Pricing software has long been a tool to help businesses analyze market conditions and optimize rates. In recent years, this technology has expanded aggressively into residential real estate, where landlords began using sophisticated algorithms to set rental prices. By 2024, major property management companies across the country were relying on software which pooled confidential pricing data from competing landlords, processed that shared information through algorithms, and generated coordinated pricing recommendations at a large scale. Rather than landlords conducting their own market research or setting rental prices based on local conditions, they could outsource these determinations to software that claimed to maximize revenue.
On Oct. 6, 2025, Governor Newsom signed Assembly Bill (“AB”) 325, amending the Cartwright Act to directly address algorithmic price coordination. The law became effective Jan. 1, 2026, and makes California the most aggressive state in regulating pricing algorithms, across all industries. The legislation makes two specific practices illegal. First, it prohibits using or distributing a common pricing algorithm as part of an agreement to restrain trade. Second, it prohibits using or distributing such an algorithm if the person coerces another to adopt the recommended price or commercial term.
The proponents of AB 325 stated: “It doesn’t matter if price fixing happens behind closed doors or through artificial intelligence, it’s wrong either way. Californians face an affordability crisis, with basic needs like food and housing increasingly priced beyond their means. Unknown to consumers, digital tools are accelerating the “price crisis,” resulting in higher costs and fewer choices. AB 325 updates California’s antitrust laws to address modern technologies being used for illegal price fixing.”
AB 325 defines a common pricing algorithm as any methodology, including software or other technology, used by two or more persons that uses competitor data to recommend, align, stabilize, set, or otherwise influence a price or commercial term. This definition is deliberately broad. The statute does not distinguish between public and non-public competitor data, meaning that even algorithms using publicly available pricing information could violate the law if they facilitate coordination among competitors. The legislation applies across all industries operating in California, not just rental housing.
AB 325 also fundamentally changed how antitrust cases can be litigated in California. Previously, plaintiffs alleging price-fixing had to meet a high pleading standard, demonstrating facts that excluded the possibility that defendants were acting independently. Under the new law, plaintiffs need only show that the existence of a conspiracy to restrain trade is plausible. This relaxed standard makes it substantially easier for cases to survive early dismissal and proceed to discovery, where plaintiffs can obtain internal documents and communications that reveal coordination.
California paired this legislation with Senate Bill (“SB”) 763, which dramatically increases penalties for Cartwright Act violations. Criminal fines for corporations jumped from $1 million to $6 million per violation. Individual violators face penalties up to $1 million per violation, increased from $250,000. The Attorney General and district attorneys can seek civil penalties up to $1 million per violation. These penalties are cumulative, meaning they can be imposed in addition to other remedies available under California law.
The significance of this legislative framework came into focus even before the law took effect, as two large settlements were announced that dealt with algorithmic pricing. On Nov. 18, 2025, California Attorney General Bonta announced a $7 million settlement with property management company, Greystar Management Services LLC. As part of the settlement, Greystar agreed to stop using any software that uses competitively sensitive information to align rent prices.
On Nov. 24, 2025, the federal Department of Justice announced a settlement with RealPage for the company to change its business practices to cease having its software use competitors’ nonpublic, competitively sensitive information to determine rental prices in runtime operation and cease using active lease data for purposes of training the models underlying the software, limiting model training to historic or backward-looking nonpublic data that has been aged for at least 12 months.
The combination of new legislation, federal enforcement, and state prosecution creates new legal risk for landlords and other real estate companies using pricing algorithms. The practical implications extend beyond the real estate industry, as AB 325 can apply to any industry using algorithmic pricing.
The legal landscape continues to evolve rapidly, with enforcement likely to intensify as prosecutors have additional tools in their arsenal to essentially catch up with technology. As artificial intelligence and machine learning become more prevalent in business operations, AB 325, and the federal and state enforcement mechanisms will govern a wide range of commercial activity. For businesses trying to set pricing with algorithms, the combination of AB 325’s broad prohibitions, dramatically increased penalties, and relaxed pleading standards creates substantial compliance obligations that require immediate attention.
Pooja S. Nair is a Partner and Chair of the Food, Beverage, and Hospitality Department at Ervin Cohen & Jessup LLP. She represents clients in real estate litigation and complex business disputes involving contracts, employment, intellectual property, and fraud. Her insights have appeared in Law360, Daily Journal, The New York Times, and the Los Angeles Times. Before joining ECJ, she led the Food and Beverage practice at TroyGould and practiced white-collar defense at Foley & Lardner. She also serves on nonprofit boards and has been recognized as one of Los Angeles’s Top Litigators and Trial Attorneys and Most Influential Women Lawyers by the LA Business Journal.
