The real estate sector has always been a popular target for investors. Perhaps because it’s considered a low-risk investment compared to other favored options such as stocks, cryptocurrencies, or annuities. It seems that investors will keep showing their interest, as the real estate sector worldwide is expected to see an annual growth of 3.41% (CAGR 2024-2028). This will result in a market volume of $729.40 tn by 2028.
However, according to PwC’s report on Emerging Trends for Real Estate in 2024, while investors are eager to acquire new assets, a lack of sales data results in lost deals as buyers and sellers can’t agree on pricing. Moreover, the report states that industry leaders don’t expect the market to return to where it was before the pandemic, and interest rates might remain high for another year or so.
Where does this leave the investors? Without clear pricing data and with no historical trends to rely on, real estate investment may become a high-risk business. However, there is a solution for investors who seek to make informed decisions — alternative data. Learning to analyze nontraditional data sources can help predict hyperlocal real estate trends and market value, which is something traditional data sources often fail to provide.
Traditional data sources are outdated and too broad
Usually, real estate market investors base their investment decisions on historical sales transaction details and market trends. However, property records don’t get updated constantly, and data can quickly become outdated.
Moreover, recent years have shown that retrospective data can become irrelevant when facing a global pandemic or dealing with intense geopolitical situations. This means that investors who rely on historical data base their decisions on information that can be irrelevant by the time the deal is made.
Another issue is that widely used information, such as market trend analysis and property valuation data, provides a very broad picture. This data may be completely irrelevant when choosing to invest in specific locations. Even seemingly simple changes in the neighborhood, such as a shopping mall opening, can impact real estate prices in the area. The more granular the data, the more accurate investment decisions can be made.
While retrospective data is still dominating real estate investment decision-making, worldwide digitalization has opened the doors to a much wider array of data resources that provide a significantly deeper look into properties. For example, property listings on Airbnb or Booking.com can indicate a property value in a certain area. Instead of only looking at delayed, generic data, investors should turn to live, hyperlocal data, which can help predict the future value of a property.
Alternative real estate data
Nontraditional data sources may have nothing to do with real estate from the first sight. The key is to put together multiple data signals, which can then predict accurate areas with increased potential for a climbing property value. According to McKinsey & Company, the number of coffee shops popping up in the area, changes in crime rates, permits issued for building swimming pools, and even mobile phone location data can become important data sources for predicting real estate market changes.
For example, a new popular restaurant or leisure complex opens up in a residential area. Initially, investors may overlook this information as irrelevant to the property value in the neighborhood. However, tracking restaurant reviews and following patterns of other establishments opening in the area can become a valuable indication of the future real estate prices around that location.
Moreover, mobile location data can indicate propitious locations for commercial real estate. Millions of people use mobile phones every minute and leave their digital tracks by continuously pinging the GPS. This creates a vast pool of data that can be used for geofencing, analyzing daily travel trends, and, of course, real estate market predictions.
The GPS signal data alone is already a great starting point, but combining it with other sources will lower investment risks. These other valuable data sources include real estate broker’s knowledge of the local market, demographic data, psychographics, and so-called geosocial data based on people’s public social media activity.
Acquiring nontraditional real estate data
Web scraping — an automated public data acquisition method — is one of the main ways to acquire alternative data points. E-commerce businesses collect publicly available data for market analysis, implementing dynamic pricing strategies, and keeping up with the competitors. The finance industry also uses web scraping to foresee market changes and make informed decisions.
Web scraping solutions can gather hyperlocalized data, which is especially relevant for the real estate industry. For example, automatically collecting data from large real estate listings offering thousands or even millions of various data points can help draw a more accurate picture of real estate trends in specific areas compared to using traditional data on the entire city.
You’ll find a number of APIs specifically designed for gathering real estate data. They provide access to various datasets, such as public property listings, home values, rental estimates, and other property details, in one place. However, carefully check their labels as some of these APIs don’t consider nontraditional data.
Building an in-house solution using ethically sourced proxy infrastructure or turning to ready-made web scraping solutions that return public data from your identified targets can be the best way to go. When looking for a web scraping solution, make sure it can return highly localized real-time data in your preferred format. Automated parsing is a huge plus, ensuring you won’t end up with tons of unstructured, hard-to-read data that is complicated to clean.
Conclusion
The real estate sector is an attractive target for investment. However, most investment decisions in the industry are still based on traditional data, which is often outdated and doesn’t represent future trends. Moreover, the real estate market today is extremely dynamic, requiring granular data, and traditional data sources don’t provide that.
Alternative data sources, such as mobile phone GPS signals, restaurant reviews, and geosocial data, can provide hyperlocalized information about real estate trends in real time. Combined with other data sources, it can help identify hidden patterns and make well-informed investment decisions. One of the ways to acquire nontraditional data is by using web scraping solutions. Scrapers help collect localized data in real-time, which are essential factors the traditional real estate data fails to provide.
