Ky. Off. of the Att'y Gen., Complaint, Commonwealth v. RealPage, Inc. (filed July 2, 2025)

Citation: Ky. Off. of the Att'y Gen., Complaint, Commonwealth v. RealPage, Inc. (filed July 2, 2025)
Jurisdiction: Kentucky (state)
Source: Official source
Original Source: https://www.ag.ky.gov/Press%20Release%20Attachments/2025.07.02%20Realpage%20C... ↗

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price units incorporating nonpublic data of their competitors, including effective rents and

occupancy rates, all of which allow landlords to raise price with more certainty.

112. As landlords appreciate, AIRM and YieldStar use competitors’ nonpublic data to

predict with more certainty the highest price that the market will bear for a particular unit. A

landlord is therefore less likely to negotiate on price. Any potential negotiation instead turns on

lease term and move-in date, for which AIRM and YieldStar adjust the pricing to avoid

overexposure for the landlord in the future.

113. AIRM and YieldStar also encourage landlords to follow each other in raising

rents. When transactional data reveal that peers are raising effective rents—particularly the

highest and lowest competitors for a given floor plan—AIRM and YieldStar follow with

recommendations to increase rental prices. This movement with the market is ingrained in the
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AIRM and YieldStar models; AIRM and YieldStar will not recommend a floor plan price that

falls below the market minimum.

114. Accordingly, as adoption of AIRM and YieldStar increases among peer

competitors, the use of AIRM and YieldStar can push prices up through a feedback effect. As

peers move up, other AIRM or YieldStar users may move up accordingly.

115. AIRM uses machine learning to train models on competing landlords’ sensitive

data. The parameters learned in this training are then applied to each AIRM client. As a result,

the model uses the same method and learned parameters to generate price recommendations from

the relevant data for each landlord.

116. This aligns and stabilizes prices in at least two ways. First, it reduces volatility in