No single party can unilaterally fix rent prices—not landlords working alone, not property management software companies, and not government. However, multiple parties working together through algorithmic systems have been caught doing exactly that, and the consequences are finally catching up. In November 2025, the Department of Justice reached a landmark settlement with RealPage, a major rental pricing software company, after the company used proprietary data from competing property management firms to recommend rents—essentially letting landlords coordinate pricing without ever talking directly. This type of algorithmic collusion affects millions of renters across the country and represents what may be the largest coordinated rent-fixing scheme in modern American history. The question “Can any party fix rent prices?” has a clear legal answer: no.
The FTC confirmed in March 2024 that “price fixing by algorithm is still price fixing,” meaning that doing through software what would be illegal to do in person remains illegal. Yet for years, landlords and property managers operated in a gray zone, arguing that data-driven pricing was just smart business. Recent enforcement actions—including 26 class action settlements totaling $141.8 million against landlords using RealPage’s software—show that regulators no longer accept that argument. The problem persists, but so does the accountability. This article examines who was involved in fixing rent prices, what mechanisms they used, why they got caught, and what safeguards now exist to prevent it from happening again.
Table of Contents
- HOW LANDLORDS USED ALGORITHMS TO COORDINATE RENT PRICES
- THE DATA THAT MADE IT POSSIBLE
- THE CLASS ACTION SETTLEMENTS THAT PROVED THE HARM
- LOCAL AND STATE GOVERNMENTS TAKING ACTION
- WHAT THE DOJ SETTLEMENT ACTUALLY CHANGES
- THE ROLE OF PROPERTY MANAGEMENT COMPANIES AND INVESTORS
- THE FUTURE OF RENT PRICE REGULATION
- Conclusion
HOW LANDLORDS USED ALGORITHMS TO COORDINATE RENT PRICES
For decades, large apartment operators set rents independently, or so they claimed. But starting in the early 2000s, property management software companies like RealPage created tools that promised to maximize rental revenue by analyzing market data. The danger emerged when these tools gained access to proprietary data from competing properties. RealPage, in particular, began using nonpublic rental data from properties managed by competing companies to train its pricing algorithms—creating a system where landlords could effectively coordinate pricing without ever meeting in a room or signing an agreement. The mechanics are straightforward and damning.
A property manager for Greystar would input their rental inventory into RealPage. RealPage would see real-time data from thousands of other properties also using its software, including those managed by Greystar’s competitors. The algorithm would then recommend rents designed to maximize revenue across the entire market, not just for one landlord. This is functionally identical to illegal price fixing—where competitors agree to set prices at a certain level—except the coordination happens through an algorithm rather than a handshake. The DOJ sued six major landlords in January 2025, including Greystar, directly alleging that they used algorithmic software to “work together” to raise rents in violation of antitrust law.
THE DATA THAT MADE IT POSSIBLE
The actual mechanics of RealPage’s system hinged on its access to a goldmine of data: confidential rental information from competing property managers. When a property manager uploaded their portfolio into RealPage’s system, they assumed their pricing and occupancy data would remain confidential. Instead, RealPage used that data to power recommendations for everyone else. This created a circular advantage: the more competitors used RealPage, the better RealPage’s algorithms became at predicting—and influencing—market rents, which only increased demand for the software. This data advantage is worth understanding because it explains why the DOJ settlement focused so heavily on restricting data access. Under the terms of the November 2025 RealPage settlement, the company can no longer use real-time or near-real-time data from competing property managers for pricing recommendations.
It cannot use geographic areas narrower than an entire state level when recommending rents. And any training data used for the algorithm must be at least 12 months old—essentially forcing RealPage to operate in the past rather than in real-time coordination with competitors. Nevada negotiated a separate settlement on similar grounds in September 2025, further limiting RealPage’s data practices within state borders. The limitation here is important: even with these restrictions, RealPage continues to operate. The company is not banned, just constrained. This means the business model persists, property managers will continue using the software, and the theoretical risk of future data misuse remains—it is simply more heavily supervised now.
THE CLASS ACTION SETTLEMENTS THAT PROVED THE HARM
Twenty-six class action settlements have been reached against landlords caught using RealPage’s pricing-fixing algorithms. These settlements represent real money—$141.8 million—paid to tenants who were overcharged as a result of algorithmic coordination. The largest single settlement came in January 2025 when Camden (Mid-America Apartment Communities), one of the nation’s largest landlords, agreed to pay $53 million to tenants in a class action. This settlement came without Camden admitting wrongdoing, a legal formality common in settlement agreements, but the payment itself is an acknowledgment that tenants were harmed. Other major settlements include $48 million paid by Invitation Homes in 2026 as part of an FTC settlement for unfair housing fee practices (which overlapped with their algorithmic pricing conduct), and $7 million paid by Greystar to settle claims in nine states.
