DOJ targets algorithmic pricing in new antitrust suit – implications and key takeaways

Background

U.S. government enforcers and private plaintiffs have been increasingly scrutinizing competitor exchanges of information and algorithmic pricing practices across various industries. Merely sharing information with competitors is not, without more, a violation of the antitrust laws.1 However, an agreement to exchange with competitors non-public, competitively sensitive information, such as information related to prices or output, may violate the antitrust laws if the agreement harms competition (among other factors). In 2023, the FTC and DOJ both withdrew policy statements, which had provided a “safe harbor” for competitor information exchanges meeting certain criteria.2 Before 2023, the agencies considered information exchanges between competitors presumptively legal if the data involved were sufficiently aggregated, anonymized by a third party, and sufficiently historical. With the withdrawal of these policy statements, there is currently no formal guidance as to what sort of information exchange, if any, antitrust enforcers would consider lawful.

Complicating the situation is the growing use of algorithmic pricing, which can sometimes incorporate competitor data but may not strictly constitute an information exchange in the traditional sense. The term “algorithmic pricing” generally means an automated decision-making tool or system that sets prices dynamically, based on pricing rules or strategies. However, there are different models and approaches to algorithmic pricing, and even nuanced differences could present widely varying antitrust risk. In recent court filings and public comments, DOJ has stated that using algorithmic software can be per se illegal under the antitrust laws. DOJ’s complaint against RealPage is the first significant litigation brought by the agency to test its theory of antitrust harm related to algorithmic pricing. 

DOJ complaint preceded by private antitrust litigation targeting algorithmic pricing 

Private plaintiffs have advanced similar theories to those alleged in DOJ’s case against RealPage in a series of cases filed over the past several years in the real estate, hospitality, and health care industries. To date, these plaintiffs have had mixed results. 

For example, the recent DOJ action was preceded by a series of putative class action lawsuits filed in 2023 by private plaintiffs against RealPage and a group of landlords that allegedly use RealPage algorithmic pricing tools. In that case, the court denied the defendants’ motion to dismiss and let the case proceed, but rejected plaintiffs’ argument that the per se standard should apply, noting that the plaintiffs had not alleged that RealPage or any of the lessors “can enforce acceptance of price recommendations,” and that “courts are hesitant to apply the per se standard to new or novel ways of doing business that have not yet been tested or studied by economists to conclusively determine that these types of conspiracies are per se anticompetitive.”3 In a similar algorithmic pricing case in the hotel industry, the court dismissed plaintiffs’ complaint altogether for failing to allege facts sufficient to infer an agreement between competitors, and noted that “[t]his case remains a relatively novel antitrust theory premised on algorithmic pricing going in search of factual allegations that could support it.”4 At least two other sets of algorithmic pricing claims have been filed this year: a putative class action against Atlantic City casinos,5 and a series of lawsuits against health insurance data analytics firm MultiPlan and several health insurance companies related to MultiPlan’s “claims repricing” services.6

DOJ’s RealPage complaint

In its recently filed complaint, DOJ—along with North Carolina, California, Colorado, Connecticut, Minnesota, Oregon, Tennessee, and Washington—alleges that RealPage violated both Section 1 and Section 2 of the Sherman Act. DOJ claims that RealPage’s revenue management software collects competitively sensitive data from landlords and generates rental pricing recommendations; and that the landlords agree to share this information because they have an understanding that their competitors will do so also, and will also receive pricing recommendations.

Specifically, the complaint alleges that the RealPage software uses competing landlords’ “nonpublic, transactional data in nearly every step of setting a recommended floor plan price.”7 Although no subscriber is formally bound to implement the software’s pricing recommendations, RealPage allegedly “has taken multiple steps to increase compliance with” pricing recommendations, including making it easier to accept recommendations than decline them, integrating auto-accept functionality that “pushes clients to adopt” price recommendations, and utilizing pricing advisors to encourage landlords to follow pricing recommendations.8 The government claims that this alleged conduct constitutes unlawful agreements between RealPage and landlords to (1) “share and exploit competitively sensitive data,” and (2) “align pricing” in violation of Section 1 of the Sherman Act.9

