Recent Developments

Recently Filed Complaints

Residential Mortgage Borrowers Allege Residential Mortgage Originators Exchange Sensitive Information and Fix Loan Prices – Mendez v. Optimal Blue, LLC, No. 25-cv-1140 (M.D. Tenn.)

  • On October 6, 2025, purchasers of residential mortgages filed a putative class action against Optimal Blue, LLC, a provider of pricing and business analytics tools for mortgage originators, and 26 financial institutions that use Optimal Blue’s software.
  • Plaintiffs allege that Optimal Blue’s business analytics tools are the mechanism through which loan originators share competitively sensitive data about prospective borrowers and desired loan characteristics. Financial institutions, according to the complaint, use real-time intelligence provided by Optimal Blue’s tools to set mortgage rates. Plaintiffs allege that defendants’ conduct constitutes price-fixing and unlawful information exchange in violation of Sherman Act § 1.
  • Plaintiffs filed in the Middle District of Tennessee, and the case has been assigned to Judge Waverly Crenshaw, who currently oversees the RealPage MDL and landlord defendants alleged to have used RealPage’s software to raise and fix residential rents. See In re RealPage, Inc., Rental Software Antitrust Litig. (No. II), No. 3:23-md-3071 (M.D. Tenn.).
  • Although the complaint alleges “Optimal Blue uses information from all participating lenders to generate pricing recommendations,” the complaint does not allege that these recommendations are binding or that the recommendations themselves are widely adopted (or adopted at any given rate) by Optimal Blue users.

Decisions

CA State Court Grants SJ to Yardi Because Source Code Foreclosed Plaintiffs’ Allegations – Mach v. Yardi Systems, Inc., 24-cv-063117 (Cal. Super. Ct.)

  • In February 2024, housing renters filed a putative class action against Yardi Systems, Inc., a provider of software that generates pricing recommendations for rental properties, and four landlords that use Yardi’s software. Plaintiffs alleged that the landlords agreed to provide competitively sensitive pricing information to Yardi for use in generating pricing recommendations and to adopt Yardi’s recommendations in setting rents, in violation of California’s Cartwright Act. In August 2024, the court denied defendants’ motion to dismiss and permitted initial discovery into how Yardi’s software uses competitor data in developing pricing and supply recommendations.
  • On October 6, 2025, the court granted Yardi’s motion for summary judgment. Relying heavily on evidence about the software’s source code, the court concluded that plaintiffs had failed to establish Yardi uses customers’ commercially sensitive data to inform pricing recommendations for other customers. The court also rejected plaintiffs’ theory that the landlords accepted an invitation to adopt a common pricing algorithm, reiterating the absence of evidence of agreement and further explaining that using a common software application is not itself an antitrust violation. The court cited the Ninth Circuit’s recent decision in Gibson v. Cendyn, which held that competitors’ independent choices to obtain pricing advice from the same company do not harm competition because they do not reduce the incentive to compete.
  • The court highlighted “Yardi’s forthright decision to produce its source code and related evidence in the initial phases of discovery was critical to answering key questions concerning the sharing and use of rental price information to generate price recommendations.” To the extent source code can debunk plaintiffs’ theories, defendants should consider producing it early.

Settlements

Tyson Pays $85 Million to Exit Pork Products Class Action – In re Pork Producers Antitrust Litig., MDL No. 21-2998, No. 18-cv-1776 (D. Minn.)

  • In June 2018, purchasers of pork products alleged that Agri-Stats, a distributor of data and analytics reports, and seven pork producers or integrators exchanged competitively sensitive information through Agri-Stats and conspired to fix prices for pork products in violation of Sherman Act § 1. The many separately filed actions are consolidated in an MDL containing three certified classes of direct and indirect purchasers as well as dozens of individual actions (including suits by Minnesota, Puerto Rico, and private parties opting out of the class actions).
  • In March 2025, the court largely denied motions for summary judgment. Certain defendants moved for both reconsideration of the summary judgment order and an order certifying the decision for interlocutory appeal. The court granted one defendant permission to file a motion for reconsideration on October 8, 2025, but has not yet ruled on interlocutory appeal. The parties’ dispute about whether the class plaintiffs’ cases or a bellwether set of individual actions should be tried first is pending.
  • On October 1, 2025, the consumer indirect purchaser plaintiffs moved for preliminary approval of a class settlement resolving claims against Tyson. Tyson agreed to pay $85 million, calculated based on its current market share.

Plaintiffs Seek Approval for Twenty-Six Settlements in RealPage Rent Price Fixing MDL – In re RealPage, Inc., Rental Software Antitrust Litig. (No. II), No. 23-md-3071 (M.D. Tenn.)

