Articles Posted in Algorithmic Pricing

Algorithmic Pricing: New DAI Case, First Appellate Decision, and the Greystar Settlement. The show Goes On! (Part 1)

September 15, 2025 | Bona Law PC

Summer is over and everyone is back at the office. If you’ve been enjoying some days off, you’ve probably missed what happened recently in the algorithmic-pricing space in the US. And, as always, we had a very busy summer here.

First, there is a new case from the Northern District of California that confirms the importance of not only the nature of the information shared with the software provider, but also whether such information is later incorporated into any final pricing recommendations.
Second, we have the first opinion from a Court of Appeals—in this case the Ninth Circuit in the Gibson v. Cendyn Group case—analyzing an antitrust claim involving algorithmic pricing.
And third, the DOJ Antitrust Division announced its recent settlement with Greystar Management Services to resolve the DOJ’s claims about the use by Greystar of RealPage’s software.

As I said, everyone has been working hard around here during the past months!

This first article explains the Dai v. SAS Inst. Inc new case. Also, if you need some background on the current cases before diving into the new developments, we’ve written several articles on algorithmic pricing:

Dai v. SAS Inst. Inc., No. 24-CV-02537-JSW (N.D. Cal. July 18, 2025)

In this new case, plaintiffs sued software provider IDeaS, Inc. (“IDeaS”), and a group of hotel operators including Wyndham Hotels & Resorts, Inc., Hilton Domestic Operating Company Inc., Four Seasons Hotels Limited, Omni Hotels Management Corporation, and Hyatt Corporation, for conspiring to fix hotel room prices (“Hotel Operators”).

Here are the allegations:

IDeaS is the dominant provider of revenue management and profit optimization software and services for Hotel Operators.

According to the complaint, Hotel Operators agreed to provide IDeaS with non-public, competitively sensitive price and occupancy information in real time, including the price paid by consumers for each room, the quantity of rooms available by room type, whether or not any consumers attempted to book a room that was no longer available, and room rates not visible to the public.

IDeaS would then plug all the information into its algorithm, generating supra-competitive pricing recommendations for each of them.

And the last—but certainly not least—piece of the puzzle: each defendant would implement IDeaS’s supracompetitive pricing, because they know all their horizontal competitors are doing the same thing.

Plaintiffs did not rely on direct evidence but rather alleged the inference of a horizontal agreement by the group of Hotel Operators using IDeaS’s software. They argued parallel conduct—when Hotel Operators began to use IDeaS’s software and to charge allegedly supra-competitive rates based on IDeaS’s recommendations—together with (i) an invitation to collude as well as the motive and the opportunity to do so; (ii) high barriers to enter the relevant market; (iii) inelastic demand for hotel rooms; and (iv) sharing of confidential information against self-interest; all as plus factors to show antitrust conspiracy.

Would this be enough to show the existence of an antitrust conspiracy? Not quite, according to the Northern District of California.

Remember, Sherman Act Section 1 antitrust cases require: (1) a contract, combination, or conspiracy among at least two different entities; (2) an intent to restrain trade; and (3) injury to competition. See here and here.

On July 18, 2025, the District Court for the Northern District of California dismissed the complaint on several grounds. What is particularly helpful for future litigants in this Opinion is the comparison the Court makes with past recent cases.

The Court in California held here that plaintiffs did not sufficiently allege parallel conduct for several reasons.

First, the Opinion compares the current case to In re RealPage, Inc., Rental Software Antitrust Litig., 709 F. Supp. 3d 478 (M.D. Tenn. 2023), where a critical level of RealPage’s software adoption explaining how defendants changed their strategy and increased prices—despite not acting simultaneously—was enough to show parallel conduct.

Then, and in contrast, it mentions Gibson v. MGM Resorts Int’l, No. 2:23-cv 00140-MMD-DJA, (D. Nev. Oct. 24, 2023), where a court dismissed the complaint for lack of parallel conduct. In that case, plaintiffs neither include information about when the defendants began to use the software and which systems they used, nor alleged facts about the rate at which the defendants accepted the software recommendations. Plaintiffs did include general allegations of the acceptance rate for the price recommendations, but that was not sufficient to make the existence of an agreement plausible according to Twombly requirements.

