highly Unlikely interest in healthy living',
    '9' => 'Extremely Unlikely interest in healthy living',
    '0' => 'Unknown / Not Attempted',
  }
  return 'Unknown' if key.size != 2 or code[key[1]].nil? or charity[key[0]].nil?
  [code[key[1]], charity[key[0]]].join(' - ')
end

.en_8538_column_name ⇒ Object

Health & Health Propensity, Self reported data



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# File 'lib/element_numbers/en_8538.rb', line 5

def self.en_8538_column_name
  'Health & Health Propensity, Self reported data'
end

.en_8538_description ⇒ Object 



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# File 'lib/element_numbers/en_8538.rb', line 11

def self.en_8538_description
  'Z health & health propensities indicates a household\'s self reported interest in healthy living. BehaviorBank Household Indicators groups similar self-reported elements into slightly broader categories. Propensities are created with sophisticated analytical models based solely on self-reported data to predict households that are likely to exhibit the same behaviors. We supplement Behavior Bank Indicators with propensities for those records where self-reported data is not available to maximize coverage and response. The propensity codes rank living units from 1 to 9 using individual, living unit and area level demographics. Field values beginning with 1 indicate the strongest probability.'
end

.en_8538_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_8538.rb', line 9

def self.en_8538_value_description(key); ''; end

.en_8538p(key) ⇒ Object 



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# File 'lib/element_numbers/en_8538.rb', line 47

def self.en_8538p(key)
  Experian::DataDictionary.en_8538(key)
end

.en_8538p_column_name ⇒ Object

Health & Health Propensity, Quick Predict Code



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# File 'lib/element_numbers/en_8538.rb', line 16

def self.en_8538p_column_name
  'Health & Health Propensity, Quick Predict Code'
end

.en_8538p_description ⇒ Object 



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# File 'lib/element_numbers/en_8538.rb', line 22

def self.en_8538p_description
  'Z health & health propensities indicates a household\'s self reported interest in healthy living. BehaviorBank Household Indicators groups similar self-reported elements into slightly broader categories. Propensities are created with sophisticated analytical models based solely on self-reported data to predict households that are likely to exhibit the same behaviors. We supplement Behavior Bank Indicators with propensities for those records where self-reported data is not available to maximize coverage and response. The propensity codes rank living units from 1 to 9 using individual, living unit and area level demographics. Field values beginning with 1 indicate the strongest probability.'
end

.en_8538p_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_8538.rb', line 20

def self.en_8538p_value_description(key); ''; end

.en_8574(key) ⇒ Object 



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# File 'lib/element_numbers/en_8574.rb', line 26

def self.en_8574(key)
  charity = {
    'Y' => 'Living unit has known to be a Volunteer',
    'U' => 'Unknown'
  }
  code = {
    '1' => 'Extremely Likely to be a Volunteer',
    '2' => 'Highly Likely to be a Volunteer',
    '3' => 'Very Likely to be a Volunteer',
    '4' => 'Somewhat Likely to be a Volunteer',
    '5' => 'Likely to be a Volunteer',
    '6' => 'Somewhat Unlikely to be a Volunteer',
    '7' => 'Very Unlikely to be a Volunteer',
    '8' => 'Highly Unlikely to be a Volunteer',
    '9' => 'Extremely Unlikely to be a Volunteer',
    '0' => 'Unknown / Not Attempted',
  }
  return 'Unknown' if key.size != 2 or code[key[1]].nil? or charity[key[0]].nil?
  [code[key[1]], charity[key[0]]].join(' - ')
end

.en_8574_column_name ⇒ Object

Volunteer & Volunteer Propensity, Self reported data



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# File 'lib/element_numbers/en_8574.rb', line 5

def self.en_8574_column_name
  'Volunteer & Volunteer Propensity, Self reported data'
end

.en_8574_description ⇒ Object 



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# File 'lib/element_numbers/en_8574.rb', line 11

def self.en_8574_description
  'Z Volunteer & Volunteer Propensity indicates a household\'s self-reported behavior to be a Volunteer. BehaviorBank Household Indicators groups similar self-reported elements into slightly broader categories. Propensities are created with sophisticated analytical models based solely on self-reported data to predict households that are likely to exhibit the same behaviors. We supplement Behavior Bank Indicators with propensities for those records where self-reported data is not available to maximize coverage and response. The propensity codes rank living units from 1 to 9 using individual, living unit and area level demographics. Field values beginning with 1 indicate the strongest probability.'
end

.en_8574_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_8574.rb', line 9

def self.en_8574_value_description(key); ''; end

.en_8574p(key) ⇒ Object 



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# File 'lib/element_numbers/en_8574.rb', line 47

def self.en_8574p(key)
  Experian::DataDictionary.en_8574(key)
end

.en_8574p_column_name ⇒ Object

Volunteer & Volunteer Propensity, Quick Predict Code



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# File 'lib/element_numbers/en_8574.rb', line 16

def self.en_8574p_column_name
  'Volunteer & Volunteer Propensity, Quick Predict Code'
end

.en_8574p_description ⇒ Object 



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# File 'lib/element_numbers/en_8574.rb', line 22

def self.en_8574p_description
  'Z Volunteer & Volunteer Propensity indicates a household\'s self-reported behavior to be a Volunteer. BehaviorBank Household Indicators groups similar self-reported elements into slightly broader categories. Propensities are created with sophisticated analytical models based solely on self-reported data to predict households that are likely to exhibit the same behaviors. We supplement Behavior Bank Indicators with propensities for those records where self-reported data is not available to maximize coverage and response. The propensity codes rank living units from 1 to 9 using individual, living unit and area level demographics. Field values beginning with 1 indicate the strongest probability.'
end

.en_8574p_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_8574.rb', line 20

def self.en_8574p_value_description(key); ''; end

.en_8580(key) ⇒ Object 



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# File 'lib/element_numbers/en_8580.rb', line 26

def self.en_8580(key)
  charity = {
    'Y' => 'Living unit has self-reported being Active Military',
    'U' => 'Unknown'
  }
  code = {
    '1' => 'Extremely Likely Active Military',
    '2' => 'Highly Likely Active Military',
    '3' => 'Very Likely Active Military',
    '4' => 'Somewhat Likely Active Military',
    '5' => 'Likely Active Military',
    '6' => 'Somewhat Unlikely Active Military',
    '7' => 'Very Unlikely Active Military',
    '8' => 'Highly Unlikely Active Military',
    '9' => 'Extremely Unlikely Active Military',
    '0' => 'Unknown / Not Attempted',
  }
  return 'Unknown' if key.size != 2 or code[key[1]].nil? or charity[key[0]].nil?
  [charity[key[0]], code[key[1]]].join(' - ')
end

.en_8580_column_name ⇒ Object

Z Active Military & Active Military Prop, Self reported data



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# File 'lib/element_numbers/en_8580.rb', line 5

def self.en_8580_column_name
  'Z Active Military & Active Military Prop, Self reported data'
end

.en_8580_description ⇒ Object 



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# File 'lib/element_numbers/en_8580.rb', line 11

def self.en_8580_description
  'Z Active Military indicates a household has self reported as being Active Military. BehaviorBank Household Indicators groups similar self-reported elements into slightly broader categories. Propensities are created with sophisticated analytical models based solely on self-reported data to predict households that are likely to exhibit the same behaviors. We supplement Behavior Bank Indicators with propensities for those records where self- reported data is not available to maximize coverage and response. The propensity codes rank living units from 1 to 9 using individual, living unit and area level demographics. Field values beginning with 1 indicate the strongest probability.'
end

.en_8580_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_8580.rb', line 9

def self.en_8580_value_description(key); ''; end

.en_8580p(key) ⇒ Object 



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# File 'lib/element_numbers/en_8580.rb', line 47

def self.en_8580p(key)
  Experian::DataDictionary.en_8580(key)
end

.en_8580p_column_name ⇒ Object

Z Active Military & Active Military Prop, Quick Predict Code



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# File 'lib/element_numbers/en_8580.rb', line 16

def self.en_8580p_column_name
  'Z Active Military & Active Military Prop, Quick Predict Code'
end

.en_8580p_description ⇒ Object 



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# File 'lib/element_numbers/en_8580.rb', line 22

def self.en_8580p_description
  'Z Active Military indicates a household has self reported as being Active Military. BehaviorBank Household Indicators groups similar self-reported elements into slightly broader categories. Propensities are created with sophisticated analytical models based solely on self-reported data to predict households that are likely to exhibit the same behaviors. We supplement Behavior Bank Indicators with propensities for those records where self- reported data is not available to maximize coverage and response. The propensity codes rank living units from 1 to 9 using individual, living unit and area level demographics. Field values beginning with 1 indicate the strongest probability.'
end

.en_8580p_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_8580.rb', line 20

def self.en_8580p_value_description(key); ''; end

.en_8581(key) ⇒ Object 



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# File 'lib/element_numbers/en_8581.rb', line 26

def self.en_8581(key)
  charity = {
    'Y' => 'Living unit has self-reported being Inactive Military',
    'U' => 'Unknown'
  }
  code = {
    '1' => 'Extremely Likely Inactive Military',
    '2' => 'Highly Likely Inactive Military',
    '3' => 'Very Likely Inactive Military',
    '4' => 'Somewhat Likely Inactive Military',
    '5' => 'Likely Inactive Military',
    '6' => 'Somewhat Unlikely Inactive Military',
    '7' => 'Very Unlikely Inactive Military',
    '8' => 'Highly Unlikely Inactive Military',
    '9' => 'Extremely Unlikely Inactive Military',
    '0' => 'Unknown / Not Attempted',
  }
  return 'Unknown' if key.size != 2 or code[key[1]].nil? or charity[key[0]].nil?
  [charity[key[0]], code[key[1]]].join(' - ')
end

.en_8581_column_name ⇒ Object

Z Inactive Military & Inactive Military Prop, Self reported data



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# File 'lib/element_numbers/en_8581.rb', line 5

def self.en_8581_column_name
  'Z Inactive Military & Inact Military Prop, Self reported data'
end

.en_8581_description ⇒ Object 



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# File 'lib/element_numbers/en_8581.rb', line 11

def self.en_8581_description
  'Z Inactive Military indicates a household has self reported as being Inactive Military. BehaviorBank Household Indicators groups similar self-reported elements into slightly broader categories. Propensities are created with sophisticated analytical models based solely on self-reported data to predict households that are likely to exhibit the same behaviors. We supplement Behavior Bank Indicators with propensities for those records where self- reported data is not available to maximize coverage and response. The propensity codes rank living units from 1 to 9 using individual, living unit and area level demographics. Field values beginning with 1 indicate the strongest probability.'
end

.en_8581_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_8581.rb', line 9

def self.en_8581_value_description(key); ''; end

.en_8581p(key) ⇒ Object 



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# File 'lib/element_numbers/en_8581.rb', line 47

def self.en_8581p(key)
  Experian::DataDictionary.en_8581(key)
end

.en_8581p_column_name ⇒ Object

Z Inactive Military & Inactive Military Prop, Quick Predict Code



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# File 'lib/element_numbers/en_8581.rb', line 16

def self.en_8581p_column_name
  'Z Inactive Military & Inact Military Prop, Quick Predict Code'
end

.en_8581p_description ⇒ Object 



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# File 'lib/element_numbers/en_8581.rb', line 22

def self.en_8581p_description
  'Z Inactive Military indicates a household has self reported as being Inactive Military. BehaviorBank Household Indicators groups similar self-reported elements into slightly broader categories. Propensities are created with sophisticated analytical models based solely on self-reported data to predict households that are likely to exhibit the same behaviors. We supplement Behavior Bank Indicators with propensities for those records where self- reported data is not available to maximize coverage and response. The propensity codes rank living units from 1 to 9 using individual, living unit and area level demographics. Field values beginning with 1 indicate the strongest probability.'
end

