According to the Department’s charges, each landlord who joined the scheme submitted confidential data on its own rental prices and availabilities, both current and anticipated, in minute detail, which it knew RealPage would combine with the same confidential data from the other landlords to calculate recommended rental prices for all the landlords, which they would all follow.

Cortland has agreed to settle with the Department, to end its involvement with RealPage, to refrain from engaging in any coordination on rental prices, to submit to monitoring by the Department, and to assist the Department in the continuing investigation and enforcement action. Cortland is one of the largest apartment managers in the United States, managing, as of 2024, more than 80,000 units and more than 220 properties in the United States.

Under the Antitrust Procedures and Penalties Act, the Department is required to publish any proposed antitrust consent decree, to give the public 60 days to comment, and to reply to the comments. The court then ultimately determines if the proposed decree is in the public interest, and if so, enters the decree as a final judgment as to the settling defendant.

CDT’s comments explain why in our view the proposed consent decree is in the public interest.

Read the full comments.

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After requesting and receiving, with the Department’s consent, two extensions, RealPage filed its response on December 3 – a motion to dismiss for failure to state a claim.^[1] Essentially, RealPage takes issue with the way the Department has defined the relevant markets for antitrust analysis, and further denies orchestrating or being involved in any pricing coordination among landlords.

Some might say that the Justice Department has come to the party late. The District of Columbia and Arizona had already brought enforcement actions under their own laws, and a federal class action was already pending in a federal district court in Tennessee.^[2] But the Department’s allegations provide far greater detail, benefitting from its greater resources combined with its stronger investigatory authority, which enables it to compel production of evidence during its investigation, before it brings an action, under the Antitrust Civil Process Act. 

As charged in the complaint, RealPage has created and advertised to apartment landlords an algorithm-powered system to collect and analyze, on a daily basis, current rental prices and planned future prices, and current availabilities and projected future availabilities for all participating landlords. This information is separately categorized for each individual rental unit, according to size, floor plan, layout, and amenities. RealPage makes explicitly clear to the landlords that it will analyze this information and provide pricing recommendations to each landlord based on this information. This kind of information is competitively sensitive, and in a healthy competitive marketplace it is closely guarded, not shared.

RealPage’s system has the hallmarks of a classic anticompetitive “hub-and-spoke”^[3] arrangement under which competitors coordinate pricing and output decisions through a central clearinghouse “hub.” This kind of arrangement has been found to violate section 1 of the Sherman Act, which prohibits contracts, combinations, or conspiracies in restraint of trade, provided that the evidence sufficiently demonstrates that the competitors along the “rim” had a “conscious commitment to a common scheme designed to achieve an unlawful objective.”^[4] It is not necessary that the competitors along the “rim” have direct communication with each other regarding the anticompetitive scheme, because they are communicating effectively through the “hub” as “spokes.”

Here, per the Department’s allegations, RealPage created the “hub” and advertised it to landlords, encouraging them to join up. RealPage explained that it would calculate pricing recommendations for them, based on pricing data submitted on a daily basis by every participating landlord in the market area. And that the recommendations would be guided by the highest prices being charged, which would enable each landlord to confidently increase its own rental prices in line with the high end of prices being charged by its competitors. 

So, per those allegations, the landlords were well aware that they would be “spokes,” participating along with their competitors, and that the result would be pricing recommendations that would result in increased prices. Or, as RealPage regularly puts it, would “raise all ships.” A RealPage revenue management vice president elaborated that this phrase means that “there is greater good in everybody succeeding versus essentially trying to compete against one another in a way that actually keeps the industry down.” And even more pointedly, that landlords using RealPage’s software would “likely move in unison versus against each other.”

Thus, the landlords who joined up were consciously committing themselves to a common scheme not to compete. Their commitment includes paying RealPage a hefty fee in recognition of the value they receive. 

But RealPage has allegedly gone beyond just creating and advertising the hub that enabled and facilitated a conscious commitment to unlawful pricing coordination. It has taken a number of calculated steps to make sure landlords follow through on that commitment. It constantly nudges landlords to follow each other’s price increases. It actively monitors prices charged on literally millions of apartment units – not only to calculate new pricing recommendations, but also to determine which landlords are complying with its recommendations and which are not. 

