On October 16, 2025, Governor Kathy Hochul signed into law S.7882, which amends New York’s antitrust law, the Donnelly Act, to prohibit the use of pricing algorithms by residential landlords to set rent prices. The legislation is one piece of a broad legislative package signed by the Governor which is centered on housing policies in New York.
S.7882 prohibits the operation or licensure of any software, data analytics service, or algorithmic device that performs a “coordinating function” on behalf of, or between and among, residential property owners or managers. A software, service, or device performs a “coordinating function” if it does all three of the following:
- Collects historical or contemporaneous prices, supply levels, or lease or rental contract information and renewal dates of residential dwelling units from two or more residential rental property owners or managers;
- Analyzes or processes the information described in category (1) using a system, software, or process that uses computation, including by using that information to train an algorithm; and
- Recommends rental prices, lease renewal terms, ideal occupancy levels, or other lease terms and conditions to a residential rental property owner or manager.
The new law takes specific aim at the use software that involves the “pooling” of data provided by two or more different property owners or managers. The potential for algorithms that use shared competitor data to affect competitive decision making, such as by making pricing recommendations, has caught the attention of antitrust enforcers and policymakers—especially in real estate management markets.
Courts have generally considered the collection of historical competitively sensitive information less likely to impact competition than collection of current data.1 However, the New York law makes no such distinction. The law also goes beyond prior judicial treatment by directly prohibiting rental property owners or managers from either “knowingly or with reckless disregard” basing certain competitive decisions on recommendations from algorithms. Note that the law does not require owners or managers to directly adopt recommendations; merely using the recommendations as a basis for pricing, occupancy, or lease term decisions may be sufficient to establish a violation. The law further makes it unlawful for a third party to operate tools performing a “coordinating function” on behalf of separate rental property owners or managers.
New York is the first state to outright ban the use of pricing algorithms by landlords that use such programs as tools to assist in setting rental prices. The enactment follows several city-wide bans across the country, including in Seattle, Philadelphia, San Diego, San Francisco, Jersey City, N.J., Minneapolis, and Providence, R.I. The New York law comes on the heels of California’s new law on algorithmic pricing, which was signed into law on October 6, 2025.
There are several differences between the New York and California laws regulating pricing algorithms. While New York’s law applies only to residential rental housing and prohibits the use of pricing algorithms in that industry, California’s law applies to any industry and does not directly prohibit the use of algorithmic pricing tools. New York’s law defines a certain category of conduct as anticompetitive, whereas the California law clarifies the standards by which algorithmic pricing cases should be evaluated under its state antitrust law, the Cartwright Act. Specifically, California’s law amends the Cartwright Act to prohibit competitors from using or distributing “common pricing algorithms” in restraint of trade and prohibits coercing other parties to accept recommendations from such algorithms.
S.7882 is New York’s second statewide law regulating algorithmic pricing this year. Earlier this year, the State enacted the Algorithmic Pricing Disclosure Act, which requires that companies that use “personalized algorithmic pricing,” or dynamic pricing derived from algorithms that use consumer data, disclose their use of these tools to consumers. On October 8, 2025, Judge Jed Rakoff of the U.S. District Court for the Southern District of New York dismissed a constitutional challenge to the law, stating that its “compelled disclosure is not rendered ‘controversial’ merely because the regulated entity does not wish to make [the] disclosure or because they would prefer to make a different statement on that same topic.”
S.7882 is set to take effect on December 15, 2025. The emerging diversity of state and federal treatments of recommendation algorithms based on pooled data from algorithm users is an important example of states asserting independent authority to define and address anticompetitive activity. Traditional understandings of risks around such algorithms based on federal court decisions and federal enforcement policies are no longer sufficient. Firms must understand what data any recommendation tool may collect from its users and where and how that data may be used. To ensure compliance with both state and federal law, firms should work with counsel to closely audit their usage and policies around recommendation tools that make use of shared data from other tool users.
[1] See Statement of Interest of the United States in In re Pork Antitrust Litigation, No. 18-cv-01776 (D. Minn. Oct. 1, 2024) (noting that courts have been “especially suspicious when competitors exchange recent, current, or future price or output”). In 2023, the U.S. Department of Justice withdrew a 30-year-old policy statement providing guidance for information sharing, stating it would analyze information exchanges moving forward on a case-by-case basis.
