The Real Reason the Rent Is Too Damn High: Greed-Driven Corporate Algorithms

Have you ever wondered why your rent is so damn high? Ask any economist or housing expert and they’ll tell you the reason is simple: There’s not enough housing. Too many tenants are competing for too few units, which drives up prices. There is some truth to this. We absolutely need more affordable housing in many US markets, and where housing is scarce, prices do rise. But the simple supply- side story misses something crucial—an explanation for why housing prices, like other prices, are higher than they should be: corporate power.

Landlords have held the upper hand. Moving is a pain—expensive, stressful, and time-consuming—and landlords know it. That’s why they often raise rents year after year, betting that most tenants would rather put up with the hike than deal with the hassle of relocating. Even those willing and able to move may stay put if they can’t find another place in the right neighborhood or school district. Scarcity in rental markets, as in any market, creates power imbalances. And in a seller’s market, many renters simply don’t have the option to vote with their feet.

In recent years, corporate consolidation and algorithmic pricing tools have made those imbalances worse. Landlords no longer need to collude over backyard barbecues to fix prices—they can do it online, aided, as we’ve seen, by a new breed of real estate tech firms. Your landlord might not be gathering the neighborhood’s other landlords to swap gouging strategies over steak (though who’s to say?), but they are coordinating. And they’re doing it with help from software built to do exactly that.

To hear him describe it, many landlords were barely running real businesses.

Stephen Winn, a hard-nosed Texan with a taste for big data, founded RealPage in 1998. The company launched its first real estate software system in 2001. The following year, it acquired a budding start-up called YieldStar—soon to become the crown jewel of RealPage’s portfolio and the backbone of its price- coordination capabilities. Two years later, RealPage also acquired Re-Opt, along with its founder and CEO, the aforementioned Jeffrey Roper, who was promptly named president and principal scientist of YieldStar. Roper merged the two systems and began developing what would become RealPage’s signature suite of AIpowered “revenue management software,” or, to dispense with the Wall Street euphemism, price- fixing software.

Jeffrey Roper describes himself as a “numbers nerd.” Sure, Roper knows his way around complex computer programming languages, but his real passion is simpler: cold, hard cash. He’s a price- hiking machine whose tenure as the director of “revenue management” at Alaska Airlines, where he helped pioneer their model of dynamic pricing, landed him in the FBI’s crosshairs—his first time, anyway. Roper claimed he didn’t realize the model constituted price- fixing, and that he was stunned when federal agents seized his papers. More than twenty years later, that explanation beggared belief.

I knew when I began this book that I had to talk to Roper. We spoke over Zoom in the spring of 2025. He wasn’t what I expected. Friendly, mild-mannered, even humble, he wore a blue T-shirt and overalls and sat in front of a coatrack crowded with cowboy hats. Every so often, he calmly took a drag from what looked like a vape pen. He seemed anxious about being misunderstood—or about sounding like a cartoon villain. After our interview, he followed up over email to say he worried he’d come off as too flippant. But it was clear that Roper saw himself as a reformer, not a profiteer. He was proud of what he’d built and eager to defend its logic. Roper is a true believer in the virtue of efficient pricing.

I asked him how it all began. He told me his career was “a bit of serendipity.” He’d spent the 1970s bouncing around the travel and hospitality business when he discovered a “remarkable proclivity” for computers. Airline deregulation was under way, and the major carriers were scrambling to survive without government-controlled pricing. They had just

come off “nineteen years of profitability,” Roper told me, “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 launched a proprietary in-house solution called revenue management.

Roper was desperate to escape customer service, so when a couple of entrepreneurs approached him about building a new revenue management system, he jumped at the chance. He said he spent several years “immersed” with them, building what became the “first commercial revenue management solutions.” Unlike American’s private system, this one could be licensed. Eventually, Roper was hired by Alaska Airlines to build out their own revenue management platform.

Once inside the industry, Roper was appalled by how airlines set prices. They were total Luddites. He was in his late twenties when he was invited to join the airlines’ tariff committee. He was thrilled. He thought: “This is great. I’m gonna go sit with the senior vice presidents of everything. The brainiacs, right, the gods of Olympus will be there, and I’m going to get to learn how big business really runs.”

Then the meeting started.

