The U.S. Department of Justice’s proposed settlement with RealPage in late November offers the first substantive signal from federal enforcers about how they intend to approach algorithmic pricing going forward. For the many industries in the economy today that utilize algorithmic pricing, the biggest takeaway may be that, despite the rhetoric around the original complaint, the settlement itself—both in structure and in substance—does not treat algorithmic pricing as inherently unlawful under U.S. antitrust law.

There was no judicial finding that algorithmic rent-setting violated the Sherman Act. And the agreement left large parts of the company’s business operations intact. When the lawsuit was filed under the prior leadership of the Antitrust Division, many commentators expected this case—arising at the intersection of competition law and artificial intelligence—to test the outer edges of existing doctrines. However, the theory that independent use of sophisticated pricing software could constitute the “contract, combination, or conspiracy” required to violate Section 1 has not taken hold. Earlier this year, the U.S. Court of Appeals for the Ninth Circuit reiterated that using the same vendor or pricing tool does not create an agreement, nor does it transform parallel conduct into collusion.

“Plaintiffs essentially argue for a rule in which a business’ entry into any contract typical in its industry, when followed by higher prices, is sufficient to trigger antitrust scrutiny under the rule of reason as to any other aspect of the business,” the ruling read. “This rule does not comport with the logic of Section 1 or our precedents.” In other words, the court rejected the idea that merely using a common vendor or industry-standard pricing tool—even if prices rise—can substitute for the actual agreement required to establish a Section 1 violation.

Given the current case law, rather than securing sweeping operational restrictions or structural remedies, the DOJ apparently opted for limited concessions in the RealPage case. These biggest changes focus primarily on the age of certain data that pricing tools may incorporate. For many companies, such provisions will not meaningfully alter existing compliance norms. For the DOJ, it is unclear if these conditions will serve as precedent for future enforcement actions or instead represent a pragmatic exit ramp from an aggressive filing that ran into legal headwinds.

For now, the settlement does not indicate that the DOJ will treat algorithmic pricing as inherently suspect. That would have represented a major departure from established doctrine, and to date, courts have been reluctant to adopt such a theory. The agreement instead tracks long-standing antitrust principles: Firms may use analytical tools—including software, machine learning, or predictive model—so long as they make independent pricing decisions and do not rely on competitors’ confidential, real-time information. This approach aligns with how numerous industries already use algorithms to incorporate demand patterns, cost trends, and operational data into pricing without triggering antitrust concerns.

For most companies, the practical implications will be modest beyond confirming existing compliance fundamentals:

• Retain independent pricing authority.
• Avoid sharing sensitive, current competitive data with rivals, directly or indirectly.
• Understand and document the inputs used by pricing tools.

These principles mirror decades of guidance on information exchanges and hub-and-spoke theories. The settlement thus reaffirms these doctrines in the context of modern analytical tools.

While policymakers may continue to target algorithmic pricing, such efforts will likely move toward agreements between firms, not the methods companies use to analyze data that make businesses more efficient. The DOJ’s resolution with RealPage affirms the difficulty of claiming that using software, without more, whether for hotel revenue management or grocery-store demand forecasting, is inherently unlawful.

Ultimately, the RealPage settlement reflects a federal enforcement agency choosing not to push an aggressive, precedential theory in court. Instead, the DOJ obtained moderate concessions that enable it to close a politically visible case without risking a public loss. For the marketplace, the clearest message is that proper algorithmic pricing—used independently and without competitively sensitive data sharing—remains well within the bounds of lawful conduct.

Reprinted with permission from law.com, © ALM Media Properties LLC. All rights reserved.