When Algorithms Became Landlords: The Strange Journey from Innovation to Legislation

When Algorithms Became Landlords: The Strange Journey from Innovation to LegislationDr. David Hatami, Ed.D., Founder & Managing Director, EduPolicy.ai

I’ve spent the past few months watching something genuinely strange unfold across American cities. Seattle passed an ordinance in late June banning algorithmic rent-setting software, imposing penalties up to $7,500 per violation. Berkeley did the same in March, though they’re now in a legal standoff that’s forced them to delay implementation until next year. Providence enacted their ban in May. San Diego followed in April. King County, Washington just passed their version in late September. What catches my attention isn’t just that these bans exist—it’s the pattern itself, the geographic spread, the timeline acceleration, and the question nobody’s really asking loudly enough: how did we get here in the first place?

The conventional narrative frames this as a housing affordability crisis meeting algorithmic price-fixing, which is accurate but incomplete. What interests me more is the trajectory—how a pricing optimization tool that started as a solution to help landlords maximize occupancy rates somehow metastasized into something that multiple attorneys general now characterize as an illegal cartel facilitation mechanism. That transformation didn’t happen overnight, and understanding it matters because we’re going to see variations of this same pattern repeated across other sectors where algorithms touch pricing.

Let me start with what we know factually, because speculation helps nobody. RealPage, the Texas-based company at the center of this controversy, developed software called YieldStar more than a decade ago. The original concept was actually somewhat elegant from a business optimization standpoint: aggregate market data, apply predictive algorithms, and provide landlords with pricing recommendations that would balance occupancy rates against revenue maximization. Jeffrey Roper, who developed the software, has stated publicly that he was concerned from the beginning about antitrust implications and deliberately designed the system to avoid using private competitor data inappropriately.

Here’s where things get interesting—and by interesting, I mean problematic. ProPublica’s 2022 investigation revealed something that should have triggered alarm bells much earlier: in Seattle’s Belltown neighborhood, seventy percent of apartments were managed by property owners using RealPage’s software. Think about that concentration for a moment. When that many competitors in a single geographic market are all receiving pricing recommendations from the same algorithmic source—even if those recommendations are theoretically based on anonymized, aggregated data—you’ve created something that walks, talks, and quacks remarkably like coordinated pricing, regardless of whether any humans ever sat in a smoke-filled room together.

The U.S. Department of Justice clearly agrees. Their August 2024 lawsuit against RealPage doesn’t mince words, alleging that the company operated “an unlawful scheme to decrease competition among landlords in apartment pricing.” California’s Attorney General joined that suit, along with seven other states. The DOJ’s complaint includes internal documents where RealPage executives acknowledged their software helps landlords avoid competing on merit, with one executive noting there’s “greater good in everybody succeeding versus essentially trying to compete against one another in a way that actually keeps the entire industry down.” If you’re looking for evidence of intent, that’s about as clear as it gets.

Now here’s what bothers me most about this entire situation: we had warning signs for years, and the industry response was essentially to double down. Former RealPage CEO Steve Winn told investors that even after the controversial 2017 merger that consolidated the market—a merger that received heightened scrutiny from the DOJ before being approved—the company wouldn’t be big enough to harm competition. Property management executives praised the software in testimonial videos, with one director of revenue management stating “the beauty of YieldStar is that it pushes you to go places that you wouldn’t have gone if you weren’t using it.” Translation: it recommends prices higher than human judgment would naturally set.

The data emerging from various investigations paints a troubling picture. Washington State’s Attorney General estimates that 800,000 leases were priced using RealPage software between 2017 and 2024 in Washington alone. Research suggests that renters in buildings using these algorithms may pay an additional $99 per month—over $1,000 annually—compared to market rates absent algorithmic coordination. In some markets, RealPage captured up to eighty percent market share among available apartment units. At that level of market penetration, you’re not just influencing prices; you’re effectively setting them.

