Common pricing algorithms can be used to coordinate prices among sellers, to the detriment of buyers. RealPage is the seminal case, but there are (alas) plenty of others. The problem is particularly acute in a two-sided transactional platform setting, where the platform influences—and sometimes coerces—the pricing decisions of its sellers.
Take the case of Airbnb. Even before considering its “Smart Pricing” tool aka common pricing algorithm (discussed below), Airbnb inflicts tremendous costs on society. The short-term rental platform keeps rents artificially high by converting capacity for long-term (e.g., monthly or annual) rentals into short-term (e.g., daily) rentals. When the supply of long-term rentals artificially contracts, holding demand for apartments fixed, rents zoom upwards.
And high rents keep residents from spending money on other things—a drag on economic activity—and even contribute to homelessness for those who are priced out of the rental market entirely. According to a study by Harvard’s Joint Center for Housing Studies, a record 12.1 million Americans in 2024 were spending at least half of their incomes on rent and utilities, putting them at increased risk of eviction and homelessness.
Economists have studied the inflationary impact of Airbnb on rents. Calder-Wang (2021) found that the presence of Airbnb in New York leads to a transfer from renters to property owners of $200 million per year or $2.7 billion in net present value. Barron, Kung and Prospero (2017) found that a one percent increase in Airbnb listings leads to a 0.018 percent increase in rents; in aggregate, the growth in home-sharing through Airbnb contributes to about one-fifth of the average annual increase in U.S. rents. Seiler, Siebert, and Yang (2022) found that Irvine’s short-term rental ban reduced contract rental prices in the long-term rental market by 2.7 percent between 2018 and 2021. (Airbnb consultants point to evidence that Airbnb puts downward pressure on hotel prices for travelers, but absent some redistribution mechanism, that purported benefit to out-of-towners cannot offset the harms to local residents from higher rents.)
To bring down rents, Barcelona recently moved to end licenses for Airbnb homes, requiring owners by 2028 to offer them as long-term lodging at capped rents or put them up for sale. Closer to home, since September 2023, New York imposed a requirement that hosts must be present for stays under 30 days, and limited guests to two, reducing available listings on Airbnb. Similarly, in Santa Monica, the host must be present during the guest’s stay and unhosted rentals are banned, and Las Vegas bans non-owner-occupied short-term rentals. New Orleans banned Airbnb rentals in the French Quarter.
Airbnb’s “Smart Pricing” tool
Converting housing into short-term rentals is not, on its own, a cognizable violation of the antitrust laws, notwithstanding the clear price and output effects. What is cognizable, however, is price fixing, or the coordination of pricing strategies and output between horizontal rivals. Here the horizontal rivals are homesharers in the same geographic market. And while evidence of an illegal agreement to fix prices is typically challenging to detect—after all, minimally savvy competitors are unlikely to leave breadcrumbs that trace back to illegal behavior—Airbnb has at least invited homesharers on its platform to participate in one such conspiracy.
Here’s how: Airbnb offers homesharers on its platform a tool called “Smart Pricing,” which is an internal pricing algorithm that automatically updates homesharers’ listing prices. Hosts can opt in to Smart Pricing and set certain parameters, including minimum and maximum prices, then Smart Pricing does the rest by pinning prices to the “competitive” price. Of course, “competitive” is a misnomer to the extent prices are no longer a function of independent price setting among homesharers, but instead an automated prediction by the algorithm of what the market will bear.
The primary harm from Airbnb’s Smart Pricing tool is inflated rents that flow from a price-fixing conspiracy. Airbnb’s Smart Pricing tool also raises concerns regarding price discrimination, as it not only considers the features of the property and economic conditions, but also the characteristics of the guests themselves—for example, Airbnb acknowledges that Smart Pricing considers guest behavior in its algorithm. While maximizing the quality of the guest experience is a worthy goal, exploiting information asymmetries to extract supra-competitive prices can evince a market failure. Alas, the antitrust laws generally condone price discrimination. (For a great example of price discrimination achieved via algorithm aka “surveillance pricing,” check out Groundwork’s recent study of Instacart, another online platform that sets prices for sellers such as Target and Safeway.)