  • On October 1, 2025, plaintiffs moved for preliminary approval of 26 settlements reached with property owners and managers. Under the proposed agreements, the settling defendants will pay a total of $141.8 million to resolve claims that they used RealPage software to inflate residential rents. The settling defendants agreed not to provide non-public data to RealPage for use in competitor pricing recommendations, to refrain from using RealPage’s revenue management software that relies on non-public competitor data to make pricing recommendations, and, if necessary, to renegotiate contract terms with RealPage to achieve those limitations.
  • On October 15, 2025, DC, Maryland, New Jersey, and Washington notified the court that they are considering filing a statement of interest regarding the proposed settlements. Their initial filings indicate concerns with how the terms of the proposed settlements could affect their own ongoing enforcement actions and limit relief for residents in their jurisdictions.

State Legislation

CA’s Amended Cartwright Act Expands Liability for Use of Algorithmic Pricing Tools

  • On October 6, 2025, California amended its Cartwright Act to clarify that it is unlawful to use a “common pricing algorithm” as part of an agreement to restrain trade and to broaden liability for using algorithmic pricing tools. The law defines “common pricing algorithm” as methodologies that use competitor data to recommend or set any commercial terms (e.g., price, wages, level of service, availability, output, etc.). The amendments take effect on January 1, 2026. The revised California law appears to diverge from federal antitrust law in multiple ways:
    • California defines common pricing algorithm to include methods that incorporate “competitor data” without reference to whether such data is public or nonpublic, current or stale. These factors have been relevant to federal courts determining whether the use of pricing tools violates the Sherman Act.
    • California prohibits a person from coercing another to set or adopt recommendations of a common pricing algorithm. This prohibition appears to target conduct such as a vendor of algorithmic pricing software requiring or pressuring users to adopt recommended prices but could sweep more broadly.
    • California lowers the bar for pleading antitrust violations. Under federal law, plaintiffs relying on circumstantial evidence to establish the existence of an agreement to restrain trade typically must plead “plus factors,” i.e., facts tending to exclude independent action. The revised Cartwright Act expressly disavows this requirement. This may mean that alleging the use of a common pricing algorithm suffices to survive a motion to dismiss.
  • The law also significantly increases criminal and civil penalties for violations.
  • Businesses within California’s jurisdiction should consider auditing pricing tools.

NY Bans Use of Shared Algorithmic Pricing Tools for Setting Rents

  • On October 16, 2025, New York amended its General Business Law to significantly restrict the use of algorithmic software to set rents. The new law deems even considering recommendations of an algorithm that uses data from two or more competing landlords to set any rental terms as an “unlawful agreement”—even if the algorithm collects only historical data. The law becomes effective on December 15, 2025. Rental algorithmic software providers are expected to challenge the law.

 Congressional Committee Investigations

  • On October 1, 2025, the US House & Senate Judiciary Committees sent letters to the College Board and several consulting firms seeking information about the software these firms use in advising colleges about enrollment management.

Thought Leadership from Competition Authorities Abroad

  • On October 6, 2025, the Competition Commission of India (“CCI”) released its market study on Artificial Intelligence. The CCI stated that algorithms can facilitate tacit collusion and suggested proactive compliance measures such as self-audits.

 Guidelines for Assessing and Mitigating Antitrust Risk
Below are some of the key issues we have seen emerge as we monitor these areas of the law and some questions you might consider asking about the software provided or used by your business.

ISSUE QUESTIONS
Inputs: Tools that rely on public, lagged, and/or anonymized data tend to be less risky than tools that rely on confidential, current data from competitors. If a tool uses nonpublic information, companies should be attentive to whether there is a risk that they can access competitors’ confidential data (and vice-versa).
  • What data is used?
  • Is the data public or nonpublic?
  • Is the company providing or receiving nonpublic commercially sensitive information?
  • How current is the data?
  • What data is available to participants?
  • Is data from competitors segregated?  Are competitors’ data pooled—either for training an algorithm, to generate recommendations, or for some other reason?
Independence: Ensuring that pricing recommendations are nonbinding reduces antitrust risk.
  • What are the terms of use?
  • Are settings customizable? Are competitors’ settings shared (directly or indirectly)?
  • What is the output?
  • Are there recommendations?  If so, what are they—prices, commercial terms, or benchmarks?
  • Are recommendations binding, or can individuals deviate from them?
  • How often do deviations occur in practice?
Marketing: Public statements touting a tool’s ability to discipline, optimize, or otherwise increase prices or revenues across an industry can be problematic.
  • How it the tool marketed and distributed? As means to increase profitability? For disciplining sales and pricing?
  • How widely is the tool used in the industry?
  • Does the company know if its competitors are using it? Would the company use it regardless?
Business Rationale: Ideally, there are documented procompetitive bases for using the tool, such as increasing efficiency or lowering prices for customers
  • What are the company’s expectations or reason for using the tool?
  • What benefits has the tool led to in practice?