IIf you rent your homethere’s a good chance your landlord is using RealPage to set your monthly payment. The company says it simply helps landlords set the most profitable price. But a series of lawsuits say it’s something else: an AI-powered price-fixing conspiracy.
The classic picture of price fixing is that executives of competing companies meet behind closed doors and secretly agree to charge the same inflated price for everything they sell. This type of collusion is one of the gravest sins that can be committed against a free market economy; the late Justice Antonin Scalia once called price fixing the “supreme evil” of antitrust law. Agreeing to price fixing can be punished with up to ten years in prison and a $100 million fine.
But as RealPage’s example shows, technology can offer a way out. Instead of joining forces with competitors and agreeing not to compete on price, you can all independently rely on a third party to set your prices for you. Property owners feed RealPage’s “property management software” with their data, including apartment prices and vacancy rates, and the algorithm – which also knows what the competition is asking for – spits out a rental recommendation. If enough landlords use it, the result could look like a traditional price-fixing cartel: uniform price increases instead of price competition, with no secret handshakes or secret meetings necessary.
Without price competition, companies lose the incentive to innovate and reduce costs, and consumers are stuck with high prices and no alternatives. Algorithmic price fixing appears to be spreading across more and more industries. And existing laws may not be enough to stop it.
In 2017then—Federal Trade Commission Chair Maureen Ohlhausen gave a speech to antitrust lawyers warning of the rise of algorithmic collusion. “Is it OK for a guy named Bob to collect confidential pricing strategy information from all market participants and then tell everyone how to set their prices?” she asked. “If it’s not OK for a guy named Bob to do that, then it’s probably not OK for an algorithm to do that either.”
The many lawsuits against RealPage vary in their specifics, but all have the same central argument: RealPage is Bob. By one estimate, 30 to 60 percent of multifamily properties in more than 40 housing markets across the United States are priced using RealPage. The plaintiffs suing RealPage, including the attorneys general of Arizona and Washington, D.C., argue that this has allowed a critical mass of landlords to collectively raise rents, exacerbating an existing housing affordability crisis. (In a statement, RealPage responded that the share of landlords using its services is far smaller, about 7 percent nationwide. RealPage’s estimate includes all rental properties, while the lawsuits focus on multifamily properties.)
According to the lawsuits, RealPage’s customers behave more like collaborators than competitors. Landlords share highly confidential information with RealPage, and many of them recruit their competitors to use the service. “This kind of behavior is a big red flag,” Maurice Stucke, a law professor at the University of Tennessee and a former antitrust lawyer at the Justice Department, told me. When companies operate in a highly competitive market, he said, they typically go to great lengths to protect any confidential information that could give their competitors an advantage.
The lawsuits also argue that RealPage pressures landlords to comply with its price suggestions — something that wouldn’t make sense if the company was simply paid to offer personalized advice. In an interview with ProPublica, Jeffrey Roper, who helped develop one of RealPage’s key software tools, acknowledged that one of the biggest threats to a landlord’s profits is when nearby properties set prices too low. “When idiots set prices too low, it costs the whole system,” he said. RealPage therefore makes it difficult for its customers to ignore its recommendations, the lawsuits say, reportedly even requiring written justification and explicit approval from RealPage employees. Former employees have said that failure to follow the company’s recommendations could result in customers being kicked out of the service. “That’s the biggest clue to me,” Lee Hepner, an antitrust attorney with the American Economic Liberties Project, an antimonopoly organization, told me. “Forced compliance is the hallmark of any cartel.”
The company disputes that description, claiming it merely offers “tailored price recommendations” and has “no power” to set prices. “RealPage customers make their own pricing decisions and acceptance rates of RealPage’s price recommendations have been greatly exaggerated,” the company says.
