Case: In re RealPage, Inc., Rental Software Antitrust Litigation (No. II), Case No. 3:23-md-03071, MDL No. 3071 (M.D. Tenn.)
Enforcement Action: United States et al. v. RealPage, Inc., No. 1:24-cv-00710 (M.D.N.C.)
Overview
For decades, proving horizontal price-fixing required showing competitors meeting in backrooms, exchanging phone calls, or signing explicit market-allocation agreements. The rise of machine learning software has upended that paradigm.
Multi-million-dollar class action settlements exceeding $350 million, alongside enforcement actions by the U.S. Department of Justice and state attorneys general, have targeted revenue-management software platforms. The core issue is no longer whether corporate executives made a handshake deal, but whether feed-in algorithms using nonpublic competitor data constitute an illegal “hub-and-spoke” conspiracy under Section 1 of the Sherman Act.
For commercial entities, real estate management firms, and corporate boards leveraging third-party data analytics tools, this litigation creates a crucial compliance threshold: delegating pricing autonomy to an algorithm carries the same antitrust exposure as a direct competitor agreement.
The Underlying Dispute
The litigation centers on RealPage’s yield-management software, which uses proprietary algorithms to generate daily rental rate recommendations and occupancy targets for residential property managers across major metropolitan markets.
Plaintiffs and federal regulators allege that RealPage functioned as an algorithmic pricing “hub” connecting horizontal competitors (“spokes”). Property managers fed nonpublic, real-time transaction data, including actual lease prices, concessions, and occupancy metrics, into the platform. The algorithm combined this proprietary data to output inflated pricing recommendations. To ensure compliance, RealPage employed pricing advisors who nudged property managers to accept recommendations and restricted individual price-outs.
The defendants argued that property managers retained final pricing authority, that recommendations were non-binding, and that using software to optimize revenue reflects rational, independent business behavior in a digital economy. However, federal courts rejected early motions to dismiss, ruling that delegating pricing variables to a shared third-party intermediary can satisfy the concerted-action requirement of antitrust law.
What the Courts & Settlements Established
The rulings and settlement momentum across MDL No. 3071 establish two structural shifts in commercial litigation:
- Algorithms count as horizontal agreement mechanisms. A formal agreement directly between competitors is no longer necessary to prove a Section 1 violation. If competing firms knowingly pool nonpublic data into a common algorithm that coordinates market pricing, courts will treat the arrangement as an actionable horizontal conspiracy.
- “Non-binding” recommendations offer limited liability shielding. Defendants argued that property managers frequently rejected algorithmic rates. The courts ruled that total adherence is not required; the deliberate pooling of sensitive data to influence market-wide pricing floor levels is sufficient to establish antitrust exposure.
The practical result: institutional defendants are agreeing to significant monetary payouts and commitments to overhaul or disable nonpublic data-sharing features within revenue software platforms.
“This litigation completely dismantles the shield of automated decision-making,” said Rafael Zahralddin, Managing Partner of QPWB’s Wilmington office and Leader of the firm’s National Business Law & Commercial Litigation Practice Group. “If your pricing model relies on a software black box that ingests confidential data from your competitors, you haven’t automated efficiency, you’ve automated antitrust liability. For corporate leaders, assuming software vendors absorb this risk is a critical misconception.”
The Unresolved Compliance Horizon
While major property management firms continue resolving private class claims, broader regulatory enforcement remains active. The DOJ and federal courts are closely examining whether algorithmic pricing tools inherently chill competitive price discovery, even when software vendors claim data is anonymized or aggregated.
This distinction matters far beyond commercial real estate. Parallel class actions and regulatory inquiries are expanding into hospitality pricing engines, airline distribution systems, freight logistics routing, and retail inventory management.
“The central question for corporate counsel isn’t limited to real estate or property management,” added Zahralddin. “It applies across supply chains, logistics, and retail. Courts are scrutinizing vendor contracts to examine whether data feedback loops restrict price competition. Corporate governance programs must evaluate third-party software tools with the same rigor used for direct competitor communications. Many CFOs and credit managers have been placed squarely into the breach of this issue, as their responsibilities have been expanded into other areas of compliance, including antitrust; this is a key new development for them.”
How This Affects Defense Strategies Moving Forward
For corporate boards, risk management officers, and commercial entities utilizing automated pricing, dynamic yield engines, or third-party market aggregators, this litigation highlights several operational defense steps:
- Audit third-party software data inputs: Determine whether your software vendors feed nonpublic, competitor-supplied transactional data into their predictive models. If a vendor’s algorithm relies on confidential data from competitors in your market, the software poses immediate regulatory exposure.
- Maintain verifiable pricing autonomy: Software outputs must function purely as informational references rather than mandates. Document instances where internal managers reject or deviate from algorithmic suggestions based on local market factors to demonstrate independent business decision-making.
- Review vendor indemnification and liability clauses: Many commercial software agreements exclude liability for regulatory or antitrust violations. Corporate clients should re-examine vendor contracts to ensure clear representations regarding data sourcing and compliance with state and federal competition laws.
- Establish algorithmic compliance protocols: Implement compliance guidelines governing how pricing teams interact with third-party software representatives. Pricing advisors or vendor representatives attempting to enforce rate recommendations can be interpreted as enforcing a horizontal agreement.
Key Takeaways
- Courts treat the pooling of nonpublic competitor data into pricing algorithms as actionable horizontal coordination under federal antitrust law.
- Operating with non-binding recommendations or retaining final approval authority does not shield an enterprise from Section 1 antitrust claims.
- Enterprise risk programs must audit third-party software tools to ensure pricing algorithms do not rely on confidential competitor inputs.
About the Author
Rafael X. Zahralddin is a Partner and Chair of the national Business Law & Commercial Litigation practice at Quintairos, Prieto, Wood & Boyer, P.A., based in the firm’s Wilmington, Delaware office. With over 30 years of experience, he oversees the group’s Business & Bankruptcy matters, advising a global clientele on complex corporate and commercial litigation, bankruptcy, creditors’ rights and workouts, and mergers & acquisitions.
If you have questions regarding this decision or would like to address algorithmic pricing, vendor compliance, and antitrust defense, Rafael can be reached at rafael.zahralddin@qpwblaw.com.
This article is intended for informational purposes only and does not constitute legal advice. Please consult with an attorney to discuss your specific legal situation.
Article reporting by Quintairos, Prieto, Wood & Boyer, P.A.. Legal update by Managing Partner Rafael Zahralddin.