Five Steps Hotel Owners, Operators, and Management Companies Can Take to Mitigate Risk in the Face of Heightened Antitrust Scrutiny and a Wave of Lawsuits Involving Revenue-Management Tools

Revenue-management tools are ubiquitous across sectors, but many hotel owners, operators, and management companies use such tools on a daily basis. As these products have improved, many have incorporated algorithms enabling the user to set prices dynamically based on changes in available inventory and expected demand. A recent wave of lawsuits has challenged a number of such products that are used by multiple competitors in the hospitality space, alleging that the common use of the same revenue-management tool enables price fixing. Federal and state antitrust enforcers are also increasingly focused on what they perceive as potentially anticompetitive effects of revenue-management tools and related pricing algorithms.

The crux of these allegations and investigations is that revenue-management tools can automate collusion among competitors to fix and raise prices and elude detection. Despite these allegations, revenue-management tools provide many beneficial and procompetitive use cases that do not violate the antitrust laws. However, given the current scrutiny in the space, particularly with respect to hotel room pricing and residential leasing activities, any company or operator that is already using, or considering using, revenue-management tools should be aware of the potential legal risks and challenges it may face. As detailed below, companies using such revenue-management tools should consider updating their compliance policies with clear guidance on how to avoid allegations of price fixing and think carefully about how their businesses deploy such technologies.

Background on Legal Challenges to Revenue-Management Tools

Over the past year, plaintiffs’ lawyers representing hotel customers and residential renters have filed numerous class action lawsuits alleging antitrust violations based on the use of revenue-management tools capable of algorithmic pricing — software such as that offered by CoStar (STR), Amadeus (Demand360), SAS Institute (IDeaS), Rainmaker/Cendyn, RealPage, Yardi, and others. These lawsuits all allege that hotel owners and/or operators use these programs to share nonpublic pricing and occupancy information and fix prices, which artificially inflates hotel room prices and residential rents and harms consumers.

There are no less than five class actions proceeding against hotel operators and their revenue-management software providers (discussed further below). With new cases emerging regularly — the most recent case, Au et al. v. Integrated Decisions and Systems Inc., filed on July 24, 2024, names a number of well-known, established brands in the hospitality industry as defendants — no end is in sight for antitrust lawsuits focused on revenue-management tools and algorithmic pricing in the hospitality space. In a related action in the Middle District of Tennessee, more than 30 cases against real estate revenue-management provider RealPage and its largest customers have been consolidated and have survived defendants’ motions to dismiss. There is a similar class action lawsuit against real estate software provider Yardi and several of its largest residential landlord customers in which plaintiffs are pursuing a nearly identical set of price-fixing claims.

Dismissal of Allegations Against Las Vegas Hotels

The wave of litigations related to hotel revenue-management tools began in January 2023 when a group of hotel customers filed a class action lawsuit against multiple hotel operators in Las Vegas (Gibson v. MGM Resorts). In this closely watched case, a Nevada federal district court judge dismissed with prejudice allegations that Las Vegas hotel operators had conspired to fix prices by agreeing to collectively use a revenue-management product. In their amended complaint, plaintiffs alleged that the software provided by Cendyn/Rainmaker (GuestRev) used the confidential information of each hotel operator, optimized hotel room pricing in a way that deviated from the traditional method of pricing based on occupancy, and recommended prices that were higher than competitive market rates.

The court focused primarily on GuestRev’s treatment of confidential information, finding the plaintiffs failed to plausibly allege a tacit agreement among the defendants to use the software to fix prices. The court found two key facts instructive: (i) that the GuestRev algorithm did not facilitate the exchange of confidential information across hotel operators; and (ii) that the plaintiffs never alleged that the confidential information of one hotel operator was used to generate recommendations for another hotel operator.

The court also found that the mere inference of exchanging nonpublic information through machine learning techniques is insufficient to show a conspiracy. This point will be most critical in other antitrust suits against hotel owners, operators, and management companies alleging that algorithms themselves facilitate the exchange of nonpublic information and are the basis of a conspiracy. See, e.g., In re RealPage Inc., Rental Software Antitrust Litig. (M.D. Tenn. Dec. 28, 2023).

