It’s easy to track prices of some things: Drive down any major street and you’ll only have to pass a few gas stations to know roughly how much gasoline costs in your area. Turn on the news during the workday, and you’ll quickly learn how the big stock market indexes are faring. Go shopping every week, and you’ll soon know how much bananas or eggs cost at your local supermarket.
Real estate is more complicated, partly because so many uncertain factors go into determining prices. Buying or selling a house always involves a bit of gambling on pricing and transaction time, and plenty of rental lease signings can end with both landlord and tenant wondering whether they got the best deal for their money. No two buildings are exactly alike, and you can’t fully predict what’s coming on the market tomorrow, nor can you predict how long a new roof will last or how neighborhoods will shift in desirability over time. And traditionally, shopping for real estate to live in or hold as an investment meant working with experienced brokers or relying on your own knowledge of properties in your area.
But recently, a number of startups and more established financial data firms are collecting troves of information to bring a more data-driven approach to real estate markets, including for apartment buildings and standalone homes. It’s a change that’s helping to reshape the markets, bringing new investors into the field and letting landlords expand into new territory.
“If you think about 20 years ago, it was a very opaque market,” says Marc Rutzen, CEO of Enodo, a Chicago-based company that uses AI to help investors find and evaluate real estate deals. “There were no data providers in real estate. You knew your local market by talking to people.”
Among the businesses in the residential data market are companies aimed at helping buyers price properties in new markets, telling owners how their properties’ values are shifting, and even telling landlords how best to set rents on vacant units on a day-to-day basis.
“Revenue optimization is huge in our industry right now,” says Stephanie Anderson, manager of industry operations at the National Apartment Association, a rental industry group. “They looked at areas like airlines and hotels, for instance, and how their prices change every single day based on supply and demand.”
The NAA’s most recent conference, which the group says saw more than 10,000 attendees in Denver this June, hosted numerous exhibits by data providers (along with an appearance by actor-comedian Mindy Kaling).
“There seems to be tech companies popping up literally every single day,” says NAA director of research Paula Munger.
The ‘Fort Knox of data’
Data providers can aggregate information from public records, online listings, and even old-fashioned polls to see where different markets are headed on a granular level.
“We have a team of about 1,800 researchers who spend their day calling up property managers and asking what’s going on at their properties,” says John Affleck, vice president of market analytics at real estate data company CoStar. “We collect about 4 million rent data points for apartments every single day.”
CoStar also operates the rental search engine Apartments.com—a rich source of data on what units are available when, and at which prices—and has researchers speak with construction managers to see how pending projects are coming. The company provides daily rent reports for various markets and even, in some cases, for individual properties, enabling clients like landlords to see how their properties are stacking up to competitors and adjust rents accordingly.
Similarly, the Santa Barbara company Yardi can gather and combine data from tools already used by big landlords.
“We put it into a massive vault, the equivalent of a Fort Knox for data,” says Jeffrey Adler, Yardi’s vice president for Matrix, its commercial and multifamily property intelligence tool. The company offers a variety of other products, including the cloud-based property management and accounting tool Yardi Voyager.
Data companies say their huge troves of information and the algorithms that process it can help improve housing markets, enabling developers, house-flipping investors, and landlords to more efficiently deliver what buyers and tenants want.
“We basically work off of a philosophy that data wants to be free—that turning on the lights makes markets function better,” says Skylar Olsen, director of economic research at Zillow, known for its Zestimate home price estimates and regional Zillow Home Value Index.
The risk, say some researchers, is that some data can be disproportionately available or useful to certain market players: Namely, big landlords and investors who have the time and resources to access it and figure out what it all means. They also benefit from other digital technologies that have let many institutional landlords take advantage of efficiencies to build up bigger portfolios in recent years. Those include systems that let tenants pay rent online and tools that efficiently route repair contractors to units that need work, according to a recent paper by Desiree Fields, an assistant professor of geography at the University of California—Berkeley. These developments came at the same time as big landlords were able to buy properties at reduced prices amid the recession in the early part of this decade, she writes.
“Such software allowed [single-family rental] companies to scale up portfolios rapidly and deploy capital to the right submarkets and neighborhoods,” she writes. “Underwriting algorithms widen the scope of potential acquisitions while reducing the time needed to evaluate and purchase properties.”
And bigger landlords can be less willing to negotiate with tenants when they temporarily struggle to pay the rent, or even when their digital payment systems get things wrong, she says.
“In the case of larger-scale landlords, there’s been a fair amount of documentation that their eviction rates tend to be higher than [those with] one or two or a handful of homes,” she tells Fast Company in an interview.
Some data platforms are also geared at helping new investors enter rental markets, or enter new geographical sectors: Jen Wang, CFO of Houston-based Entera, says the roughly three-year-old company works primarily with funds and people from wealthy investors’ family offices. The company offers a recommendation engine to direct investors to residential properties right for them and brokers who can manage inspections and bidding once they spot what they want, whether it’s a fixer-upper or a modern property already bringing in stable rents. Data Entera provides includes projected rent and occupancy. Wang says customers aren’t necessarily looking to swoop in and raise rents or cut costs, since they can benefit from having a low-vacancy rate, not just a high ratio of rents to expenses.
“When you buy a rental property, the ability for you to make returns is to actually make sure it has a high occupancy rate—your tenant is very happy and doesn’t want to leave,” she says. “You also need to maintain the properties in a condition that is satisfactory to the tenants.”
In addition to Houston, where the company is based, the company tracks opportunities in other growing markets like Nashville, Charlotte, Orlando, and Denver. That’s attractive to investors, Wang says, who are looking for deals beyond the already pricy markets like in California and the Northeast.
“A lot of the funds won’t go into the Bay Area for instance, because they can’t afford to buy either,” she says.
Big data can also help landlords quantify what amenities tenants are looking for, so they know whether it’s worth installing, say, garbage disposals, game rooms, or swimming pools.
“Before you get to revenue optimization, you’re trying to optimize a property that you’re either going to build for the ground up or you’re going to renovate,” says Rutzen. “It’s improving product-market fit, really.”
Still, there are concerns continued industry consolidation could lead to more disconnects between the people who own properties and the people who actually live in them, particularly if they’re increasingly geographically separated and connected mostly through digital systems.
“The danger with technology and all the big data is you remove yourself from all the actual buildings and infrastructure and the places where people live,” says Dallas Rogers, a senior lecturer at the University of Sydney’s School of Architecture, Design and Planning and an editor at the International Journal of Housing Policy. “If you think about the tenant-landlord relationship as something that you can break into tasks and completely automate and remove interpersonal interaction from it completely, I would argue that’s quite a problematic thing to do.”