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.
A common explanation for high rent prices is the basic economic principle of supply and demand: when there are more renters than available apartments, landlords can charge more because tenants have fewer options. This is especially true in cities where housing construction has not kept up with population growth. For example, in many U.S. markets, the number of renters competing for a limited number of units has pushed prices upward. While this explanation holds some truth, it does not fully account for why rents are higher than they would be in a truly competitive market. The article suggests that other factors, such as corporate power and pricing strategies, also play a significant role in driving up costs beyond what scarcity alone would justify.
Landlords often have an advantage over tenants because moving is expensive, stressful, and time-consuming. Many renters are reluctant to relocate even when rents increase, as finding a new home in the same neighborhood or school district can be difficult. This imbalance gives landlords the ability to raise rents year after year, knowing that most tenants will stay rather than go through the hassle of moving. The article notes that this power dynamic is typical in any market where supply is limited, but it has been amplified in recent years by corporate consolidation and the use of algorithmic pricing tools. These tools allow landlords to coordinate rent increases without needing to collude in person.
RealPage is a company founded in 1998 by Stephen Winn that provides software to landlords for setting rent prices. In 2001, RealPage acquired YieldStar, a tool that uses artificial intelligence (AI) to optimize rental income by adjusting prices based on data such as market trends, competitor rents, and occupancy rates. The software is designed to maximize revenue for landlords by recommending rent increases while resisting decreases. RealPage’s system is used by approximately 80% of the real estate revenue management market for multifamily dwellings in the U.S., controlling data from about 16 million rental units by 2024. Its largest competitor, Yardi, offers a similar tool called RENTmaximiser.
RealPage’s YieldStar algorithm includes a feature called the market minimum effective rent, or hard floor, which sets a minimum price that landlords are advised not to go below. This ensures that rents never fall below a certain threshold, even in a down market. In contrast, the maximum rent acts as a soft ceiling—it is a suggestion rather than a strict limit. The algorithm is programmed to recommend rent prices higher than this maximum when it calculates that a higher price is optimal. This asymmetry allows for fast and frequent rent increases but slow or rare decreases, a dynamic economists refer to as rockets and feathers. For example, if the optimal rent rises from one day to the next, the algorithm recommends the new higher price immediately, but if it falls, the algorithm may recommend holding steady rather than lowering the rent.
One of RealPage’s most controversial features is sold-out mode, which the Department of Justice (DOJ) describes as gouge mode. When a landlord reaches its targeted occupancy for a particular apartment type, the algorithm considers that apartment type sold out, even if units are still physically available. In this scenario, YieldStar recommends setting the rent at 100% of the highest price charged by competitors for the same apartment type, regardless of its previous lower price. Initially, the recommendation was to set the rent at 95% of the highest competitor price, but by 2022, RealPage adjusted the algorithm to push for 100%. This feature ensures that rents remain elevated even when market conditions might otherwise lead to lower prices.
RealPage’s software includes a feature called revenue protection mode, which activates when rental demand is weak. Instead of lowering rents to attract tenants, as basic economic theory might suggest, the algorithm prioritizes maintaining higher revenue by leasing fewer units at elevated prices. This strategy is similar to how luxury goods markets operate, where selling fewer items at higher prices is preferred over selling more at lower prices. However, in the rental market, this approach contradicts the traditional practice of keeping apartments occupied, even at the cost of reduced rents. RealPage admits that this strategy may seem counterintuitive, but it is designed to preserve pricing power during economic downturns. According to a RealPage data scientist, the model really wants to reduce rent but is prevented from doing so by the revenue protection restriction.
RealPage’s dominance in the rental market is the result of aggressive corporate consolidation. As of 2023, RealPage had acquired 26 competing companies and bought its largest rival, Rainmaker LRO, in 2017. This consolidation has left RealPage with only one major competitor, Yardi, which offers a similar pricing tool called RENTmaximiser. By controlling such a large share of the market, RealPage has access to data from approximately 16 million rental units nationwide. The DOJ’s complaint against RealPage highlights how its algorithmic tools, used by landlords controlling up to 70% of apartments in some neighborhoods, have contributed to keeping rents artificially high. This level of market control raises concerns about anti-competitive practices and the lack of transparency in how rents are set.
The founder of RealPage, Jeffrey Roper, began his career in the airline industry, where he developed *revenue management* systems to optimize ticket pricing. Roper worked on dynamic pricing models for airlines like Alaska Airlines, where he helped pioneer techniques to adjust fares based on demand. He later expanded these techniques to other industries, including hotels, rental cars, and cruise lines. Roper’s approach was to bring *rigor* to pricing, replacing intuitive or manual methods with data-driven algorithms. After returning to the U.S., he identified the rental housing market as an untapped opportunity for applying these same techniques, noting that rent is a *nontrivial transaction* representing up to 30% of people’s income. His goal was to professionalize the rental market by introducing algorithmic pricing, which he believed would increase efficiency and revenue for landlords.
