Real estate companies usually made their decisions based on intuition and feeling, instead of data. Nowadays, it is possible to make use of aggregated data and variables to provide insights about a certain location’s future risks and opportunities.
Research investigated the rise in the prices of homes in the US. In Boston for example, the house prices of houses within a quarter of a mile of a Starbucks, increased by 171 percent in 5 years, which is 45 percent more than all homes in the rest of the city. This impact of increased house prices is not just driven by having grocery stores nearby but driven by access to the right quantity and quality of community features. These relationships are observed in traditional ways by real estate companies.
McKinsey explored the frustration of developers and investors about the disconnection between the availability of data and the difficulty of using it for quick, actionable insights. For developers and investors, it is valuable to understand where they have to acquire property, and when they should trigger development. Also, portfol§io holders should optimize their holdings and would like to have information about their environment to capture value. Identifying trends late can lead to missed opportunities.
Figure 1: McKinsey & Company (Source: Asaftei, G. M., Doshi, S., Means, J., & Shanghvi, A. (2020, October 20).Getting ahead of the market: How big data is transforming real estate.)
Real estate developers and investors could identify hidden patterns and keep track of this data with the use of new technologies. Analysts are now looking at millions of records of data to find patterns and create insights from these data. When investors are doing this, the time when they have created insights, the best opportunities are already gone.
Besides, new and unconventional data sources, like resident surveys, phone signals, reviews, etc., are increasingly important. In addition, demographic and macroeconomic indicators could help with making a long-term market forecast. These nontraditional variables which are not traditionally considered as real estate data can be used together with other data points to provide more accurate insights.
Machine learning algorithms could make it possible to use these different data points together in one database. These algorithms make it easier to aggregate and interpret different data sources. Application programming interfaces (APIs) are used to make the data collection process automatically and connect the different databases before the analysis. After this, patterns, predictions, and forecasts are extracted and used to design new strategies.
As an example, developers can use these machine learning analytics to identify potential areas, select relevant neighborhoods, and type of buildings to develop. Also, the developer can optimize price segmentation and development timing to maximize his revenue. In contrast, an investor who wants to optimize his portfolio could use this algorithm to identify buildings in areas that are undervalued but rising in popularity. The ML- algorithm could combine macro and hyperlocal forecast to come to a result.
For the real estate industry, these advanced analytics could create a powerful data-driven approach providing useful insight. Applications using these algorithms could combine large databases of traditional and nontraditional data to forecast optimal rent and future values. An advantage of creating these applications powered by advanced analytics is the ability to use it for other scenarios. Information about the choice of properties to invest in, identifying individual valuable assets, and compare different outputs.
Today, real estate companies should change their decision-making process to a data-driven process, to optimize revenue. Companies should consider the progress in artificial intelligence as a supplement to their current business, portfolio review, and research processes. As McKinsey state: “If companies fail to act now, they run the risk of adapting too late.”
Clappform’s could application platform help companies in the real estate sector to become data driven. Our application provides you with many functionalities, for example: needed information from your assets, insight into your portfolio with over 50 different data sources and lease-up time predictions. Do you want to learn more about AI in the real estate sector? Request a free demo.
Whatever challenges you have, we are happy to help!
Do not hesitate to contact us or request a free demo.