Algorithmic Pricing Under the Antitrust Microscope: DOJ and FTC Sharpen Their Enforcement Posture

By Alexander L. Bevans and Jason Spitalnick

The Department of Justice (DOJ) and the Federal Trade Commission (FTC) have intensified their enforcement focus on algorithmic pricing tools, advancing the position that automated pricing systems can facilitate price-fixing in violation of federal antitrust law. Companies that use or provide third-party algorithmic pricing software — particularly in sectors like residential real estate, hospitality, and consumer goods — should treat this convergence of federal and state enforcement activity as a prompt to evaluate whether their pricing practices create antitrust exposure.

The enforcement landscape for algorithmic and data-driven pricing tools has reached a new level of clarity — or, more precisely, a new level of regulatory commitment. Both the Department of Justice (“DOJ”) and Federal Trade Commission (“FTC”) have now staked out positions that algorithmic pricing mechanisms can serve as the instrumentality of per se antitrust violations, including horizontal price-fixing, even where no explicit human agreement to fix prices exists. This is not an entirely novel theory, but the pace and coordination of enforcement activity across federal and state regulators in recent months marks a meaningful escalation.

The basic theory is straightforward: when competitors feed proprietary pricing data into a shared algorithmic system — or subscribe to the same third-party pricing software — and that system generates pricing recommendations or, increasingly, sets prices automatically, the result can amount to a tacit unlawful agreement to restrain trade in violation of Section 1 of the Sherman Act. The common sharing of information with the algorithm becomes the mechanism of coordination, and price alignment through the algorithm’s output becomes the unreasonable restraint on trade. What makes this enforcement moment distinct is not the theory itself, which the DOJ has been developing for several years, but the breadth of the agency action and the willingness of enforcers to pursue cases against both the vendors of pricing tools and their customers.

The DOJ’s Antitrust Division has been the most aggressive actor. Its prosecution of RealPage, Inc. — the residential real estate revenue management software provider — established the template: market participants subscribe to the same third-party pricing software; the third-party platform aggregates competitively sensitive data from ostensible competitors, processes it through a pricing algorithm, and outputs rental price recommendations that subscribers adopt at high rates. The DOJ’s complaint alleged that this arrangement constituted an unlawful agreement to align prices, notwithstanding the absence of a smoke-filled room or an explicit compact among landlords. The Department of Justice and RealPage recently reached a proposed settlement agreement. If approved, the settlement would require, among other things, that RealPage cease having its software use competitors’ nonpublic information to determine rental prices.   

The FTC, for its part, has followed the same approach to pricing algorithms as the DOJ. It also, however, is looking at the issue through the lens it describes as “surveillance pricing” — a term broad enough to encompass dynamic pricing, personalized pricing, and data-driven pricing strategies that adjust in response to consumer behavior, market conditions, or competitor activity. The Commission’s interest is not limited to antitrust. It extends to consumer protection, privacy, and civil rights dimensions — whether, for instance, algorithmic pricing tools produce discriminatory outcomes or exploit consumer data in ways that violate Section 5 of the FTC Act. The FTC issued orders to multiple companies in 2024 seeking information about their surveillance pricing practices, and follow-on enforcement appears increasingly likely.

What deserves emphasis is the convergence. The DOJ is pursuing algorithmic pricing as a species of horizontal price-fixing — per se illegal, no rule-of-reason analysis required. The FTC is approaching it from multiple statutory bases, including unfair methods of competition, privacy violations, and price discrimination. State attorneys general in Arizona, California, Colorado, Connecticut, Illinois, Massachusetts, Minnesota, Nevada, North Carolina, Oregon, Tennessee, and Washington participated as co-plaintiffs in the DOJ’s RealPage action or pursued independent enforcement. California’s attorney general indicated that “surveillance pricing” may violate the California Consumer Privacy Act. Nevada secured the first state-level consent judgment against RealPage in September 2025, as discussed further below. The result is a converging threat surface in which a single pricing practice may trigger exposure under federal antitrust law, federal consumer protection authority, and state-level privacy and competition regimes simultaneously.

One of the more consequential features of the current enforcement posture is the willingness to pursue both sides of the algorithmic pricing transaction. The DOJ’s theory does not stop at the software vendor. Subscribers who adopt algorithmically generated pricing recommendations — particularly where adoption rates are high and the underlying data includes competitor inputs — face their own antitrust exposure.

This creates a genuinely uncomfortable compliance problem. A firm that subscribes to a third-party pricing tool may not know, or may not have interrogated, whether the tool incorporates competitor data, how the algorithm generates its recommendations, or what adoption rates look like across the subscriber base. The firm may view the tool as a benign operational efficiency — an optimization product, not a coordination mechanism. That view, however, does not insulate the subscriber from potential enforcement actions if regulators view the practical effect as price coordination among competitors.

For vendors, the exposure is even more direct. A company that designs, markets, and sells a pricing tool that aggregates competitor data and outputs price recommendations is, under the DOJ’s theory, the hub of a hub-and-spoke conspiracy. The vendor need not intend to facilitate price-fixing; it is sufficient that the tool’s architecture and use produce that result. This has obvious implications for the growing universe of AI-driven pricing and revenue management products.

The antitrust dimension is the most developed, but it is not the only one. The FTC’s surveillance pricing framework implicates data collection and use practices that may independently violate consumer privacy expectations and, in jurisdictions with comprehensive privacy statutes, the law itself. The CCPA, for example, requires disclosure of data collection purposes and provides consumers with rights to opt out of the sale or sharing of personal information. If a pricing algorithm ingests consumer behavioral data to personalize prices — a practice the FTC has investigated — the firm deploying that tool must ensure its data practices comply with applicable privacy law, including notice, consent, and purpose limitation requirements. State legislatures have also begun to move beyond disclosure: Colorado’s HB 26-1210, discussed further below, would effectively ban the use of surveillance data to set individualized consumer prices entirely — a prohibition that, if enacted, would be the first of its kind in the United States.

