As Europe's leading private residential real estate company, Vonovia SE currently owns around 416,000 residential units in all attractive cities and regions in Germany, Sweden and Austria. The portfolio of it is worth approximately € 58.9 billion. Additionally, Vonovia manages around 74,000 third-party apartments. As a modern service provider, Vonovia focuses on customer orientation and tenant satisfaction. Thus, the company is committed to help current and prospective tenants and their families in finding a great place to live that best suits their individual needs and boundaries. In order to do so, Vonovia puts some tremendous effort in “value add” approaches that define the way the company deals with living in the future. In order to be successful, it will be critical to evolve such approaches as data driven as possible. Nevertheless, there is still an undefined amount of data-sets available (internally as well as externally) that is currently unused or at least not systematically exploited in combination.
The goal of this use case is to make a large number of additional external data easily accessible as layers of an interactive map. This map shall enrich existing information of buildings owned by Vonovia (e.g. location, age, number of apartments) with details of its surroundings (e.g. closest supermarket, best school within a short distance, availability of charging stations, reachability of shopping opportunities, etc.) and preferably a plentitude of “really not easily available information”, getting to know the community and the needs of local people even better.
Starting from this, it should then be possible to define individually derived “best-location layers” for specific innovation ideas, e.g. best locations for realizing a car sharing lot or best locations for additional public transportation services. By offering this feature, the tool will play a central role in finding best-suited target groups for testing and piloting new business models for Vonovia, in selecting and ranking geographic areas for specific sales activities and last but not least in quantifying the market potential for further “value add” approaches (e.g. investments into e-mobility infrastructure).
We expect the solution to be easily extendable by additional map layers and location attributes, making the tool versatile for the future and for integrating even more internal and external data sources. A dedicated data model and well documented universally usable interfaces will additionally be needed.
Help us to build our “Innovation Insights Map” and set the basis for a new data-driven approach of deciding where to place innovation!
The project can be divided into three milestones: