Data: collection, analysis and valuation to serve your assets

What is BNP Paribas Real Estate's approach to data collection and analysis?

Christophe Pineau – Overall, BNP Paribas Real Estate Group relies on three sources of information. We use the specific data collected and processed within each business. This private data is enriched with indications on market trends as well as external data including open data.

BNP Paribas Real Estate relies on its Research and Studies department. They are also a founding member of ImmoStat, an Economic Interest Group (GIE) gathering the four largest operators in the French market. ImmoStat produces real estate market data such as supply trends and rent pressures.

Cross referencing this information enables us to make market forecasts and to refine our analysis. As data is now seen as a strategic asset due to the rapid development of data sciences and modern data visualisation tools, we created a Data & Analytics department in January 2017, which I head. This department aims to develop a corporate culture around data to improve quality, increase operational efficiency and better meet the needs of our customers. Few agencies have as powerful real estate analysis tools as we do at BNP Paribas Real Estate.

Vincent Verdenne – BNP Paribas Real Estate Valuation has three main sources of information:

How useful is this data for the valuation of real estate assets?

V. V. - Today, we have entire libraries of data on the buildings we process. This mass of information allows us to select more and more relevant indicators to evaluate real estate assets. The use of this private data is done in strict confidentiality, in agreement with our customers, to evaluate a building. We make sure they don't leave our premises.

C. P. - We value this confidentiality in our information system. And even more so within the framework of the General Data Protection Regulation (GDPR). It is by cross-referencing this confidential information, in strict accordance with the project requested by our customers, with our other sources and by using modern ways that we multiply our analysis abilities.

V. V. - Statistics from private data, such as the cost of work, are never communicated on a specific asset. They are used to improve our forecasts for one type of asset. The qualitative approach favoured until now for the evaluation of a building and its environment will be reinforced tomorrow by a more quantitative approach. We will then have more detailed indicators, for example on the building’s environment.

What is the most relevant data to analyse in order to optimise real estate asset valuation?

V. V. – Rents and rates evolution is an essential criterion, as is the analysis of the risk associated with asset obsolescence. The study of the planned restoration work based on the building’s life cycle will be studied in greater depth.

Today, investors are demanding greater transparency on the building's technical and regulatory data. Whether it is to bring it up to date, for a reconversion, or even for a change of use, they wish to have an accurate assessment of the maintenance and restructuring costs of the building at the end of its life.

The integration of building information modeling (BIM) also contribute to this innovative approach. Tomorrow, the expert will be able to draw on his experience, but also on the most relevant data visualisation tools to refine his forecast and assessment. The human dimension will nevertheless remain essential in our real estate expertise business.

C. P. - We are currently testing different algorithms to improve our analysis. Artificial intelligence is an additional decision-making tool. The latter always ultimately goes back to the expert.

How do you refine your data analysis by asset type?

V. V. – From one asset to another, our thinking on the data choice will be very different. We favour an analysis focused on the uses of the building, the nature of the property, its size and key indicators that a potential buyer would analyse.

C. P. - The relevance criteria for evaluating a building depend on the type of property. In addition, they can vary widely within the same asset class. Faced with the influx of data, especially with open data development, the strength of a real estate professional lies precisely in their ability to collect the most salient information for a specific asset. Hence the constant need to refine our approach to data collection and analysis.