How Crackdowns on Rent Algorithms Are Reshaping Multifamily Pricing for Big Landlords

From revenue management to alleged rent cartels

Over the past decade, algorithmic pricing tools promised to bring airline-style revenue management to apartment rents. Products like RealPage’s YieldStar and related services used large datasets, including detailed property-level performance, to recommend daily rent levels for individual units. For many institutional owners, this seemed like a way to optimize revenue, reduce vacancies, and take emotion out of pricing.

That model began to unravel as watchdogs dug into how those tools actually worked. ProPublica’s investigations and later government filings described rent algorithms that ingested highly granular, non-public data from competing landlords and pushed them toward unified pricing strategies in local markets. Instead of simply reacting to supply and demand, critics argued, the software encouraged owners to hold out for higher rents, even if that meant more vacancy — behavior that can resemble a digital cartel when adopted by many large operators in the same submarket.[propublica.org]

Major enforcement moves against the largest landlords

By 2025, those concerns had matured into concrete enforcement actions. In a high-profile settlement highlighted by ProPublica, Greystar — which manages nearly 950,000 apartments nationwide — agreed to stop using RealPage’s algorithmic rent-setting software that federal prosecutors said could violate laws against price-fixing. The agreement bars Greystar from relying on certain rent algorithms and restricts how it can share competitively sensitive information with rivals through third-party platforms.[propublica.org]

At the state level, Oregon’s attorney general announced a proposed multimillion-dollar settlement with Greystar tied to similar rent-fixing allegations. While the dollar figure is modest relative to Greystar’s portfolio, the case underscores a broader shift: states are no longer treating these tools as neutral tech, but as potential conduits for collusion in the housing market.[koin.com]

North Carolina offers another example of how enforcers are targeting both landlords and software-driven coordination. In April 2025, the North Carolina Department of Justice announced a settlement with Cortland Management, one of the state’s largest landlords with more than 5,000 units. Under the consent judgment, Cortland must stop using non-public data from competing landlords to set rents and is barred from using third-party software or algorithms to price apartments unless that use is overseen by a court-appointed monitor.[ncdoj.gov][ncdoj.gov]

States move to limit or ban algorithmic rent-setting

Some states are going beyond case-by-case settlements and writing new rules for the entire rental market. New York’s 2025 legislation, covered by City Limits, bans landlords from using certain algorithm-based software to set rents when that software relies on private, non-public information. Lawmakers argued that these tools function as a form of price-fixing by letting landlords quietly access competitors’ proprietary data — such as renewal rates and concessions — and align rents to maximize profits.[citylimits.org]

The New York law effectively draws a line between using algorithms on your own data and using tools that pool sensitive information across landlords. It does not stop owners from using spreadsheets, basic pricing software, or public market data, but it does prohibit systems that depend on confidential competitor inputs to generate rent recommendations. That distinction is likely to influence how future products are designed and marketed to multifamily operators.

North Carolina’s settlement with Cortland and related litigation involving RealPage send a similar message: you can analyze your own portfolio as aggressively as you want, but you cannot coordinate pricing through a shared, opaque algorithm that uses others’ tenancy and leasing data to move rents in lockstep.[ncdoj.gov][ncdoj.gov]

What this means for investors, operators, and renters

For institutional owners, the immediate impact is higher compliance and litigation risk. Any rent-pricing system that ingests competitor data, recommends unified pricing strategies, or discourages normal competition on concessions now sits under a legal cloud. Contracts with software vendors will need to be revisited, and in some cases abandoned, to avoid alleged collusion. Investors who underwrote deals assuming aggressive, algorithm-driven rent growth may need to revisit their revenue projections.

Operators, especially those with regional scale but not national portfolios, may find that the safest path is a return to fundamentals: market surveys, public listing data, on-the-ground leasing feedback, and transparent revenue management that can be explained to regulators and renters alike. The North Carolina consent judgment even outlines a blueprint for compliant behavior, emphasizing bans on sharing sensitive data, court oversight of any algorithmic tools, and ongoing reporting to enforcement agencies.[ncdoj.gov]

For renters, the direct effect will depend on how widely these tools were actually influencing prices in a given market. Where RealPage-style algorithms were heavily used, limiting or banning them could lead to more variation in pricing, faster competition for vacant units, and somewhat slower rent growth over time. But housing supply, local demand, and zoning rules will still be the dominant drivers of long-term affordability.

The bigger story is that housing enforcement is catching up with the tech stack that underpins modern multifamily operations. As more states mimic New York’s approach or pursue their own cases, large landlords will be forced to choose between opaque algorithmic coordination and a more traditional, competitive approach to setting rents — and regulators have made clear which side of that line they expect the industry to be on.