Algorithmic Pricing and Antitrust Liability: The RealPage Theory Spreads Across Industries

The antitrust theory that the Department of Justice used to sue RealPage in August 2024—that a software vendor who collects competitively sensitive data from competing customers and uses that data to generate pricing recommendations creates a hub-and-spoke conspiracy in violation of the Sherman Act—did not originate with rental housing, and it is not ending there. Over the past two years, courts have applied similar theories in hotels, healthcare, and other sectors. Plaintiffs have adapted the RealPage framework to target algorithmic pricing in groceries, online retail, and consumer goods. State legislatures have passed laws banning certain uses of revenue management software. A federal appellate court issued the first major ruling on the merits of these claims. And the DOJ settled its case against RealPage in November 2025 in a manner that has become a roadmap—and a warning—for every industry that relies on shared-data pricing systems.

This post explains the hub-and-spoke theory as applied to algorithmic pricing, what the RealPage litigation established, how the theory is being extended across industries, and what the current legal landscape means for businesses that use or operate algorithmic pricing systems.

The Hub-and-Spoke Framework

A hub-and-spoke conspiracy is a form of antitrust conspiracy in which a firm at one level of a market—acting as the “hub”—coordinates competitors at another level—the “spokes”—through a series of vertical agreements, with a horizontal agreement among the spokes constituting the “rim” of the wheel. Classic examples include manufacturers who coordinate resale prices through vertical agreements with competing retailers, or buyers who use their purchasing power to align the pricing of competing suppliers.

The “rim” is legally essential. It is the element that converts a series of independent vertical agreements between the hub and each spoke into a horizontal conspiracy among the spokes themselves. Without the rim—without evidence that the spokes understood they were participating in a scheme that would bind their competitors to the same pricing behavior—the plaintiff has a series of independent vertical arrangements, not a horizontal price-fixing conspiracy.

The RealPage litigation adapted this framework to algorithmic pricing. The theory: RealPage, as the hub, collects current, non-public, competitively sensitive lease transaction data, occupancy rates, and pricing information from competing landlords, who act as the spokes. RealPage feeds that competitor data into its pricing algorithm, which then generates rental rate recommendations for each participating landlord. Each landlord’s decision to participate—to provide their data and accept recommendations calibrated against competitors’ data—constitutes their agreement to the horizontal scheme. The “give to get” structure—provide your data, receive your competitors’ data in the form of algorithm-informed recommendations—creates the rim.

The DOJ’s RealPage Case and Settlement

The DOJ filed its civil complaint against RealPage on August 23, 2024, joined by the Attorneys General of eight states: North Carolina, California, Colorado, Connecticut, Minnesota, Oregon, Tennessee, and Washington. The complaint alleged violations of Sections 1 and 2 of the Sherman Act.

Section 1 theory: RealPage contracted with competing landlords who agreed to share non-public, competitively sensitive information—lease transaction data, pricing, occupancy rates—to train RealPage’s algorithm. The algorithm then generated pricing recommendations based on that pooled competitor data. The DOJ alleged this was a conspiracy to fix rental prices through information sharing and coordinated pricing.

Section 2 theory: RealPage’s market position in revenue management software for multifamily housing gave it market power over pricing information. The software’s dominance enabled RealPage to maintain and extend that power by making landlords dependent on its recommendations and by creating a data network where the value of participation increased as more competitors joined—a competitive moat that foreclosed rival pricing tools from developing.

On November 24, 2025, the DOJ filed a proposed settlement. RealPage did not admit liability, but the settlement terms are operationally significant. The core restrictions: RealPage may not collect or use real-time lease transaction data from landlords; it may only use data that is at least 12 months old in training its pricing models; and it may not provide pricing recommendations based on geographic data more granular than the state level.

These restrictions directly target the mechanism that the DOJ alleged made RealPage’s software anticompetitive: the use of current, granular competitor data to generate recommendations that aligned prices among competing landlords. By limiting the data to 12-month-old state-level information, the settlement attempts to sever the causal link between data sharing and price coordination while allowing legitimate revenue management functions to continue.

