What Are Rent-Setting Algorithms?

Landlords today often use computer programs (algorithms) to help decide how much rent to charge. An algorithm is a set of rules or a software tool that looks at various factors (like apartment size, location, demand, time of year) and then recommends a rent price. Companies offer these rent-setting algorithms to landlords as a way to maximize their profits. Instead of manually researching the market or guessing, a landlord can rely on the software’s suggestion for the “optimal” rent. In fact, many big property management firms use these tools for tens of millions of apartments across the country. The goal is usually to charge the highest rent that people are willing to pay. One executive at a rent pricing company even admitted their software encouraged rent hikes that managers wouldn’t normally dare to do on their own .

How Do These Algorithms Track Competitors’ Prices?

These rent algorithms don’t set prices in a vacuum – they pay close attention to what other landlords are charging. The software often gathers data from nearby similar apartments to see the going rates. In some cases, companies even collect private pricing info from many landlords and feed it into a shared system . Using all this market data, the algorithm adjusts its recommendations. For example, if the apartment complex across the street bumps up its rent for a one-bedroom unit, the algorithm at your building might notice that and suggest you could raise your rent too. Conversely, if a competitor lowers prices or offers a special deal, the software will know and might respond (perhaps telling your landlord to also slightly lower rent, or to hold firm if it predicts the competitor’s drop is temporary). This is similar to how airlines or hotels set prices: constantly monitoring what others are doing and then tweaking their own prices accordingly. The key point is that landlords’ pricing software is always watching the competition and reacting to it, much faster and more consistently than any human landlord could.

When Algorithms Collude (Unintended Collusion)

“Collusion” is when businesses secretly cooperate instead of competing, usually to keep prices high. Normally, collusion is illegal – for instance, landlords aren’t allowed to sit in a room together and agree, “Let’s all charge $1,500 for one-bedroom apartments and not go lower.” But with algorithms, something similar can happen without anyone explicitly plotting. Here’s how: Imagine a city where multiple major landlords are all using the same or similar rent-setting algorithm. Because they’re using the same tool (or tools that work in similar ways), they end up following the same playbook. If the algorithm sees strong demand in the city, it might tell every landlord using it to keep rents high. If one landlord’s software raises rent, the others’ software takes note and may raise their rents, too. None of these landlords called each other on the phone to agree on a price, but in effect their software is coordinating their moves.

This phenomenon is sometimes called “algorithmic collusion” or tacit collusion. It’s “tacit” or unintended in the sense that no person sat down and said “let’s form a cartel.” Yet the outcome – uniformly high rents – is similar to what you’d see if there was a secret cartel. Regulators have noticed this problem. The U.S. Department of Justice, for example, alleges that a popular rent pricing algorithm allowed landlords to “align their rents” and avoid competing with each other . Essentially, the software became a middleman that let landlords stick to a high-price strategy together. One former FTC official gave a useful illustration: she said to imagine replacing the word “algorithm” with “a guy named Bob.” If all the landlords gave Bob their private pricing information and Bob told each of them what rent to charge, that would clearly be illegal collusion . If it’s not OK for a person to coordinate prices like that, it’s also not OK when a computer program does it. In short, using fancy software doesn’t magically make price-fixing legal. But many landlords may not even realize this is what’s happening – they just see the software’s recommendations and follow along. The result is a “silent agreement” of high prices: unintended by any single landlord, but very real for renters who have to pay those prices.

Impact on Renters: Rising Costs and Fewer Options

• Higher Rents Across the Board: When landlords coordinate (even unintentionally) on high prices, renters lose the benefit of shopping around. One analysis by the White House’s economic advisers found that algorithmic rent pricing led to renters paying about $70 more per month on average​. That’s roughly a 4% increase in rent – solely due to the pricing software’s effects – which adds up to over $800 extra per year for the same apartment. In 2023 alone, renters nationwide paid an estimated $3.8 billion more in rent because of these algorithm-driven pricing practices​
• Reduced Affordability and Choice: With nearly all landlords following similar high-price signals, renters can’t find a “cheap” option – every nearby landlord is charging top dollar. This makes housing far less affordable. Already, about half of renters in the U.S. spend over 30% of their income on rent and utilities (a common benchmark for affordability).​ If rents are inflated due to algorithmic coordination, more people will be pushed past that 30% threshold. Renters might have to cut other expenses, move farther away, or live with roommates or family just to make ends meet. In other words, the cost burden on renters increases, and those with limited incomes are hurt the most. High, synchronized rents can price some people out of certain neighborhoods entirely, contributing to housing crises and instability for families.

An Alternative Example: Buying Gas

Think of it like this: imagine all the gas stations in town used the same automatic pricing app. Each gas station owner types in some data, and the app tells them what price to put on the pump. The app also checks what all the other gas stations are charging. Now, if one station tries to lower its price to attract more customers, the app will notice and will likely tell the other stations to lower their prices a bit too, so that first station doesn’t get much of an edge. On the flip side, if the app sees that one station can successfully raise prices (say, during a big event or holiday), it will prompt the others to raise their prices as well. In the end, all the gas stations end up with pretty similar, higher prices. No owner ever called up another to agree on a price, but because they all followed the same software, the outcome is as if they had a gentle agreement not to compete. Drivers in that town would find that no matter which gas station they go to, the price per gallon is high and nearly identical.

This is essentially what happens with algorithmic rent pricing. All the landlords using the same (or similar) rent algorithms end up moving prices in unison, like a flock of birds turning together. Renters are like the drivers looking for cheaper gas – they discover that every apartment they visit has high rent, because the landlords’ software has guided them all to keep rents up. It’s as if the landlords had a friendly pact to not undercut each other, even though they never spoke directly. The analogy shows why this is a problem: when businesses should be competing on price but instead start acting in sync (thanks to an algorithm), consumers lose out. In rental housing, that means families paying more for shelter and struggling with affordability, all because of a behind-the-scenes algorithm that unintentionally encouraged a form of collusion.

Algorithmic Collusion Simulation