.en_8581p_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_8581.rb', line 20

def self.en_8581p_value_description(key); ''; end

.en_a107(key) ⇒ Object 



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# File 'lib/element_numbers/en_A107.rb', line 15

def self.en_a107(key)
  values = {
    '1' => 'Extremely Likely',
    '2' => 'Highly Likely',
    '3' => 'Very Likely',
    '4' => 'More than Likely',
    '5' => 'Likely',
    '6' => 'Somewhat Unlikely',
    '7' => 'Very Unlikely',
    '8' => 'Highly Unlikely',
    '9' => 'Extremely Unlikely'
  }
  key.empty? ? 'Unknown' : values[key]
end

.en_a107_column_name ⇒ Object

Luxury Car - Domestic



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# File 'lib/element_numbers/en_A107.rb', line 5

def self.en_a107_column_name
  'Luxury Car - Domestic'
end

.en_a107_description ⇒ Object 



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# File 'lib/element_numbers/en_A107.rb', line 11

def self.en_a107_description
  'Auto Purchase Model Luxury Car Domestic Auto Purchase Model Luxury Car Domestic is an Experian model which predicts the likelihood that a living unit will purchase this type of vehicle in the next 6 months. Auto Purchase Models rank living units from 1 to 9 for each auto category using living unit and area level demographics. Field values beginning with 1 indicate the strongest probability. Vehicles examples in the Luxury Car Domestic category include: Cadillac CTS, Cadillac DeVille, Cadillac DTS, Cadillac STS, Lincoln MKS, Lincoln MKZ, and Lincoln Town Car'
end

.en_a107_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_A107.rb', line 9

def self.en_a107_value_description(key); ''; end

.en_b000(key) ⇒ Object 



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# File 'lib/element_numbers/en_B000.rb', line 15

def self.en_b000(key)
  if key.size != 4
    return 'Non-match'
  elsif (key.eql?("H") or key.eql?("F"))
    return 'Household Match'
  elsif (key.eql?("P") or key.eql?("I"))
    return 'Person Match'
  end
end

.en_b000_column_name ⇒ Object

Behavior Bank Mandatory Append



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# File 'lib/element_numbers/en_B000.rb', line 5

def self.en_b000_column_name
  'Behavior Bank Mandatory Append'
end

.en_b000_description ⇒ Object 



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# File 'lib/element_numbers/en_B000.rb', line 11

def self.en_b000_description
  'The Behavior Bank mandatory append is required when any data from the Behavior Bank master file is appended. It consists of a match code.'
end

.en_b000_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_B000.rb', line 9

def self.en_b000_value_description(key); ''; end

.en_b2185(key) ⇒ Object 



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# File 'lib/element_numbers/en_B2185.rb', line 15

def self.en_b2185(key)
  values = {
    'Y' => 'Yes',
    'U' => 'Unknown'
  }
  key.empty? ? 'Unknown' : values[key]
end

.en_b2185_column_name ⇒ Object

Crafts: Crafts



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# File 'lib/element_numbers/en_B2185.rb', line 5

def self.en_b2185_column_name
  'Crafts: Crafts'
end

.en_b2185_description ⇒ Object 



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# File 'lib/element_numbers/en_B2185.rb', line 11

def self.en_b2185_description
  'Activities and Interests/Crafts. Direct reported survey data that represents a household\'s interest in Crafts.'
end

.en_b2185_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_B2185.rb', line 9

def self.en_b2185_value_description(key); ''; end

.en_b3010(key) ⇒ Object 



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# File 'lib/element_numbers/en_B3010.rb', line 15

def self.en_b3010(key)
  values = {
    'Y' => 'Yes',
    'U' => 'Unknown'
  }
  key.empty? ? 'Unknown' : values[key]
end

.en_b3010_column_name ⇒ Object

Lifestyle: Enjoys Smoking Cigars



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# File 'lib/element_numbers/en_B3010.rb', line 5

def self.en_b3010_column_name
  'Lifestyle: Enjoys Smoking Cigars'
end

.en_b3010_description ⇒ Object 



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# File 'lib/element_numbers/en_B3010.rb', line 11

def self.en_b3010_description
  'Lifestyles/Cigar Smoking. Direct reported survey data that represents household\'s that Enjoys Smoking Cigars.'
end

.en_b3010_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_B3010.rb', line 9

def self.en_b3010_value_description(key); ''; end

.en_b3076(key) ⇒ Object 



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# File 'lib/element_numbers/en_B3076.rb', line 14

def self.en_b3076(key)
  values = {
    'Y' => 'Yes',
    'U' => 'Unknown'
  }
  key.empty? ? 'Unknown' : values[key]
end

.en_b3076_column_name ⇒ Object 



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# File 'lib/element_numbers/en_B3076.rb', line 4

def self.en_b3076_column_name
  'Invest: Interest In Money & Investing'
end

.en_b3076_description ⇒ Object 



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# File 'lib/element_numbers/en_B3076.rb', line 10

def self.en_b3076_description
  'Lifestyles/Financial Investments. Direct reported survey data that represents household\'s that have Interest In Money Matters And Investing.'
end

.en_b3076_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_B3076.rb', line 8

def self.en_b3076_value_description(key); ''; end

.en_b3080(key) ⇒ Object 



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# File 'lib/element_numbers/en_B3080.rb', line 14

def self.en_b3080(key)
  values = {
    'Y' => 'Yes',
    'U' => 'Unknown'
  }
  key.empty? ? 'Unknown' : values[key]
end

.en_b3080_column_name ⇒ Object 



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# File 'lib/element_numbers/en_B3080.rb', line 4

def self.en_b3080_column_name
  'Invest: Mutual Funds - Currently'
end

.en_b3080_description ⇒ Object 



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# File 'lib/element_numbers/en_B3080.rb', line 10

def self.en_b3080_description
  'Lifestyles/Financial Investments. Direct reported survey data that represents household\'s that have tendancy to invest in Mutual Funds - Currently.'
end

.en_b3080_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_B3080.rb', line 8

def self.en_b3080_value_description(key); ''; end

.en_b3081(key) ⇒ Object 



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# File 'lib/element_numbers/en_B3081.rb', line 14

def self.en_b3081(key)
  values = {
    'Y' => 'Yes',
    'U' => 'Unknown'
  }
  key.empty? ? 'Unknown' : values[key]
end

.en_b3081_column_name ⇒ Object 



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# File 'lib/element_numbers/en_B3081.rb', line 4

def self.en_b3081_column_name
  'Invest: Mutual Funds - Future Interest'
end

.en_b3081_description ⇒ Object 



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# File 'lib/element_numbers/en_B3081.rb', line 10

def self.en_b3081_description
  'Lifestyles/Financial Investments. Direct reported survey data that represents household\'s that have tendancy to invest in Mutual Funds - Future Interest.'
end

.en_b3081_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_B3081.rb', line 8

def self.en_b3081_value_description(key); ''; end

.en_b3200(key) ⇒ Object 



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# File 'lib/element_numbers/en_B3200.rb', line 14

def self.en_b3200(key)
  values = {
    'Y' => 'Yes',
    'U' => 'Unknown'
  }
  key.empty? ? 'Unknown' : values[key]
end

.en_b3200_column_name ⇒ Object 



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# File 'lib/element_numbers/en_B3200.rb', line 4

def self.en_b3200_column_name
  'Lifestyle: Active Military Member'
end

.en_b3200_description ⇒ Object 



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# File 'lib/element_numbers/en_B3200.rb', line 10

def self.en_b3200_description
  'Lifestyles/Members of Military and Government Office. Direct reported survey data that represents household\'s that Active Military Member present.'
end

.en_b3200_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_B3200.rb', line 8

def self.en_b3200_value_description(key); ''; end

.en_b3225(key) ⇒ Object 



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# File 'lib/element_numbers/en_B3225.rb', line 14

def self.en_b3225(key)
  values = {
    'Y' => 'Yes',
    'U' => 'Unknown'
  }
  key.empty? ? 'Unknown' : values[key]
end

.en_b3225_column_name ⇒ Object 



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# File 'lib/element_numbers/en_B3225.rb', line 4

def self.en_b3225_column_name
  'Pets: Own A Cat'
end

.en_b3225_description ⇒ Object 



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# File 'lib/element_numbers/en_B3225.rb', line 10

def self.en_b3225_description
  'Lifestyles/Pets. Direct reported survey data that represents household\'s that Own a Cat.'
end

.en_b3225_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_B3225.rb', line 8

def self.en_b3225_value_description(key); ''; end

.en_b3226(key) ⇒ Object 



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# File 'lib/element_numbers/en_B3226.rb', line 14

def self.en_b3226(key)
  values = {
    'Y' => 'Yes',
    'U' => 'Unknown'
  }
  key.empty? ? 'Unknown' : values[key]
end

.en_b3226_column_name ⇒ Object 



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# File 'lib/element_numbers/en_B3226.rb', line 4

def self.en_b3226_column_name
  'Pets: Own A Dog'
end

.en_b3226_description ⇒ Object 



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# File 'lib/element_numbers/en_B3226.rb', line 10

def self.en_b3226_description
  'Lifestyles/Pets. Direct reported survey data that represents household\'s that Own a Dog.'
end

.en_b3226_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_B3226.rb', line 8

def self.en_b3226_value_description(key); ''; end

.en_b3227(key) ⇒ Object 



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# File 'lib/element_numbers/en_B3227.rb', line 14

def self.en_b3227(key)
  values = {
    'Y' => 'Yes',
    'U' => 'Unknown'
  }
  key.empty? ? 'Unknown' : values[key]
end

.en_b3227_column_name ⇒ Object 



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# File 'lib/element_numbers/en_B3227.rb', line 4

def self.en_b3227_column_name
  'Pets: Own A Pet'
end

.en_b3227_description ⇒ Object 



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# File 'lib/element_numbers/en_B3227.rb', line 10

def self.en_b3227_description
  'Lifestyles/Pets. Direct reported survey data that represents household\'s that Own a Pet.'
end

.en_b3227_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_B3227.rb', line 8

def self.en_b3227_value_description(key); ''; end

.en_b3251(key) ⇒ Object 



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# File 'lib/element_numbers/en_B3251.rb', line 14

def self.en_b3251(key)
  values = {
    'Y' => 'Yes',
    'U' => 'Unknown'
  }
  key.empty? ? 'Unknown' : values[key]
end

.en_b3251_column_name ⇒ Object 



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# File 'lib/element_numbers/en_B3251.rb', line 4

def self.en_b3251_column_name
  'Shop: By Mail'
end

.en_b3251_description ⇒ Object 



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# File 'lib/element_numbers/en_B3251.rb', line 10

def self.en_b3251_description
  'Lifestyles/Shopping Method. Direct reported survey data that represents household\'s that prefer to shop By Mail.'
end

.en_b3251_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_B3251.rb', line 8

def self.en_b3251_value_description(key); ''; end

.en_b5011(key) ⇒ Object 



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# File 'lib/element_numbers/en_B5011.rb', line 15

def self.en_b5011(key)
  values = {
    'Y' => 'Yes',
    'U' => 'Unknown'
  }
  key.empty? ? 'Unknown' : values[key]
end

.en_b5011_column_name ⇒ Object

Ailments Ind: Emphysema



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# File 'lib/element_numbers/en_B5011.rb', line 5

def self.en_b5011_column_name
  'Ailments Ind: Emphysema'
end

.en_b5011_description ⇒ Object 



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# File 'lib/element_numbers/en_B5011.rb', line 11

def self.en_b5011_description
  'Ailments and Medications/Ailments. Direct reported survey data that represents respondent\'s that suffer from Emphysema.'
end

.en_b5011_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_B5011.rb', line 9

def self.en_b5011_value_description(key); ''; end

.en_b5013(key) ⇒ Object 



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# File 'lib/element_numbers/en_B5013.rb', line 15

def self.en_b5013(key)
  values = {
    'Y' => 'Yes',
    'U' => 'Unknown'
  }
  key.empty? ? 'Unknown' : values[key]
end

.en_b5013_column_name ⇒ Object

Ailments Ind: Erectile Dysfunction



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# File 'lib/element_numbers/en_B5013.rb', line 5

def self.en_b5013_column_name
  'Ailments Ind: Erectile Dysfunction'
end

.en_b5013_description ⇒ Object 



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# File 'lib/element_numbers/en_B5013.rb', line 11

def self.en_b5013_description
  'Ailments and Medications/Ailments. Direct reported survey data that represents respondent\'s that suffer from Erectile Dysfunction.'
end

