Have you ever wondered why rents climb so relentlessly? Ask any economist or housing scholar, and they’ll often point to a straightforward cause: the housing stock is insufficient, so many renters contend for too few units, pushing prices upward. That part is true. We undeniably need more affordable housing in numerous U.S. markets, and in places where housing is scarce, prices tend to rise. Yet the plain supply-side narrative omits a crucial element that helps explain why housing costs—like many other prices—overshoot what they ought to be: the influence of corporate power.
Landlords have enjoyed the upper hand. Relocating is a burdensome, costly, and time-consuming ordeal, and landlords know it. That knowledge emboldens them to raise rents year after year, betting that most tenants would rather endure the increase than wrestle with the disruption of moving. Even renters who could move might stay put if they can’t locate a suitable option in the same neighborhood or school district. In rental markets, scarcity, as in any market, creates leverage imbalances. And in a seller’s market, many renters simply don’t possess the option to vote with their feet.
In recent years, corporate consolidation and pricing tools driven by algorithms have amplified those imbalances. Landlords no longer need to collude in backyards or at barbecues to fix prices—they can coordinate in the digital realm, aided by a new cadre of real estate tech firms. Your landlord might not be convening with nearby property owners to haggle over gouging strategies over a steak, though who’s to say? Still, they are coordinating—and they’re doing it with software designed to do exactly that.
To hear him describe it, many landlords were barely running real businesses.
Stephen Winn, a blunt Texan with a penchant for data, launched RealPage in 1998. The company rolled out its first real estate software in 2001. The next year, it acquired a budding startup called YieldStar—soon to become the centerpiece of RealPage’s price-coordination capabilities. Two years later, RealPage picked up Re-Opt, and its founder and CEO, Jeffrey Roper, was named president and chief scientist of YieldStar. Roper merged the two systems and began building what would evolve into RealPage’s renowned suite of AI-powered “revenue management software,” which, in plain terms, amounted to price-fixing software.
Jeffrey Roper describes himself as a “numbers nerd.” True, he knows his way around complex programming languages, but his genuine enthusiasm is for something simpler: pure cash. He’s a price-hiking machine whose tenure as the director of “revenue management” at Alaska Airlines—where he helped pioneer dynamic pricing—drew the FBI’s attention—his first encounter with federal investigators. He claimed he didn’t realize the model constituted price-fixing, and he was stunned when federal agents seized his papers. More than twenty years later, that explanation strains credulity.
When I began this book, I knew I had to speak with Roper. We spoke by Zoom in spring 2025. He didn’t fit the image I anticipated. Friendly, soft-spoken, almost unassuming, he wore a blue T-shirt and overalls and sat in front of a coat rack packed with cowboy hats. From time to time he took a calm drag from what looked like a vape pen. He appeared anxious about being misunderstood—or about sounding like a cartoonish villain. After our interview, he followed up by email to say he worried he sounded too flippant. Yet it was evident that Roper viewed himself as a reformer, not a profiteer. He spoke proudly of what he had built and was eager to defend its logic. He is a true believer in the virtue of efficient pricing.
I asked how it all began. He described his career as “a bit of serendipity.” He had spent the 1970s bouncing between the travel and hospitality industries when he discovered a remarkable facility with computers. Airline deregulation was underway, and the major carriers were scrambling to survive without price controls. They had just come off “nineteen years of profitability,” Roper said, “and we were determined to hold on to that and not lose it.” Around him, airlines were merging, folding, or panicking. That’s when American Airlines rolled out a proprietary internal solution called revenue management.
Roper was eager to escape the drudgery of customer service, so when a pair of entrepreneurs approached him about building a new revenue management system, he leapt at the chance. He spent several years “immersed” with them, building what became the “first commercial revenue management solutions.” Unlike American’s private system, this solution could be licensed. Eventually, Alaska Airlines hired Roper to develop their own revenue management platform.
Once inside the industry, Roper was appalled by how airlines priced goods. They were total Luddites. He was in his late twenties when he was invited to join the airlines’ tariff committee. He was thrilled. He imagined: “This is fantastic. I’ll sit with the senior vice presidents of everything—the brains, the gods of Olympus—and learn how true big business runs.”
Then the meeting began.