Unlike the complaints filed in the private RealPage class actions, DOJ’s lawsuit does not claim that the conduct is a per se violation of Section 1 and instead appears to be brought under the “rule of reason,” a more complex standard requiring proof that the anticompetitive effects of the alleged agreement are not outweighed by procompetitive justifications.10 The government also goes further than the private plaintiffs in focusing on RealPage’s use of non-public data related to occupancy rates, alleging that this contributes to the ability of “landlords to raise price with more certainty.”11

DOJ’s complaint also alleges that RealPage has violated Section 2 of the Sherman Act by leveraging landlords’ data to maintain a monopoly in the commercial revenue management software market. DOJ claims that RealPage has used “exclusionary conduct” to “obstruct rival software providers from competing on the merits via revenue management products that do not harm the competitive process,” and that RealPage’s “data and scale advantage is significant and creates a feedback loop that further increases barriers to competition for commercial revenue management software.”12

RealPage has issued a statement in response to DOJ’s lawsuit asserting that its “revenue management software is purposely built to be legally compliant,” and that “the claims brought by the DOJ are devoid of merit and will do nothing to make housing more affordable.”13 Among other things, RealPage contends that its customers decide their own rates, have “100% discretion” to accept or reject software price recommendations, are not punished for declining recommendations, and “accept recommendations at widely varying rates” that are lower than what has been alleged.14

Looking ahead and takeaways

DOJ’s lawsuit against RealPage is perhaps the federal antitrust enforcers’ most significant challenge to algorithmic pricing, and is likely not the last. Both DOJ and the FTC have indicated that they see algorithmic pricing and information-sharing as areas of focus for antitrust enforcement across industries.15 Assistant Attorney General Jonathan Kanter has highlighted the cross-industry application of algorithmic pricing tools, noting that the technology is emerging “throughout our economy” and “is making it much easier and more effective” to “put[] a cartel together.”16 Current DOJ leadership has also been clear that the agency considers price fixing using an algorithm to be no different than price fixing conducted by individuals.17 Most recently, DOJ Deputy Assistant Attorney General John Elias said that “[a]lthough algorithms are becoming more prevalent in pricing, we don't think of them or other new technologies as requiring totally new [antitrust] enforcement paradigms. The basic world has not changed[.]”18

Despite these broad statements, not all algorithmic tools are likely to raise the same level of antitrust concern. For example, a unilateral decision to use a proprietary algorithmic pricing tool that relies only on a firm’s own internal data should not create antitrust risk. And, as the Nevada district court recognized in dismissing the complaint against Las Vegas hotel operators, gathering public competitor data from public sources to aid in pricing decisions is not the same as exchanging non-public data, or entering into an agreement with competitors to use price recommendations, and does not alone violate the Sherman Act.19 Even in situations where algorithms allegedly rely on non-public data, the decision by the court in the RealPage class action litigation to reject application of the per se standard demonstrates that algorithmic pricing claims raise novel and challenging issues that require examining the full economic impact of the use of such tools.

Nevertheless, in the current enforcement environment, any exchange of non-public data between competitors or use of algorithms for pricing purposes has the potential to draw antitrust scrutiny. Companies can begin to evaluate their risk in this area by first determining if the organization exchanges non-public information with competitors or uses any third-party algorithmic pricing tools that may include or rely on competitor information. If so, a variety of questions follow, including:

• What is the nature and source of any data related to competitors?

• What safeguards are in place to control the use and disclosure of competitively sensitive information?

• Do competitors use the same service or tool?

• Does the service or tool make recommendations about pricing or other competitively sensitive topics? If so, how are those recommendations used?

• Are there opportunities to communicate with competitors about pricing data or decisions?

The answers to these questions can help a company analyze the likelihood that it could face an investigation or lawsuit, but they do not necessarily mean a violation has occurred. Experienced antitrust counsel, like the team at Hogan Lovells, can conduct an in-depth risk analysis, recommend appropriate courses of action, and help navigate this evolving legal landscape.