Big data has long since arrived in the real estate industry. Digitization and the intelligent use of large amounts of data will fundamentally change the industry, expect experts such as Juri Ostaschov, Chief Data Scientist of the Prea Group, a service and consulting company for real estate investments. A conversation about clean data, simple structures and forecasts.
Mr. Ostaschov, what role does big data play in real estate investments?
Juri Ostaschov: All actors can use big data to obtain relevant information on the smallest geographical levels. Some can only be analyzed at the postcode level, while other data can be analyzed at the object level. Property developers have quick access to framework conditions, for example what exactly they can do where according to the development plan. Investors get a good look at costs, where there is how much rent per square meter, why the prices are at a certain level, and whether the rent invested can still be sustainably realized for the next five years. Asset managers can find out about the risk of potential payment defaults at an early stage. Such results based on big data surveys are used to optimize the investment performance of customers and, for example, to better structure equity and debt capital. A current acute question could also be: How badly is a neighborhood affected by Corona?
Juri Ostaschov, Chief Data Scientist of the PREA Group
(Photo: PR)
What data do you access to provide such answers?
We use many different sources. This includes data that is public anyway. Online databases such as GeoMap are also interesting. We also buy data from research institutes, among others. Once we have found the sources for certain questions, the data must be cleaned, transferred and put in the appropriate context. This requires computing power. We can query up to two terabytes in 20 seconds. But that’s not enough, you have to make something qualitative out of the large amounts of data.
What does qualitative mean?
The art of data analysis is to filter out the desired answers from gigantic stocks. We work with many sources that seem to have nothing to do with each other: For example, the density of restaurants and how they are rated on certain portals can provide information about where a trendy neighborhood is developing. This is then coupled with the question of how the economy is developing in general. Converting all of this into reliable data is the challenge. As the first purely digital investment advisor in the real estate sector, Prea has developed its own AI for this purpose. It is not about using the most complex algorithm possible; simple structures make a system more secure. In order to always find the best way here, I develop the AI, but I am also responsible for the analyzes.
What can Big Data do that was previously not possible?
Data sets that are based on a geographically broad base could not previously be focused on local points. This is now possible through AI with big data. This becomes interesting, among other things, at locations that are not very frequented. Today it is no longer enough for us to consult five comparable properties and make a forecast on this basis. Our base is broader because we bundle such “comparables”; no place in Germany is unique. Clustering algorithms help here to analyze and filter the locations for similarity. It is crucial to be able to access the right sources in the required breadth. Such data can be used to create exact simulations. This is the second big step forward: Big data enables precisely quantified statements, far more precisely than predictions for vague trends.
What insights does this enable beyond abstract numbers?
We carried out a big data analysis for the seven largest German cities: Which characteristics of an apartment are where and how strongly promote or reduce prices? In doing so, you learn things that may not have been considered before. In Berlin, for example, parquet floors and an open kitchen are very price-promoting. In Düsseldorf, on the other hand, it is more important that the apartment is non-smoking and that it has a full cellar. In Stuttgart, on the other hand, very price-promoting factors are a parquet floor and a roof terrace.