Some revenue management systems based on algorithms may lead to unintended collusion and antitrust violations.

Chris K. Anderson and Fredrik Ødegaard November 04, 2025 Reading Time: 12 min

Alex Nabaum/theispot.com

**Summary: **

Several recent court cases contend that the pricing algorithms used by hotels and multifamily landlords pose antitrust risks. Federal regulators have intervened to argue that these systems can lead to unintended collusion and antitrust violations, even without explicit agreements among the parties. Businesses can reduce the risk of collusion with algorithms that rely on decentralized — as opposed to centralized — decision-making and use only public, not private, data.

Listen to “The Perils of Algorithmic Pricing” (17:16)

For decades, hotels, airlines, casinos, and other companies have used revenue management systems to help them set prices, maximize revenues, and gain competitive advantage. Now, in a series of legal cases, plaintiffs have argued that some of those systems’ pricing algorithms could be used to facilitate illegal price-fixing in violation of federal antitrust law.

Typically, collusion over pricing requires explicit coordination among competitors, the kind one might imagine occurring in the stereotypical smoke-filled room. What makes the recent lawsuits worth paying attention to is the idea, expressed by federal regulators, that the use of pricing algorithms can lead to collusion without such overt agreements — and even if companies didn’t intend to collude. If this view of collusion prevails, it could pave the way for even more antitrust lawsuits over algorithmic pricing.

In this article, we want to highlight the possible antitrust risks posed by algorithmic pricing and provide an analytical framework for understanding how the algorithms use data and guide pricing decisions in ways that can lead to illegal collusion.

The Legal Cases Alleging Collusion

Over the past few years, pricing algorithms have become the target of several class-action lawsuits alleging that the systems enable users to collude illegally in setting prices. In 2023, plaintiffs brought suit against casino hotels in Las Vegas and Atlantic City, New Jersey, and their revenue management (RM) system vendor, Cendyn Group, whose Rainmaker algorithm is used to set prices and control inventory.1 More recently, plaintiffs filed suit against large hotel brands and SAS Institute subsidiary Integrated Decisions and Systems (IDeaS), whose software is deployed at more than 30,000 hotels around the world.2 The pending suits allege that the hotels, by delegating pricing to the shared algorithm, implicitly colluded to set prices that were higher than would be found in a truly competitive market.

Two algorithm vendors serving the multifamily housing market — RealPage and Yardi Systems — face similar class-action antitrust lawsuits. In October, 27 property firms agreed to pay a total of $141 million to settle one class action (RealPage and other landlords remain defendants).3 RealPage is also the subject of a civil antitrust suit filed by the U.S. Department of Justice and attorneys general in eight states.

About the Authors

Chris K. Anderson is a professor of services management at Cornell University’s SC Johnson College of Business. Fredrik Ødegaard is an associate professor of management science at Western University’s Ivey Business School.

References

1. Cornish-Adebiyi v. Caesars Entertainment, Inc., No. 1:23-cv-02536 (D. N.J., filed 2023); and Gibson v. Cendyn Group, LLC, No. 2:23-cv-00140 (D. Nev., filed Jan. 25, 2023).

2. Gonzalez v. IDeaS Revenue Solutions, Inc., No. 1:24-cv-08262 (N.D. Ill. filed 2024); Shattuck v. SAS Institute Inc., No. 3:24-cv-03424 (N.D. Cal., filed 2024); and Au v. IDeaS Revenue Solutions, Inc., No. 1:24-cv-06324 (N.D. Ill., filed 2024).

3. In re: RealPage, Inc., Rental Software Antitrust Litigation (No. II), No. 3:23-md-03071 (M.D. Tenn., filed 2023); Duffy v. Yardi Systems, Inc., No. 2:23-cv-01391 (W.D. Wash., filed 2023); and Mach v. Yardi Systems, Inc., No. 24-CV-063117 (Cal. Super. Ct., Alameda County, filed 2024).

4. United States v. RealPage, Inc., No. 1:24-cv-00710 (M.D. N.C., filed Aug. 23, 2024).

5. Segal v. Amadeus IT Group, S.A., No. 1:24-cv-01783 (N.D. Ill., third amended complaint, April 28, 2025); and Portillo v. CoStar Group, Inc., No. 2:24-cv-00229 (W.D. Wash., filed Feb. 20, 2024).

6. Y. Cheng, “Statement of Interest of the United States (Case No. 1:23-cv-02536),” U.S. Department of Justice, 2022.

7. Preventing Algorithmic Collusion Act of 2025, S. 232, 119th Cong. (2025-2026), www.congress.gov/bill/119th-congress/senate-bill/232/text.

8. An important issue is to continuously stay updated and not succumb to algorithmic inertia. See V.L. Glaser, O. Omidvar, and M. Safavi, “Predictive Models Can Lose the Plot. Here’s How to Keep Them on Track,” MIT Sloan Management Review 64, no. 4 (summer 2023): 20-25.

9. J. Birkinshaw, “Will AI Disrupt Your Business? Key Questions to Ask,” MIT Sloan Management Review 66, no. 4 (summer 2025): 50-56.

10. Z. Brown and A. MacKay, “Are Online Prices Higher Because of Pricing Algorithms?” Brookings Institution, July 7, 2022, www .brookings.edu.

11. J.M. Meylahn and A.V. den Boer, “Learning to Collude in a Pricing Duopoly,” Manufacturing & Service Operations Management 24, no. 5 (September-October 2022): 2577-2594, https://doi.org/10.1287/msom.2021.1074.

12. Gibson v. Cendyn Grp., LLC, No. 24 -3576, slip op. at 5-6 (9th Cir. Aug. 15, 2025).