Back office ops · Production

DOJ Settles With RealPage Over Algorithmic Rent-Setting Software

The problem

RealPage's algorithmic rent-setting software used nonpublic, competitively sensitive data shared among landlords to recommend rent prices, which prosecutors said enabled landlords to compete less and boost prices in ways that could violate antitrust laws.

First attempt

The software facilitated sharing of nonpublic competitive pricing data among landlords and included features that restricted rent decreases or aligned pricing among competitors, which prosecutors characterized as algorithmic collusion replacing traditional cartel coordination.

Workflow diagram · grounded in source
1
Landlord software adoption
trigger
“algorithmic rent-setting software the tech company sold that prosecutors said enabled landlords to compete less and boost prices in apartment buildings”
2
Nonpublic data collection
integration
“RealPage will stop conducting market surveys to gather such information”
3
Algorithmic rent recommendation
ai_action
“uses nonpublic, "competitively sensitive" data shared among landlords to recommend how much to charge tenants”
4
Landlord pricing output
output
“one landlord told RealPage that it started increasing rents within a week of adopting the software and, within 11 months, had raised them more than 25%”
Reported outcome

RealPage settled with the DOJ, agreeing to stop offering software that uses nonpublic competitively sensitive data to recommend rents, cease market surveys gathering such data, and remove or redesign features that restrict rent decreases or align competitor pricing.

Reported metrics
Rent increase after software adoptionmore than 25%
Time to first rent increase after adoptionwithin a week
Reported stack
algorithmic rent-setting softwarerevenue management software
Source
https://www.propublica.org/article/using-ai-responsibly-for-reporting
Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

RealPage settled with the DOJ, agreeing to stop offering software that uses nonpublic competitively sensitive data to recommend rents, cease market surveys gathering such data, and remove or redesign features that res…

What tools did this team use?

algorithmic rent-setting software, revenue management software.

What results were reported?

Rent increase after software adoption: more than 25%; Time to first rent increase after adoption: within a week (source-reported, not independently verified).

What failed first in this deployment?

The software facilitated sharing of nonpublic competitive pricing data among landlords and included features that restricted rent decreases or aligned pricing among competitors, which prosecutors characterized as algo…

How is this back office ops AI workflow structured?

Landlord software adoption → Nonpublic data collection → Algorithmic rent recommendation → Landlord pricing output.

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