The deployment of algorithmic pricing software across competing industries raises a fundamental antitrust question: does it constitute a horizontal price-fixing conspiracy when competitors independently use the same algorithm—trained on their collective, non-public pricing data—to generate pricing recommendations they follow? The In re RealPage litigation, now spanning a massive multidistrict proceeding in the Middle District of Tennessee and a separate Department of Justice action in the Middle District of North Carolina, has become the central proving ground for this theory. For plaintiffs' practitioners evaluating whether to bring algorithmic pricing claims under the Sherman Act, understanding the hub-and-spoke framework and how courts have analyzed it is essential.
The Sherman Act § 1 Hub-and-Spoke Theory
Section 1 of the Sherman Act, 15 U.S.C. § 1, prohibits "[e]very contract, combination in the form of trust or otherwise, or conspiracy, in restraint of trade or commerce." Price-fixing among competitors is a per se violation; no inquiry into market effects is required when competitors directly agree to set prices. The more difficult case arises when the agreement is not horizontal—directly among competitors—but is mediated through a common intermediary.
The hub-and-spoke conspiracy theory addresses exactly this structure. In a hub-and-spoke conspiracy: (1) the "hub" is the central coordinating actor—here, an algorithmic pricing vendor; (2) the "spokes" are the competitors who each enter agreements with the hub; and (3) the "rim" is the implied horizontal agreement among competitors that the spoke-hub agreements collectively effectuate.
The foundational case is Interstate Circuit, Inc. v. United States, 306 U.S. 208 (1939). Interstate Circuit controlled first-run movie theaters. It sent a letter to eight film distributors demanding that they each impose certain pricing restrictions on their second-run exhibition licensees. All eight distributors complied. The Supreme Court upheld the conspiracy conviction despite the absence of any direct communication among the distributors. The Court held that when competitors act in parallel in response to a common signal, knowing that each competitor is receiving and likely responding to the same signal, the factfinder may infer an implicit agreement—the "rim"—from the consciously parallel conduct. The crucial element is that each competitor acted knowing that the others were likely to act similarly: "It was enough that, knowing that concerted action was contemplated and invited, the distributors gave their adherence to the scheme and participated in it." Interstate Circuit, 306 U.S. at 227.
The RealPage Litigation
The Private MDL
In re RealPage, Inc., Rental Software Antitrust Litigation (No. II), MDL 3071 (M.D. Tenn.), is a multidistrict proceeding consolidating dozens of private plaintiff class actions alleging that RealPage, Inc., the dominant provider of revenue management software for apartment operators, facilitated an anticompetitive hub-and-spoke conspiracy among competing landlords. The core allegations:
- RealPage's software collects non-public, competitively sensitive pricing data from each participating landlord—occupancy rates, lease transaction prices, renewal rates, concessions.
- RealPage aggregates this data and uses it to generate individualized pricing recommendations for each landlord.
- Because each landlord knows their competitors are also feeding data to and receiving recommendations from the same algorithm, each landlord effectively participates in a coordinated pricing regime that is "designed to 'raise the tide' for all landlords."
- Plaintiffs (residential renters) allege they paid artificially inflated rents as a result.
Judge Waverly Crenshaw denied defendants' motion to dismiss, finding that the allegations plausibly stated a Section 1 claim. In re RealPage, Inc., Rental Software Antitrust Litig. (No. II), 709 F. Supp. 3d 478 (M.D. Tenn. 2023). The court distinguished purely parallel conduct (insufficient for conspiracy) from conduct involving the knowing sharing of confidential data with a common intermediary that uses it to coordinate pricing recommendations across competitors—which, the court held, is sufficient at the pleading stage to support an inference of agreement.
The DOJ Action
In August 2024, the Department of Justice Antitrust Division filed a civil complaint against RealPage in the Middle District of North Carolina, alleging violations of both Section 1 (conspiracy to fix prices through the hub-and-spoke structure) and Section 2 (monopolization of the commercial revenue management software market). In January 2025, the DOJ amended its complaint to add six major landlords—Greystar, Camden Property Trust, Cortland Management, Cushman & Wakefield, LivCor, Pinnacle Property Management Services, and Willow Bridge Property Company—as defendants.
In August 2025, the DOJ reached a proposed consent decree with Greystar, one of the largest apartment operators. Greystar agreed to stop using algorithmic pricing tools that rely on non-public competitor data and to avoid RealPage-hosted meetings with other landlords. In April 2025, the state attorneys general of Colorado and North Carolina settled with Cortland Management for $100,000 in attorney fees, with injunctive relief prohibiting Cortland from using RealPage's software or similar tools relying on private rental data.
Nine state attorneys general have joined the federal suit, bringing parallel claims under their state antitrust laws.
Distinguishing Gibson: The Ninth Circuit's Contribution
The Ninth Circuit's decision in Gibson v. MGM Resorts International, affirmed in August 2025, addressed a related but distinct algorithmic pricing claim—this one involving hotel room pricing in Las Vegas, coordinated through Cendyn pricing software. The Ninth Circuit affirmed dismissal, but on reasoning that distinguishes rather than forecloses RealPage-style claims.