The municipal response has been swift but varied, and that variation tells us something important about local government capacity and risk tolerance. San Francisco led the charge in September 2024, becoming the first city to ban algorithmic rent-setting devices entirely. Their ordinance imposes penalties up to $1,000 per violation. Following San Francisco’s lead, a cascade of cities enacted similar prohibitions throughout 2025: Berkeley in March (though now delayed), San Diego in April, Providence in May, Seattle in June, and most recently King County in September. Each jurisdiction crafted their ordinance slightly differently, reflecting local legal frameworks and political considerations, but the core prohibition remains consistent—no using software that incorporates nonpublic competitor data to recommend rental rates.

The geographic pattern deserves examination. These aren’t random cities—they’re predominantly progressive urban centers with severe housing affordability crises and politically engaged renter populations. Seattle’s median rent sits at $3,000 monthly. Providence was ranked the least affordable metro area in the country in early 2024, with rents rising over sixteen percent year-over-year. San Diego requires a $275,000 salary to afford a median-priced home. These are markets where the housing crisis has created political pressure for action, any action, that might provide relief to struggling renters.

But here’s where I need to push back against simplistic narratives. RealPage isn’t solely responsible for housing unaffordability in these cities. Limited housing supply, restrictive zoning, inadequate construction of new units, high homeownership barriers, wage stagnation, and investor consolidation of rental housing all contribute substantially to the crisis. Algorithmic rent-setting is a meaningful factor—the research suggests it accounts for genuine price inflation—but it’s one variable among many. City councils passing these bans know they’re addressing a piece of the problem, not the whole problem. The question becomes whether the political capital spent on these ordinances diverts attention from harder structural reforms.

RealPage’s response has been aggressive and predictable. In April 2025, they filed a federal lawsuit against Berkeley, arguing the city’s ordinance violates the company’s First Amendment rights by restricting commercial speech. Their attorney, Stephen Weissman, characterized the ban as “sweeping and unconstitutional” and based on “an intentional campaign of misinformation.” The lawsuit appears to have had its intended chilling effect—Portland’s city council pulled back similar legislation after RealPage sued Berkeley, citing the lawsuit as reason to send their ordinance back to committee. Berkeley, facing a $27 million budget deficit, voted unanimously to delay their ban until March 2026 rather than fight an expensive legal battle. 

This litigation strategy raises uncomfortable questions about municipal capacity to regulate technology companies. If a well-resourced corporation can effectively veto local ordinances by threatening expensive First Amendment litigation that cash-strapped cities can’t afford to defend, we’ve created a troubling asymmetry in democratic governance. RealPage has indicated they may sue additional cities, and smaller jurisdictions are clearly watching Berkeley’s experience with concern.

The First Amendment argument itself deserves scrutiny. RealPage contends that their software merely facilitates information exchange—much of it publicly available rental data—and that recommending prices based on mathematical analysis constitutes protected speech. There’s legal precedent supporting some aspects of this argument; commercial speech does receive First Amendment protection, particularly when it involves truthful, non-misleading information. However, antitrust law has long recognized that certain types of information exchange among competitors, even if technically “speech,” can facilitate illegal price-fixing. The question isn’t whether the information exchange is speech—it clearly is—but whether that speech facilitates conduct (coordinated pricing) that violates antitrust law.

So why did this problem escalate to the point where government intervention seemed necessary? I’ve been thinking about this question extensively, and I think the answer involves several intersecting failures: 

First, there was a regulatory lag problem. Algorithmic pricing tools evolved faster than legal frameworks designed for more traditional forms of price coordination. By the time regulators fully understood how these systems functioned at scale, they had already achieved substantial market penetration. 

Second, there was an incentive alignment problem. Individual landlords adopting the software saw improved returns, creating rational incentives to use it despite potential systemic harms.

Third, there was an opacity problem. The algorithmic nature of the recommendations obscured what would have been obvious collusion if humans had explicitly coordinated the same pricing behavior.