The court noted additional factors undermining the plaintiffs’ conspiracy claim. First, different hotel operators had started using the software years apart, which diminished the likelihood of a conspiracy. Second, the hotel operators had different practices regarding whether they accepted the pricing recommendations generated by the algorithm, which the court found was more consistent with a lack of conspiracy.

In early June, the plaintiffs appealed the court’s decision to the Ninth Circuit, with a decision expected sometime in 2025. This will be a pivotal decision given the growing number of antitrust lawsuits targeting companies’ use of revenue-management software and algorithms to set pricing across industries.

The Gibson ruling came out markedly different from a federal judge’s ruling in the RealPage case. As noted above, in the RealPage multidistrict litigation, plaintiffs, a group of renters, alleged that defendant property managers manipulated the rental housing market using RealPage’s software to charge higher rents. In December 2023, the court denied the defendants’ motion to dismiss the multifamily housing complaint after finding that the complaint “unequivocally alleges that RealPage’s revenue-management software inputs a melting pot of confidential competition information through its algorithm and spits out private recommendations based on that private competitor data.”

In RealPage, two key distinctions stand out from Gibson related to the use of nonpublic information and the adoption rate of the pricing recommendations. The RealPage complaint alleges that the algorithm facilitates the exchange of nonpublic information among the property managers and uses the nonpublic information of one property manager in generating recommendations for another property manager. As of now, this distinction is one of the primary issues in determining whether plaintiffs will be able to assert a plausible claim of a conspiracy. Further, the plaintiffs in RealPage allege that defendants accept RealPage’s recommended rates “up to 80-90% of the time,” a point that seemed to particularly influence the court. By contrast, the judge in Gibson noted that the hotel defendants “are not required to and often do not accept pricing recommendations generated” by the revenue-management products.

Additional Hotel Lawsuits

Besides Gibson, similar allegations against hotel owners, operators, and management companies using revenue-management tools and/or related pricing algorithms are proceeding in other jurisdictions, including the District of New Jersey (Cornish-Adebiyi v. Caesars), the Western District of Washington (Portillo v. CoStar), the Northern District of Illinois (Segal v. Amadeus and Au et al. v. Integrated Decisions and Systems Inc.), and the Northern District of California (Dai v. SAS Institutes). Defendants in these cases include once again a number of established brands in the hospitality industry. While the specific revenue-management tools/products vary across these cases, the core allegations remain the same: hotel operators allegedly relied on these revenue-management tools to exchange nonpublic information, which was ultimately used to coordinate pricing decisions and artificially inflate prices in violation of the antitrust laws. The decisions to date discussed above portend that these pending cases will turn on plaintiffs’ ability to carefully connect defendants’ pricing practices with reliance on pricing algorithms that use competitors’ confidential commercial information. Companies should expect continued scrutiny and therefore should be mindful of the manner in which they use any such revenue-management software and related tools, as discussed further below.

Government Enforcement

The potential legal ramifications of using revenue-management software extends beyond exposure to class action lawsuits. For example, the Department of Justice (DOJ) and Federal Trade Commission (FTC) have weighed in three times in the past nine months by filing statements of interest in private lawsuits. Further, after long-running civil and criminal investigations into RealPage, the DOJ filed a civil antitrust lawsuit against RealPage on August 23, 2024, alleging an unlawful scheme to decrease competition among landlords in apartment pricing and to monopolize the market for commercial revenue-management software that landlords use to price apartments. And, not to be forgotten, state attorneys general (state AGs) are becoming increasingly active in the space, with several state AGs (including in Arizona; Washington, DC; and North Carolina) filing their own lawsuits in the real estate segment within the past nine months.