In December, a Tennessee judge denied RealPage’s motion to dismiss a class-action lawsuit against the company and allowed the case to proceed. But it would be a mistake to conclude from this example that the legal system has a handle on the problem of algorithmic price-fixing. RealPage could still prevail in court, and in any case, the company is not alone. Its main competitor, Yardi, is embroiled in a similar legal battle. One of RealPage’s subsidiaries, a service called Rainmaker, is facing multiple lawsuits for allegedly facilitating price-fixing in the hotel industry. (Yardi and Rainmaker deny any wrongdoing.) Similar lawsuits have been filed against companies in industries as diverse as health insurance, tire manufacturing and meat processing. But win These cases are difficult.
The challenge is this: under existing antitrust law, it is not enough to prove that companies A and B used algorithm C to increase prices. You have to prove that there was some kind of agreement between companies A and B, and you have to provide a concrete factual basis for the existence of that agreement. before They can formally request evidence of it. This dynamic can put plaintiffs in a bind: It’s hard to credibly allege the existence of price-fixing without access to evidence like private emails, internal documents or the algorithm itself. But they typically can’t uncover such materials until they get legal authority to request evidence as part of discovery. “It’s like trying to put a square peg in a round hole,” Richard Powers, a former assistant attorney general in the Justice Department’s antitrust division, told me. “It makes the job really hard.”
In the RealPage case, the plaintiffs succeeded in establishing price maintenance. But in May, a Nevada judge dismissed a similar lawsuit against a group of Las Vegas hotels that used Rainmaker. He concluded that there was insufficient evidence of price fixing because the hotels involved had not shared confidential information with each other and were not required to follow Rainmaker’s recommendations, even though they allegedly did so 90 percent of the time. “The rulings to date have set the bar very high,” Kenneth Racowski, a litigator at Holland & Knight, told me. The RealPage case “was able to overcome that hurdle, but it may prove to be an exception.”
And cases like RealPage and Rainmaker are perhaps the simplest. In a series of articles, Stucke and fellow antitrust expert Ariel Ezrachi have outlined ways in which algorithms could set prices that would be even more difficult to prevent or prosecute—including situations in which an algorithm learns to set prices without its developers or users intending to do so. Something similar could even happen if companies different Third-party algorithms for price setting. They point to a recent study of German gas stations that found that one major supplier’s margins did not change when it introduced one pricing algorithm, while both suppliers’ margins increased by 38 percent when two major suppliers introduced different pricing algorithms. “In situations like these, the algorithms actually learn to cooperate with each other,” Stucke told me. “That could make it possible to set prices on a scale never seen before.”
None of the situations described by Stucke and Ezrachi involve an explicit agreement, making them nearly impossible to prosecute under existing antitrust laws. In other words, price-fixing has entered the algorithmic age, but the laws designed to prevent it have not kept pace. Powers said he believes existing antitrust laws cover algorithmic collusion — but he feared he might be wrong. “That’s what kept me up at night,” he said of his tenure at the Justice Department. “The concern that the 100-plus-year-old case law on price-fixing could be circumvented by technology.”
Earlier this year, a handful of Senate Democrats led by Amy Klobuchar introduced a bill that would update existing laws to automatically presume price fixing when “competitors share competitively sensitive information through a pricing algorithm to increase prices.” Like so many other bills passed by Congress, this bill is not going to be enacted anytime soon. Local governments may have to take the initiative. Last week, San Francisco passed the first-of-its-kind ordinance banning “both the sale and use of software that combines nonpublic data from competitors to set, recommend, or advise on rents and occupancy rates.”
Whether other jurisdictions will follow suit remains to be seen. Meanwhile, more and more companies are finding ways to set prices using algorithms. If these do indeed enable de facto price fixing while evading legal scrutiny, the result could be a kind of pricing dystopia in which competition for better products and lower prices is replaced by coordination to keep prices high and profits buoyant. That would mean permanently higher costs for consumers – like a never-ending inflation nightmare. Even more serious, it would undermine the incentives that keep economic growth and living standards going. The basic assumption of free market capitalism is that prices are set by open competition, not by a central planner. This is true of algorithmic central planners too.
