Research Area:
Monitoring of Settlement and Open Space Development

Research databank on non-residential buildings (ENOB:dataNWG)

Research databank on non-residential buildings. Gathering of representative primary data for the statistically valid analysis and evaluation of the structure and energetic quality of Germany’s stock of non-residential buildings.

Sub-project: Gathering of data on building features and building classifiers through geo data analysis.

Project aim
is to fill knowledge gaps regarding non-residential buildings to achieve the same level of data provision as for residential buildings in order to be able to conduct scenario analyses on the entire building sector.

Research questions
What are the potentials of applying geoinformatics in combination with image processing and machine learning to national georeferenced building datasets such as official building footprints and coordinates along with other sources of data to derive results on the stock and structure of Germany’s non-residential buildings? What are the general features of the stock of non-residential buildings (spatial distribution, building categories, structural types, building sectors, areas, life cycles, building ages, etc.)? How has the structure of the stock changed over the years (comparison with theoretical spatial-economic explanatory models)? How can we analyse the energetic quality of building envelopes and technical facilities of non-residential stock? What are the processes of modernisation, such as the upgrading of energy ratings, and how fast are they proceeding? What is the relevance of the condition and upgrading rates in the non-residential buildings sector for achieving the goals of the Federal Government’s energy concept as well as national climate protection goals within the building sector? How can the condition and upgrading rates be positively influenced in order to achieve the goals of development scenarios for 2030 or 2050? Which conclusions can be drawn from the development of the condition and upgrading of the non-residential stock regarding the motivations of actors when making decisions on investment, and to what extent do these decisions depend on the legal, economic and business frameworks? How are current funding instruments used? What are their significance? How can these be optimised?

Development of a scientifically validated and practical sample-based data gathering concept that permits the successful implementation of primary data collection on the non-residential building stock in order to answer the specified research questions. The careful selection of data samples is of particular importance as there exists no list or databank of the total research population. For this reason it is necessary beforehand to classify all building footprints in the databank and to seek out suitable contact partners for the building sector. The aim is to construct a databank of buildings and attributes describing various features (e.g. land use, building floor area and other morphological features, number of house coordinates, relationship to neighbouring buildings) as well as a sophisticated classification system of non-residential building types. Here it is necessary to classify all building footprints regarding the probability that these are relevant non-residential buildings.

Desired results and contribution to the aims of the research field
For the first time, Germany’s stock of non-residential buildings will be the object of a systematic, scientific analysis in order to gather primary data. This will create a databank based on regularly updated official statistics that can be used to develop realistic scenarios on the development of the building stock, thereby helping to realise political goals of energy consumption and climate preservation. Furthermore, this will allow us to underline the relevance of the non-residential building sector to the wider economy up to a sufficient level of detail. Our knowledge will be expanded of the condition, development and investment behaviour in the non-residential building sector, allowing conclusions to be drawn on the motivations of actors in investment decisions and helping to make connections to legal, economic and business framework conditions. The project will help to answer Research Question 1 (knowledge generation from geo-base data) and Research Question 4 (application of the developed process in the provision of scientific services). The project is designed in such a way to offer long-term evaluation of results in the form of an IOER data monitoring. 

Project results 2015-2016
The following activities have already been launched or in some cases completed within the project: Investigation and creation of an overview of State Survey Laws prescribing the gathering of data on buildings; gathering of ALKIS data on owners to test the process of making contact with owners for the purpose of interviews; national quality assessment of geo data products HU-DE and GA as well as the calculation of initial analysis results (in particular histograms on building sizes). In addition to the data from HU-DE, 3D building models (LoD1) can now also be utilised. This provides additional information on building usage (derived from the ALKIS real estate cadastre) as well as information on volumes, constituting a significant improvement in project data. Disparities at the level of individual states as well as regional disparities in the LoD1 data have been analysed and processed. Following the elimination of the smallest polygons in the HU-DE, polygons were overlaid to create a uniform dataset. Using the data on usage, building footprints were assigned a code to indicate whether these represent non-residential buildings or not. Subsequently, the project partners can undertaken a binary logistic regression on an additional 40-plus derived geometric attributes in order to calculate the probability that the footprints are in fact non-residential buildings. After supplying the created databank to the project partners, the first stage of geo data processing has already been largely completed. 
Under predefined conditions, optimally-shaped districts for data gathering were specified using an innovative algorithm to allow the sampling of building footprints. Furthermore, a process was developed to survey the envelope of investigated buildings (based on the direction they face) to allow energy audits as well to optimise the path followed by the surveyors in the respective districts.


11/2015 – 05/2019


Dr.-Ing. Gotthard Meinel
Phone +49 351 4679 254


Bundesministeriums für Wirtschaft und Energie (BMWi)


  • Institut Wohnen und Umwelt GmbH (IWU, Leadpartner);
  • Institut für Markt- und Sozialforschung (IFAK);
  • Deutsche Energie-Agentur GmbH (dena);
  • Bergische Universität Wuppertal (BUW-ÖPB)