One airline executive recounted a passenger he’d spoken with during a flight from Spokane, Washington. The passenger complained about a fare increase. “A couple minutes later, [the airline] changed the price.” He was stunned. “There wasn’t a statistical analysis in sight, just a quick glance at traffic and a gut call: ‘We’ll tweak the price by ten bucks.’” Roper added, “You could have deflated me with a pen.” It was then, he said, that he recognized how crude airline pricing truly was—and how much money the industry was leaving on the table. That realization “inspired me to bring rigor to this. There’s a real opportunity here.” That opportunity, he told me, “has motivated me ever since.”
Eventually, worried that Alaska might be bought out, Roper left and teamed up with Bob Cross (another name that will surface again in this narrative) to broaden revenue management beyond airline tickets. He says they “branched out into hotels, rental cars, cruise lines, and then non-transport, hospitality work.” That expansion took him abroad—especially into Central and Eastern Europe.
What thrilled him most was the sheer potential. “In the U.S. and even Europe, we were chasing decimal-point improvements,” he said. “Still producing impressive numbers, but the real excitement came from emerging markets where 5 to 10 percent margins were clearly visible.” In other words, while shaving 0.01 percent off a metric might impress a hedge fund analyst, Roper sought the bigger prize. Emerging markets, with their inefficiencies and unprofessional systems, offered him something far more tantalizing: margin.
When he eventually returned to the United States, he did so with a mission. “I came back looking for any budding markets in the U.S.,” he told me. “There are untapped opportunities out there.” The first of those “opportunities” he pursued was event ticketing, but that quickly collapsed: Ticketmaster already had a chokehold on the market. “It was a volatile challenge,” Roper said. “It’s too risky, and we’d be investing five years to get in. One player is the gatekeeper. The odds of succeeding are slim.” So he pivoted. That’s when he stumbled into multifamily commercial real estate. The potential there exceeded his expectations.
The upside wasn’t purely theoretical. “The value of the transactions is substantial,” he noted. “Even then, a sizable share of people’s income is involved in rents.” In other words: rent itself. And Roper believed he could extract even more value by applying the same dynamic pricing techniques he had perfected for hotel nights and airline seats to this space.
What astonished him was how unregulated—and unsophisticated—the rental market seemed. The process by which tenants were chosen and how their rents were determined was “literally entirely manual and decentralized,” he explained. In other words, humans set rent prices based on intuition or local knowledge, not algorithmic optimization. For someone like Roper, that wasn’t a flaw to fix but an opportunity to exploit.
To hear him tell it, many landlords were barely operating real businesses. “I don’t want to sound condescending,” he said, “but these aren’t graduate-level operations.” They were “mom-and-pop-driven,” often resembling a frat house more than a financial instrument. “You had a parent living in the office collecting rents and handling the basics. That was about the extent of it. It wasn’t viewed as a highly commercial enterprise.” So Roper set out to transform that—professionalize the business of collecting rent and, along the way, to extract more revenue from it.
He pitched the idea as a long runway—three to five years—but with a massive market and a tremendous payoff. Yet the timing wasn’t ideal. It was just after 9/11, and capital had dried up. Convicted that the market was short-sighted, Roper discontinued his own firm and launched a new venture. He spent a couple of years consulting to stay afloat while building his real estate pricing software from scratch. He didn’t rush, taking the time to understand the space and its unique requirements. Then RealPage came calling. They bought the software. They bought Roper. And they kept him on board. “I joined them and stayed there until we took it public.”
RealPage had a plan for that too—and it was called “revenue protection mode.”
The rest, as they say, is history.
As of 2023, RealPage stood as the nation’s dominant provider of property-management software for multifamily housing. In addition to purchasing twenty-six competing firms, RealPage acquired its largest rival, Rainmaker LRO, in 2017, cementing its position as the leading supplier of rent-setting software and leaving only a single substantial competitor: Yardi, which offers a YieldStar-like program under the not-so-subtle name “RENTmaximiser.”
By 2024, RealPage controlled roughly 80 percent of the real estate revenue-management market for multifamily dwellings and held the keys to datasets from around sixteen million rental units nationwide.
According to a report from the investigative outlet ProPublica, this software was so prevalent among large landlords that in some neighborhoods as many as 70 percent of apartments were owned by companies using RealPage’s price-coordination algorithm.
