From rent to retail: New Jersey takes aim at automated pricing

In late July 2026, New Jersey enacted two laws aimed at curbing data-driven pricing practices in residential housing and consumer retail markets. The measures are part of a broader state-level effort to regulate algorithmic pricing and other forms of automated decision-making.

The FAIR Act

On July 20, 2026, New Jersey Governor Mikie Sherrill signed the Forbidding the Algorithmic Inflation of Rent (FAIR) Act into law. Effective July 1, 2027, the Act makes New Jersey the fourth state to expressly regulate algorithmic rent pricing tools.

The FAIR Act prohibits rental property owners from using algorithmic devices or third-party coordinators to perform a “coordinating function.” In practical terms, the Act focuses on tools that collect purported competitively sensitive, nonpublic information from two or more rental property owners, process that information through an algorithm, and use the output to set or recommend rents, material lease terms, or occupancy levels. The Act also prohibits using the competitively sensitive information of another property owner to train an algorithm where the algorithm or other automated process is used to set or recommend rental prices, material lease terms, or occupancy levels. The FAIR Act expressly provides that conduct barred by the Act constitutes a violation of the New Jersey Antitrust Act.

“Competitively sensitive information” under the FAIR Act means “nonpublic information” regarding prices, supply levels, security deposits, ideal occupancy levels, lease contract termination, renewal dates of residential dwelling units, or any other material lease terms. New Jersey’s treatment of nonpublic information differs from the approaches taken in several other state laws. The Act defines nonpublic information as information “not available to the public at no cost” and further provides that the combination of public and nonpublic information renders the entire dataset nonpublic. It remains unclear whether enforcement authorities will distinguish between public information obtained from private, paywalled platforms and information obtained from publicly accessible sources. The broad scope of this definition may be relevant to how regulators and private plaintiffs characterize algorithmic inputs as competitively sensitive. 

By comparison, Connecticut’s HB 8002 defines “nonpublic competitor data” as “information that is not available to the general public,” whether competitor-specific or anonymized and whether obtained directly from or through another competitor in the same or a related market. New York’s S7882 does not distinguish between public and nonpublic data, instead focusing on whether software performs a coordinating function. California’s AB 325 likewise does not turn on the public or nonpublic nature of the data, reaching common pricing algorithms that use competitor data regardless of its source. 

The FAIR Act also arrives amid increased enforcement activity. It follows the New Jersey Attorney General’s pending lawsuit against real estate software provider RealPage and others and is part of a wave of parallel enforcement actions, private class actions, and recent state legislation, including California’s AB 325 and New York’s S7882. Additional rental algorithm bills remain pending in states such as Massachusetts and Pennsylvania. 

The Fair Price Protection Act

Three days after enacting the FAIR Act, Governor Sherrill signed the Fair Price Protection Act into law, complementing the FAIR Act’s regulation of algorithmic rent-setting and the administration’s broader consumer protection initiatives. The Fair Price Protection Act makes it an unlawful practice under New Jersey’s Consumer Fraud Act for retail food stores and third-party grocery delivery platforms to use personal data to determine or vary the prices of groceries and other food products, a practice the Act defines as “surveillance pricing.”  

The Act, however, preserves several common pricing practices, including cost-based price differences that are subject to a once-per-day pricing limitation, discounts available to broadly defined groups such as teachers and veterans, and certain loyalty programs.

Additionally, the Act addresses electronic shelf labels, prohibiting labels connected to technology that determines or varies grocery prices based, in whole or in part, on personal data. It further imposes a one-year moratorium on the new use of electronic shelf labels while the New Jersey Innovation Authority, in consultation with the Division of Consumer Affairs, studies their effects and potential relationship to surveillance pricing.

The Act carries significant enforcement consequences. In addition to injunctive relief, the Attorney General may recover actual damages or statutory damages of $50,000 per violation, whichever is greater, for negligent or more culpable violations, as well as attorney’s fees, filing fees, and litigation costs. The Act’s study provisions took effect immediately upon enactment, while the electronic shelf label provisions become effective on February 1, 2027, and the remaining provisions become effective on August 1, 2027.

The Fair Price Protection Act is the latest development in a rapidly expanding state-level effort to regulate dynamic and surveillance pricing. In April 2026, Maryland became the first state to restrict data-driven grocery pricing when it enacted the Protection From Predatory Pricing Act, and legislators across the country have introduced dozens of similar proposals. 

These developments reflect the growing convergence between antitrust law and artificial intelligence (AI). DLA Piper’s Antitrust and Competition team has deep experience representing clients in litigation and investigations involving alleged algorithmic price fixing, as well as advising on antitrust compliance in the AI ecosystem.

For more information, please contact the authors.