The Private Litigation: Class Actions and Settlements

Parallel to the DOJ case, a multidistrict litigation consolidated in the Middle District of Tennessee—In re: RealPage, Inc. Rental Software Antitrust Litigation—has been proceeding since 2022. Plaintiffs are current and former renters alleging that the hub-and-spoke conspiracy inflated rental prices across markets where RealPage software was widely used.

In October 2025, plaintiffs reached preliminary class action settlements totaling approximately $141.8 million with 26 of the property manager and landlord defendants. RealPage itself, and several of the largest property owners and managers, have not yet settled.

These settlements represent significant individual defendant decisions to exit the litigation rather than face trial on the hub-and-spoke theory. From a legal risk-management perspective, each settlement is a data point about how defendants evaluate their exposure under the theory.

Hotels: Gibson v. Cendyn and the Ninth Circuit

The hotel industry has its own parallel litigation, centered on the use of hotel revenue management software—particularly products from Cendyn Group and The Rainmaker Group—that competing hotels subscribed to for room pricing recommendations.

In Gibson v. Cendyn Group LLC, plaintiffs alleged that competing hotels on the Las Vegas Strip participated in a hub-and-spoke conspiracy by each subscribing to the same pricing software, inflating hotel room rates. The District Court dismissed the complaint, and on August 15, 2025, the Ninth Circuit affirmed the dismissal—but in terms that are instructive rather than exculpatory for the industry.

The Ninth Circuit held that independently subscribing to a pricing algorithm does not, standing alone, constitute a restraint of trade under Section 1 of the Sherman Act. A hotel that independently buys a license to revenue management software and uses its recommendations has not agreed with competing hotels on price—it has made a unilateral decision to use a tool.

But the Ninth Circuit explicitly noted that the analysis would have been different if the plaintiffs had alleged—and shown—that Cendyn shared confidential competitor information from one hotel subscriber with other hotel subscribers to generate those recommendations. The “give to get” structure alleged in RealPage, where each subscriber’s confidential data is pooled and reflected back in recommendations to all subscribers, would be a different factual and legal case.

The Ninth Circuit’s ruling thus draws a line: parallel independent use of the same pricing software is not per se a conspiracy. Parallel use of software that works by pooling and sharing competitors’ confidential data is potentially a very different matter.

The FTC and DOJ had filed a statement of interest in a related hotel case in March 2024, making the same point: hotels cannot use an algorithm to coordinate pricing in ways that would be illegal if done by humans directly. The government’s position is that the form of the coordination—algorithmic rather than manual—does not change its legal character.

Healthcare: The MultiPlan Litigation

The most significant extension of the RealPage theory into another industry involves MultiPlan, a healthcare cost-management company that provides health insurers with data analytics and pricing recommendations for out-of-network medical claims.

The In re Multiplan Health Insurance Provider Litigation, filed in August 2024, alleges a hub-and-spoke conspiracy in which MultiPlan serves as the hub, competing health insurers serve as the spokes, and the shared use of MultiPlan’s algorithm to set sub-market reimbursement rates for out-of-network healthcare providers constitutes the rim. The plaintiffs—healthcare providers and their patients—allege that MultiPlan gathered confidential pricing data from competing insurers and used it to generate recommendations that coordinated reimbursement rates below competitive levels.

A federal court ruling in the MultiPlan litigation noted that “an agreement to fix prices within a below-market range through use of an algorithm is no different for antitrust purposes than an agreement to fix prices to a single point.” This formulation—that algorithm-assisted coordination is legally equivalent to direct human coordination—tracks the DOJ’s and FTC’s position in the RealPage and hotel contexts and represents the most significant judicial statement of the theory to date.

Retail, Grocery, and Consumer Goods

State attorneys general and consumer advocates have extended scrutiny to algorithmic pricing in consumer-facing retail. The concept of “surveillance pricing”—personalized pricing systems that use individual consumer data, AI models, and real-time demand signals to charge different prices to different customers—has attracted attention from the FTC and state regulators in the grocery, online retail, and consumer goods sectors.

Senator Amy Klobuchar and co-sponsors introduced the Preventing Algorithmic Collusion Act in February 2024, which would have barred companies from using pricing algorithms for horizontal collusion. The bill did not advance, but its introduction signaled legislative attention to the issue beyond rental housing.

