Algorithmic Pricing, Rising Rents, and the Future of Antitrust Law

By: Jake Smithson

Average rents across the United States have increased at a rapid pace in recent years.[1] Unsurprisingly, this rapid increase has resulted in an affordability crisis which has become a central issue in some recent high-profile political campaigns.[2] While many factors contribute to rising housing costs, one increasingly scrutinized practice has lately been the target of some unwanted legal attention: the use of algorithmic pricing software by large, corporate landlords.[3] Critics argue that this software enables landlords to indirectly coordinate rent increases, allowing for inflated prices that would otherwise be difficult to sustain in a competitive market.[4]

This debate has now moved into the courts. A wave of antitrust lawsuits have alleged that algorithmic pricing tools facilitate unlawful price-fixing in violation of Section 1 of the Sherman Act.[5] Yet early judicial decisions have produced conflicting outcomes, revealing uncertainty over how traditional antitrust doctrine applies to the modern use of commercial algorithms. The resulting split raises a critical question: when does algorithmic pricing cross the line from efficient technological practice to illegal price-fixing?

I. How Algorithmic Pricing Works in Rental Housing

At a basic level, algorithmic pricing tools, in the rental housing context, analyze data—such as vacancy rates, turnover, and operating costs—to generate “optimal” rent recommendations designed to maximize revenue.[6] While some landlords use proprietary in-house tools, the majority of large, corporate landlords use third-party platforms.[7] The market for third-party algorithmic pricing software in the rental housing sector is dominated by two companies: RealPage and Yardi.[8]

One distinguishing feature between RealPage and Yardi’s tools versus proprietary in-house pricing software is their reliance on pooled, non-public data. A landlord using a proprietary software may receive pricing recommendations based solely on its own data or market signals. Yardi or RealPage’s algorithm, on the other hand, collects commercially sensitive data from all of its customers, including competing landlords within the same market, and aggregates it.[9] The software then generates rent recommendations derived from this collective dataset.[10] In addition, these companies have allegedly instructed clients that the effectiveness of these algorithms depends on their uniform acceptance of its recommendations.[11] In other words, landlords are encouraged to outsource pricing decisions to a shared algorithm optimized to produce supra-competitive rents.[12] Plaintiffs argue that this structure allows the competing landlords to coordinate, or fix, prices indirectly through a common intermediary.

II. Section 1 of the Sherman Act and Algorithmic Collusion

Section 1 of the Sherman Act prohibits any agreements or conspiracies that unreasonably restrain trade.[13] Therefore, to prevail on a Section 1 claim, plaintiffs must show (1) the existence of an agreement or conspiracy; (2) that such agreement or conspiracy imposes an unreasonable restraint on competition; and (3) that this restraint impacts interstate commerce (although this final prong has been broadly construed).[14]

Agreements can take many forms. Of particular relevance here is the “hub-and-spoke” style of conspiracy, in which horizontal competitors coordinate through a vertical intermediary.[15] Most often, such conspiracies are proven through circumstantial evidence, which includes showing parallel conduct between competitors as well as “plus-factors” which suggest the conduct was not the result of independent decision-making.[16]

Once a conspiracy is established, courts must determine whether it is unlawful under one of two primary standards: the Rule of Reason or the Per Se rule.[17] The Rule of Reason requires a detailed market analysis and proof of anticompetitive effects.[18] The Per Se rule, by contrast, applies to a narrow class of restraints, such as horizontal price-fixing, that are deemed “always or almost always” anticompetitive and requires no market analysis or proof of anticompetitive effects.[19]

III. Diverging Judicial Approaches

Early algorithmic pricing cases have split sharply. In two cases involving casino-hotels, Gibson v. MGM Resorts and Cornish-Adebiyi v. Caesars, courts dismissed the claims outright, holding that the plaintiffs failed to plausibly allege a conspiracy.[20] The key fact was that the software at issue did not pool or share competitors’ non-public data.[21] Without such data exchange, there was no “rim” connecting the spokes of the alleged hub-and-spoke conspiracy.[22]

By contrast, the courts in the RealPage and Yardi cases found that the plaintiffs did plausibly allege a conspiracy.[23] Unlike the casino software, RealPage’s and Yardi’s tools aggregate confidential data from competing landlords and use it to inform pricing recommendations to their clients.[24] The complaints also alleged a striking shift in landlord behavior: rather than prioritizing occupancy (the traditional “heads in beds” approach), landlords allegedly began prioritizing rent maximization, holding rents above market levels even as vacancies rose.[25] Both courts found that this conduct would have been irrational in the absence of collusion.[26]

