June 10, 2026
The settlement agreements between the United States and defending parties RealPage1 and Agri Stats2, together with new legislation in California (AB 325) and New York (S. 7882 / A. 1417B), highlight the factors likely to guide assessments of algorithmic pricing (AP) under antitrust law. This emerging framework also emphasizes several important roles for economic analysis.
AP uses data-driven tools, including machine-learning, to set or recommend prices in real time based on information such as demand conditions and competitors’ prices. In principle, AP can intensify competition by making prices more responsive to supply and demand. Combined with greater online visibility of alternatives, this responsiveness can move prices toward competitive levels more quickly. In some cases, however, individual plaintiffs and antitrust enforcers have brought claims under Section 1 of the Sherman Act alleging that a common pricing algorithm functioned as a centralized platform (“hub”) through which competing firms (“spokes”) pooled data to facilitate a price -fixing agreement. They have also alleged that the resulting information exchange harmed competition.
The November 2025 settlement between RealPage and the United States Department of Justice (“DOJ”) provides guidance on federal antitrust enforcement against coordination through AP. In 2024, DOJ sued RealPage, alleging that its data management software served as a mechanism for landlords to coordinate prices. DOJ alleged that landlords fed private data to RealPage’s algorithm, such as lease and occupancy rates, and received price and capacity recommendations. The complaint treated the collective submission of sensitive non-public information to a common pricing system as conduct violating Section 1 of the Sherman Act in the U.S. rental housing market. The use of software rather than interfirm communications was not viewed as a redeeming factor. DOJ also identified software features that limited possible price reductions and increased price alignment, as violations.
The settlement imposes four key constraints:
More recently, the May 2026 proposed settlement between DOJ and Agri Stats addressed alleged anticompetitive information sharing among competing meat processors. DOJ alleged that Agri Stats collected price, output, and cost information from meat processors’ accounting systems, and then pooled, analyzed, and distributed it back to meat processors. Agri Stats allegedly distributed the resulting analyses only among processors, but not to meat buyers. Unlike RealPage, Agri Stats provided a price benchmarking service but not direct individualized price recommendations. According to DOJ, this information exchange enabled processors to sustain above-competitive prices and coordinate output.
The Agri Stats Settlement required Agri Stats to:
State legislation is also defining the boundaries of lawful algorithmic pricing. New York Senate Bill S7882, signed into law in October 2025, prohibits landlords from using software that serves a “coordinating function.” The statute reaches not only rent, but also lease terms and occupancy levels, prohibiting reliance on recommendations from algorithms that pool competitors’ data. It also prohibits entities from performing the coordinating function through software and algorithms. California AB 325 took effect on January 1, 2026, and amended the Cartwright Act to prohibit both the use and distribution of “common pricing algorithms” as part of a conspiracy to restrain trade. It also made it a violation to coerce firms to adopt AP recommendations. Notably, California’s legislation adopts an expansive definition of a common pricing algorithm: it defines “algorithm” broadly as “any methodology,” and does not expressly exempt systems that rely only on public data.
DOJ’s settlements and California and New York legislation highlight how common software platforms that facilitate data sharing can function as a digital cartel. Taken together, these developments suggest that antitrust risk increases when AP is embedded in a hub-and-spoke platform that pools data that is:
Within this framework, economic analysis can play several distinct roles:
Audit algorithm design and inputs. Assess whether data inputs are likely to generate antitrust risk, whether observed pricing rigidities reflect built-in algorithmic constraints or benign factors such as adjustment or menu costs, and whether the pricing algorithm includes mechanisms for information sharing, recommendation, or enforcement.
Analyze adoption and potential coercion. Analyze firms’ adoption of AP recommendations and evaluate possible coercive mechanisms, such as lower search rankings or similar penalties imposed on firms that deviate.
Model counterfactual outcomes. Construct pricing models that exclude rivals’ data, whether public or private, and current or historic. Compare outcomes with models that incorporate rivals’ prices. Higher, more stable, or more synchronized prices may be consistent with coordinated effects.
Identify lower-risk benchmarks. Simulate pricing models trained only on competitors’ lagged data aggregated at the state level and assess how those models respond to broader state-level market trends rather than live competitor behavior in submarkets.
Whether a given algorithm harms competition remains an empirical question. Recent settlements and legislation, however, make increasingly clear which forms of conduct are likely to be treated as antitrust red lines, or, at a minimum, as conduct carrying especially high antitrust risk. Economic analysis can help identify when AP poses higher antitrust risk and measure the impact of any questionable feature.
If you would like to discuss the issues raised in this article, please reach out to Dr. Stuart Gurrea. Dr. Gurrea is a Managing Director at Secretariat and has offered testimony in Federal Court about the statistical identification of economic impacts. Dr. Gurrea participated in the Antitrust West Coast 2026 Conference as a discussant on the panel “Antitrust & Algorithmic Pricing.”
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