Algorithmic Price-Setting and Competition Law: A Closer Look

Introduction

Imagine searching for an apartment in a major city, only to find that rents across different buildings owned by different landlords are suspiciously similar and climbing in lockstep. Or booking a hotel room and noticing that prices across competing properties move together.

This isn't coincidence. It's the work of pricing algorithms that are reshaping how businesses compete, and regulators are taking notice.

The promise of algorithmic pricing is efficiency. Software can optimise prices to match supply and demand in real time.

The danger is collusion: when competitors feed their sensitive pricing data into the same algorithm, they effectively outsource price coordination to a machine, achieving what would be illegal through direct communication.

What is Algorithmic Price-Setting?

Algorithmic price-setting uses software, including artificial intelligence, to recommend or automatically adjust prices based on market data, consumer information, and competitors' inputs. The technology processes real-time data including supply, demand, historical sales, and user-specific factors such as location or browsing history.

While enabling dynamic pricing in housing, e-commerce, and hospitality, it has attracted scrutiny under competition laws due to risks of coordinated pricing among competitors.

Legal Framework

In the United States, Section 1 of the Sherman Act prohibits agreements among competitors that unreasonably restrain trade, including price-fixing conspiracies. These can occur through direct communication or indirect means, such as sharing competitively sensitive information via third-party algorithms.

In the United Kingdom, the Competition Act 1998 bans anti-competitive agreements, with the Competition and Markets Authority (CMA) enforcing against algorithm-facilitated price-fixing.

In the European Union, the European Union's Artificial Intelligence Act, effective from 2024, empowers regulators to access AI system details for competition assessments.

These frameworks treat algorithmic coordination as equivalent to traditional cartels, requiring proof of intent or effect on competition. Legal practitioners from leading corporate law firms are increasingly advising clients on compliance strategies for algorithm-driven pricing models, reflecting the growing intersection of technology and competition regulation.

Key Cases

USA

In August 2024, the U.S. Department of Justice (DOJ), alongside attorneys general from eight states, filed a civil suit against RealPage Inc., a data analytics firm providing rental pricing software to landlords.

The complaint alleges that RealPage facilitates a hub-and-spoke conspiracy by collecting non-public pricing data from competing landlords, aggregating it, and recommending rents that align prices upward, reducing competition in multifamily housing markets.

The suit claims this practice contributed to $3.8 billion in excess rents in 2023. RealPage defends that its tool merely analyses market data. The case remains ongoing.

In a parallel development, the DOJ reached a proposed settlement with Greystar Management Services, the largest U.S. landlord and a RealPage user. Under the terms, Greystar agreed to cease sharing sensitive information with competitors and to refrain from using algorithms that incorporate rivals' data for pricing recommendations. In August 2025, the Ninth Circuit Court of Appeals in the Gibson v. Cendyn Group, LLC case dismissed antitrust claims against Las Vegas Strip hotels accused of colluding via a shared pricing algorithm. Plaintiffs alleged that inputting data into the common software led to coordinated room rate increases.

The court ruled that mere use of the same algorithm and data sharing with the provider did not suffice; evidence of an agreement to follow recommendations or exchange non-public information was required. This decision establishes evidentiary thresholds in algorithmic cases.

UK The CMA investigated Trod Limited and GB eye Limited (trading as GB Posters), two competing sellers of licensed posters and frames on Amazon's UK platform.

From March 2011 to July 2015, the companies agreed not to undercut each other's prices in specified cases, using automated repricing software to monitor and align pricing, in violation of competition law.

In August 2016, the CMA fined Trod £163,371 (reduced 20% for cooperation) and granted full leniency to GB eye; additionally, Trod's then managing director received a five-year director disqualification.

Regulatory Developments In October 2025, California enacted Assembly Bill 325, prohibiting the development, sale, or use of pricing algorithms that incorporate competitors' non-public data to recommend or set prices in sectors like housing and e-commerce.

The law raised corporate fines for antitrust violations from $1 million to $6 million per violation, or twice the gross gain or loss, and individual fines from $250,000 to $1 million.

On May 9, 2025, New York Governor Kathy Hochul signed the Algorithmic Pricing Disclosure Act (Assembly Bill A3008), effective July 8, 2025.

The law requires businesses using algorithms to set prices based on personal consumer data, such as browsing history or location, to disclose this practice at the point of sale. It prohibits use of sensitive data like health or financial information. Federally, the Preventing Algorithmic Collusion Act of 2025 (S. 232), introduced on February 6, 2025, proposes Sherman Act amendments that would presume anticompetitive effects for pricing algorithms accessing competitors' non-public data.

The bill mandates disclosure of algorithmic pricing practices and bans collusion-facilitating tools. It awaits action in the Senate Judiciary Committee.

Challenges and the Path Forward

Enforcement of competition laws against algorithmic price-setting encounters significant obstacles. The inherent opacity of AI systems hinders regulators and courts from establishing intent or direct causation in anticompetitive outcomes.

Tacit collusion, wherein algorithms autonomously align on supercompetitive prices without explicit human coordination, challenges legal standards for proving "agreements" under statutes like the Sherman Act.

Judicial decisions, such as the Ninth Circuit's ruling in Gibson, confirm that mere adoption of common software does not constitute collusion absent evidence of data sharing or adherence to outputs. As regulatory oversight expands, an experienced AI law firm can help businesses navigate emerging legal risks tied to automated pricing and competition law compliance.

Legal frameworks will require greater transparency, cooperation across jurisdictions, and clearer evidentiary standards to effectively regulate algorithmic pricing.