One airline executive told a story about a passenger he’d spoken with on a flight from Spokane, Washington. The passenger had complained about a fare hike. As Roper tells it, a “couple minutes later, [the airline] changed the price.” He was stunned. “There was not a statistical analysis in sight, there was just kind of a, you know, ‘Well, how’s traffic doing?’ ‘Yeah, it looks pretty good.’ ‘We’ll change the price by ten bucks or something.’” Roper added: “You could have deflated me with a pen.” It was then, he said, that he realized how rudimentary airline pricing really was—and how much money the industry was leaving on the table. The experience “reinforced my commitment that, man, we need to bring some rigor to this. There’s an opportunity here.” That opportunity, Roper told me, “has inspired me ever since.”

Eventually, worried that Alaska might be acquired, Roper jumped ship. He joined forces with Bob Cross (another name that will resurface later in the book) to expand the use of revenue management systems beyond airline tickets. According to Roper, we “branched more into hotels, rental cars, cruise lines, and then nontransportation, hospitality work.” That expansion took him abroad—most notably to Central and Eastern Europe.

What thrilled him most was the raw potential. “In the US and even in Europe, we were chasing second- decimal precision improvements,” he said. “Still producing huge numbers, but it really wasn’t quite as interesting as going into these emerging markets where you had 5 and 10 percent upsides that were just so clearly visible.” In other words, while shaving 0.01 percent off a metric might impress a hedge fund analyst, Roper was after the bigger prize. Emerging markets, with their inefficiencies and unprofessional systems, offered him something far more tantalizing: margin.

When Roper eventually returned to the United States, he came back with a mission. “I kind of came back looking for any emerging markets in the US,” he told me. “There’s some untapped gems out there.” The first of those “gems” he pursued was event ticketing, but that quickly turned into a bust: Ticketmaster already had a “death grip” on the market. “It was a flammable hurdle,” Roper said. “It’s too risky, and we’re going to invest five years getting in. One player is the gatekeeper. Every chance that you risk it all and lose.” So he pivoted. That’s when he stumbled into multifamily commercial real estate. What he found there took his breath away.

The potential wasn’t just theoretical. “The ticket value of the transactions is significant,” he said. “Even then, 30 percent of people’s income and stuff like that. This is a nontrivial transaction.” In other words: rent. Because the rent is so damn high, there was real money to be made. And Roper believed he could squeeze even more out of the system by applying the same dynamic pricing techniques he had perfected on hotel nights and airline tickets.

What shocked him was how unregulated—and unsophisticated—the rental market seemed. The process by which tenants were being selected, and by which their rent was being determined, was “literally entirely manual and decentralized,” he explained. In other words, human beings were setting rent prices based on intuition or local knowledge, not algorithmic optimization. For someone like Roper, this wasn’t a flaw but a business opportunity.

To hear him describe it, many landlords were barely running real businesses. “I don’t want to sound patronizing or condescending,” he said, “but these are not graduate students.” Operations were “mom and pop driven” and often resembled more of a frat house than a financial instrument. “You had basically a parent living in the office collecting

rents and just kind of doing the basics of looking after things. But that was really it. It wasn’t looked at as a particularly commercial endeavor.” So Roper set out to change that—to professionalize the business of collecting the rent, and along the way, to collect more of it.

He pitched the idea: a long runway, sure—three to five years—but a massive market and a huge opportunity. But the timing wasn’t great. It was just after 9/11, and capital had dried up. Roper, convinced the market was being shortsighted, walked away from his firm and launched his own company. He spent a couple of years consulting to keep the lights on while building his real estate pricing software from scratch. He didn’t rush, making sure he understood the space and its requirements. Then he built the product. RealPage—already on the hunt for a revenue optimization tool after a couple of failed attempts—came calling. They bought the software. They bought Roper. And they kept him on board. “I went with 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 was the country’s dominant property management software vendor for multifamily housing. In addition to acquiring twenty-six competing companies, RealPage bought its largest rival, Rainmaker LRO, in 2017, making it the nation’s top provider of rent-setting software and leaving it with only one sizable competitor: Yardi, which offers a YieldStar-like program with the not-so-subtle moniker “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 approximately sixteen million rental units nationwide.

According to a report from the investigative news outlet ProPublica, this software was so prolific among large landlords that as many as 70 percent of apartments in some neighborhoods were owned by companies using RealPage’s price- coordination algorithm.