Where the courts diverged, however, was in determining how to evaluate the unreasonableness of these practices. In RealPage, the court applied the Rule of Reason.[27] Although it acknowledged the presence of horizontal coordination with regard to pricing, it emphasized features it viewed as “imperfections” in the alleged conspiracy: staggered entry into the conspiracy, lack of direct communication, incomplete delegation to the algorithm, absence of enforcement mechanisms, and the novelty of algorithmic pricing.[28] According to the court, these imperfections prevented the alleged conspiracy from being classified as a “straightforward  form of horizontal price-fixing conspiracy,” to which the Per Se rule applies.[29] After analyzing the defendants’ conduct under the Rule of Reason, the court ultimately dismissed one of the plaintiffs’ complaints.[30]

In Yardi, the Middle District of Tennessee reached the opposite conclusion. Explicitly rejecting RealPage’s reasoning, the court held that the alleged conduct clearly amounted to horizontal price-fixing and therefore warranted per se treatment.[31] In its view, “imperfections” relating to communication, timing, and enforcement go to the issue of whether a conspiracy exists in the first place, not to whether price-fixing is inherently anticompetitive.[32] Moreover, the court held that the novelty of algorithmic pricing does not remove it from the ambit of established antitrust law.[33]

IV. Why the Split Matters

This disagreement is not merely academic. The choice between the Rule of Reason and the Per Se rule can determine whether a case even survives the motion to dismiss stage.[34] As RealPage illustrates, the heightened scrutiny and market analysis under the Rule of Reason can doom otherwise plausible claims, especially in markets such as rental housing where search costs, relocation barriers, and lock-in effects distort traditional measures of competition and may not be accurately captured by the Rule of Reason’s market analysis.[35]

More broadly, overapplication of the Rule of Reason (especially to conduct that closely resembles price-fixing) increases the risk of false negatives, allowing genuinely anticompetitive behavior to persist unpunished. While antitrust law generally tolerates some false negatives to avoid chilling legitimate conduct, that tradeoff becomes less acceptable when structural features of a market make this risk more likely.[36]

V. Reconciling Antitrust Doctrine with Algorithmic Pricing

This emerging split can be resolved by focusing on the proper role of antitrust standards. The Rule of Reason is the default, but the Per Se rule exists precisely because some conduct, including horizontal price-fixing, so reliably harms competition that elaborate market analysis is unnecessary.[37] The concerns cited in RealPage do not justify departing from this principle. A conspiracy does not cease to be price-fixing simply because firms join at different times, communicate indirectly, or retain nominal discretion over final prices. Antitrust law has long recognized that tampering with any part of the price structure—even upstream inputs or pricing recommendations—constitutes price-fixing if it predictably affects the prices that consumers ultimately pay downstream.[38]

Nor does the novelty of algorithmic technology change the analysis. New tools may alter the means of coordination, but they do not change its economic effect. Horizontal agreements to influence pricing remain “always or almost always” anticompetitive, regardless of whether they are implemented through back-room deals or machine-learning models. At the same time, not all algorithmic pricing is suspect. Where software relies solely on public data or a firm’s internal information, without aggregating competitors’ confidential data, claims should be evaluated under the Rule of Reason, as illustrated by the casino cases.

Conclusion

Algorithmic pricing presents a genuine challenge for antitrust law, not because it defies existing doctrine, but because it tests courts’ willingness to apply that doctrine consistently. When pricing algorithms aggregate and disseminate competitors’ non-public data to influence rents, they facilitate classic horizontal price-fixing through modern means. Courts should therefore adopt the approach articulated in Yardi: apply the Per Se rule where algorithmic tools enable horizontal coordination through shared commercially sensitive data, and reserve the Rule of Reason for cases which lack such collusion. Doing so would preserve doctrinal coherence, reduce false negatives, and ensure that antitrust law keeps pace with technological changes.

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[1] Mary Cunningham, These U.S. Cities Have Seen the Biggest Rent Increases Since 2020, CBS: Money Watch (Dec. 4, 2025, at 17:27 ET) https://www.cbsnews.com/news/rent-apartments-cities-near-me-biggest-increases/ (“In the 50 largest U.S. cities, the rent for a one-bedroom apartment climbed an average of $457 per month, or 41% . . . between 2020 and 2025. . . .”).

[2] Ryan Bourne, Zohran Mamdani’s “War on Prices”, Cato Inst. (June 13, 2025) https://www.cato.org/commentary/zohran-mamdanis-war-prices (describing then New York City mayoral candidate Zohran Mamdani’s pledge to freeze rents in the city for a period of four years).