Individual landlords have also faced liability in specific states. In Arizona, the state’s Attorney General Kris mayes announced in February 2026 the first settlement in a rental price-fixing case, targeting Weidner Property Management. These settlements collectively prove that landlords knew—or should have known—that using this software to coordinate pricing with competitors was unlawful. What these settlements do not do is fully compensate tenants for the overcharges. The settlements are calculated based on estimated harm, court costs, and attorney fees, but many tenants impacted by years of inflated rents may not receive significant compensation. For those in high-demand markets where RealPage pricing had the most impact, the harm has already been done.
LOCAL AND STATE GOVERNMENTS TAKING ACTION
Local and state governments have begun directly banning the use of algorithmic rent-fixing software. Santa Ana, California became one of the first cities to pass an ordinance explicitly banning automated rent pricing systems. Portland, Oregon; San Diego, California; and Berkeley, California followed with similar bans. These cities recognized that waiting for federal antitrust enforcement was too slow when renters were facing months and years of inflated housing costs.
California took the most aggressive stance when it passed AB 325 in 2026, a state-level law prohibiting the “anticompetitive use of algorithmic pricing software for rent.” This law is broader than just targeting RealPage—it restricts any algorithmic pricing tool that allows landlords in a market to coordinate rents. However, the law contains a critical limitation: it applies only to the use of algorithms that access competitors’ data or that restrict housing supply in some way. Landlords can still use algorithmic pricing for their own properties independently. The challenge for enforcement is proving when an algorithm crosses the line from independent optimization to competitive coordination.
WHAT THE DOJ SETTLEMENT ACTUALLY CHANGES
The November 2025 RealPage settlement is the most comprehensive enforcement action against algorithmic rent fixing to date, and understanding its scope reveals both its strengths and limitations. RealPage is required to cease using real-time and nonpublic data from competing rental management companies when generating pricing recommendations. It cannot derive geographic pricing effects narrower than entire states. Model training data must be at least 12 months old, which effectively prevents the algorithm from responding to current market conditions in ways that competitors might recognize. Yet the settlement leaves major questions unresolved.
RealPage is not required to divest its data or business. It is not required to cease operations entirely. Landlords are not prohibited from using RealPage; they are only prohibited from using it in ways that coordinate with competitors. The burden of enforcement falls on regulators who must monitor RealPage’s practices and landlords’ usage patterns—a challenge when algorithms operate invisibly and settlements are difficult to interpret in real-world practice. A limitation for tenants is that there is no “clawback” mechanism that would force RealPage or landlords to return overcharges to renters across all markets. The settlement applies only to conduct going forward.
THE ROLE OF PROPERTY MANAGEMENT COMPANIES AND INVESTORS
Large institutional investors—including private equity firms that own rental properties—have been central to the price-fixing problem. Companies like Greystar, which manages over 1 million apartment units across North America, have the scale to make algorithmic pricing decisions that affect entire metropolitan markets. When Greystar uses RealPage to set rents, it is not just optimizing one building; it is potentially influencing rents across dozens of cities.
This scale is what made the DOJ’s 2025 lawsuit significant: the department alleged that six major landlords collectively controlled enough rental inventory to meaningfully suppress housing supply and raise rents through coordinated algorithmic use. The settlement and legal actions send a signal to institutional real estate investors: algorithmic pricing coordination is not a gray area anymore. It is illegal antitrust conduct. Investors who continue to use software known to enable coordination with competitors now face clear regulatory and legal risk.
THE FUTURE OF RENT PRICE REGULATION
The trajectory of enforcement suggests that algorithmic rent fixing will become increasingly difficult to execute, though not impossible. More states are likely to follow California’s lead in passing AB 325-style laws. The FTC and DOJ have signaled their commitment to monitoring this space closely. But the larger question is whether these enforcement actions will materially change rents for tenants.
Settlements and restrictions on algorithmic data use do not automatically lower housing costs; they only prevent unlawful coordination that pushed rents higher than competitive market rates would support. Looking forward, expect the rental market to operate in a more fractured way, with some states banning algorithmic pricing outright, others restricting it, and some remaining permissive. This patchwork creates compliance challenges for national property management companies. It also means that in states without strong enforcement, landlords using compliant versions of algorithmic pricing may still enjoy advantages over those who price independently. The competitive rental market is not yet truly competitive again.
Conclusion
No single party can fix rent prices without breaking the law, but multiple parties coordinating through algorithms came close—and did so for years before consequences arrived. The RealPage settlement, class action judgments totaling $141.8 million, and state laws banning algorithmic coordination represent a significant shift in how regulators treat rent pricing. Landlords can no longer hide behind the claim that algorithms are objective market forces immune to antitrust law.
The FTC made that clear in 2024, and enforcement actions in 2025 and 2026 made it unmistakable. Tenants harmed by algorithmic rent fixing have some recourse through settlements, though full compensation remains limited. The more important outcome is deterrence: landlords now know that using shared data and algorithms to coordinate rents is explicitly illegal. For renters moving forward, this means some protection against systematic algorithmic collusion—though it does not solve the underlying affordability crisis, which is driven by insufficient housing supply, not just illegal pricing coordination.