Algorithmic Pricing and Antitrust

Short Answer

Pricing software does not by itself establish an antitrust violation. Risk increases when competitors exchange nonpublic pricing information through a common provider or agree to follow common recommendations. The DOJ’s RealPage complaint and the May 19, 2026 Final Judgment in that case (No. 1:24-cv-00710-WO-JGM, M.D.N.C.) illustrate that theory. A complaint is not proof of its allegations; the entered judgment resolves the United States’ claims against RealPage by consent, without trial or adjudication of liability. Remaining state and landlord-defendant claims were addressed in a September 30, 2026 opinion denying dismissal motions, a pleading-stage ruling rather than a trial liability finding. Business and Commerce Code § 15.05 and federal Sherman Act Section 1 apply according to their elements. Review data sharing, independence and the actual use of recommendations before adopting a shared tool.

Which Laws Apply

Texas AI-specific: Business and Commerce Code § 552.003 (preemption of local AI regulation).

Generally applicable Texas law: Texas Free Enterprise and Antitrust Act, Business and Commerce Code § 15.05; Deceptive Trade Practices-Consumer Protection Act (DTPA) for pricing representations.

Federal: Sherman Act sec. 1, 15 U.S.C. § 1; Federal Trade Commission (FTC) Act sec. 5.

The Theory

Sherman Act Section 1 and Business and Commerce Code § 15.05(a) address agreements restraining trade. A common vendor is relevant to the inquiry but is not itself proof of an agreement. The DOJ’s RealPage complaint alleged pooling nonpublic, competitively sensitive information and features supporting coordinated rents; the entered judgment now bars the specified data uses and constrains the recommendation features described below. Buyers should identify what competitor information enters the system, whether independent decisions remain possible and how departures from recommendations are treated.

The Entered RealPage Judgment

On May 19, 2026, the U.S. District Court for the Middle District of North Carolina entered a Final Judgment resolving the United States’ antitrust claims against RealPage, Inc. (No. 1:24-cv-00710-WO-JGM, ECF 194). The judgment grew out of the Justice Department’s November 24, 2025 proposed final judgment and was entered by consent after Tunney Act public comment and a public-interest finding. Its relief includes: no use of current or historical unaffiliated-property data in the runtime operation of revenue management products; model training limited to historical backward-looking data at least 12 months old and not from active leases; restrictions on recommendation features, including automatic acceptance without user-set parameters, one-sided pricing guardrails, and any impediment to rejecting or overriding recommendations; and appointment of a monitor. The judgment was entered without trial or adjudication and states it is not evidence of or an admission of liability. It terminates only the complaint’s claims against RealPage; it does not resolve the plaintiff states’ claims or claims against other defendants. On September 30, 2026, the court denied motions to dismiss the remaining Sherman Act and related state-law claims against RealPage and several landlord defendants (ECF 223), a pleading-stage ruling that the complaint states claims, not a trial finding of liability.

Other State Measures

The Texas Responsible Artificial Intelligence Governance Act (TRAIGA), Business and Commerce Code § 552.003, preempts political-subdivision regulation regarding the use of AI systems. Whether a pricing ordinance falls within that language depends on its text and the software’s function. Statewide antitrust law and an independently supported antitrust claim require their own analysis.

Practical Steps

Ask what data the tool uses and whether competitors’ nonpublic data feeds recommendations.

Keep pricing decisions independent and documented; do not commit to follow recommendations.

Avoid features that discourage price decreases or align prices with competitors.

Review vendor marketing that promises above-market results through shared data.

Illustrative Example (Hypothetical)

Hypothetical: a Texas apartment owner adopts a pricing tool using public listings and its own leasing data. Managers retain independent authority. Those facts differ from pooling competitors’ nonpublic current lease data and enforcing common recommendations. Ask what information is exchanged, whether an agreement exists and how the recommendations are used; the label AI does not answer the antitrust question.

What Is Unsettled

Agreement, information exchange and competitive effects remain fact-dependent. A complaint, a proposed settlement and an entered judgment have different legal effects. Preserve the data-source and recommendation-use records needed to test the relevant elements.