.en_b5013_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_B5013.rb', line 9

def self.en_b5013_value_description(key); ''; end

.en_b5014(key) ⇒ Object 



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# File 'lib/element_numbers/en_B5014.rb', line 15

def self.en_b5014(key)
  values = {
    'Y' => 'Yes',
    'U' => 'Unknown'
  }
  key.empty? ? 'Unknown' : values[key]
end

.en_b5014_column_name ⇒ Object

Ailments Ind: Frequent Headaches



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# File 'lib/element_numbers/en_B5014.rb', line 5

def self.en_b5014_column_name
  'Ailments Ind: Frequent Headaches'
end

.en_b5014_description ⇒ Object 



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# File 'lib/element_numbers/en_B5014.rb', line 11

def self.en_b5014_description
  'Ailments and Medications/Ailments. Direct reported survey data that represents respondent\'s that suffer from Frequent Headaches.'
end

.en_b5014_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_B5014.rb', line 9

def self.en_b5014_value_description(key); ''; end

.en_b5016(key) ⇒ Object 



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# File 'lib/element_numbers/en_B5016.rb', line 15

def self.en_b5016(key)
  values = {
    'Y' => 'Yes',
    'U' => 'Unknown'
  }
  key.empty? ? 'Unknown' : values[key]
end

.en_b5016_column_name ⇒ Object

Ailments Ind: Gastritis



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# File 'lib/element_numbers/en_B5016.rb', line 5

def self.en_b5016_column_name
  'Ailments Ind: Gastritis'
end

.en_b5016_description ⇒ Object 



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# File 'lib/element_numbers/en_B5016.rb', line 11

def self.en_b5016_description
  'Ailments and Medications/Ailments. Direct reported survey data that represents respondent\'s that suffer from Gastritis.'
end

.en_b5016_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_B5016.rb', line 9

def self.en_b5016_value_description(key); ''; end

.en_d105n(key) ⇒ Object 



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# File 'lib/element_numbers/en_D105N.rb', line 15

def self.en_d105n(key)
  values = {
    'A' => '$1,000 - $14,999',
    'B' => '$15,000 - $24,999',
    'C' => '$25,000 - $34,999',
    'D' => '$35,000 - $49,999',
    'E' => '$50,000 - $74,999',
    'F' => '$75,000 - $99,999',
    'G' => '$100,000 - $124,999',
    'H' => '$125,000 - $149,999',
    'I' => '$150,000 - $174,999',
    'J' => '$175,000 - $199,999',
    'K' => '$200,000 - $249,999',
    'L' => '$250,000+',
    'U' => 'Unknown'
  }
  key.empty? ? 'Unknown' : values[key]
end

.en_d105n_column_name ⇒ Object

EST HOUSEHOLD INCOME RANGES V4



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# File 'lib/element_numbers/en_D105N.rb', line 5

def self.en_d105n_column_name
  'EST HOUSEHOLD INCOME RANGES V4'
end

.en_d105n_description ⇒ Object 



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# File 'lib/element_numbers/en_D105N.rb', line 11

def self.en_d105n_description
  'Estimated Income is the total estimated income for a living unit, and incorporates several highly predictive individual and household level variables. The income estimation is determined using multiple statistical methodologies to predict which of 12 income range a living unit is most likely to be assigned. When there is insufficient data to match a customer\'s record to ConsumerView for estimated income, a median estimated income based on the Experian modeled incomes assigned to other living units in the same ZIP+4 area is used. In the rare case that the ZIP+4 is not on the record, median income is based on the incomes assigned to other records in that ZIP region.'
end

.en_d105n_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_D105N.rb', line 9

def self.en_d105n_value_description(key); ''; end

.en_d105s(key) ⇒ Object 



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# File 'lib/element_numbers/en_D105S.rb', line 14

def self.en_d105s(key)
  values = {
    'A' => '$1,000 - $14,999',
    'B' => '$15,000 - $24,999',
    'C' => '$25,000 - $34,999',
    'D' => '$35,000 - $49,999',
    'E' => '$50,000 - $74,999',
    'F' => '$75,000 - $99,999',
    'G' => '$100,000 - $124,999',
    'H' => '$125,000 - $149,999',
    'I' => '$150,000 - $174,999',
    'J' => '$175,000 - $199,999',
    'K' => '$200,000 - $249,999',
    'L' => '$250,000+',
    'U' => 'Unknown'
  }
  key.empty? ? 'Unknown' : values[key]
end

.en_d105s_column_name ⇒ Object 



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# File 'lib/element_numbers/en_D105S.rb', line 4

def self.en_d105s_column_name
  'ENH EST HH INCOME RANGES V4'
end

.en_d105s_description ⇒ Object 



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# File 'lib/element_numbers/en_D105S.rb', line 10

def self.en_d105s_description
  'Enhanced Estimated Household Income Code V4 Enhanced Estimated Income is the total estimated income for a living unit, and incorporates several highly predictive individual, household, and geographical level variables including Summarized Credit Statistics. The income estimation is determined using multiple statistical methodologies to predict which of 12 income ranges a living unit is most likely to be assigned. When there is insufficient data to match a customer\'s record to ConsumerView for estimated income, a median estimated income based on the Experian modeled incomes assigned to other living units in the same ZIP+4 area is used. In the rare case that the ZIP+4 is not on the record, median income is based on the incomes assigned to other records in that ZIP region.'
end

.en_d105s_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_D105S.rb', line 8

def self.en_d105s_value_description(key); ''; end

.en_d200(key) ⇒ Object 



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# File 'lib/element_numbers/en_D200.rb', line 15

def self.en_d200(key)
  values = {
    '0' => 'Not eligible',
    '1' => 'Married with likely 1 income or not married',
    '2' => 'Married with likely dual incomes in living unit'
  }
  key.empty? ? 'Unknown' : values[key]
end

.en_d200_column_name ⇒ Object

Working Couples



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# File 'lib/element_numbers/en_D200.rb', line 5

def self.en_d200_column_name
  'Working Couples'
end

.en_d200_description ⇒ Object 



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# File 'lib/element_numbers/en_D200.rb', line 11

def self.en_d200_description
  'Working Couples is a model which works to predict Living Units that are likely to have two incomes. The value appended of 2 identifies living units that are married between the ages of 19-80 and are likely to have two incomes. The value of 1 identifies living units that are either married with only 1 income, living units where there is not known data to identify if the couple is married, or living units that only have 1 adult between the ages 19-80. The value of 0 represents the individuals within the Living Unit are over the age of 80 and are not eligible to be scored.'
end

.en_d200_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_D200.rb', line 9

def self.en_d200_value_description(key); ''; end

.en_f031(key) ⇒ Object 



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# File 'lib/element_numbers/en_F031.rb', line 15

def self.en_f031(key)
  values = {
    '01' => 'High profitability, high likelihood to perform',
    '02' => 'High profitability, high likelihood to perform',
    '03' => 'High profitability, high likelihood to perform',
    '04' => 'Good profitability, medium likelihood to perform',
    '05' => 'Good profitability, medium likelihood to perform',
    '06' => 'Fair profitability, medium likelihood to perform',
    '07' => 'Fair profitability, medium likelihood to perform',
    '08' => 'Poor Profitability, unlikely to perform',
    '09' => 'Poor Profitability, unlikely to perform',
    '10' => 'Poor Profitability, unlikely to perform',
    '11' => 'Low Profitability, unlikely to perform',
    '12' => 'Low Profitability, unlikely to perform',
    '13' => 'Low Profitability, unlikely to perform'
  }
  key.empty? ? 'Unknown' : values[key]
end

.en_f031_column_name ⇒ Object

ConsumerView Profitability Score



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# File 'lib/element_numbers/en_F031.rb', line 5

def self.en_f031_column_name
  'Working Couples'
end

.en_f031_description ⇒ Object 



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# File 'lib/element_numbers/en_F031.rb', line 11

def self.en_f031_description
  'ConsumerView Profitability Score works to identifying households likely to pay their debts and ranks households that allow marketers to target the best prospects based on: profitability approval rates response rates likelihood to perform as prescribed'
end

.en_f031_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_F031.rb', line 9

def self.en_f031_value_description(key); ''; end

.en_g2001(key) ⇒ Object 



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# File 'lib/element_numbers/en_G2001.rb', line 15

def self.en_g2001(key)
  return 'Unknown' if key.size != 4 or key[0...3].to_i < 0000 or key[0...3].to_i > 1000
  ((key.to_i.to_f)/10)
end

.en_g2001_column_name ⇒ Object

2000 % Population Age < 4



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# File 'lib/element_numbers/en_G2001.rb', line 5

def self.en_g2001_column_name
  '2000 % Population Age < 4'
end

.en_g2001_description ⇒ Object 



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# File 'lib/element_numbers/en_G2001.rb', line 11

def self.en_g2001_description
  '% Population Age < 4 is a 2000 Census demographic statistic. 2000 Census data reflects information collected on 118 million housing units and 281 million people by the US Census Bureau about households and individuals within a geographic area. These statistics are provided at the lowest possible geographical level (i.e. census tract or block group). This element represents a 100-percent characteristic (short form), where a limited number of questions were asked of every person and housing unit in the United States. Information such as: household relationship, sex, age, Hispanic or Latino origin, race, tenure (whether the home is owned or rented) and vacancy characteristics.'
end

.en_g2001_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_G2001.rb', line 9

def self.en_g2001_value_description(key); ''; end

.en_g2514(key) ⇒ Object 



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# File 'lib/element_numbers/en_G2514.rb', line 15

def self.en_g2514(key)
  values = {
    '0' => 'Decile 1',
    '1' => 'Decile 2',
    '2' => 'Decile 3',
    '3' => 'Decile 4',
    '4' => 'Decile 5',
    '5' => 'Decile 6',
    '6' => 'Decile 7',
    '7' => 'Decile 8',
    '8' => 'Decile 9',
    '9' => 'Decile 10'
  }
  key.empty? ? 'Unknown' : values[key]
end

.en_g2514_column_name ⇒ Object

Current State Estimated Family Income Decile



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# File 'lib/element_numbers/en_G2514.rb', line 5

def self.en_g2514_column_name
  'Current State Estimated Family Income Decile'
end

.en_g2514_description ⇒ Object 



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# File 'lib/element_numbers/en_G2514.rb', line 11

def self.en_g2514_description
  'Current state Estimated Family Income Deciles is an Experian derived statistic using current Estimated Median Household Income indexed within State and ranked on a decile basis. 2000 Census data reflects information collected on 118 million housing units and 281 million people by the US Census Bureau about households and individuals within a geographic area.'
end

.en_g2514_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_G2514.rb', line 9

def self.en_g2514_value_description(key); ''; end

.en_g2516(key) ⇒ Object 



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# File 'lib/element_numbers/en_G2516.rb', line 15

def self.en_g2516(key)
  values = {
    '0' => 'Decile 1',
    '1' => 'Decile 2',
    '2' => 'Decile 3',
    '3' => 'Decile 4',
    '4' => 'Decile 5',
    '5' => 'Decile 6',
    '6' => 'Decile 7',
    '7' => 'Decile 8',
    '8' => 'Decile 9',
    '9' => 'Decile 10'
  }
  key.empty? ? 'Unknown' : values[key]
end

.en_g2516_column_name ⇒ Object

Current CBSA Estimated Family Income Decile



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# File 'lib/element_numbers/en_G2516.rb', line 5

def self.en_g2516_column_name
  'Current CBSA Estimated Family Income Decile'
end

.en_g2516_description ⇒ Object 



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# File 'lib/element_numbers/en_G2516.rb', line 11

def self.en_g2516_description
  'Current CBSA Estimated Family Income Deciles is an Experian derived statistic using current Estimated Median Household Income indexed within CBSA and ranked on a decile basis. 2000 Census data reflects information collected on 118 million housing units and 281 million people by the US Census Bureau about households and individuals within a geographic area.'
end

