RealPage offers revenue and property management software, including tools that multifamily landlords use to set floorplan- and unit-level rents. In 2024, the DOJ and multiple states sued RealPage and several large property managers, alleging that RealPage used nonpublic, competitively sensitive information from competing landlords in its pricing recommendations and discouraged independent pricing decisions, thus facilitating price alignment in violation of § 1 of the Sherman Act. The complaint also alleges that RealPage leveraged its access to a large pool of nonpublic data and imposed contractual terms to entrench its position in violation of the monopolization provisions (§ 2) of the Sherman Act.
On November 24, 2025, DOJ and RealPage agreed to a proposed settlement, subject to court approval, that would set out detailed restrictions on RealPage’s use of nonpublic data, the operation of its pricing tools, and its handling of competitively sensitive information.
The proposed final judgment focuses on two core concepts: runtime operation (how the software generates pricing recommendations) and model training (how algorithmic models are built and refined using data). It would impose different limits on RealPage’s use of nonpublic, competitively sensitive data in each area.
Runtime (live pricing):
Model training (backward-looking only):
Although the settlement generally focuses on the price output, recommendations involving non-price factors like quantity, when based on competitively sensitive data, could also warrant similar scrutiny.
Further, the agreement would bar RealPage from sharing any competitor’s nonpublic, competitively sensitive data through its products, and RealPage advisors would not be permitted to disclose such data when assisting landlords. Going forward, the company would also be prohibited from conducting new “market surveys” (e.g., call-arounds or emails) to collect nonpublic data for use in its recommendations.
The settlement also targets how RealPage’s tools are designed, focusing on features that could steer competing landlords toward aligned pricing. If approved, the settlement would require landlords to remain in control of pricing, meaning “auto-accept” and similar features would need to be configurable and capable of being overridden, and RealPage would not be allowed to incentivize users to adopt its recommended rents. Product features would not be permitted to default in favor of rent increases (for example, by making it easier to move prices up than down).
Although the RealPage settlement and earlier resolutions with two landlord co-defendants offer practical guidance, they do not provide an absolute safe harbor. The state enforcers that joined DOJ’s complaint have not signed onto the settlement and may continue to litigate if they view the relief as insufficient, and multiple private actions involving RealPage and other revenue management tools remain active, with courts already applying divergent approaches in related cases against one such provider.
At the same time, several city and state legislative bodies are moving ahead with their own, potentially stricter, limits on algorithmic pricing. New York State, for example, has enacted a first-of-its-kind statute that broadly prohibits rent-setting software drawing on data from multiple unaffiliated landlords and treats violations as a state antitrust offense, regardless of whether the inputs are public or nonpublic. Similar legislative efforts in other jurisdictions may ultimately impose constraints that go well beyond the DOJ settlement’s terms.
1. Separate what the tool does “in the moment” from how it learns over time.
Under the proposed settlement, RealPage’s real-time rent recommendations cannot incorporate competitors’ current nonpublic data at all, but its models can still be trained on that type of data if sufficiently historic and with careful aggregation and anonymization protections (e.g., encompassing a broad geographic scope). The split between live outputs and model training is a useful signal of where enforcers are headed, even though the rules in this space are still developing.
2. Designing features that support, not substitute for, client decision making.
Features like auto-accept, guardrails, volume or target settings, and price floors or ceilings may draw scrutiny when they make it difficult for users to move away from recommended prices or tend to push prices higher in lockstep. Enforcers are likely to focus not just on what the tool recommends, but how easy it is for users to make a different choice.
3. Build in governance, not just code.
Companies offering or using pricing tools should know what data goes in, how recommendations come out, and what ability users have to exercise independent judgment. They should consider mapping data sources, documenting model logic at a high level, allowing user customization, and engaging counsel on product design and use questions as the tools, and the enforcement landscape, continue to evolve.
Fenwick Associate Vaibs Srikaran contributed to this alert.