Does your rent violate antitrust laws?

ANDif you rent your homethere’s a good chance your landlord is using RealPage to set your monthly payment. The company describes itself as simply helping renters set the most profitable price. But a series of lawsuits say it’s something else: a conspiracy to set prices using artificial intelligence.

The classic image of price fixing involves executives from competing companies meeting behind closed doors and secretly agreeing to charge the same inflated price for everything they sell. This type of collusion is one of the gravest sins against a free-market economy; the late Justice Antonin Scalia called price fixing the “ultimate evil” of antitrust law. Agreeing to fix prices is punishable by up to 10 years in prison and a fine of $100 million.

But as RealPage’s example suggests, technology can offer a workaround. Instead of meeting with rivals and agreeing not to compete on price, you can independently rely on a third party to set prices for you. Landlords feed RealPage’s “property management software” their data, including unit prices and vacancy rates, and an algorithm—which also knows what competitors are charging—spits out a rent recommendation. If enough landlords take advantage of it, the result could look the same as a traditional pricing cartel: steady price increases instead of price competition, without the need for a secret handshake or secret meeting.

Without price competition, companies lose the incentive to innovate and cut costs, and consumers are stuck with high prices and no alternatives. Algorithmic pricing appears to be spreading to more and more industries. And existing laws may not be able to stop it.

ANDin 2017Then-Federal Trade Commission Chairwoman Maureen Ohlhausen gave a speech to antitrust lawyers warning of the rise of algorithmic collusion. “Is it fair for a guy named Bob to gather confidential pricing strategy information from all the market participants and then tell everyone how they should set their prices?” she asked. “If a guy named Bob can’t do it, then an algorithm probably can’t do it either.”

The many lawsuits against RealPage vary in their details, but they all rest on the same central argument: RealPage is Bob. In more than 40 U.S. housing markets, an estimated 30 to 60 percent of multifamily housing units are priced using RealPage. Plaintiffs suing RealPage, including the attorneys general of Arizona and Washington, allege that this has allowed a critical mass of landlords to raise rents in concert, further exacerbating the existing housing affordability crisis. (In a statement, RealPage responded that the percentage of landlords using its services is much lower, about 7 percent nationally. RealPage’s estimate includes all rental properties, while the lawsuits focus on multifamily housing units.)

Christopher Beam: Welcome to Price Hell

RealPage’s customers, the lawsuits say, behave more like collaborators than competitors. Landlords give RealPage highly confidential information, and many of them recruit their rivals to use the service. “That kind of behavior raises a lot of suspicion,” 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 might give their rivals an advantage.

The lawsuits also allege that RealPage pressures landlords to follow its price suggestions—which wouldn’t make sense if the company was only paid to offer personalized advice. In an interview with ProPublica, Jeffrey Roper, who helped develop one of RealPage’s main software tools, admitted that one of the biggest threats to a landlord’s profits is when nearby properties set their prices too low. “When idiots are undercutting, it costs the entire system,” he said. RealPage makes it harder for customers to bypass its recommendations, according to the lawsuits, allegedly even requiring written justification and explicit consent from RealPage staff. Former employees said that failing to follow the company’s recommendations can result in customers being removed from the service. “That’s the biggest telltale sign to me,” Lee Hepner, an antitrust attorney at the American Economic Liberties Project, an antitrust watchdog, told me. “Enforced compliance is the hallmark of any cartel.”

The company disputes that description, saying it only offers “individual pricing recommendations” and has “no authority” to set prices. “RealPage customers make their own pricing decisions, and RealPage’s pricing recommendation acceptance rates have been greatly exaggerated,” the company says.

In December, a Tennessee judge denied RealPage’s motion to dismiss the class action, allowing the case to proceed. But it would be a mistake to conclude from this example that the legal system has the algorithmic pricing problem under control. RealPage can still prevail in court, and it is not alone in doing so. Its main competitor, Yardi, is involved in a similar lawsuit. One of RealPage’s subsidiaries, a service called Rainmaker, is facing multiple legal challenges for allegedly facilitating price fixing in the hotel industry. (Yardi and Rainmaker deny wrongdoing.) Similar complaints have been filed against companies in industries as diverse as health insurance, tire manufacturing and meatpacking. But victorious such cases prove to be difficult.

The challenge is that under current antitrust law, showing that Firms A and B used Algorithm C to raise prices is not enough; you must show that there was some kind of agreement between Firms A and B, and you must provide a specific factual basis that would support such an agreement. before you can formally demand evidence. This dynamic can put plaintiffs in a bind: Credibly alleging a pricing agreement is difficult without access to evidence, such as private emails, internal documents, or the algorithm itself. But they typically can’t discover such materials unless they have the legal authority to demand evidence in a discovery proceeding. “It’s like trying to fit a square peg into a round hole,” Richard Powers, a former deputy assistant attorney general in the Justice Department’s antitrust division, told me. “That makes it really difficult.”

In the RealPage case, the plaintiffs were able to fit a peg. But in May, a Nevada judge dismissed a similar case against a group of Las Vegas hotels that used Rainmaker, finding that there was insufficient evidence of a price-fixing agreement because the hotels involved did not share confidential information with each other and were not required to accept Rainmaker’s recommendations, even though they allegedly did so about 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 “has managed to raise that bar, but it may prove to be an exception.”

Eric Schlosser: Do we really want a food cartel?

And cases like RealPage and Rainmaker may be the easy ones. In a series of articles, Stucke and his colleague, antitrust expert Ariel Ezrachi, have outlined ways in which algorithms could set prices that would be even harder to prevent or prosecute—including situations in which an algorithm learns to set prices without the intentions of its creators or users. Something similar could happen even if companies used miscellaneous third-party algorithms for pricing. They point to a recent study of German gas stations that found that when one major player adopted a pricing algorithm, its margins didn’t budge, but when two major players adopted different pricing algorithms, both saw their margins increase by 38 percent. “In these situations, the algorithms learn to collude with each other,” Stucke told me. “This could enable pricing on a scale we’ve never seen before.”

None of the situations Stucke and Ezrachi describe involve an explicit agreement, making it nearly impossible to prosecute them under current antitrust laws. In other words, price fixing has entered the age of algorithms, but the laws designed to prevent it haven’t kept up. Powers said he believes current antitrust laws cover algorithmic collusion—but he worries he might be wrong. “That’s what kept me up at night,” he said of his time at the Justice Department. “The fear that more than 100 years of case law on price fixing could be circumvented by technology.”

Earlier this year, a handful of Democratic senators led by Amy Klobuchar introduced a bill that would update existing laws to automatically presume a price-fixing agreement when “competitors share competitively sensitive information through a pricing algorithm for the purpose of raising prices.” That bill, like many other congressional bills, likely won’t become law anytime soon. Local governments may have to take the lead. Last week, San Francisco passed a first-of-its-kind law that would ban “both the sale and use of software that combines nonpublic competitor data for the purpose of setting, recommending, or advising on rents or occupancy levels.”

The question is whether other jurisdictions will follow suit. In the meantime, more and more companies are discovering ways to use algorithms to set prices. If they do enable de facto price setting and manage to escape legal scrutiny, the result could be a kind of pricing dystopia, in which competition to make better products and lower prices is replaced by coordination to keep prices high and profits flowing. That would mean permanently higher costs for consumers—like an inflationary nightmare that never ends. More fundamentally, it would undermine the incentives that keep economies growing and living standards high. A fundamental assumption of free-market capitalism is that prices are set by open competition, not by a central planner. And that includes algorithmic central planners.