.en_g2516_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_G2516.rb', line 9

def self.en_g2516_value_description(key); ''; end

.en_g2601(key) ⇒ Object 



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# File 'lib/element_numbers/en_G2601.rb', line 15

def self.en_g2601(key)
  key.empty? ? 'Unknown' : key.to_i
end

.en_g2601_column_name ⇒ Object

2000 CBSA Code



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# File 'lib/element_numbers/en_G2601.rb', line 5

def self.en_g2601_column_name
  '2000 CBSA Code'
end

.en_g2601_description ⇒ Object 



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# File 'lib/element_numbers/en_G2601.rb', line 11

def self.en_g2601_description
  'CBSA (Core Based Statistical Area) Codes have been developed by the Office of Management and Budget (OMB) to re-define the 1990 Metropolitan Statistical Areas (MSA). The terms Metropolitan, Primary Metropolitan (PMSA), and Consolidated Metropolitan Statistical Areas (CMSA) have been replaced by Core Based, Micropolitan, Metropolitan, and Combined Statistical Areas and Metropolitan Divisions. These are based on county populations. The terms Primary and Consolidated Metropolitan Statistical Area are also no longer used. A Metropolitan Statistical Area has a population of 50,000 or greater. There are 49 new Metropolitan Statistical Areas defined bringing the total to 362 from the previous 313. Metropolitan Statistical Areas that have a population of 2,500,000 or greater may be further divided into Metropolitan Divisions. Eleven Metropolitan Statistical Areas have been divided into a total of 29 Metropolitan Divisions. A Micropolitan Statistical Area is a newly defined entity that has a population between 10,000 and 49,999. There are 560 Micropolitan Statistical Areas. Collectively, Micropolitan and Metropolitan Statistical Areas are referred to as Core Based Statistical Areas (CBSAs). CBSAs may be rolled up into Combined Statistical Areas (CSAs). There are presently 116 CSAs comprising 314 CBSAs. The Office of Management & Budget further notes that while cities and towns (NECTA\'s) have also been re-defined and made available (similar to 1990 standards), it recommends users adapt to county standards for consistency of comparison.'
end

.en_g2601_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_G2601.rb', line 9

def self.en_g2601_value_description(key); ''; end

.en_g2602(key) ⇒ Object 



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# File 'lib/element_numbers/en_G2602.rb', line 15

def self.en_g2602(key)
  values = {
    'A' => 'Metropolitan',
    'B' => 'Micropolitan',
    'C' => 'Rural'
  }
  key.empty? ? 'Unknown' : values[key]
end

.en_g2602_column_name ⇒ Object

2000 CBSA Type



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# File 'lib/element_numbers/en_G2602.rb', line 5

def self.en_g2602_column_name
  '2000 CBSA Type'
end

.en_g2602_description ⇒ Object 



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# File 'lib/element_numbers/en_G2602.rb', line 11

def self.en_g2602_description
  'CBSA (Core Based Statistical Area) type has been developed to differntiate between Metropolitan, Micropolitan and Rural areas / CBSA codes. A Metropolitan Statistical Area has a population of 50,000 or greater. A Micropolitan Statistical Area is a newly defined entity that has a population between 10,000 and 49,999.'
end

.en_g2602_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_G2602.rb', line 9

def self.en_g2602_value_description(key); ''; end

.en_g2603(key) ⇒ Object 



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# File 'lib/element_numbers/en_G2603.rb', line 15

def self.en_g2603(key)
  values = {
    '1' => 'Counties in Metro Areas with population 1,000,000 or more',
    '2' => 'Counties in Metro Areas with population 250,000 - 1,000,000',
    '3' => 'Counties in Metro Areas with population less than 250,000',
    '4' => 'Urban population of 20,000 or more, adjacent to a Metro Area.',
    '5' => 'Urban population of 20,000 or more, not adjacent to a Metro Area.',
    '6' => 'Urban population of 2,500 to 19,999, adjacent to a Metro Area.',
    '7' => 'Urban population of 2,500 to 19,999, not adjacent to a Metro Area.',
    '8' => 'Completely rural or less than 2,500 urban population, adjacent to a Metro Area.',
    '9' => 'Completely rural or less than 2,500 urban population, not adjacent to a Metro Area.'
  }
  key.empty? ? 'Unknown' : values[key]
end

.en_g2603_column_name ⇒ Object

2000 Rural-Urban County Size Code



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# File 'lib/element_numbers/en_G2603.rb', line 5

def self.en_g2603_column_name
  '2000 Rural-Urban County Size Code'
end

.en_g2603_description ⇒ Object 



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# File 'lib/element_numbers/en_G2603.rb', line 11

def self.en_g2603_description
  'Rural-Urban County Size Code Codes is a 1 position Rural/Urban continuum code from Economic Research Services. The Rural-Urban County Size Code codes are Rural-Urban Continuum Codes developed by The Economic Research Service of the U.S. Department of Agriculture (ERS/USDA). These codes distinguish counties within Metropolitan Statistical Areas (Metro) by the population of the CBSA, and counties not in Metropolitan Statistical Areas (Non-Metro)-they may be in Micropolitan Statistical Areas or not in any CBSA at all-by degree of urbanization and adjacency to one or more Metropolitan Statistical Areas. The Metro and Non-Metro counties have been subdivided into three and six subgroups, respectively, resulting in nine county classifications. This allows for analysis and selection by finer distinction than the simple Metro and Non-Metro classification, CBSA type, or Nielsen County Size Code.'
end

.en_g2603_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_G2603.rb', line 9

def self.en_g2603_value_description(key); ''; end

.en_geo6(key) ⇒ Object 



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# File 'lib/element_numbers/en_GEO6.rb', line 29

def self.en_geo6(key)
  key.empty? ? 'Unknown' : key.to_i
end

.en_geo6_column_name ⇒ Object

2010 Geo Code



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# File 'lib/element_numbers/en_GEO6.rb', line 5

def self.en_geo6_column_name
  '2010 Geo Code'
end

.en_geo6_description ⇒ Object 



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# File 'lib/element_numbers/en_GEO6.rb', line 11

def self.en_geo6_description
  'Geo Codes are numeric symbols assigned for the identification of geographic entities such as state, county, tract, block, groups, etc., by the U.S. Census Bureau. Every geographic
entity recognized by the Census Bureau is assigned one or more geographic codes. A geo code is geographic presentation that shows the geographic entities in a
superior/subordinate structure. In this system of relationships among geographic entities, each entity (except the smallest one) is subdivided into lower-order units that in turn may be
subdivided further. For example, states are subdivided into counties, which are subdivided into both county subdivisions and census tracts.
State Code (2 position) States and equivalent areas. Besides the 50 states, the Census Bureau treats the District of Columbia, Puerto Rico, and the Island areas (the U.S. Virgin
Islands, Guam, American Samoa, and the Northern Mariana Islands) as state equivalents for statistical presentation.
County Code (3 positions) Counties and equivalent areas. These are the primary divisions of most states, Puerto Rico, and the Island Areas. They include counties in 48 states;
parishes in Louisiana; boroughs and census areas in Alaska; municipios in Puerto Rico; independent cities in Maryland, Missouri, Nevada, and Virginia; and other entities in the
Island Areas.
Census Tract Code (6 positions) These small statistical subdivisions (averaging about 4,000 persons) of counties generally have stable boundaries and, when first established, were
designed to have relatively homogeneous demographic characteristics.
Census Block Group (1 position) Block groups are a collection of census blocks within a census tract, sharing the same first digit of their four-digit identifying numbers.
Census Block ID (3 positions) Block ID within a block group
CBSA (5 positions) A statistically based geographic level consisting of county or counties associated with at least one area of at least 10,000 population. Source: US Office of
Management and Budget (OMB)'
end

.en_geo6_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_GEO6.rb', line 9

def self.en_geo6_value_description(key); ''; end

.en_l000(key) ⇒ Object 



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# File 'lib/element_numbers/en_L000.rb', line 15

def self.en_l000(key)
  census = {
    '5' => 'Input Zip Code matched, but the Zip+4 Code did not match or was not provided as input.',
    '9' => 'Input Zip and Zip+4 Code matched the master file.',
    'S' => 'Street address match',
    'A' => 'Auxiliary file match'
  }
  street_match = {
    'Z' => 'ZIP Code not found in Street address database',
    'S' => 'Street not found in ZIP Code',
    'H' => 'House number not found on street',
    'L' => 'Lat/long not determined on Auxiliary File match'
  }
  return 'Input Zip Code did not match the master file.' if key.size == 0
  return census[key[0]] + ' - Successful match' if key.size == 1 and !census[key[0]].nil?

  [street_match[key[1]], census[key[0]]].join(' - ')
end

.en_l000_column_name ⇒ Object

2000 Geo Mandatory Append



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# File 'lib/element_numbers/en_L000.rb', line 5

def self.en_l000_column_name
  '2000 Geo Mandatory Append'
end

.en_l000_description ⇒ Object 



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# File 'lib/element_numbers/en_L000.rb', line 11

def self.en_l000_description
  'The Geo mandatory append is required when any data from the geo code process (L and K file types) is appended. It consists of a match level and street match level.'
end

.en_l000_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_L000.rb', line 9

def self.en_l000_value_description(key); ''; end

.en_p213e(key) ⇒ Object 



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# File 'lib/element_numbers/en_P213E.rb', line 15

def self.en_p213e(key)
  code = {
    '1' => 'Extremely Likely (Household level code)',
    '2' => 'Highly Likely (Zip 4 level code)',
    '3' => 'Likely (Zip level code)'
  }

  values = {
    '01' => 'Less than $25,000',
    '02' => '$25,000 - $49,999',
    '03' => '$50,000 - $74,999',
    '04' => '$75,000 - $99,999',
    '05' => '$100,000 - $249,999',
    '06' => '$250,000 - $499,999',
    '07' => '$500,000 - $749,999',
    '08' => '$750,000 - $999,999',
    '09' => '$1,000,000 - $1,999,999',
    '10' => '$2,000,000+',
    '00' => 'Unknown / Not Attempted',
    '99' => 'Unknown / Default'
  }

  return 'Unknown' if key.size != 3 or code[key[0]].nil? or values[key[1..2]].nil?
  [values[key[1..2]], code[key[0]]].join(' - ')
end

.en_p213e_column_name ⇒ Object

Inc Producing Assets IPA V3



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# File 'lib/element_numbers/en_P213E.rb', line 5

def self.en_p213e_column_name
  'Inc Producing Assets IPA V3'
end

.en_p213e_description ⇒ Object 



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# File 'lib/element_numbers/en_P213E.rb', line 11

def self.en_p213e_description
  'Income Producing Assets (IPA) is one of the models that are used to derive the P$YCLE Financial Markets. This model specifically tries to predict real worth or asset value of the household. IPA estimates affluence within ten ranges.'
end

