Antitrust & Other Legal Risks in the Age of Algorithmic Pricing

by: Max Landaw and John Allaire

In recent years, the use of algorithmic pricing (sometimes called “surveillance pricing”) has become more prevalent as companies leverage data-driven systems to optimize prices in real-time. However, the growing reliance on these automated systems has raised significant concerns among regulators about potential violations of antitrust laws, and as we highlighted in a previous post, algorithmic pricing has recently drawn attention from both private litigants and government agencies. While algorithmic pricing itself is not inherently illegal, concerns arise when this practice may facilitate price coordination among competitors, and use of common algorithm vendors may allow prices to be autonomously aligned. 

There have been a number of private civil antitrust complaints filed in federal court alleging that certain providers of algorithmic pricing tools and their users have violated the Sherman Act. A number of those have zeroed in on real property and hotel room rate algorithms. In the recent case, Duffy v. Yardi Systems, a federal Court in Washington state recently allowed claims to proceed against Yardi Systems, where renters alleged the company's software facilitated collusion among property managers to increase rents by sharing non-public pricing data.

Another notable case, In re: RealPage, Inc. Rental Software Antitrust Litigation, has advanced beyond the pleading stage. This is a private class action filed by renters against property owners who use algorithmic pricing to price rent.  What’s striking about this case is that plaintiffs and regulators are also pursuing the  software developers (RealPage) who provide the algorithms which engage in dynamic pricing.  Last month, the Department of Justice filed an amended complaint in its case against RealPage’s software, which it announced in August of last year. The amended complaint claims that six major landlords, collectively managing over 1.3 million units across 43 states and D.C., engaged in an illegal scheme by using RealPage's algorithm (among other things) to reduce competition in apartment pricing, thereby harming millions of American renters. You can read the DOJ’s announcement here.

The FTC has also made the study of surveillance pricing a priority by conducting studies investigating surveillance pricing “aiming to better understand the shadowy market that third-party intermediaries use to set individualized prices for products and services based on consumers’ characteristics and behaviors, like location, demographics, browsing patterns and shopping history.” The staff perspective found that the following types of consumer-facing businesses were among the clients of such algorithmic pricing software providers: grocery stores, apparel retailers, health and beauty retailers, home goods and furnishing stores, convenience stores, building and hardware stores, and general merchandise retailers such as department or discount stores. 

This is clearly an area inquiry that plaintiffs and regulators are going to continue to vehemently pursue and it is not going to go away any time soon. Part of the reason is that plaintiffs and regulators can pursue claims in the domain of algorithmic pricing from a number of angles and legal theories including antitrust, UDAP (unfair, deceptive, and abusive practices), and data privacy (since algorithmic pricing can include profiling based on personal characteristics). We have even seen a Senate bill out of California that aims to reduce algorithmic pricing in its entirety.

In order to mitigate risks, it’s important to understand how your algorithms work and the inputs that go into them. It is very possible for a company to develop or license software to set prices which is unwittingly being used for what the FTC or DOJ would be considered illicit purposes (e.g. highly individualized surveillance or price fixing). As we have seen, according to the DOJ/FTC, plaintiffs do not need to show direct communications between competitors in an algorithmic price-fixing case in order to establish a violation.

Therefore, by conducting thorough due diligence and risk assessments of any AI or algorithmic pricing tools before deployment, companies can make more informed decisions and better assess the costs and benefits of using such tools. Such due diligence should also include an assessment of what types of personal data such software collects and whether such data is shared with competitors.

Originally published by InfoLawGroup LLP. If you would like to receive regular emails from us, in which we share updates and our take on current legal news, please subscribe to InfoLawGroup’s Insights HERE.