The Arguments Against RealPage Aren’t Real, Part Two
Part One of this series described the August 2024 antitrust lawsuit filed by the U.S. Department of Justice (DOJ) against Texas-based RealPage, a Texas-based developer of revenue-management (RM) software for rental housing, which accused RealPage of unlawfully enabling collusion among property managers in order to reduce competition in local rental markets and thereby raise costs for apartment renters. Part One also showed that the DOJ had failed to demonstrate the first of two traditionally recognized elements of antitrust violations—namely, anticompetitive intent. As will be shown presently, the DOJ also has not satisfied the second traditional antitrust standard, specifically, that RealPage’s actions have resulted in anticompetitive outcomes.
Anticompetitive Outcomes
There are four primary reasons why the DOJ’s lawsuit fails this second antitrust test:
First, the monopolistic price outcomes asserted by the DOJ lawsuit requires monopolistic power—something that RealPage demonstrably does not have. Specifically, a monopoly in the rent-recommendation software market, which RealPage arguably does possess, is not the same as monopoly power in the rental market. While Washington, D.C. authorities claim that more than 90 percent of the large apartment buildings in the D.C. metropolitan area were priced using RealPage software, the nation’s capital is the exception: Across the United States, only 7 percent of all rental rates—and only around 30 percent in major metropolitan areas—are set with RealPage products. That is not even remotely monopolistic power. As one analyst explains, “If the vast majority of landlords don’t use any RealPage products, its clients couldn’t possibly have enough market power to act as a price-setting cartel reaping above-market returns … You can only reap monopoly rents when you actually have a monopoly.”
Second, revenue optimization is not the same thing as monopolistic price-fixing. Revenue optimization in rental housing is nothing more than a technique to maximize profits, not to maximize rents, by setting rental rates that balance the supply of rental housing with the demand. Indeed, rather than being uniquely anticompetitive behavior, revenue optimization and revenue management are common practices across industries. Nor is RealPage’s “dynamic pricing”—the practice of changing prices in real time in order to achieve a supply/demand balance—a malicious practice. In fact, according to the Harvard Business School, “[W]hen implemented well, [dynamic pricing] can be an efficient means for companies and customers to share value” with customers. Such algorithmic, real-time pricing is routinely employed in a wide array of business sectors, from airlines to entertainment and sporting venues to hotels and ride-sharing.
Third, not only does RealPage not possess monopolistic market power, but it also lacks a monopoly on the data that underlies its rent-recommendation algorithms. While RealPage does use some nonpublic information, two-thirds of its data comes from publicly available sources, and the limited private data it uses is anonymized and aggregated, making it impossible for users to identify its specific data competitors. RealPage also allows customers to remove nonpublic data from its algorithms if they desire. Significantly, even without RealPage’s algorithms property managers still could set profit-maximizing rents. As the online publication Slate explained, “Finding the maximum price for a unit is a big landlord’s job. They do it every day. Whether there’s a RealPage or not, they will find all the information they can to make that decision, even if that means paying for it.”
Fourth, RealPage software’s use does not necessarily lead to higher rents. As one report emphasized, “landlords aren’t in the business of maximizing rents. They’re in the business of maximizing profits.” And maximizing profits can require different responses depending upon market conditions, such as increasing rents to take advantage of rising demand or, alternatively, reducing rents to try to retain tenants when demand falls. In fact, academic research suggests that, because rent-recommendation algorithms provide additional information that otherwise would remain unavailable, they actually make landlords more willing to either raise or lower rents as market conditions might dictate. On this point, one research study concluded that, “[d]uring the Great Recession (2008- 2010), adopters of the algorithm lowered rents and increased occupancy compared to non-adopters in the same submarket and building class.” For example, Dallas and Phoenix have lower rental prices than in San Francisco and Los Angeles, but Dallas and Phoenix actually have more rental units owned by landlords using RealPage products than the two California cities do.
The bottom line: Not only is anticompetitive intent not borne out in reality (despite the DOJ’s claims to the contrary), neither are anticompetitive outcomes.
Next up, Part Three: Why Rental Costs Are Really Rising