.en_p213e_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_P213E.rb', line 9

def self.en_p213e_value_description(key); ''; end

.en_p213h(key) ⇒ Object 



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# File 'lib/element_numbers/en_P213H.rb', line 15

def self.en_p213h(key)
  code = {
    '1' => 'Extremely Likely (Household level code)',
    '2' => 'Highly Likely (Zip 4 level code)',
    '3' => 'Likely (Zip level code)'
  }

  values = {
    '01' => 'The Wealth Market - Wealthy Older Mostly w/o Kids When it comes to American affluence, The Wealth Market is home to the richest addresses in the nation. Consisting of older suburban couples, this is where to find millionaires in the P$YCLE system. Most of the households boast more than $1 million in income-producing assets, and invest in a broad range of stocks, corporate/municipal bonds, mutual funds, and real estate. These consumers also excel in buying annuities and other investment-style life insurance products. In The Wealth Market, residents are known for both making money and spending money in grand style, splurging on foreign travel and cultural events as well as hiring a small army of financial managers, estate planners, and full-service brokers the better to accumulate more assets.',
    '02' => "Globetrotters - Upper Mid Mature Mostly w/o Kids Members of Globetrotters have reached an age over 65 years old and a level of financial comfort many have six-figure incomes that allow them to indulge their passion for foreign travel. Consisting mostly of suburban couples, households in this segment have amassed substantial IPA, such as variable-rate annuities, government securities, and corporate/municipal bonds. Admittedly risk-averse in their financial behavior, they buy a variety of insurance products, including long-term care, medical, and residential coverage. But playing it safe doesn't apply when it comes to exploring other countries; members of Globetrotters have been known to take more than three foreign trips a year. And many get their news about world events from US News & World Report and Condé Nast Traveler.",
    '03' => "Business Class - Wealthy Older Mostly w/o Kids As the segment most likely to belong to a country club, Business Class is known for its lavish spending style. But many of its fifty-something executive couples have begun to divert their high incomes to build up long- neglected nest eggs. Segment households rank at the top for having Keogh plans, cash management accounts, and unit investment trusts. But they have only one-third the level of income-producing assets of The Wealth Market, and many are trying to make up for lost time by aggressively investing in stocks, mutual funds, and investment-style insurance. Located mostly in pricey suburban areas, Business Class scores high for business and pleasure travel, high-end catalog shopping, listening to classical radio, and reading business publications.",
    '04' => "Golden Agers - Upper Mid Mature Mostly w/o Kids One of the oldest financial segments, Golden Agers is a collection of over-65-year-olds whove amassed substantial levels of IPA. These senior singles and couples, typically living in large suburban homes, tend to be fiscally conservative; they rank at the top for having corporate/municipal bonds, government securities, fixed-rate annuities, and savings accounts. In addition, theyre more than twice as likely as average Americans to own multiple annuities and long-term care insurance. Enjoying their cushy retirements, they fill their days with golf (both playing and watching), the arts, and public TV and radio. Like many affluent seniors, they leave their investment decisions to brokers at full-service brokerage firms.",
    '05' => "Power Couples - Wealthy Older w/o Kids The most affluent Younger Years segment, Power Couples seems to have it made: six-figure incomes, designer-decorated houses, and high balances in their income-producing assets. As investors, these mostly 45- to 54-year-old couples boast retirement accounts containing a welldiversified mix of options, stocks, and mutual funds. Typically college-educated and holding management jobs, they also tend to be cultured consumers who travel the world, subscribe to publications like Forbes and Architectural Digest, and shop at swanky stores like Lord & Taylor and Nordstrom. When it comes to managing their money, however, they put their faith in the pros, exhibiting high rates for using asset managers, estate planners, and full-service brokers. Internet savvy, they track how well their investments are doing online.",
    '06' => "Civic Spirits - Midscale Mature Mostly w/o Kids They may be retired, but they're not retiring. The over-65-year-old couples who make up Civic Spirits tend to be community activists who participate in civic events, write newspaper editors, and contact elected officials at high rates. Their financial behavior is less adventurous, with households preferring investments like CDs, corporate/municipal bonds, government securities, and annuities, particularly those purchased for tax shelters. Civic Spirits members also rank high for buying long-term care insurance and residential insurance for their condos. When they're not volunteering, these Americans keep up with their civic interests by watching news programs on television, tuning in to news/talk radio stations, and reading mature market magazines.",
    '07' => "Family Fortunes - Wealthy Middle Age w/Kids The members of Family Fortunes rank at the top in many financial categories: investing in futures and options, owning mutual funds and U.S. Savings Bonds, and acquiring first mortgages worth over $150,000. They need to: these 35- to 54-year-old suburbanites have more children than any other P$YCLE segment. With high incomes and expensive homes, they make a prime target for high-value life and homeowners insurance. No segment has more collegeeducated residents, and they enjoy an energetic lifestyle: traveling around the world, shopping at high-end department stores, owning luxury vehicles, and enjoying pricey sports like skiing and tennis. Not surprisingly, their favorite media outlets include business and travel magazines, which they read at the highest rate in the nation.",
    '08' => "Domestic Bliss - Wealthy Older Family Mix Domestic Bliss is home to fashionable couples in their peak earning years. Consisting mostly of adults between the ages of 45 and 64, the White, Black, and Asian households in this segment report six-figure incomes and income-producing assets of over $100,000. Generally college graduates, they work in management, own their home, and hold extensive investments in stocks,mutual funds, bonds, and real estate. They have solid credit profiles and typically have second mortgages, home equity loans, and personal lines of credit, not to mention auto, term life, longterm care, and investment-style insurance policies. Active and cultured, Domestic Bliss members travel abroad, attend the theater, and enjoy tennis, golf, and skiing. With their sizable portfolios, they have high rates for using full-service brokers and estate planning services.",
    '09' => "Big Spenders - Wealthy Middle Age Mostly w/ Kids The suburban Boomers who comprise Big Spenders are the ultimate financial jugglers. With teenagers at home and retirement looming, theyve managed to craft both enviable lifestyles and fat portfolios filled with stock options, mutual funds, securities, and bonds. Higher than average incomes help, but they also rank at the top for using home equity lines of credit and second mortgages. Big Spenders represents an attractive market for a range of insurance products: term life, auto, disability and long-term care coverage. But these dual-income households know how to enjoy their money: theyre twice as likely as average Americans to drive new cars, frequent casual dining restaurants, build basement gyms, and take frequent trips around the world. Their top-rated mediaairline magazinesserves as a guide to worldly living.",
    '10' => "Capital Accumulators - Upper Mid Older Mostly w/o Kids Capital Accumulators is a collection of 55+ year-old suburbanites dedicated to growing their IRAs and 401(k) retirement accounts. Theyre twice as likely as average Americans to own securities, mutual funds, and real estate investments. Many households are home to white-collar professionals who have parlayed upper middle class incomes into substantial income-producing assets. They tend to lead very active lifestyles, traveling abroad, skiing at exclusive resorts, and paying for investment advice from stockbrokers and financial planners. With their brains and bucks, Capital Accumulators consume a variety of media, reading the Wall Street Journal and Cigar Aficionado, and watching pay-per-view movies at high rates.",
    '11' => "Savvy Savers - Lower Mid Mature Mostly w/o Kids The living is easy in Savvy Savers, a segment of well-invested retirees scattered across the nations suburban and exurban communities. These cautious investors rank near the top for owning CDs, money market funds, municipal bonds, and fixed- and variable-rate annuities. Together, these assets provide them sizable nest eggs, though they pursue only midscale lifestyles, characterized by watching golf on TV and socializing at the fraternal order. These are the folks who take full advantage of senior discounts and are coupon users at drugstores, grocery stores, movie theaters, and restaurants. When they go on vacation, theyre typically value-oriented travelers who drive to a domestic destination and stay at a Comfort Inn.",
    '12' => "Feathered Nests - Wealthy Middle Age Family Mix There's money tucked away in Feathered Nests, a collection of middle-aged families with high incomes and above-average investable assets. A mix of college-educated Whites and Asians,these 35- to 54-year-olds typically hold management jobs and have begun filling their retirement accounts with mutual funds, stocks, bonds, and CDs. They're also a trong credit market, often carrying jumbo mortgages and home equity lines of credit. Insurance omnivores, they own annuities, term and whole life, disability coverage, and auto insurance for their multiple cars. And they enjoy good-life pursuits, like golfing and sailing, boutique shopping for children's toys and collectibles, and tuning their high-end TVs to ice hockey matches and public roadcasting programs. For investment tips, they read a variety of business magazines.",
    '13' => "Annuity-ville - Lower Mid Mature Mostly w/o Kids No place has more seniors, fewer children, and a greater passion for fixed-rate annuities than Annuity-ville. These upper-middle-class suburbanites living in established communities score near the top for staid investments like government securities, CDs,and money market funds. Many members are preoccupied with preserving their wealth, and they boast high rates for having tax shelters, unit investment trusts, and cash management accounts. But theyre also willing to spend their money shopping at upscale department stores and donating to PBS. Leading somewhat sedentary lifestyles, they enjoy tuning in to news, old movies, and sports such as golf and tennis on TV.",
    '14' => "Financial Independents - Upscale Older Mostly w/o Kids The members of Financial Independents like to go it alone. Upscale Boomers living in suburbia, they trade online, read self-help business books, and find investment ideas in The Wall Street Journal. But their investment style leans towards safety, with segment members preferring to fill their IRAs with mutual funds and money market accounts at high rates. And they offer only a middling market for insurance products, with high indices only for disability insurance, variable-rate annuities, and high-value homeowners insurance policies. Married and mostly without children at home, these professional couples have pleasant lifestyles, traveling often, shopping at warehouse clubs, and surfing the web. Their entertainment tastes reflect their inquisitive minds: talk and all-news radio, jazz and classical music, and travel and epicurean magazines.",
    '15' => "Midlife Highlife - Wealthy Younger Mostly w/ Kids Big homes, diversified retirement accounts and high-value life insurance that's the skinny on Midlife Highlife. This upscale segment is filled with investment-savvy, 25- to 44-year-old Whites and Asians who own mutual funds, stocks, stock options, and savings bonds at high rates. With their college degrees and white-collar jobs, they typically have high incomes and above-average investable assets. But because many are married and raising young children, they also score high for acquiring term life, disability, and long-term care insurance to protect their growing families. Active and sophisticated, Midlife Highlife members enjoy attending museums and dance performances, as well as going biking, backpacking, and jogging. They're also fans of the virtual world, where they buy books, trade stocks, and acquire mutual funds. 16 = Leisure Land - Lower Mid Mature Mostly w/o Kids Located in a mix of suburban and exurban communities, Leisure Land residents have below average to moderate portfolios and lower midscale incomes. These households of over",
    '16' => "Leisure Land - Lower Mid Mature Mostly w/o Kids Located in a mix of suburban and exurban communities, Leisure Land residents have below average to moderate portfolios and lower midscale incomes. These households of over-65-year-old couples, widows, and divorcees have average educations, and home values. But theyve managed to sock away enough money to create solid investment portfoliosalbeit characterized by a conservative assortment of annuities, CDs, money market accounts, and mutual funds. As a result, they can afford to spend their time socializing at fraternal orders, and taking the occasional all-inclusive-package vacation. At home, members of Leisure Land also rank high for watching television, especially talk shows, cultural programs, and, befitting their aspirations, Who Wants to be a Millionaire?",
    '17' => "Home Sweet Equity -Upscale Older Mostly w/o Kids Upscale incomes, above average assets, and home-owning couples make this segment an attractive market for large-balance credit products. In Home Sweet Equity, many of the 45- to 64-year-old residents are tapping the value of their older homes with home equity loans, second mortgages, and home improvement loans. With well-paying white-collar jobs, these traditionalists rank high for buying fixed-rate annuities and using credit unions and savings and loan associations. Their above- average presence in exurban areas also helps explain their fondness for hunting, power boating, and buying from mail-order catalogs. Given their busy schedules, they get what information they can about life, medical, and mortgage insurance from newspaper ads and TV and radio commercialsespecially oldies radio stations.",
    '18' => "Travel & Antiques - Travel & Antiques comes by its name honestly: The members of this segment love both traveling and collecting, especially antiques and coins. Mostly empty-nesting couples between 55 and 64 years old, these households have filled their IRAs with a range of investments: stocks, mutual funds, CDs and money market accounts. They qualify for personal and home equity loans, and likely use them for home renovation projects. Residents of Travel & Antiques have high rates for traveling in the U.S. and abroad, favoring excursions to Britain and car trips across America. And when theyÆve returned to their suburban homes, they often take armchair journeys, reading travel magazines, watching the Travel Channel and rarely missing an installment of The Antiques Roadshow.",
    '19' => "New Money Upscale - Middle Age Family Mix Middle age, upscale suburban and town families make up New Money, a segment of mostly thirty and forty-something households who invest heavily in stocks, futures, and mutual funds. With their college degrees and management careers, these dual-income upscale households have above average levels of income-producing assets. They also make for financially savvy consumers, trading stocks online, using credit cards with rewards programs, and getting financial ideas from investment magazines and websites. In addition to staying financially fit, New Money members also try to stay physically healthy, pursuing aerobic sports such as skiing, bicycling, and in-line skating. And they work to protect their dependents from undue financial burdens with lots of high-balance term and whole life insurance.",