But fourth, and I think most importantly, there was a failure of industry self-regulation. RealPage and their clients had opportunities to implement guardrails that might have prevented reaching this crisis point—limiting market share in individual cities, excluding certain types of competitor data, creating more transparency about recommendation methodologies, or establishing clear occupancy floors below which recommendations wouldn’t reduce supply. Instead, the company aggressively marketed the revenue-maximizing aspects of their software while downplaying competitive concerns.

Jeffrey Roper, the software’s original developer, told ProPublica he was “highly sensitized” to antitrust issues and “just don’t do it” regarding misuse of private data. That may have been his original intent, but somewhere between concept and commercialization, those sensitivities appear to have been subordinated to revenue growth imperatives. When your marketing materials promise to help clients “outperform the market 3% to 7%” and executives explain how using competitor data can turn a “$10 increase into a $50 increase for the day,” you’ve moved well beyond neutral market analysis into territory that reasonably raises price-fixing concerns.

The municipal ordinances themselves vary in important ways that reflect different regulatory philosophies. Seattle’s ban prohibits landlords from using rent-setting software but also creates a private right of action, allowing renters to sue for damages. King County’s ordinance similarly allows tenants to seek up to $7,500 per violation plus actual damages and attorney’s fees. San Diego’s version includes specific exemptions for software using only public market data and tools used for affordable housing compliance. Berkeley’s original ordinance was broader, banning any rent-setting algorithm regardless of whether it used nonpublic information, though that breadth may have made it more legally vulnerable.

These variations matter because they’ll shape how courts eventually evaluate these ordinances. Narrower bans focused specifically on nonpublic competitor data sharing have stronger antitrust foundations. Broader prohibitions that might sweep in legitimate market analysis tools face tougher constitutional scrutiny. Cities are essentially experimenting with different regulatory approaches, and we’ll learn from observing which versions survive legal challenge.

The irony here is that algorithmic pricing tools could theoretically serve legitimate, even beneficial purposes in rental markets. More transparent pricing could reduce discrimination in negotiations. Data-driven vacancy management could improve housing utilization. Predictive analytics could help match renters with appropriate units more efficiently. The technology itself isn’t inherently problematic—it’s the application, the market concentration, and the misalignment between optimization objectives and public interest that created the problem.

I keep coming back to a fundamental question: why didn’t federal antitrust enforcement prevent this situation earlier? The DOJ approved RealPage’s 2017 acquisition of competitor LRO after a second-look review. That merger consolidated the market and arguably created the conditions for the current crisis. Perhaps regulators underestimated how algorithmic coordination would function differently than traditional explicit collusion. Perhaps they focused too narrowly on traditional market concentration metrics and missed how shared algorithmic recommendations could achieve similar effects. Or perhaps they were simply understaffed and overwhelmed by the volume of merger reviews.

Whatever the reason, the result is that cities are now attempting to regulate through local ordinances what might have been better addressed through federal antitrust enforcement years ago. This creates inefficiencies—companies face a patchwork of local regulations rather than clear national standards—but it also represents local democracy functioning as intended. When federal systems fail to address problems affecting constituents, local governments step in, even if imperfectly.

Looking forward, I see several possible trajectories. First, RealPage’s litigation strategy may succeed in blocking or substantially delaying these local ordinances, forcing the issue back to federal courts through antitrust litigation. Second, enough jurisdictions may successfully implement bans that RealPage fundamentally changes their business model or exits certain markets. Third, we may see legislative solutions at the state level—California has multiple bills pending—that create more comprehensive frameworks. Fourth, the current federal antitrust case may result in remedies that make local ordinances unnecessary.

What I find most valuable about studying this situation is what it reveals about how technology regulation actually happens, not how we wish it would happen. In an ideal world, we’d have proactive regulatory frameworks that anticipate how new technologies might create harms and establish guardrails before problems metastasize. In reality, we get reactive patchwork responses after harms have already manifested, driven by political pressure from affected constituencies, shaped by municipal capacity constraints and litigation risk calculations.