Last, the Northern District of California states that plaintiffs did not provide enough facts in this case to explain when the Hotel Operators began to outsource their pricing decisions to IDeaS; when they started to change their strategy and increased prices; or when and how they started to adopt IDeaS’s pricing recommendations to their room prices.

In other words, according to the Court, plaintiffs did not have to show that each defendant acted at the exact same moment in time or acceptance rate. But plaintiffs did have to plead additional facts to render the allegations of parallel conduct plausible, which as explained below, they didn’t do.

Indeed, the Court reasoned that plaintiffs did not allege enough plus factors:

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Quick Update on Algorithmic Pricing: Yardi and the “Per Se” Standard

February 24, 2025 | Bona Law PC

Author: Luis Blanquez

In simple terms algorithmic pricing takes place when competitors make use of a software platform to share competitively sensitive information, which the pricing algorithm uses to recommend prices for all users.

Algorithmic pricing has been in the antitrust spotlight over the past few years.

The FTC has Algorithmic Price-Fixing in its Antitrust Crosshairs

New Antitrust Cases and Statements of Interests About Algorithmic Collusion

The main reason? Antitrust laws apply to algorithms implementing human agreements.

How to Show the Existence of an Agreement: Direct and Circumstantial Evidence

Remember that under US antitrust law, there are two ways to show the existence of an agreement:

Through direct evidence (sometimes this is a “smoking gun”), and;
Through circumstantial evidence: Alternatively, it’s more common to show the existence of an agreement through a combination of parallel conduct and “plus factors,” i.e., a common motive to conspire, evidence that shows that the parallel acts were against the apparent individual economic self-interest of the alleged conspirators, and/or evidence of a high level of interfirm communications.

But finding an express agreement between companies to fix prices is not super common these days. So, what happens when there is no agreement involved, and the algorithm “only” facilitates tacit collusion between the companies using it? Things get much murkier. That happens, for instance, when competitors use the software platform to share sensitive commercial information.

Agreements to Exchange Information: “Per Se” or Rule of Reason?

Section 1 of the Sherman Act prohibits every contract, combination or conspiracy that restrains trade, so long as those restraints are unreasonably restrictive of competition in a relevant market. This includes both an agreement and tacit collusion.

Restraints analyzed under the “per se” rule are those that are always (or almost always) so inherently anticompetitive and damaging to the market that they warrant condemnation without further inquiry into their effects on the market or the existence of an objective competitive justification. Business practices considered per se illegal under antitrust laws include: (a) horizontal agreements to fix prices, (b) horizontal market allocation agreements, (c) bid rigging among competitors; (d) certain horizontal group boycotts by competitors; and (e) sometimes tying arrangements.

On the other hand, a contract, combination or conspiracy that unreasonably restrains trade and does not fit into the per se category is usually analyzed under the so-called rule of reason test. This test focuses on the state of competition within a well-defined relevant agreement. It requires a full-blown analysis of (i) definition of the relevant product and geographic market, (ii) market power of the defendant(s) in the relevant market, (iii) and the existence of anticompetitive effects. The court will then shift the burden to the defendant(s) to show an objective procompetitive justification. Most antitrust claims are analyzed under this test.

Depending on the type of unlawful information exchange, it might be categorized as:

“Per se”unlawful conduct, when facilitates price fixing, bid rigging, or market allocation, so plaintiffs do not need to show actual harm to competition, or;
Unlawful conduct under the rule of reason, if the exchange of information leads to some anticompetitive effect, based on factors such as the structure of the industry involved, and the nature of the information exchanged, among others.

This is an important distinction to keep in mind if you want to understand why the District Court for the Western District of Washington in Duffy v. Yardi Systems Inc. recently denied the defendants’ motion to dismiss, while stating—for the first time involving an antitrust case on algorithmic pricing—that plaintiffs’ allegations were sufficient to allege a per se unlawful antitrust conspiracy.