Each day, RealPage sends updated pricing recommendations to each landlord. RealPage makes it easy for the landlord to accept the recommendations in bulk – it can be done with a single keystroke, or even programmed for auto-accept, which RealPage strongly encourages landlords to adopt. Diverging from the recommendation, in contrast, requires the landlord’s property manager to affirmatively give RealPage a “strong sound business-minded” justification for each divergence, based on something the algorithm is not accounting for, such as local construction or renovations occurring in the building. And whenever RealPage disagrees with the justification, which is usually, the matter is escalated to the property manager’s supervisor, and upward, with increasing aggressiveness.

Internal training explained that RealPage wanted to “widen auto accept parameters” by introducing the feature and then “creating enough trust so that over time we have client[s] that are willing to let auto accept run with very wide parameters… AKA – accept all recommendations.” RealPage trains pricing advisors to have an “accountability conversation” or a “refresher on short term vs long term goals” for clients that show less tolerance for increasing auto-accept parameters.

The result, according to the complaint: more than 85% of final floor plan prices are within 5% of RealPage’s recommendation.

The Department further charges that RealPage reinforces its algorithm-driven coordinated upward pricing recommendations by discouraging landlords from offering renters discount “concessions” – such as a free month’s rent or waived fees – as landlords in a competitive marketplace would have incentives to offer. In its “best practices” for landlords, RealPage’s guidance is simple: “Eliminate concessions.” A landlord’s agreement to refuse to offer concessions is bolstered by its awareness that competing landlords are receiving the same advice from RealPage.

Essentially, landlords are encouraged, and then pressured, to turn over rental pricing decisions to RealPage’s algorithm, knowing that it is coordinating pricing among participating landlords, pushing prices higher.

The Department alleges that RealPage’s pricing algorithm ratchets in only the upward direction. It resists recommending the kind of price decreases in response to a decrease in demand that would occur in a marketplace with healthy competition. Instead, the algorithm overrides its normal functioning to coordinate recommended reductions in supply – taking units off the market temporarily – the classic tactic used by price-fixers to reinforce inflated prices. RealPage refers to this as “revenue protection mode” or “sold out mode.”

Interestingly, the Department has brought the case under both section 1 and section 2 of the Sherman Act. Section 1 prohibits contracts, combinations, and conspiracies in restraint of trade – often referred to as collusion. It involves a de facto agreement – a “meeting of the minds” – between two or more entities. The prime example of a section 1 violation is price-fixing, essentially what is alleged here. Section 2 prohibits monopolization or attempts to monopolize. It involves having monopoly or market power, and using it to sabotage the competitive efforts of rivals through exclusionary conduct. A section 2 violation can be committed by one entity acting alone.

In this case, the section 1 collusion claims focus on anticompetitive benefits to landlords, and secondarily on the fees RealPage charges the landlords for participation in the scheme. The section 2 monopolization claims focus on the anticompetitive benefits to RealPage from the massive data it collects from bringing so many landlords into its scheme. The thrust of the claim is that rival apartment rental management software providers cannot compete with RealPage without entering into similar anticompetitive schemes, and even then, RealPage has the overwhelming and entrenched advantage in having signed up so many landlords under its own anticompetitive scheme and amassing all their data.

The section 1 claim describes long-recognized hub-and-spoke collusion. The section 2 claim is not long-recognized. The factual allegations describe a market share RealPage has achieved, and the barriers to entry resulting from the vast data RealPage has amassed that potential entrants could not get similar access to. There are not, however, allegations of actions that RealPage has taken to maintain a monopoly by sabotaging the competitive efforts of potential rivals – at least not in a way familiar in past antitrust cases. Instead, the alleged monopolization is more of a by-product of the collusive scheme. Indeed, RealPage has moved to dismiss the section 2 claim on the basis that it has not engaged in any exclusionary conduct – that amassing the data is not exclusionary.