The broader pattern here extends well beyond rental housing. Algorithmic pricing affects airline tickets, concert tickets, hotel rooms, retail goods, and increasingly, services of all kinds. The fundamental tension between optimization algorithms designed to maximize seller revenue and competitive markets that should benefit consumers exists across all these sectors. We’re going to see variations of this same conflict repeatedly as algorithms become more sophisticated and market penetration deepens.

For government officials reading this—and I know many of you are—here’s what I think matters most:

 First, these ordinances are a reasonable response to a real problem, but they’re not a complete solution to housing affordability. Don’t oversell what they can achieve. 

Second, the legal challenges are serious, and smaller jurisdictions should carefully evaluate their capacity to defend ordinances against well-resourced litigation. Coordination among cities sharing legal costs and strategies might help. 

Third, the most durable solutions will probably need to come from federal legislation or antitrust enforcement that can create uniform national standards.

For technology companies and the people who run them: this is what happens when optimization proceeds without sufficient attention to systemic effects and regulatory risk. RealPage built an effective product that generated substantial value for clients, but they appear to have insufficiently weighted the antitrust risks of market concentration and competitor data sharing. The resulting backlash—federal lawsuits, state lawsuits, private class actions, and municipal bans—now threatens their entire business model. That’s a predictable consequence of prioritizing revenue growth over thoughtful consideration of competitive dynamics.

For citizens and renters: these bans may provide some relief at the margins, but the housing affordability crisis requires much more comprehensive responses. Increased housing construction, zoning reform, tenant protection policies, rent stabilization measures where appropriate, public housing investment, and wage growth all need to be part of the conversation. Algorithmic rent-setting is a real problem worth addressing, but it’s one piece of a much larger structural challenge.

I started this piece saying I’ve been watching something strange unfold. What makes it strange isn’t that cities are banning algorithmic rent-setting—that’s a logical response to an identified harm. What’s strange is that we allowed a situation to develop where this response became necessary. We had a decade to think carefully about how algorithmic pricing tools should function in housing markets, what safeguards were needed, and what market concentration thresholds should trigger concern. Instead, we let market forces run largely unchecked until the political pressure for action became irresistible.

What’s most revealing is how the regulation evolved: not from a sweeping federal law, but from city councils with four lawyers and a broken printer taking on a billion-dollar company. These ordinances are imperfect and reactive, but they are democracy’s rough edge working as designed. Over time, they’ve converged around four shared principles: ban the use of nonpublic competitor data for multi-landlord recommendations; establish safe harbors for public-data tools; add private enforcement mechanisms; and require auditable data provenance. That’s not technophobia—it’s governance.

The federal antitrust case will eventually set precedent. Yet even before that ruling arrives, the market has already changed. Vendors now advertise “public data only” modes. Cities are coordinating on ordinance language. The phrase “algorithmic price coordination” has entered policy vocabulary. And for the first time in a decade, tenants have leverage in a conversation long dominated by software.

This could have been avoided. The DOJ approved RealPage’s 2017 merger that consolidated much of the rent-optimization market. A deeper understanding of algorithmic coordination back then might have kept this from metastasizing. But here we are. The fix, messy as it is, might finally create the conditions for something better—technology that optimizes efficiency without eroding competition. 

References

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Associated Press. (2025a, October 3). Greystar and other landlords agree to $141M deal to settle rent-setting lawsuit. https://apnews.com/article/5a52c3cea866757a508d2efa8e12d0b5

Associated Press. (2025b, April 10). RealPage sues California city officials over rental algorithm ban. https://apnews.com/article/7eed2dc41ad657745f9bdc674386abfd

Axios San Diego. (2024, December 18). RealPage software raises San Diego-area rents, report finds. https://www.axios.com/local/san-diego/2024/12/18/realpage-pricing-software-rent-landlords

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Dr. David Hatami specializes in AI policy development for educational and government institutions. His work focuses on practical implementation strategies that prioritize public service and organizational effectiveness.