There is also a civil rights dimension that regulators have begun to articulate, though enforcement in this area remains nascent. Algorithmic pricing tools that produce systematically different prices for consumers based on characteristics correlated with race, national origin, or other protected categories could trigger fair lending, fair housing, or broader civil rights scrutiny. The theory is still developing, but it is not speculative — the FTC and several state regulators have signaled interest in precisely this intersection.

The federal enforcement framework does not operate in isolation. Several states have moved — through legislation, enforcement actions, or both — to address algorithmic pricing independently. The following developments in Arizona, Colorado, Nevada, Oregon, Utah, and Washington are particularly relevant.

Colorado. Colorado’s HB 26-1210, which passed the House on a 39-24 vote and is awaiting Senate action, represents the most significant state legislative development in this area. The bill would effectively prohibit the use of surveillance data — including, among other things, an individual’s online habits, preferences, or affiliations — fed through automated decision systems to determine individualized prices for consumers or individualized wages for workers. If enacted, it would make surveillance-based individualized price setting a deceptive trade practice under the Colorado Consumer Protection Act, enforceable by the attorney general.

Two features distinguish the Colorado bill. First, it would impose an effective ban on surveillance-based personalized pricing, not merely a disclosure obligation — making it potentially the first law of its kind in the United States. Second, it extends to employer use of automated systems to set individualized wages, a dimension not addressed by most other state proposals. Governor Polis vetoed a separate algorithmic rent-setting bill (HB 25-1004) in 2025, however, introducing uncertainty about whether HB 26-1210 will be signed even if it clears the Senate.

Nevada. Nevada’s most significant contribution to this area is enforcement-based rather than legislative. In September 2025, Nevada’s attorney general secured the first state-level consent judgment against RealPage, predating the DOJ’s own proposed settlement. The consent judgment permits RealPage to continue using nonpublic data from other properties in its rent recommendations only if that data is at least three months old, anonymized, and aggregated to at least 10 properties. RealPage must also maintain an antitrust compliance program and submit annual compliance certifications for five years. The Nevada settlement’s three-month data-age standard is notably less stringent than the 12-month standard the DOJ later proposed in its federal settlement — a divergence that creates a compliance planning issue for firms operating across jurisdictions, which must satisfy the more restrictive federal standard regardless.

Washington. Washington’s SB 5469 would make it illegal for landlords to use algorithmic rent-setting software that collects and analyzes data from two or more landlords and public or private databases. The bill passed the Washington Senate on a 29-19 vote and cleared the House Housing Committee, but was placed in the Senate Rules “X” file — a procedural hold — in January 2026. It remains alive in the current biennium but is not actively advancing. Washington’s attorney general also joined the DOJ’s original RealPage action as a co-plaintiff, reinforcing the state’s enforcement interest in this area.

Arizona. Arizona’s attorney general was among the state enforcers that pursued independent litigation against RealPage’s algorithmic rent-setting practices. On the legislative side, HB 2847, introduced in the 2025 session, would have prohibited the use of algorithmic pricing incorporating nonpublic competitor data to set residential rental rates and would have created a rebuttable presumption of an antitrust violation subject to rebuttal only by clear and convincing evidence. The bill did not advance out of committee, but new algorithmic pricing and surveillance pricing bills have been introduced for the 2026 session, and the state’s enforcement posture remains active.

Utah. Utah’s SB 293, a Republican-sponsored bill introduced in February 2026, similarly prohibited suppliers from using personal data, biometric data, purchase history, or inferences derived from that information to increase prices. It further required suppliers using automatic pricing systems to retain pricing data for at least one year — a distinct compliance obligation focused on data retention. The bill’s Republican sponsorship is notable, signaling that legislative interest in algorithmic pricing is not confined to Democratic-controlled legislatures. The bill, however, died in the Senate in March 2026.

Oregon. Oregon joined the DOJ’s original RealPage action. On the legislative side, Oregon’s SB 722, introduced in 2025, would bar landlords from using software relying on nonpublic competitor data to set rents. The bill, however, did not pass in 2025. Uniquely, the City of Portland has passed its own ordinance prohibiting the use and sale of algorithmic devices for setting rents or managing occupancy levels in the city. These developments illustrate that legislative action, on some levels, is beginning to keep pace with enforcement activity.

Companies that use or provide algorithmic pricing tools should undertake a targeted assessment of their antitrust and privacy exposure. The assessment should address at least the following:

Whether the DOJ and FTC’s current enforcement posture toward algorithmic pricing ultimately reshapes the market for pricing technology — or whether it produces a more targeted set of precedents limited to the most egregious cases — remains to be seen. The trajectory, however, is unmistakable. Federal and state enforcers have committed to the theory that algorithms can be instruments of antitrust violation, and they are pursuing that theory against both the architects and the users of pricing tools. That momentum is not confined to the federal level: state legislatures and attorneys general are pursuing parallel tracks of enforcement and legislation that, in some cases, impose obligations distinct from or more restrictive than the federal framework. Companies that rely on algorithmic pricing should not wait for a subpoena or civil investigative demand to evaluate whether their practices fall within the regulators’ expanding zone of interest. The time to conduct that assessment is now, while the enforcement framework is still developing and before exposure hardens into liability.