The kernel of the Department’s section 2 monopolization claim is that, by creating and managing the hub-and-spoke collusive scheme, RealPage has intentionally made it impossible for a rival to offer the same benefits to landlords without engaging in a similar collusive scheme – in other words, impossible for them to compete on the merits lawfully – and which would likely be futile even if they did attempt it. According to the Department’s information, RealPage controls at least 80 percent of the market for apartment rental management software. No other revenue management company could hope to match RealPage’s access to landlords’ nonpublic, competitively sensitive rental data.

One potential advantage to the Department of bringing a section 2 claim is that the potentially available remedies go beyond ordering the cessation of the unlawful conduct, and include structural relief – that is, in this case, requiring RealPage to divest some of the parts of its operation that collectively enable the unlawful coordination among landlords. Structural relief is not generally available to remedy a violation of section 1. The Department may believe that the anticompetitive conduct alleged here cannot effectively be remedied without breaking apart the arrangement. 

It is also noteworthy that the Department has brought a civil enforcement action, not a criminal prosecution. Ordinarily, when the alleged conduct is clearly price-fixing among competitors, a criminal case is warranted. The courts have long considered price-fixing to be a per se violation, and do not accept any mitigating justifications.^[5] Three considerations likely influenced the Department’s decision. 

First, although the caselaw is ostensibly clear that price-fixing is per se unlawful, the courts have in practice recognized exceptions, when there are novel factual circumstances that courts have not previously considered. The Department may have decided that the factual allegations in this case were potentially too complex to rely on criminal prosecution and the requirement to prove the violation beyond a reasonable doubt. Indeed, the district court in the federal class action in Tennessee ruled that that case could not be brought as a per se violation. 

Second, although section 2 of the Sherman Act explicitly provides for criminal penalties equal to those in section 1, section 2 violations had not, until very recently, been criminally prosecuted for decades. And of the 168 criminal section 2 cases – brought between 1903 and 1977 – all but 20 were multiple-conspirator cases that could have been brought under section 1, and many were. Of the 20 single-defendant cases, only 12 resulted in guilty findings, most as a result of a nolo contendere plea and a fine. Only three cases resulted in prison; two of those involved crimes of violence, and in the other, the individual served only a single month in prison.^[6]

The Biden Antitrust Division revived section 2 criminal prosecutions for the first time in almost 50 years. It has brought three, none of which has reached a verdict or resulted in prison. All were against individuals – not companies – whose conduct clearly met long-established standards for guilt. Two of them involved multiple conspirators and were also brought under section 1.^[7]

Third, if the case were pursued criminally, the Department would have had to go it alone; the states would not have been able to join. Notably, the enforcement actions the Department is pursuing against Google and Apple for monopolization under section 2, each joined by several states, have been civil, not criminal. 

As the case moves forward, RealPage will have ample opportunity to justify its marketing and use of the pricing algorithm.

More broadly, the issue will not be whether the services RealPage provides include some that are lawful and even procompetitive. It will be whether the algorithm-directed pricing system specifically is permissible under the antitrust laws.

And the mere fact that an algorithm is driving the coordinated pricing will not excuse it. As then-Acting FTC Chair Maureen Olhausen remarked, antitrust enforcers evaluating the use of an algorithm in commerce will follow the “guy named Bob” rule – “Everywhere the word ‘algorithm’ appears, please just insert the words ‘a guy named Bob’ … If it isn’t okay for a guynamed Bob to do it, then it probably isn’t ok for an algorithm to do it either.”^[8]

^[1] Available at https://ecf.ncmd.uscourts.gov/doc1/13314442744.

^[2] RealPage, Inc., Rental Software Antitrust Litig. (No. II), 2023 U.S. Dist. LEXIS 230200 (M.D. Tenn. Dec. 28, 2023).

^[3] E.g., United States v. Apple, Inc., 791 F.3d 290, 314 (2d Cir. 2015).

^[4] E.g., id. at 315. See Interstate Circuit v. United States, 306 U.S. 208, 227 (1939) (“Acceptance by competitors, without previous agreement, of an invitation to participate in a plan, the necessary consequence of which, if carried out, is restraint of interstate commerce, is sufficient to establish an unlawful conspiracy under the Sherman Act.”)

