Outside the Beltway

AIgorithmic Price-Fixing

Why the rent is too damn high.

James Joyner · Saturday, August 10, 2024 · 16 comments

The Atlantic’s Rogé Karma asks, “Is Your Rent an Antitrust Violation?“

The classic image of price-fixing involves the executives of rival companies gathering behind closed doors and secretly agreeing to charge the same inflated price for whatever they’re selling. This type of collusion is one of the gravest sins you can commit against a free-market economy; the late Justice Antonin Scalia once called price-fixing the “supreme evil” of antitrust law. Agreeing to fix prices is punishable with up to 10 years in prison and a $100 million fine.

But, as the RealPage example suggests, technology may offer a workaround. Instead of getting together with your rivals 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” their data, including unit prices and vacancy rates, and the algorithm—which also knows what competitors are charging—spits out a rent recommendation. If enough landlords use it, the result could look the same as a traditional price-fixing cartel: lockstep price increases instead of price competition, no secret handshake or clandestine meeting needed.

[…]

In 2017, Federal Trade Commission Chair Maureen Ohlhausen gave a speech to antitrust lawyers, warning them about the rise of algorithmic collusion. “Is it okay for a guy named Bob to collect confidential price strategy information from all the participants in a market and then tell everybody how they should price?” she asked. “If it isn’t okay for a guy named Bob to do it, then it probably isn’t okay for an algorithm to do it either.”

The many lawsuits against RealPage differ in their details, but all make the same central argument: RealPage is Bob.

Offhand, that strikes me as reasonable. I don’t rent my home but my family rents a place for our summer vacation and a friend group and I rent a place a couple times a year for gatherings around Alabama football games.

In those cases, I fully understand why the property owner—almost always a private individual renting a second home—would want to rely an such a program. They lack the expertise to adjust pricing to maximize returns. Set them too low, and they miss out on revenue they could have earned. Set them too high, the place goes un-rented. And, since demand fluctuates wildly from day-to-day and week-to-week depending on what’s going on, it makes sense to outsource this function. Offhand, doing so doesn’t strike me as nefarious.

These lawsuits, however, are over long-term rentals for primary residences.

According to one estimate, in more than 40 housing markets across the United States, 30 to 60 percent of multifamily-building units are priced using RealPage. The plaintiffs suing RealPage, including the Arizona and Washington, D.C., attorneys general, argue that this has enabled a critical mass of landlords to raise rents in concert, making an existing housing affordability crisis even worse. (In a statement, RealPage has responded that the share of landlords using its services is far lower, about 7 percent nationwide. RealPage’s estimate includes all rental properties, whereas the lawsuits focus on multifamily-building units.)

According to the lawsuits, RealPage’s clients act more like collaborators than competitors. Landlords hand over highly confidential information to RealPage, and many of them recruit their rivals to use the service. “Those kinds of behaviors raise a big red flag,” Maurice Stucke, a law professor at the University of Tennessee and a former antitrust attorney at the Department of Justice, told me. When companies are operating in a highly competitive market, he said, they typically go to great lengths to protect any sensitive information that could give their rivals an edge.

This is obviously more problematic. It’s one thing for landlords to do market research to determine the going rate on comparable properties. After all, realtors do that all the time when pricing home sales. (Indeed, it’s now relatively easy for prospective buyers and sellers to get this information themselves.) But if there’s actually coordination between landlords to ensure that others aren’t undercutting the market, it’s clearly price-fixing. Where that line is drawn is not clear.

And this is even more problematic:

The lawsuits also argue that RealPage pressures landlords to comply with its pricing suggestions—something that would make no sense if the company were merely being paid to offer individualized advice. In an interview with ProPublica, Jeffrey Roper, who helped develop one of RealPage’s main software tools, acknowledged that one of the greatest threats to a landlord’s profits is when nearby properties set prices too low. “If you have idiots undervaluing, it costs the whole system,” he said. RealPage thus makes it hard for customers to override its recommendations, according to the lawsuits, allegedly even requiring a written justification and explicit approval from RealPage staff. Former employees have said that failure to comply with the company’s recommendations could result in clients being kicked off the service. “This, to me, is the biggest giveaway,” Lee Hepner, an antitrust lawyer at the American Economic Liberties Project, an anti-monopoly organization, told me. “Enforced compliance is the hallmark feature of any cartel.”

