Landlord Settles in Algorithmic Rental Pricing Case

May 29, 2025

Landlord Settles in Algorithmic Rental Pricing Case

Mogin Law reports that Cortland Management LLC, a large landlord with properties across 13 states, has become the first defendant to settle in ongoing federal and state antitrust lawsuits targeting algorithmic rental pricing practices.

The settlement marks a significant shift in the highest-profile case, United States of America v. Realpage, Inc.

Realpage’s rent-pricing software, YieldStar, has drawn scrutiny for allegedly enabling price-fixing among large landlords, but the case highlights broader concerns over how technology is being used in the housing market and raises critical antitrust implications.

In January, the US Department of Justice and several states filed suit against major real estate firms, including Cortland, Greystar, LivCor, Camden, Cushman & Wakefield, Pinnacle, and Willow Bridge. These firms were accused of feeding sensitive rental data into RealPage’s software to set rents in a coordinated, anti-competitive manner.

RealPage, valued at $3.5 billion, faces both government scrutiny and numerous private lawsuits from renters. Some jurisdictions, such as San Francisco, have already banned algorithmic pricing tools like YieldStar in response to concerns about affordability and fairness.

Cortland’s consent decree with North Carolina and Colorado requires it to halt the sharing of private rental data and mandates monitoring to ensure future compliance.

While Cortland admitted no wrongdoing and paid only $100,000 in attorney’s fees, it must assist in the broader case against RealPage. The agreement allows Cortland to use rent-setting software that relies solely on public data.

This outcome may encourage other defendant landlords to settle similarly, potentially isolating RealPage as the case proceeds.

Attorneys may find that settlements like Cortland’s offer a template for negotiated resolutions that avoid liability while aiding enforcement. They must consider advising clients who use AI-based pricing tools to anticipate potential regulatory scrutiny and make adjustments to prevent antitrust exposure.

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