It does not appear that the DOJ and FTC’s focus on these revenue-management tools and concerns regarding the use of pricing algorithms are going to wane anytime soon, particularly given the DOJ’s recent lawsuit against RealPage after an extended investigation. While the DOJ and FTC have not adopted rulemaking to date, we expect that investigations, subpoenas, and additional lawsuits may materialize in the near future. Further, given the number of antitrust litigations currently pending that have been brought by state AGs, we anticipate that additional state AGs will not just bring their own lawsuits but also initiate conduct investigations into how hotel operators or property management companies use revenue-management tools and algorithmic pricing.

Practical Guidance

Companies in the hospitality space should be mindful of these developments and mitigate risk. As discussed above, while it is not an antitrust violation for companies to use revenue-management tools to assist in making their own independent decisions, including setting their own prices and managing room inventory, even meritless lawsuits and investigations can impose enormous expense, exposure, and business disruptions.

Given this, hotel owners, operators, and property managers would be well served to implement robust antitrust policies and compliance programs that specifically address these issues regarding revenue-management tools and any other emerging technologies. The following steps can help insulate a company from antitrust scrutiny:

1. Do not disclose or discuss the revenue-management tools used or company pricing policies or practices. As a general matter, a company should not discuss or disclose the revenue-management tools it uses publicly or otherwise to competitors. Do not discuss the company’s usage of the revenue-management tool or algorithmic pricing recommendations at any vendor-sponsored conferences, trade association events, or other events where competitors may be present.
2. Use revenue-management tools and pricing suggestions only as recommendations. A revenue-management tool should be just one input among others when hotel owners, operators, and property managers are setting prices and managing room inventory. Do not have a blanket policy in which the company accepts any pricing recommendations generated by the revenue-management tool (or accepts such recommendations a certain percentage of the time); recommendations generated by the revenue-management tools and related software should be treated exactly as that — recommendations. Each decision-maker or user of the products should independently decide to accept or reject any such pricing recommendation or suggestion on a case-by-case basis.
3. Document each decision in writing and properly retain such records. Each decision-maker or user of revenue-management tools should document in real time the reasons for their independent decision-making. Companies should remember that revenue maximization is distinct from price maximization, as maximizing revenue often involves reducing prices to sell more volume, which revenue-management tools often recommend. Having a clear record of this decision-making — including the reasons behind the decisions — can help companies quickly show government enforcers or courts that any concerns regarding collusion over price-setting are meritless.
4. Know the revenue-management tools and data you are using. Companies should understand how revenue-management tools and related software products work, including what data sources a tool relies on in generating recommendations. Revenue-management tools that do not rely on nonpublic, commercially sensitive data from competitors regarding pricing, inventory, and other information present a lower risk than software that does include such information. For clarity, this is not necessary to comply with antitrust laws but can help avoid being involved in costly and burdensome litigations or investigations.
1. When possible, for any new or forthcoming contractual arrangements with revenue-management tool providers, choose software that does not use the nonpublic information of competitors. If it is unclear whether the revenue-management tool uses nonpublic information, the safest practice is to inform the vendor that the company does not authorize the use of its information for other parties’ products, nor does the company wish to use others’ data for any of the company’s pricing decisions or recommendations generated by the revenue-management tool.
5. Provide clear antitrust training and a written antitrust compliance policy. Companies should adopt written antitrust compliance policies that are accompanied by regular training on the topic. This can significantly reduce antitrust risk. Training of senior executives and other key stakeholders can lead to the identification of any potential concerns, which can be addressed with antitrust counsel up front. Companies that do not have in-house counsel to implement such programs would be well served to engage with antitrust counsel at Goodwin to help prepare an appropriate policy, administer training, and address any reports or concerns within the protections of attorney-client privilege.

Hotel operators and owners seeking to establish and implement robust antitrust policies and compliance programs should consider seeking advice from counsel with expertise in this field who have monitored, and will continue to monitor very closely, cases such as those described in this article.

This informational piece, which may be considered advertising under the ethical rules of certain jurisdictions, is provided on the understanding that it does not constitute the rendering of legal advice or other professional advice by Goodwin or its lawyers. Prior results do not guarantee a similar outcome.