For a long time, the commercial real estate industry made decisions based on property data and “gut” instinct. But the amount of change in real estate markets—and interest rates, for that matter—are making it too risky to make these large decisions based on assumptions. Thanks to technological advances, commercial real estate investors have a growing number of information sources—specifically, non-property data—to consult when considering a purchase or planning a development. Of course, a prospective buyer still needs to know things like NOI, occupancy, and tenant strength so that property data or supply-side data remains valuable. But non-property data, culled through cutting-edge technology, is playing an increasingly important role in investment decision-making by providing additional forms of analytical intelligence.
Among the different options investors can consult in the expanding world of tech-based non-property data is sentiment analysis, which can provide insight into tenants’ various attitudes toward the apartment, office, or retail market. This data is particularly useful for the development community. One company that is at the forefront of multifamily real estate sentiment analysis is RCKRBX (pronounced “rocker box”). Powered by an AI-backed platform, the company provides real-time information based on responses from thousands of detailed, quarterly updated surveys of prospective renters within specific markets. The platform, which evolved from the science behind political campaign research, gets into the head of the rental population, collecting key primary data from the actual potential tenant.
Having primary audience research combined with secondary market data allows investors to essentially test assumptions they may have in their underwriting by comparing them to how the project is likely to perform once it’s on the market. “Insight from the actual end-user, the tenant in a commercial office building or the resident in a multifamily product, is so important because you can use their perspectives, their priorities, their preferences to evaluate everything else that you’re collecting in the secondary source information,” said Michael Broder, CEO of RCKRBX. “The more information you have about what people are looking for in their next space, why they’re looking for it, and the premium they’re willing to pay to be in that type of environment is critically important today.” Broder equates making property decisions with supply-side data to driving to a new destination by looking in the rearview mirror.
Sentiment analysis is just one segment of the non-property data pool that real estate investors are turning to for guidance on their activities. Geospatial data (also referred to as geolocational data or just geodata) has become exceedingly popular in the real estate industry. This GIS-based location intelligence provides a plethora of information on a specific geographic location, offering details on such factors as property boundaries, land quality, transportation networks, and demographics. Collecting and analyzing geodata essentially gives insight into what changes may materialize in the future at a particular site.
Geodata can also help property investors understand the risk associated with each property, including climate risk, something that many in the industry are hyper-focused on right now. As the world grapples with the consequences of climate change, geospatial data that provides information on such aspects as weather patterns, water tables, and fire zones can be invaluable in selecting a development location or incorporating resilience features into a project.
Predictive analytics is also a key component of non-property data. Software platforms can cull detailed historical data and combine it with numerous real-time market data sources to predict shifts in market trends as well as changes in property valuation. It’s all about the analysis of data—a wide variety of data. “It’s not any one piece of information that helps you make decisions; it’s the complex relationship between lots of pieces of data,” according to Joshua Panknin, director of Real Estate AI Research & Innovation at Columbia University Engineering. “311 calls give us one piece of information; building permits give us another; building performance gives us another.” The Columbia team also looks at such factors as online data around the businesses near a property. By analyzing the ratings, reviews, and new business openings, investors can, with the help of powerful software, identify real-time fluctuations in the market. “No data is all that valuable by itself. It’s valuable when you combine all the different data sets, and when they point in the same direction, you have a stronger indicator of change,” Panknin said.
The more data investors have about prospective tenants, site geospatial factors, potential market changes, and other non-property indicators, the better positioned they will be to find long-term success with their acquisitions or development endeavors. Right now, the disruption in the property market has opened up a lot of opportunities, but it also comes with a lot of risk. Demand-side data and predictive analytics can be used to evaluate property assumptions in ways never before possible. Investors’ use of non-property data in investment decisions is on the rise, but it won’t be fully embraced by the industry overnight. Old habits die hard, and yesterday’s practice of basing decisions on location, location, location has evolved. “It’s location, demand, and analytics that matter,” Broder explained. “And I think you’re seeing people move towards that, but it’s going to take some time.”