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One Man’s Quest To Label Real Estate Data Companies as Cartels

“It is quite true that land monopoly is not the only monopoly which exists, but it is by far the greatest of monopolies—it is a perpetual monopoly, and it is the mother of all other forms of monopoly.”

Winston Churchill

Apartment landlords have always had a bit of an upper hand on tenants. The most obvious reason is that they own a product that fulfills their most basic of human needs: shelter. There is a less obvious advantage as well. Landlords also enjoy an information arbitrage over those looking to rent their wares. Potential renters know much less about the unit, the building, the landlord they are applying to than they know about them. Renters have to open up their personal and financial histories in order to compete for a place to rent. Landlords, for the most part, don’t. Plus, property owners and leasing teams are experts on the markets they compete in. Most renters have little more than a few hours of online browsing under their belt before they are required to make a rather important life decision.

This has been the balance of power since the fall of feudalism and so will it continue until our next world order replaces the current one. But something has amplified the landlord’s advantage in this zero-sum economic game. A number of technology companies have grown to service a large part of the property landscape with everything from leasing to accounting platforms. These companies provide valuable service, one that helps property companies complete the complicated task of managing their buildings. But by doing so they have become the gatekeeper for huge amounts of the industry’s data, which often gets packaged into additional analytical products.

What landlords know about their own buildings, the vacancies, the demand, and ultimately what they are leasing for, can now be extended to include other properties around their neighborhood or across the country. Landlords can also learn a lot more about prospective tenants than before. The credit system is helpful in determining a person’s eligibility for a rental, but even more powerful is history about their rental history, something that data platforms are now able to provide. 

Technology platforms have harnessed powerful computing techniques like AI weaponize their data in order to help them maximize their profitability. One man has taken it upon himself to prove just how powerful of a weapon this technology actually is. James Nelson recently published a book called Stealing Home: How Artificial Intelligence is Hacking the American Dream. In it he claims that, because software has given landlords access to each other’s data, areas that have more landlords using these platforms have had their rents go up much more than areas that haven’t. 

When I saw the pitch for the book I was incredulous but intrigued. I set up an interview with James to learn more about his accusations. When I got him on the phone he was a bit rattled, his website had been hacked the day before and he was worried that it was retribution for his book. He started by telling me about his childhood. He was raised on a family farm not far from the Arizona/Mexico border. He told me how he grew up poor but was able to get degrees in economics and business from BYU. His alma mater should tell you that he is Mormon. While he never said that his religion played into his pursuit of fairness it was easy to get a sense of his strong moral stance. He went on to become a federal banking regulator and then, later in his career, a commercial mortgage broker working on multifamily refinances. It was there that he began to notice a disparity in rental price increases between areas that had large, tech enabled property managers and those that didn’t. 

Then he decided to move to Seattle. This put him in the market for a rental and gave him first hand experience about just how competitive the apartment rental market can be in certain thriving metros. At that point he made the decision to take a sabbatical from his work in order to do a deep dive into what was happening in the rental property market. “I noticed how much more technology was being used by landlords, particularly artificial intelligence,” he said. “AI has disrupted pretty much every industry it touches so I wanted to understand what its effects were on the property industry.”

To get some empirical evidence of the impact of AI and data sharing on property markets he created a study with two very different samples. In one group he looked at three counties in Washington state, King County (home to Seattle and its suburbs), Thurston County (home to the State’s capital of Olympia), and Spokane County (home to the city of Spokane, the fourth most populated city in the state). For contrast he chose Yakama, Wallawalla, Pascal, and Kinnawick counties, all rural/semi-rural areas comprising smaller towns. Besides the difference in population density, Nelson made this distinction because the more populous areas have a large number of properties that are owned by large national property management companies while the more rural ones have mostly local ownership.

What he found was rather telling. “The three areas where AI is used for a large percentage of the rental housing stock prices, on average, increased 96 percent between 2013 and 2018. In the area with less corporate ownership, where AI is largely not used, they only went up by seven percent,” Nelson said. He factored in local economies, population growth, and housing starts, none of which made up for this large disparity in price increase. At this point, Nelson started reaching out to regulators and writing his book.

Nelson explained that many leasing platforms operate under a data sharing agreement, which began in 2006 and has become widely used in the industry. The platforms share private information on tenants as well as information on over 18 million units managed with their software. There are well over 12 thousand members of the agreement, operating in more than 180 markets across the country and 400 markets worldwide. The problem, according to Nelson, is that this collaboration shares data between the members that would not otherwise be available to managers and owners were it not for their association with the platforms. 

“The way that these real estate tech companies allow property managers to share information that isn’t privy to buyers makes them cartels,” Nelson said. Cartel is a legal term that is used to describe a group of independent organizations that join together to fix pricing. In the U.S., cartels are subject to antitrust laws to help prevent monopolies and reinforce consumer protections. Nelson thinks that each state will have its own response to this problem and that the practice of data sharing will come under much more scrutiny if the housing crisis continues. “If you go out and get your own data, that is fine,” he said. “But when all the companies use the same data, it becomes a cartel. The same thing happened with the airlines.” In 2018, all of the major American airline companies were fined for an alleged agreement to limit the number of domestic flights in order to constrain the supply and boost margins. Google is currently under antitrust scrutiny as well, which shows that regulators are ratcheting up their response to what could potentially be considered monopolistic behavior.

Nelson also explained that AI is helping landlords with their leasing strategy in three ways. First, it sets high, fixed-rates of rents, which accounts for the increase in rental rates between 25 and 50 percent over the past ten years. Second, to lock tenants into the long-term contract, aggressive leasing tactics such as unreasonable early termination fees. Lastly, AI is pushing up effective rental rates with a program that can set real rate of rents over the advertised or stated price by some 22 to 45 percent. He calculates that the programs pay a return on investment to investors (property owners) of up to 880 percent. His worry is that increasing housing prices has cost-burdened many Americans that are now paying above 30 percent of their income on housing, an important threshold according to HUD, and is exacerbating the eviction crisis that we are starting to see in the wake of the pandemic.

At this point, I think that it is important to remember that this is all just opinion. None of this has been brought to court so there is nothing but speculation to these claims. There are certainly holes in Mr. Nelson’s argument as well. Cartels are created by monopolies and even in cities with a lot of large owners sharing data, their overall ownership of the rental stock is likely nothing close to a majority. Large landlords also don’t control the supply of the product into the market, anyone can develop more housing—although many large landlords are certainly some of the main financers for large developments in most cities. There are also plenty of instances of companies sharing consumer data to influence their pricing decisions. Cell phone and social media companies have entire arms of their organizations dedicated to selling user data to all types of industries for a myriad of purposes.

Despite whether you agree with Mr. Nelson or not, this adds an important new fold to the property data conversation. The regulatory landscape is now turning on companies like Facebook and Amazon in an attempt to break up some of their power over the consumer. With housing affordability being one of the biggest issues facing many Americans, will we see similar scrutiny on real estate technology companies? Progressive cities and states already have laws restricting rent increases, so it doesn’t seem too far-fetched to think that they could limit the ability for properties to share pricing and renter information as well. If this were to happen it would certainly erode some of the value of many technology providers. It would also shift the balance of power between tenants and landlords, something that has happened very few times in the long history of this economic relationship.

Propmodo is a global multimedia effort to explore how emerging technologies affect our built environment.

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