    '20' => "Comfortably Retired - Lower Mid Mature Mostly w/o Kids The members of Comfortably Retired could be poster children for living below ones means. Despite their lower-middle class incomes, these over-65 singles and couples have solid net worthmostly due to their homes. Their small investments include a well-diversified mix of conservative fixed-rate annuities, variable-rate annuities, and unit investment trusts. Together, these investments provide Comfortably Retired households with tranquil lifestyles. With a disproportionate number of residents living in exurban areas, many households enjoy pursuits such as bird watching, socializing at church, and watching TV, especially cultural and game shows. In Comfortably Retired, big-time excitement means Bingo Night.",
    '21' => "Leveraged Life - Upper Mid Older Family Mix They may be upper-middle-class, but Leveraged Life members often maintain that status by living on credit. These older homeowners index high for having second mortgages, using home equity lines of credit, and maxing out the rewards on their charge cards. Typically having attended at least some college, they tend to earn upper midscale paychecks from white-collar jobsand carry death and disability insurance to protect them. But this group has only modest assets in conservative investments like U.S. savings bonds and Treasury bills. Fond of the outdoors, many enjoy skiing, camping, whitewater rafting, and hunting. Media-wise, members of this group enjoy watching country music TV, reading fishing and hunting magazines, and listening to classic rock radio.",
    '22' => "Bargain Lovers = Upper Mid Middle Age w/o Kids Call them thrifty or call them economical; just dont call them cheap. The members of Bargain Lovers have upper middle class incomes and moderate levels of income-producing assets, but they still love a dealwhether its buying stocks through discount brokers or cashing in credit card rewards for free airline tickets. A mix of under 55 year-old singles and couples, these urban and suburban households are heavy users of mortgage products, high-end credit cards, and auto leases. They also fill their Roth IRAs and 401(k)s with mutual funds, stocks, and money market funds. Internet fans, they go online to bank, comparison shop, get travel information, and trade stocks. Their media tastes are also progressive: they enjoy new age music and soft rock as well as computer and sports magazines.",
    '23' => "School Daze - Upper Mid Middle Age w/ Kids
A collection of large, suburban households, School Daze represents one of the nations best markets for borrowing. These upper-middle-class families score near the top for having first mortgages, home
improvement loans, and auto loans. Yet these Gen-X parents have only begun to think about retirement, and theyve acquired only moderate levels of income-producing assets such as stocks and mutual funds.
Being in their early childrearing years, theyre a prime audience for term life insurance, which they tend to buy from independent agents whenever a new child is born. With one of the nations highest
concentrations of children, School Daze features kid-centric lifestyles characterized by minivans, toys, video games, and lots of sports. Their favorite media: parenting magazines and classic rock radio.
",
    '24' => "Corporate Climbers - Upper Mid Middle Age w/o Kids
The members of Corporate Climbers are singularly focused on their careers. Mostly 35-54 years old and childless, these educated White and Asian singles and couples have professional jobs and upper-middle-class
incomes but only moderate assets, invested mostly in mutual funds and retirement accounts. To maintain their busy lifestyle, they use credit liberally, taking out auto loans, using home equity lines of credit, and
carrying high-end credit cards with revolving
balances. They fill their leisure time with sports and entertainment: mountain biking and skiing, playing tennis and going sailing, frequenting bars and attending nightclubs. Determined to improve their financial lot,
they index high for reading self-help business books, watching Bloomberg Television, and subscribing to the Wall Street Journal.",
    '25' => "Safe at Home - Upper Mid Older Mostly w/o Kids
The members of Safe at Home are aging gracefully in financial comfort. Mostly empty-nesting couples between 45 and 64, they work in white-collar jobs and earn upper-middle-class incomes. Theyve already
amassed decent nest eggs, reporting investable assets between
$100,000 and $250,000. Though theyre fiscal conservatives who favor CDs, saving bonds, and money market funds, they buy few insurance products other than term life coverage. These mature folks pursue wide-
ranging leisure activitiesfrom reading and gardening to boating and skiing. And their media preferences are similarly eclectic: they listen to sports and oldies radio; watch CNBC and the Sci-Fi Channel, and read
travel, and computer magazines. More than three-quarters claim to have a conservative outlook.",
    '26' => "Early-Bird Specials - Lower Mid Mature Mostly w/o Kids
With their moderate net worth and lower midscale incomes, members of Early-Bird Specials have crafted comfortable retirements in their older homes. These risk-averse singles and couples have filled their
retirement accounts with government securities, fixed-rate annuities,
CDs, and money market funds. Theyve also tapped a variety of insurance productsauto, homeowners, long-term care, and whole lifefor added protection. Concentrated in exurban communities, segment
residents pursue quiet pastimes: going bird watching, reading
gardening and shelter magazines, and shopping at Sears. When dining out, they head for the local Dennys or Cracker Barrel restaurant, especially if it offers senior discounts.",
    '27' => "Conservative Couples - Downscale Mature Mostly w/o Kids
Most of the members of Conservative Couples have retiredat least those who can afford to. This mostly retired segment is filled with over-65-year-old couples who are wary investors. Their downscale incomes
and moderate income-producing assets make them only a modest
market for most financial investments other than CDs, mutual funds, and savings accounts. But they do buy a number of insurance products and rank number one for medical coverage. Located in both small town
and rural communities, these seniors pursue an easygoing lifestyle
with a fondness for bird watching, needlepoint, and belonging to Veterans clubs. When it comes to media, their tastes lean towards TV news, daytime game shows, and skating competitions.",
    '28' => "Senior Solitaire - Downscale Mature Mostly w/o Kids
The elderly singles of Senior Solitaire are Americas fiscal conservatives. One of the oldest groups in the nation, they include disproportionate numbers of veterans, widows, and nursing home residents. With
downscale incomes and below average to moderate assets, they buy low-risk investments such as CDs, annuities, and money market funds. Their advanced age also makes them a receptive audience for long-term
care insurance. But these older traditionalists care for few banking services other than direct depositing their Social Security checks; they dont even carry credit cards. Sedentary in their lifestyles, they watch a lot of
television: daytime game shows, evening newscasts, and sports like golf and skating. Just about the wildest thing they do is risk imaginary dollars on Jeopardy, one of their favorite TV shows.",
    '29' => "Retirement Ready - Midscale Older Mostly w/o Kids
The nearly-retired Americans in this segment enjoy comfortable lifestyles on middle-class incomes. Although not asset-rich, members of Retirement Ready do invest in real estate and variable-rate annuities, and
theyve built up enough home equity to take out second
mortgages, and home equity loans. Many of these singles and couples have turned high school educations into well-paying white-collar jobs. Today, they have sufficient disposable cash to make significant
purchases through L.L. Bean and QVC. Their idea of a big night out is
heading for Shoneys, which ranks at the top among this segments restaurant choices. Their media habits are also old fashioned: old movies and game show re-runs.
",
    '30' => "Fiscal Rookies - Upper Mid Younger w/o Kids
The young couples and singles of Fiscal Rookies are financially inexperienced Americans. Despite relatively high incomes, these 25- to 44-year-olds are not saving a lot of money and have only moderate levels of
income-producing assets. Many of these exurban households carry debt from student and auto loans as well as their first home mortgages, and theyve only recently begun dabbling in investment-style insurance
and mutual funds for their 401(k)s. For now, a lot
of their money supports active lifestyles and media preferences. Their top activities include skiing, in-line skating, baseball, and racquetball. When theyre not exercising, they have high rates for listening to rock
radio, reading fitness magazines, and watching comedy shows.",
    '31' => "hakis & Credit - Upper Mid Older Family Mix
Theyre a mix of families and couples, town and rural folk. But one thing the Boomer households in Khakis & Credit share is a fondness for credit to maintain their middle-class lifestyles. Many enjoy incomes that
allow them to qualify for home improvement loans,
installment credit, and auto loans for the four-plus cars that many own. And they also have high rates for using prestige credit cards. Given the mix of household types, the segment shows a wide range of leisure
activities, with above average rates for attending high school sports, buying electronic games and educational toys, as well as playing soccer and participating in civic affairs. And though Khakis & Credit members
dont own a lot of income-producing assets, many hedge their credit-happy ways by buying disability and life insurance.",
    '32' => "Family Sprawl - Upper Mid Younger w/ Kids
Their homes may be filled with tykes, bikes, and toys, but the younger, upper-middle-class families of Family Sprawl also make a solid market for financial products ranging from installment credit and U.S. savings
bonds to auto and term life insurance. Highly leveraged, these mostly thirty-something households are typically paying off mortgages and auto loans. And though many have recently opened 401(k) accounts and
Roth IRAs, they still score low for
investing in stocks, bonds, and money market funds. For now, they're inundated with childrearing expenses: This segment ranks at the top for buying children's toys, bicycles, electronic games, and videos. With
many residents having only recently moved into their first
houses, they're also big users of Internet Yellow Pages to locate retailers and insurance companies.",
    '33' => "Cut-Rate Country - Midscale Older Mostly w/o Kids
The rural Boomers in Cut-Rate Country rarely pass up a dealwhether its getting a sale price at Wal-Mart or using a Discover card for the cash-back feature. These 45- to 64-year-old couples and families with
teenagers have midscale incomes and wallets full of credit cards.
Although not asset-rich, they do own older homes and exhibit a high rate for taking out second mortgages, home improvement loans, and installment credit. They also buy insurance products like variable annuities,
homeowners insurance, and mortgage life insurance. While theyre willing to have some fun with their moneycamping and traveling in their motor homesthese persuadable consumers typically play it safe and
buy insurance on the advice of a friend or insurance agent.",
    '34' => "Online Living - Upscale Younger Family Mix
No segment scores higher for using Internet Banking than Online Living. This group of mostly 25- to 44-year-old singles, couples, and families ranks number one for stock trading, and bill paying, as well as buying at
retail websites. Many of these web-surfers live in suburban sprawl areas, own their homes and have upscale incomes, but they have only modest levels of incomeproducing assets and are still paying off student and
personal loans. They also have begun buying their first insurance policiesfor term life, disability, auto, and medical coveragetypically from online websites. When they go offline, the members of Online Living
prove to be fitness, music, and travel buffs. Their favorite media outlets are alternative rock radio, VH1, and fitness and health magazines.",
    '35' => "Hunters & Collectors - Lower Mid Older Mostly w/o Kids
It's a world where hunters reign supreme, whether their target is big game, small fish, or rare coins. Hunters & Collectors ranks at the top for collecting coins but near the bottom for most other financial activities.
These 55 plus year-old homeowners, disproportionately found in exurban communities, care little about stocks, bonds, and other investments. But they do take out personal and home improvement loans, mostly to
upgrade their older homes. And they have above-average rates for buying auto, residential, and disability insurance. For leisure, Hunters & Collectors households rank at the top for hunting and a number of other
popular country pursuits: boating, camping, and reading Country Living and Town & Country.
",
    '36' => "Paying It Down - Midscale Middle Age Family Mix
A haven for Generation-Xers pursuing energetic lifestyles, Paying It Down residents rank high for enjoying hard rock, fast cars, and all manner of outdoor activities. These diverse households a mix of families and
couples between 35 and 54 years old have middle class
incomes, below-average levels of IPA, and a slowly-awakening financial sensibility. With many residents having recently married or begun having children, they represent a solid market for life
insurance and first mortgages. But they're limited in their financial investments because theyre still paying off credit card balances, installment credit, and personal and auto loans. Found in communities all over the
map, they enjoy a range of leisure activities, from baseball and basketball to skating, camping, and biking.",
    '37' => "Rural Roots - Upper Mid Middle Age Mostly w/ Kids
Cash-rich but asset-poor, Rural Roots is a collection of 35- to 54-year-old homeowners raising large families out in the country. Racially mixed with some college education, the members of this segment have mid to
upper middle class incomes but low income-producing assets. Many of these families have no tradition for investing in the stock market, and they rank low for owning stocks, bonds, and mutual funds. However, Rural
Roots does make a strong market for debt products, showing high indices for using auto loans, home mortgages, and installment credit. Kid-centered in their lifestyles, Rural Roots households buy lots of toys, eat at
fast-food
restaurants, and enjoy going bowling. When it comes to media, they score high for parenting magazines, Nick at Night, and contemporary radio.",
    '38' => "Old Homesteaders - Downscale Mature Mostly w/o Kids
Old Homesteaders should not be confused with Old Money. The segment has one of the highest concentrations of nursing home residents in the nation. Nearly three-quarters of these elderly singles and couples
have extremely low incomes and many depend on Medicaid and
their monthly Social Security checks for survival. With income-producing assets only onethird the national average, they buy few financial products other than CDs. However, they do own low-balance insurance
products and boast the highest indices for life insurance valued under $20,000 and homeowners insurance valued under $25,000. Home centered, they tend to watch a lot of television, especially news, game shows,
and 700 Club.",
    '39' => "ATM Nation - Midscale Middle Age Mostly w/o Kids
As hip as they are, the members of ATM Nation are, nevertheless, financially unsophisticated. Many of these city apartment dwellers boast midscale incomes and have yet to accumulate appreciable assets. They
rarely set foot inside banks, preferring to use ATMs and online billpaying when accessing their interest-bearing checking accounts. They also prefer to go online to buy a handful of insurance productsauto, term life,