^[5] United States v. Socony-Vacuum Oil Co., 310 U.S. 150, 218 (1940).

^[6] See Daniel A. Crane, Criminal Enforcement of Section 2 of the Sherman Act: An Empirical Assessment, 84 Antitrust L.J. 753 (2022).

^[7] Two of those cases were resolved with a guilty plea. Zito pled guilty in September 2022 to attempted monopolization, with an invitation to divide markets that would have given him a monopoly in highway crack-sealing services in Wyoming and Montana. He was sentenced to six months’ home confinement followed by three years’ probation. Tomlinson pled guilty in April 2024 to conspiring to rig bids for forest fire-fighting fuel truck services in Idaho and Nevada and to de-prioritize bids of others. He is apparently still awaiting sentencing. The case charged violations of both section 1 and section 2. https://www.justice.gov/atr/case/us-v-ike-tomlinson-and-kris-bird. The third case is still pending. Martinez and his co-conspirators are charged with violations of both section 1 and section 2 – price-fixing of transmigrante forwarding agency services for shipping vehicles from the United States through Mexico to Central America, and conspiring to monopolize by threatening potential competitors with violence. https://www.justice.gov/atr/case/us-v-carlos-favian-martinez-et-al.

^[8] Should We Fear the Things That Go Beep in the Night? Some Initial Thoughts on the Intersection of Antitrust Law and Algorithmic Pricing,” Maureen K. Ohlhausen, Acting Chairman, U.S. Federal Trade Commission, May 23, 2017, p. 10, https://www.ftc.gov/system/files/documents/public_statements/1220893/ohlhausen_-_concurrences_5-23-17.pdf.

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According to the district court’s recitation of the facts, Fubo and other independent streaming services have been seeking, for years, a license to offer consumers a narrower “skinny bundle” of live sports programming. The Defendants have refused, and have offered live sports programming licenses only in bigger packages that include programming that many consumers have no interest in. The additional undesired programming comes at additional cost to the independent streaming services, which has impaired their ability to offer competitively attractive live sports streaming packages to consumers, and has undermined the promise of the internet to provide consumers with a greater variety of innovative choices. 

Read the full brief.

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A corollary exposure to consumers courtesy of untrammeled data collection and powerful algorithms is enabling a seller to tailor the price it offers to what the seller knows about the particular consumer’s identity, preferences, and situation. This practice has been variously referred to as “individualized pricing” – or “dynamic pricing,” of which the practice is a subspecies – by those who wish to emphasize its potential benefits, or as “surveillance pricing” by those who want to highlight its invasiveness and its potential to be weaponized against vulnerable consumers without their awareness, let alone their consent.[2]

A more apt name might be “bespoke pricing,” as the price is being custom-tailored by the seller to fit the particular customer – made-to-measure.

Bespoke pricing is not entirely new. Some products and services innately require the seller to charge different prices to fit the particular consumer’s situation. Insurance and credit are two prominent examples, where underwriting is used to determine the price to charge the particular buyer based on the risk posed – because the actual cost to the seller – for the claims made on the policy, or for the default on the credit – cannot be determined until after the sale.[3] And it is also customary for larger consumer purchases like homes and cars, where prices are typically the result of negotiations between buyers and sellers.

Dynamic pricing in the broader sense includes charging different prices based on a seller’s sensitivity to fluctuations in supply and demand, which can have the effect of discerning different categories of buyers expected to be willing to pay different prices. One example is airline tickets that for the same flight are priced lower when purchased in advance – more likely by individuals and families planning travel for personal reasons, on a budget – and priced higher when purchased closer to the time of the flight – more likely by persons traveling for business, on a corporate expense account, or persons with an urgent, last-minute need to travel, and no flexibility.

Bespoke Pricing’s Ancient Origins

Bespoke pricing has ancient antecedents, hearkening back to the beginning of commerce and trade.[4] Even in prehistoric times, we can imagine that every exchange that did not occur by force was between two individuals who knew each other, or came to know each other, based on a recognition of each other’s needs and wants. Importantly, it was presumably a mutual recognition. 

