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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“you’ll be pricing your renewals in the dark without insight into actual lease transaction data that
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YS uses to help you make pricing decisions. This is critical to price renewals right[,] especially

in a downturn.”

4. RealPage Revenue Management Software Uses Nonpublic,
Competitively Sensitive Data to Recommend Prices

73. AIRM and YieldStar are built upon similar code and leverage competitive data in

similar ways. LRO, on the other hand, was originally developed outside of RealPage and takes a

different approach.

74. AIRM uses competitors’ nonpublic, transactional data in three separate stages of

the pricing process: (1) model training, (2) floor plan price recommendations, and (3) unit-level

prices. YieldStar uses competitors’ nonpublic, transactional data in stages two and three of its

process.

5. AIRM Model Training Relies on Competitively Sensitive Data to
Generate Learned Parameters.

75. In the first stage, RealPage trains its AIRM models using nonpublic data from

OneSite and other property management software, totaling millions of executed lease

transactions, new lead applications, renewal applications, and guest cards filled out by visiting

potential tenants. This data is run through a machine learning model to generate learned

parameters for supply and demand models that are then used for all AIRM clients across the

country. Like the coefficients in a regression model, the learned parameters are applied to the

data of a landlord’s specific property, and to the data of its competitors, when AIRM makes

pricing recommendations. RealPage generally retrains the models three to four times per year

using updated nonpublic data.

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6. AIRM and YieldStar Incorporate Competitors’ Nonpublic Data to
Generate Floor Plan Price Recommendations.