Local Data Science

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How location-based data science is changing the real estate investment market

Investing in real estate is more than just investing in a specific building or object. Real estate investing also means investing in a location, cluster or region. After all, the way in which an entire region develops can also influence the return on investment of a specific real estate object. By using data science and handy analysis tools, it becomes possible to translate historical developments of real estate prices into trends.

The use of big data has become an indispensable part of the investment world. Thus Analysts at Goldman Sachs Asset Management say that thanks to the growth and availability of nontraditional data sources such as web traffic, patent filings and satellite imagery, they are using more nuanced and sometimes unconventional data to gain information advantage and make better investment decisions. The data allows analysts to better respond to investment themes such as momentum, value, profitability and sentiment.

A treasure trove of data has also been collected over the past decades within the real estate market. Information about real estate transactions and price developments could initially be dyed. However, a KPMG study shows see that more and more investment companies and large real estate companies are using data science to make strategic choices within their real estate portfolios.

Particularly when it comes to the valuation of real estate objects, big data and data analysis can offer absolute added value in determining real estate prices and expected returns. But how does that work in practice?

Different techniques

The price and value development of real estate is primarily dependent on macroeconomic developments. Figures on employment, consumer confidence and gross domestic product (GDP) can have a significant effect on real estate value.

Nevertheless, fluctuations in the real estate value depend on many more factors. The biggest challenge to the accurate valuation is the many variables. As a result, the value of, for example, two apartments within the same building can differ significantly due to properties such as size, appearance and composition. To equalize these variables, various methods have been developed, such as the “Hedonic Regression” technique and the “repeat sales” method.

Nevertheless, the valuation of real estate is not limited to the physical characteristics of real estate objects. The location and immediate surroundings of a property are just as important. Commercial real estate in particular is highly dependent on the location and composition of the local population for a favorable return. A store that only sells high-end products will most likely perform poorly in a region where the average income of the population is relatively low.

Explore the environment

Data science also makes it possible to gain better insight into the past, present and even the future of regional real estate development. One of the methods to gain that insight is by using so-called “Geographic Information Systems” (GIS). Well-known examples of some GIS tools are ´ Quantum Gis ´ and ´ ArcGis ´.

As municipalities and local authorities make more and more information and data available, such GIS tools can help analyze local real estate trends. Also consider data about the local population, project development plans and area development that are available for analysis.

In practice, a GIS system can be used to highlight specific real estate objects within an area. For example, it is possible to see which office buildings are within a certain radius from the nearest train station. After all, the presence of such public facilities can have a positive effect on the value of real estate. Consider, for example, the travel time for employees. Based on this type of data, the price difference per square meter can also be calculated for office buildings that are further away from a train station.

A GIS system can also provide information on all real estate transactions within a specific area over a specific period. All in all, GIS makes it easier and faster for real estate investors to find a suitable real estate object that suits their needs, for example to find the right retail location.

In this way, big data and data ana servelyse as a kind of matchmaking tool for real estate and investors.

Data analysis based on clusters

Real estate performance can vary greatly from location to location, for example due to macroeconomic factors. Not only per country, but also at an urban level. Consider, for example, local factors such as economic activity and supply. Within a city, some neighborhoods or districts can also perform very differently.

In order to be able to predict the real estate market to some extent, the value development of several real estate objects is compared. For example, trends can be searched, whereby groups with comparable real estate objects show the same development. This form of data science is also called cluster analysis. It exposes data patterns to determine which properties are expected to perform equally and which properties may be able to distinguish themselves.

A practical example is the clustering of supermarkets outside the Randstad. How does the value of this cluster group respond to macroeconomic developments or certain legislative changes? In this case, no data at the local level of a single supermarket is used, but all supermarkets that are located within a rural area are merged. This data can then also be compared with supermarkets that are located within the Randstad.

In this way, cluster analysis can help real estate investors to gain more insight into certain trends of comparable real estate objects. For example, if it appears that supermarkets within the Randstad are more vulnerable to fluctuations in employment figures, a real estate investor can include this knowledge in his decision-making process or as part of risk spreading within his real estate portfolio.

What location means for your real estate return

The characteristics and properties of a real estate object are important for the valuation. However, the characteristics and development of the location and immediate surroundings are even more decisive for estimating the potential return.

How vulnerable is a potential real estate investment to the development of macroeconomic figures for the region concerned? How does a real estate object perform compared to comparable real estate objects? Thanks to the use of data science, investors can answer these questions better and more accurately.[/fusion_text][/fusion_builder_column][/fusion_builder_row][/fusion_builder_container]

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