The Ninth Circuit held that ordinary license agreements between a software vendor and competing hotels—without more—do not constitute a horizontal conspiracy. The court emphasized that the Cendyn agreements merely imposed obligations between each hotel and Cendyn "as to each other"; they did not restrict any hotel's ability to compete with other hotels or set its own prices. Each hotel retained independent pricing authority.
The court, however, explicitly identified two factors that would support antitrust scrutiny:
- If competitors agreed among themselves to use the same software and follow its recommendations—that would be "undoubtedly" anticompetitive.
- If the software pooled competitors' confidential, non-public data to generate pricing recommendations for participants—that would raise distinct antitrust concerns because it enables a "melting pot of confidential competitor information."
Both factors are present in the RealPage allegations. This is the critical distinction: RealPage is alleged to use competitors' non-public data to generate recommendations—unlike Cendyn, which did not pool competitor confidential information. The Ninth Circuit's Gibson opinion therefore functions as a roadmap for RealPage-type plaintiffs, not a bar.
The Agreement Requirement in Hub-and-Spoke Cases
The central proof challenge in any hub-and-spoke case is establishing the "rim"—the implied horizontal agreement among competitors. The Supreme Court in Interstate Circuit held that proof of agreement can be circumstantial, inferred from conscious parallel conduct taken with knowledge of competitors' participation.
In the algorithmic context, key factual elements that support an inference of the rim:
Shared understanding of competitor participation. If the software vendor's marketing materials, contracts, or communications disclosed to each landlord that competitors were also participating and their data was being used, each landlord was on notice that it was entering a coordinated scheme—not merely entering an independent bilateral contract.
Delegation of pricing authority. Where a landlord systematically follows the algorithm's pricing recommendations without independent review or override, that landlord has effectively delegated pricing authority to the hub. The Supreme Court's language in Interstate Circuit about "adherence to the scheme" maps onto this scenario.
Reciprocal data contribution. Contributing one's own non-public pricing data as a condition of receiving the algorithm's recommendations is fundamentally different from purchasing a software license. It constitutes the functional equivalent of sharing price information with competitors.
Communications at industry events. RealPage allegedly hosted user group meetings at which competing landlords and RealPage personnel discussed pricing strategies. The DOJ has alleged these meetings facilitated explicit coordination beyond the software's technical operation.
State Antitrust Claims: Florida and Alabama
State antitrust statutes independently provide causes of action for price-fixing conspiracies that affect state residents.
Florida Deceptive and Unfair Trade Practices Act (FDUTPA) and Florida's little Sherman Act (Fla. Stat. § 542.18) both prohibit contracts, combinations, and conspiracies in restraint of trade in Florida. Florida courts generally apply federal Sherman Act analysis to § 542.18 claims. Florida renters affected by RealPage-coordinated pricing have viable state claims.
Alabama's State Antitrust Statute, Ala. Code § 8-10-1 et seq., prohibits trusts, combinations, and conspiracies in restraint of trade or commerce within Alabama. Like Florida's statute, Alabama courts look to federal Sherman Act precedent for interpretive guidance. Alabama renters who can demonstrate that algorithmic pricing affected Alabama apartment markets have parallel state claims.
State claims are valuable in algorithmic pricing litigation for two reasons: (1) they may carry different standing requirements or damages mechanisms than federal antitrust law; and (2) they subject defendants to state AG enforcement and private treble damages remedies under state consumer protection frameworks.
Practice Notes for Plaintiffs' Practitioners
Class certification. Antitrust class certification requires predominance of common questions, which in algorithmic pricing cases typically focuses on whether the algorithm's pricing recommendations affected all class members uniformly. Economic expert testimony establishing market-wide price effects attributable to the algorithm is essential.
Statistical evidence. The damages model in algorithmic pricing cases requires econometric analysis isolating the "but-for" price—what rents would have been absent the alleged conspiracy—from actual rents paid. This requires economic experts with expertise in hedonic pricing models and regression analysis.
Standing. Direct purchasers (here, renters who paid algorithmically elevated rents) have standing under federal antitrust law. Indirect purchasers have standing under state antitrust statutes in many jurisdictions, though not under federal law (Illinois Brick Co. v. Illinois, 431 U.S. 720 (1977)).
Coordination with state AG actions. Where state AGs are actively litigating parallel claims against the same defendants, private plaintiffs may benefit from the government's investigative resources and settlement precedents. Monitor the DOJ and state AG proceedings for factual developments relevant to private cases.
Talk to Yates Anderson
If you are litigating a matter in this area — or weighing whether to — the working analysis above only goes so far. Request a case evaluation and a Yates Anderson attorney will respond within one business day.
Informational only. Not legal advice. No attorney-client relationship is created by reading this post. Consult a licensed attorney in your jurisdiction.