While Smart Pricing offers homesharers a convenient tool for managing their prices, Airbnb’s interests may not be aligned with the interests of homesharers. Some hosts have expressed frustration at the Smart Pricing algorithm for automatically pinning prices to the high end of their price range. Other hosts have complained that applying additional “rule sets” to their properties kicks them out of the Smart Pricing tool, creating friction for hosts who otherwise prefer to automate their prices. In other words, Airbnb’s Smart Pricing tool betrays a conflict of interest, and what’s good for the platform may not be, in the end, what’s good for individual homesharers. Insight into where precisely a listing will be ranked is limited because Airbnb’s recommendation algorithm is private. For example, the algorithm might punish non-adopters of the Smart Pricing tool by lowering their placing on the results page.
Airbnb incentivizes use of its Smart Pricing tool by telling homesharers that setting a “competitive” price helps improve a property’s ranking in search results. And the easiest way to set a “competitive” price without “constantly monitoring it”? Well, by using Airbnb’s Smart Pricing tool, of course. Want to automatically change your price in response to “travel trends” in your area? Turn on Smart Pricing. Although Airbnb offers that homesharers can override Smart Pricing at any time, some hosts have expressed frustration at the inability to apply additional “rule sets” without being kicked out of Smart Pricing.
Even when a homesharer declines Smart Pricing and elects to use its own pricing algorithm, doing so does not extinguish the concern of inflated prices. The economics literature recognizes how independent algorithms can learn to collude with each other by avoiding price wars. Moreover, the mere existence of a default option establishes a price floor around which all other prices are established.
If not dispositive of an illegal price fixing scheme, these facts provide at least circumstantial evidence that Airbnb is coercing homesharers into adoption of its price coordination tool, including by withholding access to consumers through search page rankings. If so, both Airbnb and participating homesharers may be on the hook.
An unwelcoming legal environment
Companies across a large swath of industries, from meat processing to hotels to real estate, are increasingly using common algorithms to set prices—and facing federal enforcement actions for doing so. Despite a defendant-friendly legal terrain, many of these arrangements have been challenged by either private or public enforcers. In large part, these cases focus on the exchange of competitively sensitive, often non-public, information between competitors, from which courts have begun to infer the existence of an illegal agreement. Self-styled “revenue management” or price- and rate-setting services like RealPage or Yardi in the rental housing industry, Cendyn in the hospitality industry, or Agri Stats in the poultry processing industry, have in recent years defended themselves against protracted litigation alleging their facilitation of these information exchanges. (Disclosure: I served as the economic expert for plaintiffs in two Agri Stats cases.)
That the DOJ recently settled its litigation with RealPage on decisively unfavorable terms suggests an unfriendly legal terrain. (Alternatively, it could reveal the subversion of law enforcement by a politicized agency.) And if there was any doubt about the steep evidentiary hurdles faced by plaintiffs, one needs only look at a strange and economics-free decision in the Ninth Circuit.
The Ninth Circuit’s decision in Gibson v. Cendyn reveals a basic misunderstanding of the economics of pricing. To dismiss any vertical relationship between Cendyn and its hotel clients, the Court claimed that “While hotels may use Cendyn’s revenue-management software to maximize profits, the software is not an input that goes into the production of hotel rooms for rentals.” (emphasis added) Yet the revenue-management software is precisely an input in the selling of hotel rooms, the output that forms the relevant product market (not producing or constructing hotel rooms from scratch). While hotels could technically function without it, the common pricing tool improves a hotel’s ability to extract additional surplus from their guests in the sale of the relevant product (again, not constructing hotel rooms).
