Is big data useful for real estate investments?

9:00 am

7 October 2020

A report by the online US real estate information website Zillow dating back to 2015 captured the attention of real estate market players for the conclusions it reached.


For instance, in Boston, the price of homes within half a kilometre of a Starbucks jumped by more than 171 percent between 1997 and 2014, 45 percentage points more than all homes in the city. At the same time, Seattle apartment buildings within 1.5 km of specialty grocery stores like Whole Foods and Trader Joe’s appreciated in value faster than others.


These were obviously trends of which operators were well aware, but the report drew their attention to a broader question: the power of non-traditional data.

Investing in Real Estate: how big data is changing the market

While the impact of proximity might be intuitive, home prices are not driven by this alone. Rather, they are driven by access to the right quantity, mix, and quality of community features. More is not always better; for example, though having two specialty food stores within half a kilometre correlates with an increase in property prices, having more than four of them within that same distance correlates with lower prices.


These non-linear relationships are observed everywhere.  And the intersection of density and proximity to community amenities varies among cities and even neighbourhoods, obscured by a growing mass of data that is increasingly difficult to gather and analyse.


Industry players have always sought to understand where to acquire property and when to trigger development. Portfolio holders need to optimize their holdings and regularly assess conditions that lead them to divest or capture value. Being slow to identify subtle trends means leaving money on the table. Conversely, being a first mover on a winning opportunity translates into significant advantage.


But how can real estate investors keep track of so much data and quickly find hidden patterns—and harness them for profitable investments?

Which data should we consider?

Using conventional analytical methods, analysts must sift through tens of millions of data points to discern clear patterns, with few supporting tools to help glean insights from that material. By the time an investor can collect, compile, and process the data needed to decide on an investment, the best opportunities are gone.


At the same time, new and unconventional data sources are becoming increasingly relevant. Resident surveys, mobile phone signal patterns, and online reviews of local restaurants can help identify “hyperlocal” patterns—granular trends at the city block level rather than at the city level.  Macroeconomic and demographic indicators, such as an area’s crime rate or median age, also inform long-term market forecasts.


Thousands of non-traditional variables can be linked to diverging, location-specific outcomes. A few examples:


  • number of permits issued to build swimming pools
  • change in number of coffee shops within a 1.6 km radius
  • building energy consumption relative to other structures in the same postal code
  • tone of reviews for nearby businesses


This information is not traditionally considered real estate data, but stitching such data points together can more accurately predict hyperlocal areas with outsized potential for price appreciation.

There is too much relevant data for real estate investments: how can we use it?

One way to stitch together the data through advanced analytics is to use machine learning algorithms, which make it significantly easier to aggregate and interpret these disparate sources of data.  After all, it is not the raw data that creates value, but the ability to extract patterns and forecasts and use those predictions to design new market-entry strategies.


Let’s say you are an investor who wants to identify underused but high-value parcels of land.  Data sources on previous transactions exist and are widely used to source information on both residential and commercial real estate assets.  However, these databases have limited value for anticipating future potential, not having been designed for that purpose. Advanced analytics can quickly identify areas of focus, then assess the potential of a given parcel with a predictive lens. An investor can thus quickly access hyperlocal community data, paired with land use data and market forecasts, and select the most relevant neighbourhoods and type of buildings for development. Further, that investor can optimize development timing, mix of property uses, and price segmentation to maximize the value of their investment.


Alternatively, for an asset manager who wants to expand and optimize a portfolio of multifamily buildings, machine learning algorithms can rapidly combine macro and hyperlocal forecasts to prioritize cities and neighbourhoods with the highest demand for multifamily housing. This allows the asset manager to identify buildings in areas that are undervalued but rising in popularity.


Advanced analytics are not a crystal ball. In most cases, it should only support investment hypotheses, not generate them But when it comes to these classic real estate conundrums, advanced analytics can rapidly yield powerful input that informs new hypotheses, challenges conventional intuition, and sifts through the noise to identify the most useful information.



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