[3] See generally In re RealPage, Inc., Rental Software Antitrust Litig. (No. II), 709 F. Supp. 3d 478 (M.D. Tenn. 2023); Duffy v. Yardi Sys., Inc., 758 F. Supp. 3d 1283 (W.D. Wash. 2024).

[4] In re RealPage, 709 F. Supp. 3d at 492; Duffy, 758 F. Supp. 3d at 1289.

[5] See generally Gibson v. MGM Resorts Int’l, No. 2:23-CV-00140-MMD-DJA, 2023 WL 7015996 (D. Nev. Oct. 24, 2023) (concerning the use of algorithmic pricing tools by casino-hotels in Las Vegas); Cornish-Adebiyi v. Caesars Entm’t, Inc., No. 1:23-CV-02536-KMW-EAP, 2024 WL 4356188 (D.N.J. Sept. 30, 2024) (concerning the use of algorithmic pricing tools by casino-hotels in Atlantic City); In re RealPage, 709 F. Supp. 3d 478; Duffy, 758 F. Supp. 3d 1283.

[6] See, e.g., In re RealPage, 709 F. Supp. 3d at 493–94.

[7] See id. at 526.

[8] See id.

[9] See, e.g., id. at 492.

[10] See, e.g., Duffy, 758 F. Supp. 3d at 1289.

[11] In re RealPage, 709 F. Supp. 3d at 493–94.

[12] See, e.g., Duffy, 758 F. Supp. 3d at 1292–93.

[13] 15 U.S.C. § 1.

[14] See Standard Oil Co. v. United States, 221 U.S. 1, 59–68 (1911); McLain v. Real Estate Bd. of New Orleans, Inc., 444 U.S. 232, 241–43 (1980).

[15] See Total Benefits Plan. Agency, Inc. v. Anthem Blue Cross & Blue Shield, 552 F.3d 430, 435 (6th Cir. 2008).

[16] See Bell Atl. Corp. v. Twombly, 550 U.S. 544, 556 (2007).

[17] See Hahn v. Or. Physicians’ Serv., 868 F.2d 1022, 1026 (9th Cir. 1988).

[18] See Nat’l Soc’y of Prof’l Eng’rs v. United States, 435 U.S. 679, 692 (1978); Brown Shoe Co. v. United States, 370 U.S. 294, 336 (1962).

[19] Broadcast Music, Inc. v. CBS, 441 U.S. 1, 19–20 (1979).

[20] Gibson v. MGM Resorts Int’l, No. 2:23-CV-00140-MMD-DJA, 2023 WL 7015996, at *4 (D. Nev. Oct. 24, 2023); Cornish-Adebiyi v. Caesars Entm’t, Inc., No. 1:23-CV-02536-KMW-EAP, 2024 WL 4356188, at *5 (D.N.J. Sept. 30, 2024).

[21] Cornish-Adebiyi, 2024 WL 4356188, at *5; Gibson, 2023 WL 7015996, at *4.

[22] See, e.g., Cornish-Adebiyi, 2024 WL 4356188, at *7.

[23] In re RealPage, Inc., Rental Software Antitrust Litig. (No. II), 709 F. Supp. 3d 478, 518 (M.D. Tenn. 2023); Duffy v. Yardi Sys., Inc., 758 F. Supp. 3d 1283, 1294 (W.D. Wash. 2024).

[24] In re RealPage, 709 F. Supp. 3d at 492.

[25] Id. at 506.

[26] Id. at 510; Duffy, 758 F. Supp. 3d at 1292–93.

[27] In re RealPage, 709 F. Supp. 3d at 520–21.

[28] Id. at 519–20.

[29] Id. at 520.

[30] Id. at 533–34.

[31] Duffy, 758 F. Supp. 3d at 1296.

[32] Id. at 1296.

[33] Id. at 1296–97.

[34] In re RealPage, 709 F. Supp. 3d at 526–27.

[35] See id. at 527.

[36] See id.

[37] See Hahn v. Or. Physicians’ Serv., 868 F.2d 1022, 1026 (9th Cir. 1988); Broadcast Music, Inc. v. CBS, 441 U.S. 1, 19–20 (1979).

[38] United States v. Socony-Vacuum Oil Co., 310 U.S. 150, 221 (1940) (“Any combination which tampers with price structures is engaged in an unlawful activity.”); Palmer v. BRG of Georgia, Inc., 498 U.S. 46, 48 (1990) (“Under the Sherman Act a combination formed for the purpose and with the effect of raising, depressing, fixing, pegging, or stabilizing the price of a commodity in interstate or foreign commerce is illegal per se.”).