The Cendyn decision also asks, “Why don’t the independent choices of Hotel Defendants to obtain pricing advice from the same company harm competition, as alleged here? Because here, obtaining information from the same source does not reduce the incentive to compete.” Yet the entire purpose of a common pricing algorithm is to reduce the incentive to compete unilaterally. If firm A knows that its rival, Firm B, is going to default to the joint profit-maximizing price as determined by the common pricing algorithm, it is in Firm A’s interest to mimic that price and not undercut it. In this sense, the algorithm facilitates a coordinated monopoly outcome that would not be as easily achievable in its absence. And for many common pricing algorithms, the clients are further incentivized to accept the recommended pricing for fear of being disappeared in search results.
The Cendyn decision also identified the sharing of competitively sensitive information as a key ingredient that enables collusion. This is also wrong as a matter of economics. Turning over one’s pricing authority to a common agent—whether a dude named Bob or an AI-based algorithm—increases the chances of reaching the monopoly price relative to a world in which companies make independent pricing decisions. This is true even when information about a rival’s costs or capacity is commonly known. Can firms in an oligopoly setting with complete information feel their way to the monopoly price in a repeated setting? Perhaps. But at least with independent pricing, there’s a chance that your rival will undercut your inflated price to gain share. And that threat tempers one’s enthusiasm to raise prices. Once rivals agree to turn over pricing to a common agent, however, that threat is extinguished. (This is not to say that sharing of confidential information isn’t a viable pathway to a finding of liability. It just shouldn’t be a necessary condition. In any event, homesharers are likely sharing confidential information with Airbnb, including the number of days for which the seller plans to occupy her home.)
That’s enough of the economics. For a nice explainer on the legal flaws in the panel’s decision, check out this brief by the American Antitrust Institute (AAI), urging the Ninth Circuit Court of Appeals to grant rehearing en banc. By insisting on a causal link between the licensing agreements and a restraint in the relevant market, AAI’s brief explains, the panel confused proof of an agreement with proof of the agreement’s anticompetitive effects. The brief also explains how the decision conflicts with Board of Trade of the City of Chicago v. United States, 246 U.S. 231 (1918), by creating a new category of agreements not subject to rule-of-reason analysis.
The case against Airbnb
Common pricing algorithms, like Airbnb’s Smart Pricing tool, can erode the fair functioning of markets when they deprive competitors of their independent decision-making authority. In a well-functioning competitive market, a series of (ideally, atomistic) suppliers would set their price independently. But when sellers can coordinate their prices, it is easier to move from a competitive output to something that approximates the monopoly outcome. Despite the propensity for market distortion, enforcing the antitrust laws against common pricing algorithms may prove challenging absent additional circumstantial evidence of an acceptance of that invitation to collude.
Airbnb’s facilitation of pricing decision among horizontal competitors should be assessed under the per se standard, which eliminates any consideration of efficiencies and obviates the need to establish market power. If assessed under the more burdensome rule-of-reason standard, plaintiffs would have to establish that Airbnb has market power, either directly, via evidence that it has the power to raise prices over competitive levels, or indirectly, via evidence that it commands a high share of a relevant product market. Empirical evidence that Airbnb’s smart pricing algorithm has led to higher short-term rents on the platform would suffice for direct proof. Regarding indirect proof of Airbnb’s power, per one estimate, Airbnb commands 43 percent of the U.S. market for online travel agents (aka short-term rentals), with Vrbo and Booking.com occupying significantly smaller shares. This estimate includes “direct bookings” in the relevant market, however, which arguably do not provide the same services as Airbnb and thus could plausibly be removed from the market, resulting in an even higher Airbnb share.
Airbnb isn’t just facilitating a data exchange; it is incentivizing or coercing homesharers on its platform to participate in a common pricing scheme. A coercion-based approach to enforcement should obviate the need to provide heightened evidence of acceptance, because participation in the scheme is a condition of a participation on the platform. Platforms wielding access to non-price business services, like advertising or market research services, on the condition that sellers accept price recommendations deprives sellers of their independent pricing authority. Airbnb’s “Smart Pricing” tool coerces their participation in a price-fixing cartel. With luck, the authorities are watching.