The Department of Justice’s complaint against RealPage offered a rare glimpse under the hood of its YieldStar algorithm, revealing how the system kept rents elevated—even in a down market. One of its key features was the market minimum effective rent, or what the DOJ called RealPage’s “hard floor.” YieldStar surveyed all leases for a particular floor plan and generated a pricing chart that included both a minimum and maximum rent based on the available data. But, according to the court filings, YieldStar was programmed never to recommend a rent below the minimum rent, effectively creating that “hard floor” for rent prices.

So what about the maximum rent? Was there a “hard ceiling” to match? Nope. YieldStar treated the maximum rent as a bar to clear. It was merely a “soft ceiling,” a suggestion—not a constraint. The algorithm recommended rent prices higher than the so-called maximum for a given floor plan.

Another feature, “the governor,” was designed to monitor daily fluctuations in rent prices and favor increases over decreases. When the optimal price rose from one day to the next, the governor ensured that the new higher price was recommended. But when the model calculated that the optimal price had dropped, it recommended holding steady rather than pursuing a corresponding decrease, even when it might benefit the landlord.

YieldStar’s pricing recommendations were asymmetric: They allowed for hikes but resisted cuts. This created broader distortions in the rental market, similar to a dynamic that economists call “rockets and feathers.”

Here’s how it works. Say you own a gas station. If the price of crude oil spikes, you raise prices at the pump immediately—like a rocket. But when crude prices fall, you take your sweet time passing the savings along. Prices drift down slowly—like a feather. Because of the asymmetric way YieldStar was programmed, rent increases could be fast and frequent. Rent reductions, if they came at all, were likelier to be slow and rare.

Perhaps the most sinister component 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 considers that floor plan ‘sold out’ even though units may still be physically available. In that situation, AIRM and YieldStar recommends the maximum rent charged by a property’s competitors, even if the floor plan’s previous price was far lower.” In other words, the moment a market tightened, YieldStar kicked into overdrive.

Initially, sold-out mode was programmed to push rent prices to at least 95 percent of the highest comparable rent recently seen. But by 2022, RealPage decided to go for broke: It instructed the algorithm to match 100 percent of the top competitor price.

RealPage had a plan for every scenario: a plan to take advantage of a tight market, a plan to raise prices quickly and drop them slowly, and a plan to ensure rents never fell below a “hard floor.” But what about an economic downturn, when demand was low? Surely then the algorithm recommended rent reductions? Nope. RealPage had a plan for that too—and it was called “revenue protection mode.”

Revenue protection mode activated when rental demand was weak. According to basic economic theory, lower demand should lead to lower prices. Ask any economist what happens in a sluggish rental market and they’ll finish your sentence for you: Rents fall. But in the real world, if your landlord used RealPage, that’s not always what happened.

If the algorithm is the brain of the operation, RealPage’s employees were the brawn.

Instead, revenue protection mode—according to the DOJ—prioritized maintaining revenue by leasing fewer apartments at higher prices. This strategy is common in luxury goods markets, where selling fewer handbags at higher prices is more desirable than selling many at lower ones. But in real estate, the conventional wisdom has long been to “keep heads in beds”—even if that means cutting a tenant a deal. RealPage flipped that mantra. Its goal was to weather downturns by maximizing revenue, even if that sometimes meant reduced occupancy.

Even RealPage admitted that this “may seem counterintuitive.” But that’s how the system was designed, to preserve pricing power, even when market fundamentals suggested otherwise. One RealPage data scientist put it plainly: “The model really wants to reduce rent but is prevented from doing so by the revenue protection restriction.”

But couldn’t landlords override these recommendations if they wanted to fill units? In theory, yes. In practice, it wasn’t so easy. Its “autoaccept” feature defaulted many landlords into outsourcing their pricing decisions altogether. As one RealPage economist put it, they’re “trust[ing] the science.”

Landlords who opted in to autoaccept agreed to accept any rent recommendations

that fell within default thresholds: up to 3 percent daily or 8 percent weekly price changes. There’s also a “partial autoaccept” mode, which accepted only the portion of a recommendation that fit within those limits. For instance, if YieldStar recommended a 4 percent daily hike, partial autoaccept would implement only the default 3 percent.