The company disputes this but I don’t believe them.

There are a series of lawsuits ongoing against RealPage, its subsidiaries that do the same thing in similar industries like hotels, and its competitors.

The challenge is this: Under existing antitrust law, showing that companies A and B used algorithm C to raise prices isn’t enough; you need to show that there was some kind of agreement between companies A and B, and you need to allege some specific factual basis that the agreement existed before you can formally request evidence of it. This dynamic can place plaintiffs in a catch-22: Plausibly alleging the existence of a price-fixing agreement is hard to do without access to evidence like private emails, internal documents, or the algorithm itself. But they typically can’t uncover those kinds of materials until they are given the legal power to request evidence in discovery. “It’s like trying to fit a square peg in a round hole,” Richard Powers, a former deputy assistant attorney general in the DOJ antitrust division, told me. “It makes the job really hard.”

This is clearly a case where the law hasn’t kept up with shifting technology.

And this is rather wild:

And cases like RealPage and Rainmaker may be the easy ones. In a series of papers, Stucke and his fellow antitrust scholar Ariel Ezrachi have outlined ways in which algorithms could fix prices that would be even more difficult to prevent or prosecute—including situations in which an algorithm learns to fix prices withouts its creators or users intending it to. Something similar could occur even if companies used different third-party algorithms to set prices. They point to a recent study of German gas stations, which found that when one major player adopted a pricing algorithm, its margins didn’t budge, but when two major players adopted different pricing algorithms, the margins for both increased by 38 percent. “In situations like these, the algorithms themselves actually learn to collude with each other,” Stucke told me. “That could make it possible to fix prices at a scale that we’ve never seen.”

Again, this is a situation where the law is simply outdated.

None of the situations Stucke and Ezrachi describe involve an explicit agreement, making them almost impossible to prosecute under existing antitrust laws. Price-fixing, in other words, has entered the algorithmic age, but the laws designed to prevent it have not kept up. Powers said he believes existing antitrust laws cover algorithmic collusion—but he worried that he might be wrong. “That’s the thing that kept me up at night,” he said about his tenure at the Department of Justice. “The worry that all 100-plus years of 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 a price-fixing agreement whenever “competitors share competitively sensitive information through a pricing algorithm to raise prices.” That bill, like so much congressional legislation, is unlikely to become law anytime soon. Local governments might have to take the lead. Last week, San Francisco passed a first-of-its-kind ordinance banning “both the sale and use of software which combines non-public competitor data to set, recommend or advise on rents and occupancy levels.”

Whether other jurisdictions follow suit remains to be seen. In the meantime, more and more companies are figuring out ways to use algorithms to set prices. If these really do enable de facto price-fixing, and manage to escape legal scrutiny, the result could be a kind of pricing dystopia in which competition to create better products and lower prices would be replaced by coordination to keep prices high and profits flowing. That would mean permanently higher costs for consumers—like an inflation nightmare that never ends. More profound, it would undermine the incentives that keep economies growing and living standards rising. The basic premise of free-market capitalism is that prices are set through open competition, not by a central planner. That goes for algorithmic central planners too.

Figuring this out via lawsuits strikes me as incredibly inefficient and impractical. It’s incredibly expensive and takes years. Further, juries simply lack the expertise to understand this sort of thing.

Which is why ending Chevron deference was a mistake. It makes far more sense for highly-trained experts to figure this sort of thing out and adjust regulations to regularly-changing conditions that rely on the whims of the Justice Department (which are naturally going to be influenced by the politics of the sitting administration) or the judgment of untrained jurors.