New Legal Standard for Algorithmic Pricing Antitrust Cases? Maybe…

We’ve seen several government and private antitrust lawsuits on algorithmic pricing during the past years, claiming that the use of a software platform to set prices constituted an anticompetitive conspiracy under the antitrust laws.

We’ve previously discussed all these cases in detail here. In a nutshell:

In 2022 plaintiffs in Realpage, Inc. Software Antitrust Litigation sued RealPage and its landlord-customers alleging that a management software tool helped them coordinate on prices by collecting non-public information on rents and vacant units. In January 2024, the Court denied the motion to dismiss––plaintiffs were able to show that RealPage’s software uses confidential competitor information through its algorithm to spit out price recommendations based on that private competitor data.

Second, it rejected claims alleging a horizontal price-fixing conspiracy (no agreement and no absolute delegation of their price-setting to RealPage) ––which would have been “per se” illegal––but concluded that those same landlords vertically conspired with RealPage.

In 2023, plaintiffs in Gibson v. MGM Resorts International and Cornish-Adebiyi v. Caesar’s Entertainment, Inc., alleged that hotels in Las Vegas and Atlantic City used a pricing algorithm to facilitate collusion, by providing hotel and casino room pricing and occupancy information. The district courts dismissed both cases, on May 8 and September 30, 2024, respectively, based on several grounds. First, plaintiffs did not show a horizontal agreement: the hotels were not using the platforms around the same time, did not agree to be bound by such price recommendations, and did not charge the same prices. And second, plaintiffs failed to show that the pricing recommendations were based on nonpublic, competitively sensitive information.
In Duffy v. Yardi Systems, Inc. plaintiffs similarly alleged that competing landlords violated Section 1 of the Sherman Act, by unlawfully agreeing “to use Yardi’s pricing algorithms to artificially inflate” multifamily rental prices. On December 4, 2024, the court denied the motion to dismiss and allowed the case to proceed into discovery. There are two important nuances to highlight here.

First, and similarly to the RealPage case, the Court sided with plaintiffs and agreed on the existence of an antitrust conspiracy. This decision was not only based on defendants’ acceptance of Yardy’s invitation to trade sensitive information, which allow them to charge increased rents, but also on defendants’ parallel conduct (while contracting with Yardi), and “plus factors,” such as the exchange of nonpublic and competitive sensitive information, which suggested defendants acting for their mutual benefit.

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New Antitrust Cases and Statements of Interests About Algorithmic Collusion

June 9, 2024 | Bona Law PC

Author: Luis Blanquez

We recently wrote about the Federal Trade Commission’s blog post explaining how relying on a common algorithm to determine your pricing decisions might violate Section 1 of the Sherman Act.

The FTC has Algorithmic Price-Fixing in its Antitrust Crosshairs

It was just a matter of time until the first cases would hit the courts. That’s why during the last couple of years, we’ve seen four main federal antitrust cases alleging that algorithmic pricing might violate the antitrust laws. In three of them, the antitrust agencies also filed Statements of Interest (SOI), outlining the agencies’ opinion about what the legal principles applicable to claims of algorithmic price fixing should be.

Realpage, Inc. Software Antitrust Litigation

This multidistrict litigation in the Middle District of Tennessee involves unlawful price-fixing schemes against multifamily housing developers and managers, and student housing developers and managers, both organized by RealPage––a software algorithm company. RealPage developed software to collect property owners’ and managers’ data, used for pricing and inventory strategies, that later shared with its clients.

In January 2024, the Court: (i) denied the motion to dismiss the multifamily housing cases––the renters plausibly alleged an antitrust violation, but (ii) rejected claims alleging a horizontal price-fixing conspiracy among landlords, which would have been per se illegal. The Court, however, concluded that those same landlords vertically conspired with RealPage. The Court also dismissed the student housing plaintiffs’ complaint.