The Department of Justice’s complaint against RealPage opened a rare window into YieldStar’s inner workings, revealing how the system kept rents elevated—even when the market softened. A core feature was the market minimum effective rent, or what the DOJ described as RealPage’s “hard floor.” YieldStar examined all leases for a given floor plan and produced a pricing chart with both a minimum and a maximum rent based on the available data. But court filings show the algorithm was programmed never to suggest rents below the minimum, effectively establishing that floor.
As for the maximum, there was no hard ceiling to match it. YieldStar treated the maximum as a mere target to clear. It was a “soft ceiling,” a suggestion rather than a constraint. The algorithm often proposed rents above the stated maximum for a given floor plan.
Another feature, called “the governor,” tracked day-to-day rent fluctuations and tended to favor increases over decreases. When the optimal price rose from one day to the next, the governor ensured the new, higher price would be recommended. But when calculations indicated a lower optimal price, the model recommended staying put rather than cutting prices, even if doing so would help the landlord.
YieldStar’s recommendations exhibited asymmetry: price hikes were allowed to move quickly, while reductions lagged behind. This created broader distortions in the rental market, a dynamic economists describe as “rockets and feathers.”
Here is how it played out. If you own a gas station and crude oil shoots upward, you raise pump prices immediately—like a rocket. But when crude oil drops, you take longer to pass on the savings—prices drift downward like a feather. Because of YieldStar’s asymmetric programming, rent increases could be fast and frequent, while decreases tended to be slow and rare.
Perhaps the most troubling element of RealPage’s algorithm was “sold-out mode,” which might as well have been called gouge mode. According to the DOJ complaint: “Once a landlord reaches its targeted capacity for a particular floor plan, the model marks that floor plan as ‘sold out’ even if units remain available. In that situation, AIRM and YieldStar recommends the maximum rent charged by competitors, even if the floor plan’s previous price was much lower.” In other words, when the market tightens, YieldStar goes into overdrive.
Initially, sold-out mode was programmed to push rents to at least 95 percent of the highest comparable rent recently observed. By 2022, RealPage decided to aim for the top: the algorithm was instructed to match 100 percent of the leading competitor’s price.
RealPage had a plan for every scenario: a strategy to exploit tight markets, a plan to raise prices quickly and ease them down slowly, and a plan to ensure rents never dipped below a hard floor. But what about an economic downturn, when demand wanes? Surely the algorithm would advocate price reductions, right? Not necessarily. RealPage had a plan for that too—called “revenue protection mode.”
Revenue-protection mode activated when rental demand sagged. In classical economic theory, a softening market should lead to lower prices. Ask any economist what happens when demand slows in the rental sector, and you’ll hear: rents fall. But with RealPage at the helm, that’s not always what occurred.
If the algorithm is the brain of the operation, RealPage’s employees were the brawn.
Instead, revenue protection mode prioritized preserving revenue by leasing fewer apartments at higher prices. This approach mirrors strategies used in luxury goods markets, where selling a smaller number of high-priced items is preferable to moving a lot of cheaper ones. Yet in real estate, the conventional wisdom has long been to keep a full house—even if that means offering concessions to tenants. RealPage flipped that conventional wisdom, aiming to weather downturns by maximizing revenue—even if occupancy would suffer.
Even RealPage acknowledged that this approach “may seem counterintuitive.” But that’s how the system was designed: to preserve pricing power even when market fundamentals argued otherwise. A RealPage data scientist simply put it: “The model really wants to reduce rent but is prevented from doing so by the revenue-protection constraint.”
But couldn’t landlords override these recommendations if they chose to fill vacancies? In theory, yes. In practice, it wasn’t so simple. Its “autoaccept” feature defaulted many landlords into outsourcing pricing decisions altogether. As one RealPage economist explained, they were “trusting the science.”
Landlords who opted into autoaccept agreed to accept any rent recommendations that fell within default thresholds: changes of up to 3 percent daily or 8 percent weekly. There was also a “partial autoaccept” mode, which accepted only the portion of a recommendation that fit within those limits. For example, if YieldStar proposed a 4 percent daily hike, partial autoaccept would implement only the standard 3 percent.
But RealPage’s pricing schemes were only as strong as the participation of landlords. If too many landlords balked and undercut prices, the system’s force faded—that’s where the human element came in. If the algorithm is the brain of the operation, RealPage’s staff were the muscle. They didn’t merely write code; they gathered sensitive data, organized meetings for landlords to share information, and enforced algorithmic discipline.
