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Smart Data

In addition to aggregating and processing raw data, we determine scores for micro and macro locations in order to be able to evaluate and compare them objectively. Using these scores, both reliable statements can be made about location potentials, and profiles for strengths and weaknesses can be created.

Our database

Our extensive database comprises more than 2,000 location indicators, based on millions of raw data and individually determined scores. These indicators can be analyzed for specific geographic areas and evaluation levels, including their rates of change. Here you will find an initial overview:


Age structures, population forecasts, commuters, inhabitants by origin

Hotel & Tourism

Sights, occupancy rates, overnight stays, theme parks


Flooding, heat days, e-charging stations, inequity distributions

Real Estate & Prices

Rent levels, rental and ownership quotes, vacancy rates


Purchasing power data, unemployment rate, taxes, retail trade indicators

Sector overviews

Classification of companies by sector, size and turnover


Internet connectivity, supermarkets, outlet centers, hospitals, schools


Pedestrian frequencies, retail purchasing power, purchasing power retention

Connectivity & Mobility

Freeway, bus, subway, e-charging stations, car sharing


Income for households, household sizes

Initiator and data partner of DIN Spec for an ESG standard

As a partner of the DIN Spec consortium, we are committed to the standardization of ESG-compliant real estate valuation. What exactly the DIN Spec is, you can read here. The focus for the formation of a DIN standard lies in the first step on the environment criteria, since these are precisely measurable and their changes in practice are the greatest.

The basis for this is data that is collected digitally in order to analyze it in turn. In addition to building-specific information, such as energy and water consumption or CO2 emissions, site factors relating to climatic changes, environmental risks or contaminated sites are determined.

We offer over 50 ESG site criteria for macro and micro location in RELAS to enable opportunity-risk analyses for real estate management.


Our data faq

What role does data play for 21st Real Estate?

21st Real Estate (21st) is a company that offers RELAS (Real Estate Location Analytics Software), a market price and location analysis software for professional users. 21st's target groups include project developers, asset managers, banks and valuers. Data provides the foundation of the business model. 21st's data division aggregates, validates and cleanses data. 21st provides this data nationwide for Germany in RELAS. The database covers almost all types of use, such as residential and commercial real estate, senior living, student living and logistics.

What data does 21st use?

For 21st, data related to real estate markets are of primary relevance. In addition to supply and transaction prices in the rental and purchase segment, this primarily includes data that serve the valuation of a property: economic indicators such as income data or employment structure, demographic data e.g. on age groups and population forecasts, or ESG indicators such as energy consumption or climatic changes. The data basis of the situation indicators for the macro and micro situation includes rates of change to show positive and negative developments. 30-, 60-, and 90-minute catchment areas are included to achieve synergy effects from neighboring communities. In addition, digitized rent indexes and maps of standard land values or cadastral data provide further aids for a well-founded location analysis.

Where does 21st get the data?

21st researches and categorizes data through strategic partnerships with recognized and long-established data suppliers. These include Immoscout and the Gesellschaft für Konsumforschung (GfK). In addition, 21st uses publicly available data from state statistical offices or industry directories. 21st invests a 6-digit amount in data procurement every year.

How accurate is the data from 21st?

The data in RELAS is available for every municipality in Germany. This enables a comparison of locations within a city or a comparison across municipalities in the microlocation. The data is processed according to a strict control principle. In order to provide data in RELAS, enough data points must be available to determine representative values. Data are validated and adjusted for outliers to smooth out non-relevant fluctuations. The data is updated regularly, although the respective update cycles vary. Google's points of interest are always updated on a daily basis, comparables are updated monthly, and the "green spaces" location indicator, for example, is adjusted once a year, since few changes are observed here.

What does the "21st scoring" model mean?

RELAS scoring allows comparative values on a micro and macro level. In the microscale, scores are evaluated within a tile system of 200 x 200 meters, while the nearly 11,000 communities in Germany represent the macroscale. The scores range from 0 to 100, covering the entire range of values in the raw data. All values in between are equally distributed within the range. A tile score of 23 - for example, for the "supermarket" indicator in Potsdam - indicates that the location in this tile has comparatively low access to supermarkets. Conversely, there are 77 percent of tiles in Potsdam that have better accessibility to supermarkets. A scoring value of 91 for annual housing completions in Bonn within a 90-minute drive says that only nine percent of the other communities within a 90-minute drive have a higher number of housing completions.

Specifically asked: Is the per capita purchasing power of €22,080 for Leipzig a high or a low value?

21st's scoring procedure allows this figure to be compared both within the city of Leipzig and in a regional or national comparison. Looking at Leipzig within Saxony, the state's largest city performs better than other cities, receiving a score of 68 for purchasing power per capita. Comparing Leipzig's purchasing power per capita with all other municipalities in Germany, Leipzig receives a score of 26. Looking at Leipzig in the context of the other 13 B-cities, Leipzig receives a score of 8, putting it in second-to-last place among the B-cities. Further comparative possibilities for the score distribution in the macro location result from the definition of the context. It is therefore crucial for a well-founded market price and location analysis to always consider the data in the appropriate context in order to be able to make well-founded decisions.

How reliable are machine learning prices?

Machine learning algorithms are particularly superior when the supply density is low and the characteristics of properties (year of construction, area, condition) exhibit strong regional heterogeneity. Compared to the standard comparable value methods, this is especially the case for retail and office rents. For retail rents, an average improvement in price determination of 32 percent and for office rents even 46 percent could be demonstrated. Rents for apartments and houses can be calculated more accurately by 23 percent, as can house purchase prices. For the purchase prices of condominiums, the improvement is even 26 percent. We have been experts in machine learning for 4 years and have regularly tested our results together in the market and with customers. In the end, all users were always convinced of the reliability of machine learning prices, from transaction managers to appraisers.

Bubble index report

The topic of overheating tendencies on the German real estate market is repeatedly in the media spotlight. 
The chart below shows an overview of the bubble risk for the asset classes residential, office and retail. For the calculation, the established method of Phillips, Wu and Yu was applied, which tests whether sales prices have decoupled from rents.

The latest study on price overheating can be found here.

Bubble Index Residential

Proportion of German cities & municipalities with bubble risk on the housing market

Data security

Risk minimization and access control

  • Regular internal review of policies and technology used

  • Regular checks of accesses and authorizations of 21st employees

  • Assignment of authorizations by your administrators

  • Information security management since 2018

  • Software processes compartmentalized from each other for security and stability

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