Imagine you need to find a new apartment. You spend months combing Apartments.com, RentCafe, Padmapper, Zillow and other sites. Finally, you find a steal – comfortable, convenient and somehow priced way under the typical cost in your community.
You move in. But months later, your landlord sends you the next year’s lease. Your rent will increase from $1,800 to $2,250 – a 25% hike in a single year.
Such staggering rent increases are all too common – and potentially illegitimate, according to a US Department of Justice (DOJ) lawsuit filed in August 2024. The DOJ accused a company called RealPage of engaging in 21st-century price-fixing schemes to hike rents nationwide. Not only does RealPage allegedly engage in price fixing, but the lawsuit also claims that RealPage prevents landlords from lowering the rent on individual units, even when they are vacant for months at a time.
Currently, RealPage controls 80% of the commercial revenue management market. The company uses algorithmic software to help landlords set prices – but the DOJ believes RealPage is effectively facilitating price-fixing schemes that exacerbate the affordable housing crisis.
A closer look at RealPage’s inner workings not only helps understand what your landlord uses to make decisions. It may also provide an opportunity to create algorithms that can help tenants instead of hurting them. Where are current laws and government action to regulate this technology falling short? Can algorithmic services make rental housing more affordable instead of less?
What is RealPage?
RealPage is a revenue management software company that landlords use to manipulate the price of rent, spiking prices far higher than they would normally be without the use of their software. If you are, based on 2023 estimates, one of the approximately 45 million renters in the country, pay attention. What you’re about to read will disturb you.
RealPage’s software products like AIRM, OneSite, and YieldStar encourage competing landlords to share nonpublic, sensitive information to feed its algorithms. Among other data, it uses your private information such as rent, lease terms and move-in and move-out dates to train its algorithm to produce daily rent recommendations for each landlord’s available units.
RealPage is likely the reason you’re being rent-gouged. Its software helps its landlord clients push rents 3-7% higher than “competitive levels.” The August 2024 lawsuit identified submarkets where landlords sharing sensitive information to inform pricing “have harmed […] competition and thus renters.” Landlords use RealPage products like AIRM, OneSite, and YieldStar in diverse areas across the United States. Some of these include: Washington, DC; Phoenix, Arizona; Atlanta, Georgia; and Nashville, Tennessee to name a few.
RealPage’s actions are devastating because of the immense influence over the rental market it wields. RealPage’s clients control 19.7 million out of 22 million “investment-grade” housing units in the US – almost 90% of the market for multifamily rental housing units – according to a Washburn Law Journal analysis. Their hegemonic control of rent-setting in dozens of local multifamily rental markets eliminates virtually all competition, allowing RealPage to effectively become the de facto market in cities around the country. The use of similar price-setting algorithms poses significant challenges to the natural functioning of healthy markets by encouraging bad actors to drive the prices of essential services, such as housing, to the highest rates possible, often to the detriment of the average person.
According to the DOJ lawsuit, the products tamper with supply and demand and other facets of the rental market. Instead of lowering prices when apartments are vacant, the revenue protection model prohibits the algorithm from suggesting lower rents. Instead, AIRM and YieldStar artificially inject scarcity into the market by recommending that landlords make fewer leases available for higher prices.
RealPage also engages in highly coercive tactics that make it nearly impossible for landlords to decline these recommendations, the DOJ complaint asserts. By default, AIRM and YieldStar have an auto-accept function for rent recommendations. If a landlord tries to decline a recommendation, they are required to justify why they declined it. If the pricing advisor disagrees with the landlord’s justification, they can escalate the matter to the property manager’s supervisor, who they often pressure to accept RealPage’s price recommendations. Even if a landlord wants to charge a lower price, RealPage makes the process extraordinarily difficult, often forcing landlords to submit a justification for charging the lower price.