But RealPage’s pricing schemes didn’t work if too many landlords defected and undercut prices—that’s where the muscle came in. If the algorithm is the brain of the operation, RealPage’s employees were the brawn. They weren’t just writing code. They were collecting sensitive data, convening landlords for information-sharing sessions, and enforcing algorithmic discipline.

The key 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 the justification. Then, according to the DOJ’s complaint, “If the pricing advisor 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 called your boss to snitch.

In one of RealPage’s new client trainings reviewed by the DOJ, a senior pricing adviser referred to the pricing advisers as part of a system of “checks and balances.” Sure sounds to me a lot more like something you’d hear from the mob or a drug-trafficking cartel than a boring apartment rental transaction. Oh, that 250-pound guy in the black SUV following you to your next job? That’s just our system of checks and balances!

One of the pricing advisers’ most essential functions was pushing landlords to default into “autoaccept mode.” RealPage trained its advisers to persuade skeptical landlords to enable a nearly toothless version of autoaccept that did not actually require them to accept any daily or weekly rent adjustments. The logic was simple: Once landlords selected into autoaccept mode, even in its weakest form, pricing advisers could begin the real work of convincing them to accept the software’s pricing recommendations. It’s a little like those timeshare salespeople who just want you to sit through a slideshow. Once they get your butt in the chair, they know they can start the hard sell.

According to one of RealPage’s internal training materials, pricing advisers aimed to build “enough trust so that over time we have client[s] that are willing to let auto accept run with very wide parameters . . . AKA—accept all recommendations.” Advisers were also trained to have “accountability conversations” with landlords who resisted using—or expanding—their autoaccept setting. These conversations were an opportunity to give recalcitrant landlords a “refresher” on company goals. As the DOJ complaint puts it, RealPage advisers were trained to ask themselves: “[I]f a partner is not ready to use auto acceptance, are they ready to use revenue management?” In other words, if you weren’t going to accept RealPage’s prices, maybe 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 facilitated and encouraged in-person coordination between competing landlords through monthly “user group” meetings. These meetings were no joke. Representatives from some of the country’s largest landlords and property management companies attended. Topics ranged from how to price amenities—rooftop pools, communal barbecues, that sort of thing—to how to maintain pricing discipline during moments of crisis, like the COVID-19 pandemic.

At the very start of the pandemic, while most Americans were hunkered down, frightened for their lives and worried about job loss, RealPage hosted a virtual pep talk for landlords. Attendees shared strategies to not “give away the farm” on rent and encouraged each other to “use transactional market data” to “be more aggressive” with pricing—because apparently nothing says solidarity in a national emergency like coordinated rent hikes.

User group meetings often featured guest speakers. But landlords didn’t just attend user groups and happy hours to learn and swap information. They also collaborated on proposals to improve RealPage’s products. They voted on suggestions, participated in email threads, and contributed to landlords- only data exchanges. These groups sure sound less like software support forums and more like a fraternity house for corporate landlords.

In addition to user groups and pricing advisers, RealPage operated a call center that made more than fifty thousand calls each month to landlords across the United States. The scale was astonishing—something like a private Census Bureau but for landlords. During these calls, RealPage employees collected data on occupancy rates, floor plans, rent levels, concessions, and a slew of other details about owners, managers, and properties. They also made sure to ask 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 country, landlords were feeding RealPage competitively sensitive information, helping it amass an ever- larger trove of data to train and fine-tune its algorithm.

Given the human scale of this operation, it’s fair to ask if this allegation was truly an example of high-tech price-fixing, or just old-fashioned price-fixing dressed up in fancier clothes. The answer, I think, is that RealPage’s scheme was bigger than the sum of either its human or algorithmic parts. The company’s sprawling portfolio, its system of “checks and balances,” and its data empire made it possible to coordinate price hikes on a scale never seen before. But the technology also gave landlords and property managers something equally valuable: cover. It gave them plausible deniability—the sense that they weren’t acting like greedy price-fixers, just doing what the smart software said.

RealPage both enabled price-fixing and sanitized it. It made participating feel less like a conspiracy and more like good business. If a competitor called me and said, “Hey, let’s collude fix prices,” I’d probably tell him to get lost. But if that same competitor told me he was using new software that increased profits by 15 percent, I might ask where 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.