State-level action has been more concrete. San Francisco and Philadelphia both passed local laws in 2024 banning certain uses of revenue management software that incorporates non-public competitor data for residential rental pricing—directly codifying the theory at the heart of the RealPage litigation at the local level.

The grocery sector has attracted particular scrutiny following consumer complaints about algorithmic pricing by major chains. The FTC has been examining the practice of dynamic pricing—adjusting prices in real time based on demand signals, inventory data, and competitive intelligence—in connection with its ongoing study of grocery pricing and food retail consolidation.

What the Current Legal Landscape Means for Businesses

The state of the law as of mid-2026 can be summarized as follows:

Independent algorithm use is not per se illegal. The Ninth Circuit’s ruling in Gibson v. Cendyn confirms that a company’s independent decision to use the same pricing software as its competitors does not automatically create a Section 1 conspiracy. Parallel conduct without coordination—even parallel conduct that yields similar prices—is not enough.

Sharing competitor data through the algorithm creates serious risk. The critical legal line runs through the “give to get” structure: when the value of using the pricing tool comes from the fact that it pools and shares current, granular, non-public data from competing firms, the risk profile changes substantially. That is the structure the DOJ targeted in RealPage, and it is the structure that the Ninth Circuit flagged as distinguishable from the simple software-license scenario in Cendyn.

The DOJ settlement sets behavioral guardrails. The RealPage settlement—no real-time data, 12-month data lag, no sub-state geographic granularity—is both a resolution of one case and a statement of what the government views as the outer bounds of acceptable algorithmic pricing in a shared-data context. Companies designing revenue management systems in any industry should treat these constraints as a reference point.

Healthcare and financial services are next. The MultiPlan litigation is being closely watched as the first major post-RealPage application of the hub-and-spoke theory in a different industry. A decision sustaining the theory in healthcare would make the framework directly applicable to any industry where a dominant analytics vendor pools confidential competitor data to generate pricing recommendations that are widely adopted by competing firms.

Legislative risk is real. State legislatures have already acted in the rental housing context. As algorithmic pricing spreads to other consumer-facing sectors, similar legislation targeting other industries is a foreseeable risk—particularly in states with active consumer protection legislatures.

Practical Implications for Businesses Using Algorithmic Pricing Tools

If your company uses an algorithmic pricing system—whether for revenue management, competitive pricing, supply chain optimization, or any other commercial pricing function—the following questions should be reviewed with antitrust counsel:

Data sourcing. Does the pricing algorithm use data provided by or about your competitors? Is that data current (real-time or near-real-time)? Is it specific to individual competitors’ transactions, pricing decisions, or capacity data? The more granular and current the competitor data, the closer the system’s design resembles the structure that the DOJ targeted in RealPage.

Data sharing structure. Does the vendor pool data from multiple competing subscribers and use that pooled data to generate recommendations for all subscribers? If the value proposition of the system depends on “give to get”—contributing your data so the system can calibrate against competitors’ data—that structure warrants a careful antitrust review.

Adoption rates. Even a well-designed pricing tool poses elevated risk if it is used by a high percentage of competitors in a concentrated market. High adoption by market participants creates circumstantial evidence of the horizontal coordination that plaintiffs and the DOJ need to establish the “rim” of the hub-and-spoke theory.

Vendor contracts. Your contract with your pricing software vendor should specify exactly what data is collected from your systems, how that data is used, whether it is shared with other subscribers or used in training models alongside other subscribers’ data, and what your rights are to audit the vendor’s data practices. Vendors that cannot or will not answer these questions clearly are vendors who are creating legal risk for their customers.

The RealPage theory is no longer a novel legal argument in a single industry. It is an established enforcement framework with a DOJ settlement, a multihundred-million-dollar private class action, a federal appellate decision, and active litigation in healthcare and other sectors behind it. Companies that have not yet examined their algorithmic pricing arrangements through an antitrust lens should do so before regulators or plaintiffs do it for them.

This post is for general informational purposes only and does not constitute legal advice. Reading this post does not create an attorney-client relationship. If you have questions about your specific situation, consult a qualified attorney.