How Current Antitrust Law Fails to Prosecute RealPage
Despite RealPage’s egregious alleged actions, the high bar for the burden of proof and its design may allow it to evade current antitrust laws. Current antitrust theory has a high standard of proof as the Sherman Act establishes. Demonstrating that companies used an algorithm to set prices does not meet the burden of proof. An agreement needs to have existed between companies before someone requests evidence of it. This creates a predicament where evidence of intentional price-fixing is a requisite (e-mails, internal documents, or even the algorithm) to prove that the crime happened, but the evidence cannot be accessed until someone receives the legal authority to request it. Even with such information in hand, RealPage might not face legal repercussions – some scholars suggest – if courts find that its business model cloaks the firm from prosecution due to the algorithm’s use of data that landlords share to set prices.
Although the standard of proof is high, it does not mean it is impossible to prosecute RealPage. RealPage potentially meets this standard with the multiple in-person and virtual spaces it provides landlords to engage in interdependent pricing, as a group of multifamily housing tenants argued in a 2023 filing against the company. According to this same lawsuit, landlords have multiple opportunities to conspire: online forums, standing committees where landlords advise pricing strategies, and an annual 3-day “RealWorld” conference. If proven true, these particular allegations may constitute “collusive communications.” Though RealPage’s actions are more difficult to detect than a traditional cartel, there is some hope; however, the law needs to catch up to the complicated technology that exists.
Why the Government Hasn’t Acted to Curb RealPage’s Price Fixing
Unfortunately, the federal government does not have the tools to solve RealPage’s price-fixing because it does not set a national affordability standard for non-subsidized housing, only subsidized housing. Currently, HUD sets income limits for its federal rent subsidy programs such as Housing Choice Vouchers, Project Based Rental Assistance, Section 202, Section 811, and public housing. The standard the federal government sets that most closely resembles a national affordability standard is its designation that housing is affordable if Americans spend 30% of their income or less on housing and utilities.
The federal government does not have updated rent data because it relies on surveys that are collected over days and years.
How Algorithms Work and How They Should Work
Although RealPage’s algorithm is trained to raise rents using a combination of different landlords’ pricing information and tenants’ lease information, it is just as easy to train an algorithm to maintain rents at affordable rates for many renters. In plain terms, algorithms are the sum of data and instructions on how to process data. Data that is entered into the algorithm will generate an “end result.” In other words, input in is input out. If you train an algorithm using biased data, it will produce a biased algorithm that builds bias into its results.
Now, examine how an algorithm could potentially lower rents. If someone designs an algorithm to push rents lower for a significant amount of time, then renters will benefit as the market adjusts to lower price trends. If this technology exists, why hasn’t it been used to lower rents? Why hasn’t the federal government created a system that can set affordability in real time and monitor price fixing?
Towards a Better Alternative
To use this technology to lower rents, a series of public and open-source pricing models should be developed for different rental markets. This necessitates multiple approaches at the design level, including pricing models that apply the same price to every product within a given category (linear pricing models), and pricing models that take in more data regarding the characteristics of a product to set the price (nonlinear pricing models). These models’ datasets could be similar to the design of the Home Mortgage Disclosure Act dataset. Pricing transparency rules could be based on different state-level Truth In Lending Laws, such as New York’s, which could directly benefit renters by encouraging price competition between landlords. Given the differences between rental markets, no one algorithm could fit all local markets. Nonlinear pricing models are most likely to be successful in affordable housing markets where there is little variation between the characteristics of individual housing units.
Given these systems’ complex design, federal agencies, housing advocates and university researchers should work together to develop pilots and frameworks to inform data standards used to feed housing pricing models. Localities should receive public investment to advance their infrastructure and develop the expertise required to deliver solutions that can understand the decision-making of renters in their area.
Landlords should also track changes in consumer demand for their properties over time (demand elasticity). When shared with pricing model designers, this data would require landlords to receive renters’ input as prices change.
Although we have discussed how algorithmic pricing poses significant challenges to affordable rental housing, such technologies may soon be adopted in various other parts of the economy. Different companies can use this same technology to increase the price of various products, such as groceries or even concert tickets. Engaging local and federal agencies that focus on consumer protection issues can play an important role in highlighting this problem for both policymakers and the general public. Additionally, antitrust laws must be updated to account for the usage of such algorithms to close existing regulatory loopholes.
Nichole Nelson, Ph.D., is a Senior Policy Advisor at NCRC.
Bakari Levy is a Government Affairs Associate at NCRC.
Photo by turkeychik via Flickr.