The core enforcers were RealPage’s “pricing advisers,” assigned to individual landlords to monitor compliance. When a property manager rejected a recommended rent, the adviser reviewed the rejection and its justification. Then, according to the DOJ complaint, “If the pricing adviser disagrees with the rejection or justification, the disagreement is escalated for resolution to a landlord’s regional manager, who typically supervises the property manager.” In other words, RealPage would reach out to your boss to report concerns.
In one of RealPage’s new-client trainings reviewed by the DOJ, a senior pricing adviser described the pricing advisers as part of a system of “checks and balances.” That phrase, to me, sounds less like a software-support forum and more like a mob or drug-trafficking cartel’s brag—“the checks and balances, you know.” Oh, that 250-pound guy in the black SUV tailing you to your next job? That’s simply our system of checks and balances!
One of the advisers’ essential roles was steering landlords toward enabling “autoaccept.” RealPage trained its advisers to persuade skeptical landlords to activate a nearly toothless iteration of autoaccept that did not actually require them to approve daily or weekly adjustments. The logic was straightforward: once landlords opted into autoaccept, even in its weakest form, advisers could begin the real work of selling them on the software’s pricing recommendations. It’s a bit like the moment when timeshare salespeople get you to sit through a presentation; once you’re seated, the hard sell begins.
According to one internal RealPage training document, pricing advisers aimed to build “enough trust so that over time we have clients who are willing to let auto accept run with very wide parameters . . . AKA—accept all recommendations.” Advisers were also instructed to conduct “accountability conversations” with landlords who resisted expanding the autoaccept setting. These discussions served as an opportunity to refresh the company’s goals. As the DOJ complaint notes, RealPage advisers were trained to ask themselves: “[I]f a partner is not ready to use auto acceptance, are they ready for revenue management?” In other words, if you won’t accept RealPage’s prices, perhaps you don’t belong in the club at all.
These groups sure sound less like software support forums and more like a fraternity house for corporate landlords.
RealPage also fostered and encouraged in-person coordination among competing landlords through monthly “user group” gatherings. These meetings were not a joke. Delegates from the country’s largest landlords and property-management firms attended. Sessions covered everything from pricing amenities—rooftop pools, common-area grilling spaces, and so on—to sustaining pricing discipline during crises, such as the COVID-19 pandemic.
Right at the onset of the pandemic, when most Americans stayed home and worried about their health and livelihoods, RealPage hosted a virtual pep rally for landlords. Attendees shared strategies to avoid giving away rent and urged each other to “use transactional market data” to be more aggressive with pricing—because nothing demonstrates solidarity in a national emergency like coordinated rent increases.
User-group meetings frequently featured guest speakers. Yet landlords didn’t simply attend these groups for camaraderie and information; they collaborated on product proposals, voted on ideas, contributed to landlords-only data exchanges, and participated in email threads. These gatherings felt less like software-support forums and more like a fraternity house for corporate landlords.
Beyond user groups and pricing advisers, RealPage ran a call center that conducted more than fifty thousand outreach calls each month to landlords across the United States. The scale was astonishing—roughly a private census for landlords. During these calls, RealPage employees gathered data on occupancy rates, unit types, rents, concessions, and a host of other details about owners and properties. They also confirmed whether the landlord used revenue-management software—YieldStar or, in rare cases, a competitor. According to the DOJ, “these market surveys cover over 11 million units.” Across the nation, landlords were sharing competitively sensitive information with RealPage, helping to grow an ever-larger trove of data to train and fine-tune its algorithm.
Given the sheer scope of this operation, one might wonder whether this was merely high-tech price-fixing or old-fashioned price-fixing wrapped in a more modern package. The answer, in my view, is that RealPage’s scheme was greater than the sum of its parts. The company’s sprawling portfolio, its system of checks and balances, and its data empire enabled price hikes on a scale never before seen. At the same time, the technology gave landlords and property managers something equally valuable: plausible deniability—the sense that they weren’t acting as greedy colluders, merely following smart software.
RealPage both enabled price-fixing and sanitized it. It made participation feel less like conspiracy and more like prudent business. If a rival called me up and proposed collusion, I’d probably dismiss it. But if that same rival claimed their software could boost profits by 15 percent, I might be tempted to sign up.
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From Gouged by Lindsay Owens, published by Viking, an imprint of Penguin Publishing Group, a division of Penguin Random House, LLC. Copyright © 2026 by Lindsay Owens.
