Nexiga data finder

Know more, achieve more. More certainty for successful strategies.

Sociodemographics | Market data

*Area level that is directly available for the feature. Further area levels on request.

*Building, for the data level County, Municipality, Neighbourhood, Street segment, Zip code, Mikro-Zip code

Social class

This variable incorporates a wide range of information on the income situation from various sources, such as data on household income, occupation or level of education in combination with data on development structure, purchasing power and residential location. Households of the lower, middle and upper classes as well as their intermediate categories are shown. The dominant social class at the house level is divided into the following five classes:

  • Upper class
  • Upper middle class
  • Middle class
  • lower middle class
  • Underclass
*Building, for the data level County, Municipality, Neighbourhood, Street segment, Zip code, Mikro-Zip code

Age

The dominant age of the occupants of a house (usually the age of the head of the household) is described in six age classes:

  • Until 30 years
  • 31 till 40 years
  • 41 to 50 years
  • 51 to 60 years
  • 61 to 70 years
  • over 70 years
*Building, for the data level County, Municipality, Neighbourhood, Street segment, Zip code, Mikro-Zip code

Family structure

Family structure describes the proportion of singles, couples and families with children at the house level in seven classes:

  • Singles
  • rather singles
  • mixed/couples with singles
  • mixed/pairs
  • mixed/couples with families
  • rather families with children
  • Families with children
*Building, for the data level County, Municipality, Neighbourhood, Street segment, Zip code, Mikro-Zip code

Share of foreigners

The percentage of foreigners living on a street is divided into the following categories:

  • No foreigners
  • below-average number of foreigners
  • Average number of foreigners
  • Above-average number of foreigners
  • very many foreigners
*Building, for the data level County, Municipality, Neighbourhood, Street segment, Zip code, Mikro-Zip code

Profitability

This variable explains the liquidity of residents at the street segment level, taking into account data on purchasing power and payment behavior. It takes on the following characteristic values:

  • Very low profitability
  • below-average profitability
  • average profitability
  • above-average profitability
  • Very high profitability
*Building, for the data level County, Municipality, Neighbourhood, Street segment, Zip code, Mikro-Zip code

Share of titleholders

The percentage of academic title holders (Prof., Dr., etc.) in a street is classified as follows:

  • 0 %
  • over 0 - 1 %
  • over 1 - 2 %
  • over 2 - 3 %
  • above 3 - 4
  • above 4 - 5
  • over 5 %
*Building, for the data level County, Municipality, Neighbourhood, Street segment, Zip code, Mikro-Zip code

Mail order activities

This characteristic describes the incidence of household mail-order activity at the street segment level and is described in the following four specifications:

  • 1 - high
  • 2 - lifted
  • 3 - average
  • 4 - low
*Building, for the data level County, Municipality, Neighbourhood, Street segment, Zip code, Mikro-Zip code

Affinity for home ownership in 9 classes

This characteristic describes the affinity of people who are more likely to buy a Building or apartment. The probability of ownership is output in 9 classes from 1 - very low affinity to 9 - very high affinity.

Building, for data level County, Municipality, Neighbourhood, Street segment, Zip code, Micro-Zip code

 

Affinity for Beeing At Home 

The characteristic describes the probability of people who spend a lot of time at home. The probability is given in 9 classes from 1 - very low affinity to 9 - very high affinity.

More questions about our data?

Are you interested in a specific data characteristic or would you like to learn how it can be used for analyses and with our systems? Please use the contact form without any obligation. We are looking forward to your questions!