In parallel, the DOJ opened an investigation and filed a SOI. Among other things, the DOJ highlighted:

The fact that today software algorithms process more information more rapidly than humans and can be employed to fix prices. The technical capabilities of software can enhance competitors’ ability to optimize cartel gains, monitor real-time deviations, and minimize incentives to cheat.
Section 1 prohibits competitors from fixing prices by knowingly sharing their competitive information with, and then relying on pricing decisions from, a common human pricing agent who competitors know analyzes information from multiple competitors. The same prohibition applies where the common pricing agent is a common software algorithm.
Factual allegations in both complaints point to evidence of an invitation to act in concert followed by acceptance—evidence that is sufficient to plead concerted action. Among other things, RealPage required each user to submit real-time pricing and supply data to it, and RealPage’s marketing materials allegedly “touted” its use of “non-public data from other RealPage clients,” enabling them to “raise rents in concert”; as well as the algorithms’ ability to “facilitate collaboration among operations” and “track your competition’s rent with precision.”
The complaints then allege that the landlords “gave their adherence to the scheme and participated in it.” In particular, the landlords allegedly sent RealPage the non-public and competitively sensitive data (as RealPage proposed), and overwhelmingly priced their units in line with RealPage’s suggested prices (80-90%). Indeed, the complaints also contain ample allegations on how RealPage directly constrained the “deviations” from its suggested prices, including by enforcing and monitoring compliance with those prices, so the landlords effectively delegated aspects of their pricing decisions.
Relatedly, the multifamily plaintiffs allege that the landlords jointly delegated aspects of decision making on prices to RealPage. They allege that, by using RealPage’s pricing algorithms, each client defendant “agreed” to a common plan that involved “delegat[ing] their rental price and supply decisions to a common decision maker, RealPage.” Indeed, RealPage allegedly touted this feature—stating in a press release that it gives clients “the ability to ‘outsource daily pricing and ongoing revenue oversight,’” such that RealPage could “set prices” as though it “own[ed]” the clients’ properties “ourselves.’”
Jointly delegating any part of the decision-making process reflects concerted action. That the delegation is to a software algorithm, rather than a human, makes no difference to the legal analysis. Just as “surrender[ing] freedom of action. . . and agree[ing] to abide by the will of the association” can be enough for concerted action, so can be relying on a joint algorithm that generates prices based on shared competitively sensitive data.
The “per se” rule prohibiting price fixing applies to price fixing using algorithms. And the analysis is no different simply because a software algorithm is involved. The alleged scheme meets the legal criteria for “per se” unlawful price fixing. Although not every use of an algorithm to set price qualifies as a per se violation of Section 1 of the Sherman Act, it is per se unlawful when, as alleged here, competitors knowingly combine their sensitive, nonpublic pricing and supply information in an algorithm that they rely upon in making pricing decisions, with the knowledge and expectation that other competitors will do the same.

The District of Columbia Attorney General has also filed a similar action in the Superior Court of D.C., alleging violations of the D.C. Antitrust Act.

Duffy v. Yardi Systems, Inc.

In this case from the US District Court for the Western District of Washington, plaintiffs allege that competing landlords violated Section 1 of the Sherman Act, by unlawfully agreeing “to use Yardi’s pricing algorithms to artificially inflate” multifamily rental prices.

The Agencies also filed a SOI to explain the two legal principles applicable to claims of algorithmic price fixing. First, a competitors’ agreement to use an algorithm software with knowledge that other competitors are doing the same thing constitutes evidence of a contract, combination or conspiracy that may violate Section 1. Second, the fact that defendants deviate from the pricing algorithm’s recommendations––for instance, by just setting initial starting prices or by starting with prices lower than the ones the algorithm recommends—is not enough to get them “off the hook” for illegal price fixing (even if no information is directly shared between the parties).