and renters insurance. Despite paying off student and personal loans, ATM Nation residents live rich lives offline, showing high indices for traveling nationally, going skiing, whitewater rafting, and backpacking. When
it comes to media, this segment scores high for listening to NPR, watching the Late Show w/ David Letterman, and reading: favorites include Elle, Vanity Fair, and The New York Times.",
    '40' => "Timeless Tenants - Midscale Older Mostly w/o Kids
Theyre over 55, closing in on retirement, and rent their homes, but the members of Timeless Tenants boast solid middle-class incomes. After having attended college and worked at a variety of white-collar jobs,
many are now receiving healthy pensions. Though they own few
income-producing assets like stocks, bonds, and mutual funds, they're more likely than average to have CDs, collectibles, and liquid savings. And they often make use of bank offerings like credit cover their
expenses. Their insurance needs are typically limited to
renters insurance and auto insurance. But they live comfortably, taking home study courses, buying collectibles, and playing the lottery all at above average rates. And their media tastes
reflect an educated mindset, favoring Smithsonian and Metropolitan Home magazines and PBS and CNN on TV.",
    '41' => "Finance Chargers - Midscale Middle Age Mostly w/ Kids
Small-town, working-class families characterize Finance Chargers, a segment whose households are asset-light and loan-heavy. These lower-middle-class, 35- to 54-year-old homeowners use a range of installment
credit and finance productsincluding auto, home improvement, and personal loans. Many are blue-collar workers employed in farming and construction, and they're typically conservative in their financial behavior.
Members of Finance Chargers rank near the bottom for acquiring stocks and mutual funds though they do have respectable rates for owning auto, accidental death and dismemberment insurance. Most households
are more concerned with
the costs of raising kidsdevoting their funds to toys, games, and family media. This segment ranks high for parenting magazines and religious radio.
",
    '42' => "Sunset Times - Low Income Mature Mostly w/o Kids
With incomes and assets significantly below the national average, Sunset Times qualifies as a weak financial market at best. This group is dominated by elderly, racially mixed singles and couples, with a high
concentration of retirees. Other than owning their relatively low-value homes, they possess few retirement, investment, or credit products. And like other seniorfilled segments, Sunset Times ranks high for Social
Security direct deposits. Their insurance coverage is limited to low-balance whole life, auto, residential, and funeral expense policies. And though they engage in few activities above the national average, they're
more likely than the general population to play bingo, hunt, and belong to veterans clubs and fraternal orders.
They also enjoy listening to gospel radio, reading fraternal magazines, and watching soaps, comedy, and news.",
    '43' => "Payday Prospects - Midscale Middle Age Family Mix
They may earn midscale salaries, but members of the Payday Prospects segment often find themselves living paycheck to paycheck. This ethnically mixed group of singles and small families tend to rent their
exurban homes, many of which are mobile homes. With some college
education, they work at white-collar or service industry jobs. But many are saddled with student, personal, and auto loans, and they own few investments or insurance products beyond renters and auto coverage.
Young at heart, they exhibit high rates for going to bars, dance clubs, movie theaters, and billiards halls. And their fondness for ATMs, no-interest checking, and online banking reflects their footloose lifestyle. But
their fear of outlasting their retirement savings leads them to read the Wall Street Journal, subscribe to Barrons, and tune in Bloomberg Television.",
    '44' => "Homespun Families - Midscale Middle Age Family Mix
Homespun Families is a classic country lifestyle. Its 35- to 54-year-old homeowners, including a disproportionate number of farm families, are big fans of pickup trucks, country music, and outdoor sports like hunting
and fishing. Financially, theyre a strong credit market, with high rates for using installment credit, mortgages, home improvement loans, and auto loans for their multiple vehicles. What they lack in income-producing
assets, these middle-class households
make up in a variety of insurance productsbuying residential, mortgage, auto, and accidental death and dismemberment coverage. While many of these policies are bought through a bank or
farm bureau, segment members also are loyal consumers, faithful to their insurance agentswhether independents or single-company representatives.",
    '45' => "Greenback Acres - Lower Mid Older Family Mix
In Greenback Acres, cash is king. With its lower middle class incomes and low levels of income-producing assets, this segment has relatively few investments, retirement savings, or even credit card debt. But these
45- to 64-year-old Americans do own modest homes
their homeowners insurance is for under $100,000which theyve used to land home improvement loans. And they show solid indices for buying auto, residential, credit card, and mortgage insurance. Greenback
Acres has a high concentration of rural households,
and its households like to buy insurance through farm bureaus, banks, and credit unions. This segments ranks high for owning pets, and fishing gear, but in this segment, they pay for everything with cash.",
    '46' => "Settling Down - Midscale Middle Age Family Mix
Settling Down contains middle-aged apartment dwellers living mostly in the metropolitan sprawl. A mix of races, ethnicities and family types, members have midscale incomes but low levels of income-producing
assets. Heavy users of installment credit, theyre fans of auto loans, and online banking. With average educations, Settling Down members work at white-collar and service jobsand own disability and death
insurance to protect their family income. They also
have high rates for acquiring term life, renters and auto insurance. With family-centered lifestyles, they enjoy playing baseball, going to movies and buying toys and DVDs. This group also makes a strong market for
gaming and parenting magazines, MTV and Comedy Central
cable channels and Hispanic and contemporary hit radio.",
    '47' => "Middle Ages - Lower Mid Older Family Mix
The members of Middle Ages paint a portrait of diversity. Racially mixed couples and families, these working-class rural households share low indices for many investment products and banking services. Instead,
they look to the insurance industry for investmentstyle
insurance, and they leverage the value of their homes for personal and home improvement loans. Mostly 45 to 64 years old, Middle Ages residents pursue comfortable if low-key lifestyles. For leisure, they enjoy
fishing, horse races, going online, and dining at fast-food restaurants. This segment ranks near the top for a variety of mainstream and black-oriented media: nightly news, BET, and the Dr. Phil Show as well as Soap
Opera Digest, and Black Enterprise.
",
    '48' => "New Nests Upper - Mid Younger Mostly w/ Kids
Living in small cities and towns, the young families in New Nests are experiencing many firsts. These households rank at the top for acquiring their first mortgages, auto loans, and installment
credit for furnishing their first homes. Most are still too young to have amassed many assets, but their midscale incomes allow them to lead home-centered lifestyles filled with toys and games, aerobic exercise,
chess, and shopping on eBay. Theyre big Internet fans, frequenting websites that sell term and whole life insurance. And their off-line media tastes reflect their youthful sensibilities as well. Theyre twice as likely
as average Americans to read baby and parenting magazines, and more likely than the general population to listen to country and rock radio, and watch VH1 and the Cartoon Network.",
    '49' => "Loan Rangers - Midscale Younger Family Mix
Loan Rangers is a group of 25- to 44-year-old singles, couples, and families leading mobile lifestyles. These households form one of the top-ranked markets for student loans and new car insurance, and they show
strong indices for personal loans, installment credit, and interest checking. With their midscale incomes and low levels of income-producing assets, members of Loan Rangers are blasé about investing for retirement.
However, they do buy a variety of
insurance products, including medical and disability coverage, in part because theyve recently changed jobs or apartments. Away from work, they enjoy drinking, dancing, going online, and checking out rap radio or
music magazines.",
    '50' => "Urban Essentials - Lower Mid Middle Age Family Mix
The households of Urban Essentials are significant in what they dont do financially. With their lower income wages and low levels of assets, they rank at the bottom for savings, investments, and retirement
accounts. And many of these urban renters go without auto, life, or medical insurance as well. A racially diverse mix of younger urban singles, couples, and families, this group is generally limited in its financial
behavior to using debit cards at ATMs. They rarely buy insurance other than renters or disability coverage. Some members of Urban Essentials are starting to raise children and they enjoy purchasing kids toys and
reading parenting magazines. This segment also ranks high for listening to gospel music, and consuming all types of Black and Hispanic oriented media.",
    '51' => "Starter Ranches - Lower Mid Younger Mostly w/ Kids
Theyre young, living in small towns, and working class. The households in Starter Ranches tend to have much less income-producing assets than that of the average Americanand it shows. These 25- to 44-year-
old couples and families rank near the bottom for owning stocks, mutual funds, and savings accounts. Many are still paying off
student loans on top of new car loans, mortgages, and installment credit. Like other young consumers, theyve taken to automated financial services such as debit cards, banking over the phone, and ATM
machines. But they will go inside a bank or credit union to purchase homeowners and whole life insurance. Preoccupied with early childrearing, Starter Ranches residents enjoy outdoor sports like fishing and
camping, and indoor media such as baby magazines, country radio, and TV wrestling.",
    '52' => "Country Cottages - Low Income Older Mostly w/o Kids
Typically found in rural and small-town settings, Country Cottages epitomizes oldfashioned, unpretentious living. Consisting mostly of ethnically diverse singles and couples over 45, they report lower incomes and low
income-producing assets. Because threequarters
of Country Cottages members no longer work, they cant afford most investment instruments and dont qualify for many credit and debt products. Even their insurance coverage is limited. Cash-and-carry
consumers, they have little interest in online banking but occasionally parlay their money by betting on a horse race or bingo. And like other older segments, Country Cottages ranks high for watching television,
especially early evening newscasts, soaps and comedy and variety shows.",
    '53' => "Social Insecurity - Low Income Mature Mostly w/o Kids
The most downscale of the mature segments, Social Insecurity is filled with ethnically diverse widows and widowers who rely on Social Security and Medicare/Medicaid for survival. With extremely low incomes and
income-producing assets, these elderly singles barely register for owning stocks, mutual funds, and real estate investments. Nor can they muster the funds to buy insurance products other than some medical and
whole life policies acquired earlier in
their working lives. Financially strapped, most Social Insecurity residents lead quiet lifestyles in their older city apartments: there's little money for travel, nightlife, or dining out. Instead,
this segment is the top-ranked audience for daytime television, particularly game shows, Spanish language shows, and soaps.
",
    '54' => "City Strivers - Midscale Younger Family Mix
Filled with younger apartment dwellers, City Strivers consists mostly of under-35 year olds with lower-middle-class to midscale incomes and few income-producing assets. Diverse in both ethnicity and family type,
most attended college and now work at a mix of white-collar and
service jobs. But their entry-level salaries don't go far, and many are paying off student, car and personal loans. With a majority having children, theyre also preoccupied with the here-and-now
expenses of early childrearing; few tend to have long-term investments, retirement savings or life insurance. This segment scores high for going to movies, roller skating, playing volleyball,
dancing and buying family-friendly toys. City Strivers members can be found reading gaming and baby magazines; listening to rock and Hispanic radio; and watching reality TV shows.",
    '55' => "Getting-By Blues - Low Income Older Mostly w/o Kids
Life can be a financial challenge in Getting-By Blues. An ethnically diverse segment of 45- to 64-year-olds, they typically rent older apartments in urban and second-city neighborhoods. With low incomes and few
assetsabout two-thirds are unemployed these consumers rank near the bottom for most banking and insurance products. But they
do exhibit above-average rates for owning renters and whole life insurance. Pursuing quiet lifestyles, they enjoy camping, renting videos, and reading about their favorite form of entertainmentsoap operas.",
    '56' => "Economizers Downscale - Middle Age Family Mix
With nearly all its households earning under $30,000 a year, Economizers is one of the nations poorest financial groups. These racially mixed singles and single-parent families have few investments or other
assets. Typically living in apartments in big-city neighborhoods, they show low indices for buying most insurance products other than low value renters, car, and whole life insurance. And Economizers households
have little discretionary cash for traveling, dining out, or enjoying big-ticket sports. With a lifestyle
influenced by the presence of young children, this segment ranks near the top for a variety of ethnic- and family-targeted media: watching TV wrestling and BET, reading baby and parenting magazines, and listening
to gospel radio.",
    '57' => "Young Urban Renters - Downscale Younger Family Mix
Young Urban Renters ranks near the bottom for income and income-producing assets. Members of this segment are younger, single, and ethnically diverse. Many are raising small children in one parent households.
Theyre still paying off student and personal loans as well as installment credit used to furnish their new city apartments. Despite having low rates for buying insurance products, consumers here have begun
exploring auto, medical, and renters coverage. With their limited financial resources, theyre much more likely than average Americans to spend their leisure time indoorslistening to the radio or going online to
visit chat rooms and job websites.
Young Urban Renters is one of the top segments for reading music, parenting, and womens fashion magazines.",
    '58' => "Bottom-Line Blues - Low Income Middle Age Family Mix
Bottom-Line Blues is the most financially challenged of all P$YCLE segments. No segment has fewer income-producing assets, and few rank lower when it comes to income or home ownership. Concentrated in
inner-city neighborhoods, the segment is the address for mostly
younger, multi-ethnic singles and single-parent families living in low-cost apartments. Many residents have low educations and insecure jobs, surviving on cash instead of bank or insurance products. Surveys show
that members of Bottom-Line Blues have modest ifestyles, spending their leisure time going online, eating at fast-food restaurants, and listening to music. This segment ranks number one for tuning in to black-
oriented media, including BET, urban
contemporary radio, and magazines like Jet, Essence, and Ebony."
  }
  return 'Unknown' if key.size != 3 or code[key[0]].nil? or values[key[1..2]].nil?
  [values[key[1..2]], code[key[0]]].join(' - ')
end

