The Rise of Algorithm-Driven Price Fixing: A Threat to Free Market Competition

The Role of RealPage in Rent Setting and Its Legal Implications

If you are renting your home, there is a high chance that your landlord uses RealPage, a company that provides software to help landlords determine the most profitable rent. While RealPage describes its service as merely assisting landlords in setting competitive prices, a series of lawsuits has accused the company of facilitating an AI-enabled price-fixing conspiracy. RealPage’s software collects data from landlords, such as unit prices and vacancy rates, and uses an algorithm to recommend rent increases. This practice has led to allegations that RealPage is essentially acting as a central planner, enabling landlords to raise rents in lockstep, thereby reducing competition and inflating prices.

The lawsuits against RealPage argue that the company has created a system where landlords, by using its software, effectively collude to set high rents without directly communicating with each other. This form of collusion is akin to traditional price-fixing but uses technology as a workaround. RealPage disputes these claims, stating that it merely provides pricing recommendations and that landlords ultimately decide their own prices. However, former employees and legal experts have raised concerns about the company’s enforcement of these recommendations, suggesting that landlords who do not comply may face penalties or even expulsion from the service.

The Spread of Algorithmic Price Fixing Across Industries

The issue of algorithmic price fixing is not limited to the rental housing market. It appears to be spreading across various industries, including health insurance, tire manufacturing, and meat processing. RealPage’s main competitor, Yardi, and its subsidiary, Rainmaker, are also facing similar lawsuits for allegedly facilitating price fixing in the hotel industry. This trend suggests that companies are increasingly relying on third-party algorithms to set prices, potentially bypassing traditional antitrust laws that require evidence of direct agreements between competitors.

The legal challenges in addressing this issue are significant. Current antitrust laws require proof of an explicit agreement between companies to fix prices, which is difficult to establish when algorithms are used. Plaintiffs often face a catch-22: they need evidence of an agreement to proceed, but such evidence is typically only accessible through legal discovery, which requires permission from the court. This hurdle makes it challenging to prove that companies are using algorithms to collude, even when the effects of price fixing are evident.

The Legal Hurdles in Prosecuting Algorithmic Collusion

The legal system is struggling to keep up with the rapid development of algorithmic pricing strategies. In the case of RealPage, plaintiffs were able to proceed after a Tennessee judge rejected the company’s motion to dismiss the case. However, a similar case against Rainmaker in Nevada was dismissed due to insufficient evidence of a price-fixing agreement. This inconsistency highlights the difficulties in applying existing laws to new technologies.

Experts like former DOJ antitrust attorney Richard Powers worry that traditional antitrust laws may not be sufficient to address algorithmic collusion. The concern is that algorithms could enable price fixing on a scale that has never been seen before, potentially undermining the foundations of free-market capitalism. Without robust legal frameworks, consumers may face permanently higher prices, reduced innovation, and decreased competition.

The Potential for Algorithmic Collusion Without Explicit Agreements

Maurice Stucke, a law professor and former antitrust attorney, along with Ariel Ezrachi, has outlined scenarios where algorithms could facilitate price fixing without any explicit agreement between companies. For example, if two companies use different pricing algorithms that learn to collude over time, they could increase prices without direct communication. A study of German gas stations found that when two major players adopted different pricing algorithms, their profit margins increased significantly, suggesting that algorithms can independently lead to price-fixing behaviors.

These scenarios pose a significant challenge to existing antitrust laws, as they do not involve explicit agreements that can be easily proven. This raises the question of whether current legal frameworks are equipped to handle the complexities of algorithmic collusion. If left unchecked, such practices could lead to a "pricing dystopia" where competition is replaced by coordination, resulting in permanently high prices and stifled economic growth.

Legislative and Regulatory Responses to Algorithmic Price Fixing

In response to these challenges, some legislators are proposing new laws to address algorithmic collusion. A bill introduced by Senate Democrats, led by Amy Klobuchar, aims to update antitrust laws to presume price-fixing agreements when competitors share sensitive information through pricing algorithms to raise prices. While this bill is unlikely to become law soon, it reflects a growing recognition of the need for updated legal frameworks.

At the local level, San Francisco has taken a proactive step by passing an ordinance banning the use of software that combines non-public competitor data to set or recommend rents and occupancy levels. This ordinance is the first of its kind and could serve as a model for other jurisdictions. However, the effectiveness of such measures remains to be seen, and it is unclear whether other cities or states will follow suit.

The Future of Free-Market Competition in the Age of Algorithms

The rise of algorithmic price fixing threatens the fundamental principles of free-market capitalism, where prices are determined by open competition rather than central planners or algorithms. If left unchecked, this could lead to a future where inflation is perpetual, innovation is stifled, and consumers bear the brunt of higher prices. The legal system must evolve to address these challenges, but until then, companies will continue to find ways to use algorithms to their advantage, potentially escaping legal scrutiny.

In conclusion, the spread of algorithmic price fixing poses a significant threat to competition and consumer welfare. While some legal and regulatory efforts are underway to address this issue, much more needs to be done to ensure that antitrust laws are equipped to handle the complexities of the digital age. The stakes are high, as the consequences of inaction could undermine the very foundations of our economic system.