The Agencies SOI’s focus was on the second point: Defendants retaining pricing discretion. The Agencies stress in the SOI that it is “per se” illegal for competing landlords to jointly delegate key aspects of their pricing to a common algorithm, even if the landlords retain some authority to deviate from the algorithm’s recommendations. Although full adherence to a price-fixing scheme may render it more effective, the effectiveness of the scheme is not a requirement for “per se” illegality. Consistent with black letter conspiracy law, the violation is the agreement, and unsuccessful price-fixing agreements are also per se illegal.

Casino-Hotel Operators Cases

Two new algorithmic pricing antitrust cases are also ongoing against casino hotel operators in Las Vegas and Atlantic City.

In Cornish-Adebiyi v. Caesar’s Entertainment, Inc., a case pending in the U.S. District Court for the District of New Jersey, plaintiffs allege a conspiracy against eight Atlantic City casino-hotel operators, and the Cendyn Group LLC, which is a provider of the algorithmic software platform, called “Rainmaker,” used to fix, raise, and stabilize the prices of casino-hotel guest rooms in Atlantic City. Rainmaker allegedly gathers real-time pricing and occupancy data to generate “optimal” room rates for each participating casino hotel, which the software then recommends to each casino hotel.

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The FTC has Algorithmic Price-Fixing in its Antitrust Crosshairs

May 20, 2024 | Bona Law PC

Author: Luis Blanquez

Are you delegating your pricing decisions to a common algorithm software platform? If so, you might violate the antitrust laws. It may not even matter whether you actually communicated with your competitors. All it might take is for the antitrust agencies—The Department of Justice or the Federal Trade Commission—to allege illegal collusion is the use by your company of an algorithm-software tool trained using competitively sensitive data, with knowledge that some of your competitors are doing the same thing. Even deviation from the algorithm’s recommended pricing might not save you from antitrust liability.

The FTC’s Blog Post: Price Fixing by Algorithm is Still Price Fixing

On March 1, 2024, the Federal Trade Commission (FTC) published a blog post explaining how relying on a common algorithm to determine your pricing decisions might violate Section 1 of the Sherman Act.

In the blog post, the FTC includes a previous Statement of Interest (“SOI”) filed in the Duffy v. Yardi Systems, Inc. case to explain the legal principles applicable to claims of algorithmic price fixing. First, price fixing through an algorithm is still price fixing. Second: (1) you can’t use an algorithm to evade the law banning price-fixing agreements, and (2) an agreement to use shared pricing recommendations, lists, calculations, or algorithms can still be unlawful even where co-conspirators retain some pricing discretion or cheat on the agreement.

The blog concludes with two important remarks:

“Agreeing to use an algorithm is an agreement. In algorithmic collusion, a pricing algorithm combines competitor data and spits out the suggested “maximized” rent for a unit given local conditions. Such software can allow landlords to collude on pricing by using an algorithm—something the law doesn’t allow IRL. When you replace once-independent pricing decisions with a shared algorithm, expect trouble. Competitors using a shared human agent to fix prices? Illegal. Doing the same thing but with an agreed upon, shared algorithm? Still illegal. It’s also irrelevant that the algorithm maker isn’t a direct competitor if you and your competitors each agree to use their product knowing the others are doing the same in concert.
Price deviations don’t immunize conspirators. Some things in life might require perfection, but price-fixing arrangements aren’t one of them. Just because a software recommends rather than determines a price doesn’t mean it’s legal. Setting initial starting prices or recommending initial starting prices can be illegal, even if conspirators deviate from recommended prices. And even if some of the conspirators cheat by starting with lower prices than those the algorithm recommended, that doesn’t necessarily change things. Being bad at breaking the law isn’t a defense.”

This is a bold statement from the FTC. Algorithmic collusion is not only on the agency’s radar now, but it is also one of its priorities.

Final Conclusions

Algorithm collusion is on the crosshairs of the FTC and DOJ, so expect more cases soon. And not only in the real-estate industry, as highlighted from existing investigations on the online retailing and meat processing industries. Indeed, it is becoming common practice for more industries and businesses to implement and rely on algorithms to set their pricing strategies.

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