.en_p213h_column_name ⇒ Object

Psycle HH V3



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# File 'lib/element_numbers/en_P213H.rb', line 5

def self.en_p213h_column_name
  'Psycle HH V3'
end

.en_p213h_description ⇒ Object 



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# File 'lib/element_numbers/en_P213H.rb', line 11

def self.en_p213h_description
  'P$YCLE NE is a household-level segmentation system which assists marketers to predict financial behavior.'
end

.en_p213h_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_P213H.rb', line 9

def self.en_p213h_value_description(key); ''; end

.en_p213w(key) ⇒ Object 



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# File 'lib/element_numbers/en_P213W.rb', line 15

def self.en_p213w(key)
  code = {
    '1' => 'Extremely Likely (Household level code)',
    '2' => 'Highly Likely (Zip 4 level code)',
    '3' => 'Likely (Zip level code)'
  }
  values = {
    '01' => '$0 - $24,999',
    '02' => '$25,000 - $49,999',
    '03' => '$50,000 - $74,999',
    '04' => '$75,000 - $99,999',
    '05' => '$100,000 - $149,999',
    '06' => '$150,000 - $249,999',
    '07' => '$250,000 - $499,999',
    '08' => '$500,000 - $749,999',
    '09' => '$750,000 - $999,999',
    '10' => '$1,000,000+',
    '00' => 'Unknown / Not Attempted',
    '99' => 'Unknown / Default'
  }

  return 'Unknown' if key.size != 3 or code[key[0]].nil? or values[key[1..2]].nil?
  [values[key[1..2]], code[key[0]]].join(' - ')
end

.en_p213w_column_name ⇒ Object

Net Worth V3



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# File 'lib/element_numbers/en_P213W.rb', line 5

def self.en_p213w_column_name
  'Net Worth V3'
end

.en_p213w_description ⇒ Object 



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# File 'lib/element_numbers/en_P213W.rb', line 11

def self.en_p213w_description
  'Net Worth is defined as a household\'s total financial assets minus its liabilities. Assets include financial holdings such as deposit accounts, investments and home value. Liabilities include loans, mortgages and credit card debt.'
end

.en_p213w_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_P213W.rb', line 9

def self.en_p213w_value_description(key); ''; end

.en_p400(key) ⇒ Object 



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# File 'lib/element_numbers/en_P400.rb', line 36

def self.en_p400(key)
  values = {
    '1' => 'Behavioral Greens',
    '2' => 'Think Greens',
    '3' => 'Potential Greens',
    '4' => 'True Browns',
    '0' => 'Unknown'
  }
  key.empty? ? 'Unknown' : values[key]
end

.en_p400_column_name ⇒ Object

GreenAware



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# File 'lib/element_numbers/en_P400.rb', line 5

def self.en_p400_column_name
  'GreenAware'
end

.en_p400_description ⇒ Object 



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# File 'lib/element_numbers/en_P400.rb', line 11

def self.en_p400_description
  'GreenAware Segmentation targeting system includes attitudes, opinions, lifestyle, buying behavior, and media usage. Based on the distinctive mindset of consumers towards the environment, we can better
understand four distinct consumer segments:
1. Behavioral Greens: This group of people thinks and acts green, holds negative attitudes toward products that pollute, incorporate green practices on a regular basis.
2. Think Greens: This group of people thinks green but does not necessarily act green.
3. Potential Greens: They neither behave nor think along particularly environmentally conscious lines, and remain on the fence about key green issues.
4. True Browns: these groups of people are not environmentally conscious, and may in fact have negative attitudes about green movement.
'
end

.en_p400_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_P400.rb', line 9

def self.en_p400_value_description(key); ''; end

.en_p400a(key) ⇒ Object 



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# File 'lib/element_numbers/en_P400.rb', line 47

def self.en_p400a(key)
  values = {
    '01' => 'thru 10 Behavioral Greens',
    '11' => 'thru 20Think Greens',
    '21' => 'thru 30Potential Greens',
    '31' => 'thru 40True Browns',
    '00' => 'Unknown'
  }
  key.empty? ? 'Unknown' : values[key]
end

.en_p400a_column_name ⇒ Object

GreenAware Tiers



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# File 'lib/element_numbers/en_P400.rb', line 22

def self.en_p400a_column_name
  'GreenAware Tiers'
end

.en_p400a_description ⇒ Object 



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# File 'lib/element_numbers/en_P400.rb', line 28

def self.en_p400a_description
  'GreenAware Segmentation targeting system includes attitudes, opinions, lifestyle, buying behavior, and media usage based on the distinctive mindset of consumers toward the Environment. GreenAware Tiers offers
ten levels of additional ranking detail for each of the four GreenAware segments which include: Behavioral Greens, Think Greens, Potential Greens and True Browns. The higher the number within the index, the
more likely the prospect will fit the profile within that segment.
GreenAware and GreenAware Tiers are created utilizing ConsumerView demographics and Simmons Market Research data and offer an even higher level of refinement to improve targeting, prospecting and overall
campaign effectiveness.'
end

.en_p400a_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_P400.rb', line 26

def self.en_p400a_value_description(key); ''; end

.en_t2000(key) ⇒ Object 



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# File 'lib/element_numbers/en_T2000.rb', line 15

def self.en_t2000(key)
  values = {
    '4' => 'Zip+4 level data',
    'L' => 'Living unit level data',
    'Z' => 'Zip level data'
  }
  return 'Unknown' if key.size != 6 or values[key[0]].nil? or key[1..6].to_i < 888 or key[1..6].to_i > 87900
  [values[key[0]], [key[1..6]]].join(' - ')
end

.en_t2000_column_name ⇒ Object

Discretionary Spend Estimate



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# File 'lib/element_numbers/en_T2000.rb', line 5

def self.en_t2000_column_name
  'Discretionary Spend Estimate'
end

.en_t2000_description ⇒ Object 



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# File 'lib/element_numbers/en_T2000.rb', line 11

def self.en_t2000_description
  'Discretionary Spend Estimate predicts the dollar estimate of annual spend on non-essential, discretionary expenses such as household furniture, alcohol and tobacco, donations, dining out, education, reading, personal care and entertainment (fees, audio/visual equipment, toys, hobbies, pets, playground equipment and other equipment). Represents an actual dollar amount.'
end

.en_t2000_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_T2000.rb', line 9

def self.en_t2000_value_description(key); ''; end

.en_v000(key) ⇒ Object 



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# File 'lib/element_numbers/en_V000.rb', line 15

def self.en_v000(key)
  values = {
    'Z' => 'Zip Level Match',
    '4' => 'Zip +4 Level Match'
  }
  key.empty? ? 'Non-match' : values[key]
end

.en_v000_column_name ⇒ Object

New Median Mandatory Append



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# File 'lib/element_numbers/en_V000.rb', line 5

def self.en_v000_column_name
  'New Median Mandatory Append'
end

.en_v000_description ⇒ Object 



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# File 'lib/element_numbers/en_V000.rb', line 11

def self.en_v000_description
  'The New Median mandatory append is required when any data from the New Median file is appended. It consists of a match level.'
end

.en_v000_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_V000.rb', line 9

def self.en_v000_value_description(key); ''; end

.en_y000(key) ⇒ Object 



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# File 'lib/element_numbers/en_Y000.rb', line 15

def self.en_y000(key)
  values = {
    'S' => 'Census State',
    'C' => 'Census County',
    'T' => 'Census Tract',
    'B' => 'Census Block Group'
  }
  key.empty? ? 'Non-match' : values[key]
end

.en_y000_column_name ⇒ Object

Census Demographic Match Level Mandatory Append



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# File 'lib/element_numbers/en_Y000.rb', line 5

def self.en_y000_column_name
  'Census Demographic Match Level Mandatory Append'
end

.en_y000_description ⇒ Object 



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# File 'lib/element_numbers/en_Y000.rb', line 11

def self.en_y000_description
  'The Census Demographic Match Level mandatory append is required when any 2000 Census demographic data is appended. It consists of a match level.'
end

.en_y000_value_description(key) ⇒ Object 



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# File 'lib/element_numbers/en_Y000.rb', line 9

def self.en_y000_value_description(key); ''; end

.lookup(column, key) ⇒ Object 



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# File 'lib/experian.rb', line 6

def self.lookup(column, key)
  column = column.to_s.downcase.gsub(/\s+/, '')
  key = key.to_s.strip
  self.send("en_#{column}", key)
end

.method_missing(method, *args, &block) ⇒ Object 



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# File 'lib/experian.rb', line 27

def self.method_missing(method, *args, &block)
  method_parts = method.to_s.split('_')
  if (2..4) === method_parts.size and method_parts[0] == 'en'
    column = method_parts[1]
    if /^[0-9]{1,3}$/ =~ column
      letters = ''
      column = "%04d" % column
      #new_method = method_parts.size == 3 ? "en_#{column}_description".to_sym : "en_#{column}".to_sym
    elsif /[a-z]$/ =~ column
      letters = column.match(/[a-z]+$/)[0]
      column = "%04d" % column.to_i
      #new_method = method_parts.size == 3 ? "en_#{column}#{letters}_description".to_sym : "en_#{column}#{letters}".to_sym
    end

    method_parts.shift(2)
    method_sufix = method_parts.join('_')
    new_method = method_sufix.size > 0 ? "en_#{column}#{letters}_#{method_sufix}".to_sym : "en_#{column}#{letters}".to_sym

    if self.methods.include?(new_method)
      return self.send(new_method, *args, &block)
    else
      super(new_method, *args, &block)
    end
  else
    super(method, *args, &block)
  end
end

.value_description(column) ⇒ Object 



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# File 'lib/experian.rb', line 17

def self.value_description(column)
  column = column.to_s.downcase.gsub(/\s+/, '')
  self.send("en_#{column}_value_description")
end