The filing is procedural, another motion to dismiss in a Tennessee federal court. The question underneath it is one of the most consequential in American antitrust law right now: when competitors feed their private pricing data into the same software and all of them raise prices, is that a cartel or a coincidence?
The case is Mendez v. Optimal Blue, LLC, filed in October 2025 in the Middle District of Tennessee. Four homeowners allege that Optimal Blue and 26 of the nation's largest mortgage lenders conspired to fix mortgage rates through a data-sharing platform, turning competitors into collaborators. The named defendants include CrossCountry Mortgage, Guaranteed Rate, Guild Mortgage, and, tellingly, RealPage.
The alleged mechanism
The plaintiffs' theory is specific, which is what makes it more than a fishing expedition. It centers on Optimal Blue's Competitive Analytics and Competitive Data License tools, which the complaint says require lenders to surrender non-public, competitively sensitive, granular, real-time data on every component of their mortgage pricing, from profit margins and concessions to loan-officer compensation and borrower credit characteristics.
In exchange, each lender sees its competitors' pricing intelligence. The alleged result: lenders adjust rates and fees in lockstep rather than compete openly, since at least 2019.
The complaint's sharpest evidence comes from the vendor's own marketing. It quotes Optimal Blue's pitch that its pricing tool lets a lender see how its current pricing compares in any given market segment, in real time, so it can easily adjust margins and republish as necessary. And it cites a customer case study in which Beeline Loans reported nearly doubling its margins, from 1.78% in November 2024 to 3% in July 2025.
The defense, and why one word carries it
The defendants' current dismissal bid argues that the plaintiffs failed to allege that the software products at the center of their suit made pricing recommendations. Earlier, they contested the case on the ground that the vendor doesn't operate a pricing algorithm and that its tools don't dictate or recommend prices.
That sounds like a technicality. It is closer to the load-bearing wall of algorithmic antitrust law.
Traditional price-fixing requires an agreement among competitors. Algorithmic pricing cases have tried to establish that agreement through a shared intermediary: if rivals all delegate pricing to the same algorithm, and the algorithm tells each of them what to charge, courts have been receptive to the argument that they effectively agreed on price through the machine. Strip that out and the case gets much harder. If the software merely aggregates and displays market data, and each lender independently decides what to do with it, the defendants can characterize the conduct as ordinary competitive intelligence, which is legal, rather than agreeing with rivals on what to charge, which is not.
The plaintiffs' answer, in their June opposition, is that they sufficiently alleged price-fixing for residential mortgages. Their structural argument is that the exchange of non-public, granular, real-time data among direct competitors is itself the anticompetitive act, regardless of whether a machine issues a recommendation.
Why RealPage's presence tells you what this really is
The most revealing name on the defendant list is RealPage, whose software was the subject of Justice Department and state antitrust actions alleging that landlords used it to coordinate rents.
Its appearance here signals that Mendez is a deliberate port of the RealPage theory into a new market. Same structure: a dominant software vendor sits at the center, competitors feed in confidential pricing data, and the plaintiffs allege the resulting transparency replaces competition with coordination. Only the product changes, from apartment rents to mortgage rates. The plaintiffs' bar is testing whether the algorithmic-collusion theory generalizes, and the defendants' "no recommendation" argument is the industry's attempt to draw a boundary.
The scale, and the timeline
The stakes are unusual even by class-action standards. The case could span millions of affected consumers, covers a period running from 2019, and seeks treble damages under federal antitrust law, the automatic tripling that makes antitrust exposure existential.
And it is slow. Court filings have described deadlines running to a 2029 trial. The repeated dismissal attempts, an initial bid in spring, plaintiffs' opposition in June, another push in July, reflect exactly that: with treble damages and a 2029 horizon, getting out early is worth almost any amount of motion practice.
What to watch
Nothing has been proven. This is a motion-to-dismiss fight, meaning no court has weighed evidence or found that anyone fixed prices, and the defendants deny the allegations. Many antitrust class actions with plausible-sounding theories do not survive the pleading stage.
But the framing question is worth carrying beyond this case. Software that gives every competitor in a market real-time visibility into everyone else's pricing produces a genuine legal puzzle: it is simultaneously a legitimate business tool and a mechanism that could make tacit coordination effortless. The specific test here is narrow, did the software recommend a price, and the defendants say no. If courts accept that as the dividing line, vendors have a clear roadmap: aggregate and display, never suggest.
Antitrust law was built around agreements between people. It is now being asked whether an agreement can exist when the only thing competitors share is a subscription.