AI Revenue Management and Antitrust Implications

BY: Rob Stigile

Following the publication of a blockbuster report detailing the use of apartment rent pricing software by some of the nation’s largest multifamily landlords, dozens of plaintiffs have filed suit alleging that use of the software violates section of the Sherman Act. These lawsuits may serve as an invitation for judicial reconsideration of certain antitrust concepts to better align with issues emerging from the 21st Century economy.

Although several of the complaints draw comparisons to pricing software used by the airline industry in the 1990s, the current litigation perhaps better resembles more recent litigation involving the ride-sharing app developer Uber Technologies, Inc. The plaintiffs in the Uber action failed to demonstrate how the app’s services constituted an agreement between competitors that violated antitrust regulations – a challenge that may sink these new lawsuits alleging landlord price fixing.

The software at the heart of this litigation – originally called YieldStar and later rebranded as AI Revenue Management – was developed by property management service provider RealPage, Inc. As detailed in extensive reporting by ProPublica, YieldStar collects a raft of information from its clients detailing real-time apartment leasing activity. It then calculates suggested asking rents and other lease conditions for individual apartments at properties that use the YieldStar service. As a result, property managers have described how the YieldStar recommendations led them to aggressively push asking rents and absorb higher-than-usual vacancy rates, resulting in dramatic increases in revenue.

The plaintiffs in the pending lawsuits claim, in some form or another, that this scheme amounts to unlawful collusion between ostensible market competitors to fix prices and otherwise distort the market for rental apartments. For their part, RealPage maintains that YieldStar “uses aggregated market data from a variety of sources in a legally compliant manner.”

Before being hired as RealPage’s principal scientist in 2004 to develop YieldStar, Jeffrey Roper helped write the price-fixing software that landed several large airlines in trouble. This program allowed airlines to communicate with one another about potential changes to their airfares and service schedules, which may have led to billions in inflated ticket prices.

Despite having the same architect, it does not yet appear that the YieldStar platform facilitates this sort of competitor communication, a potential infirmity that might sink the RealPage litigation in the same way the Uber lawsuit was tossed. As with the Uber lawsuit, the RealPage plaintiffs essentially argue the defendants’ actions constitute a hub-and-spoke conspiracy, by which the landlords (the “spokes”) feed information to RealPage (the “hub”), which then fixes prices for all participants in the cartel. However, without direct communication between the spokes (the “rim” of the wheel), courts have declined to find any violation of antitrust laws.

As highlighted by the failed Uber lawsuit and these current complaints against RealPage, the 21st Century data and algorithm-driven service economy falls into an antitrust void that has been recent source of concern among legal scholars. Non-public market information can be accumulated by a third-party service provider and used to benefit participating competitors, who can insulate themselves by not directly coordinating business activity. Without new legislation, consumers will need to rely on the courts to reconsider their definition of collusive activity in this new economy.


Heather Vogell, Rent Going Up? One Company’s Algorithm Could Be Why, ProPublica (Oct. 15, 2022),

See Complaint, Alvarez et al. v. RealPage, Inc. et al., No. 22-cv-01617 (W.D. Wash. Nov. 10, 2022) for an example of the lawsuits pending against RealPage.

Organisation for Economic Co-operation and Development, Hub-and-Spoke Arrangements – Note by the United States 2 (2019) (describing hub-and-spoke collusion under United States antitrust law).

Cary Coglianese & Alicia Lai, Antitrust by Algorithm, 2 Stanford Computational Antitrust J., 2022, at 5 (describing how the Uber lawsuit failed for lack of a “rim” to the hub-and-spoke conspiracy).

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