Real Estate Data Lake
The real estate sector is gradually going digital, and recent constructions now incorporate equipment and connectivity to allow both use and maintenance to be optimised. However, as the renovation of the stock is naturally slow, the majority of these assets are not “connected”.
Nevertheless, a great deal of data is available and constitutes an important tool for improving performance:
- location (accessibility/transport, environment, etc.);
- land (nature, constructibility, utilities/roads, diagnostics, etc.);
- building (type, surface area, condition, energy consumption, etc.);
- occupation (users, vacancy, uses, etc.);
- financial performance (costs, profitability, etc.);
- transactions (price, time to find a tenant, accompanying measures, etc.);
- form of holding (acquisition/leasing, structuring, etc.).
Overall, little use is made of such data in comparison with other sectors (insurance, banking, retail, etc.)
...but still with little structure
While access to information is increasingly facilitated by the spread of databases (both open data and data provided by professional bodies), gathering and structuring your own data seems a more complicated matter.
In part this is because players in the sector, whatever their role (including users), have gradually developed their databases as and when required. Often these databases are infrequently shared and have scope for improvement. Data quality is often uneven in terms of completeness and update frequency, and its collection is not always exhaustive. Further, all the players have their own definition of key data, sometimes even within the same enterprise.
Progress has been made just recently: some associations have established data formats or precise definitions of ratios and cost breakdowns so as to provide relevant benchmarks for their members.
The emergence of data science: fiction or reality?
In the medium term, some aspects of real estate should be widely automated (asset valuation, visual due diligence and data rooms, etc.) necessitating a more consistent approach to data. It may well be the case that more reliable and better shared data across the sector will enable the development of artificial intelligence and predictive analysis, or even, like the social credit system used in China, make it possible to give a score to a developer, provider or tenant based on past performance, with a corresponding adjustment of fees, insurance premiums or rent.
Obviously, it is still hard to predict the future of the sector and new technologies will certainly emerge, but it is undeniable that players with good databases will be one step ahead and enjoy a decisive competitive advantage.