31 / 01 / 23 - 3 minute read
The measurement of the world has advanced considerably through digitalisation. This has also increased the demands on the investment management of real estate.
Today, a comprehensive valuation of a property takes into account not only its features, but also the surrounding area to a much greater extent and in greater detail than was the case just a few years ago. The scope and quality of location-based data and the possibilities for evaluating them have improved enormously.
These days, it is possible to determine much more precisely what characterises a good location for which tenant group and, above all, how the respective location will develop in the future. This gives investment managers important insights into where investments are particularly promising and how properties can best be developed. The search for the best location, therefore, becomes considerably more precise.
Today, a wealth of location-related data on cities and sites is available. Data on public transport, schools, green spaces, shops, restaurants, playgrounds and other important amenities are available through data providers and online map services. More than 250,000 data points for London, more than 400,000 for Paris or 56,000 for Munich, for example, flow into PATRIZIA's database, which currently comprises more than 25 million data points - and the data is evolving.
To aid analysis, this location-based database is enriched with key statistics on the real estate market, as well as socio-economic information. The evaluation requires a structured collection of data, as well as IT systems with sufficient computing power. The greater challenge, however, is how to intelligently evaluate this extensive database.
This requires a proprietary methodology analysis with meaningful variables, like the one PATRIZIA has developed for its investment management. Artificial intelligence (AI) methods are superior to classic regression analyses, as they can better map the dynamics in the real estate market.
The concept of the ‘15-minute city’ by the French-Colombian urban developer, Carlos Moreno, for example, offers a starting point for the analysis of residential real estate. According to the concept, a location is attractive if important facilities - for example, for education, work, mobility and local recreation - can be reached within 15 minutes on foot or by bicycle.
Such accessibility can be determined by location-specific data which will help determine the attractiveness of any residential location. The decisive factor here is the weighting of the individual criteria – for example, what influence a nearby school has on the attractiveness of the respective residential location compared to an easily accessible supermarket or a park.
The input of local investment managers is important for such a weighting system. For example, special cases that are not recorded on the databases, such as famous neighbours or country-specific differences, such as the proximity to cafés in Madrid or to pubs in English cities, can increase the value of a location.
With the help of automated analysis, investment managers receive an assessment of how attractive any given location is within a few minutes. This value can be compared with the rent level which can reveal an over/undervaluation, measured by the attractiveness of the location.
In addition to the overall value, the metrics aligned to the principles of the 15-minute city are particularly informative here. The data can drill down into how attractive a location is for different tenant groups which can inform which types of properties are in most demand. Over time, development dynamics in the respective city crystallise. It becomes apparent which districts have become more attractive and where there is potential for further improvement of the residential environment.
The analyses are important decision-making aids for investment managers. They form part of the due diligence for any investment decision. Regular trend analyses can also highlight anomalies and differences in valuation. They help, therefore, to identify investment potential and to develop new investment ideas.
The AI alone, however, is not enough and expert interpretation by the investment manager is crucial. For example, if an investment manager is looking for locations for a project development, less-developed locations, which would be highlighted as less attractive, may offer opportunities not captured by the data.
The possibilities of data analysis go far beyond evaluating the environment for residential properties. Other asset classes, such as offices or retirement homes, also benefit from it. Data analysis is a helpful tool for investment managers when identifying attractive real estate investment opportunities in keeping with growing demand in the sector.