Similarly, in a medieval marketplace, merchants exercised their own form of bespoke pricing, sizing up each prospective customer for how much they’d be willing to pay, and testing them with a higher, even exorbitant price initially. But customers could similarly size up the merchant, and could exercise their own recourse, not just to refuse to pay until the price seemed reasonable, but to walk through the marketplace to see what price other merchants were asking and what price they would accept. There was still mutuality, with a rough equivalence of transparency, and of bargaining power – especially when there were competing merchants.

Arrival of the Anonymous Buyer

Bespoke pricing continued into the Industrial Age, as selling increasingly moved into brick-and-mortar stores. Then in the 1870’s, a marketing revolution occurred – the price tag was introduced at a department store, the Grand Depot in Philadelphia, and quickly caught on.[5] Sellers realized they were not as apt to know their customers, and it became more efficient to post – to advertise – a price that all customers could see. This did not always prevent a customer from trying to bargain for a discount. But most customers came to accept the posted price, for most products. And store owners competed by setting the advertised price at a level calculated to appeal to as many consumers as possible, at a high enough price, to maximize profits. The price tag ushered in anonymity for buyers, and sellers coped by standardizing their prices.

Online Shopping Shifts the Balance

The internet promised to be a boon to consumers, making it far easier and more convenient for them to comparison shop across a vast, potentially unlimited number of sellers. And it largely delivered on that promise, and brought new, competitive pressure on sellers to make sure they were offering comparatively attractive deals. But online commerce also engendered a new market, for consumer data, which has been collected, organized, distributed, sold, and cross-referenced on a massive scale – sometimes referred to as “big data.”

Big data has created the potential for sellers to regain the upper hand, fundamentally reversing the anonymity equation. Now the seller’s pricing decisions can be obscured, while the buyer becomes an open book.

Pro-competition? Pro-consumer? Or Neither?

Proponents of bespoke pricing have touted it as a vehicle for promoting competition. And in theory, by enabling a seller to tailor its offer to a particular consumer’s situation, bespoke pricing could incentivize the seller to lower its price for that consumer to a point that wins the sale from some rival seller. Multiple rival sellers engaging in this practice simultaneously could, theoretically, create new possibilities for competition, bringing the efficient allocation of resources to new heights.

In practice, however, it is questionable whether more competition, and lower prices for some consumers, would actually be the result. It would depend on where the rival sellers’ profit-maximizing incentives lead them. Marshaling this increased amount of data into their pricing calculations might well lead them to monitor and coordinate with each other’s pricing, promoting collusion rather than competition.[6]

Moreover, the consumer would be left entirely in the dark about a seller’s price-setting context, while the consumer would be utterly visible to the seller. The seller would have access to vast amounts of data about the consumer, such as the consumer’s previous purchases of or searches for the product or service, and for similar and related products; the consumer’s income, assets, debts, and financial condition and history; other purchases that reveal the consumer’s propensity to spend; activities the consumer and the consumer’s family engage in that manifest a need for or benefit from the product or service; any urgency for that need or benefit; and broadly, any characteristics revealed by the consumer’s web-browsing history, or by other behaviors tracked and fed into the big data maw, that may indicate the consumer is more susceptible to sales-pitch puffery or pressure. 

Granted, those insights might enable a seller to spot a consumer who has thus far not been willing, and is not likely, to purchase at the original offered price, but who has a sufficient need or desire or use for the product or service to be more likely to purchase it at a somewhat reduced price. The seller could even sequentially reduce the offered price until that consumer is willing to buy its product or service rather than a competitor’s product or service. Isn’t that the essence of healthy competition?

If the engagement actually occurred in that fashion, that would indeed constitute competition, and that particular consumer would come out ahead in that instance, as would the seller.

The question is how likely bespoke pricing is to occur in that fashion. The same digital resources that would enable a seller to target price reductions can just as easily enable targeted price increases for consumers who the seller determines are willing to pay more.[7] Those digital resources can also enable the seller to coordinate with other sellers to avoid price reductions when they perceive anticompetitive price-fixing to be in their collective net interest.[8] Sellers engaged in such a price-fixing conspiracy have the incentive and, with those digital resources, the potential ability to retaliate against a seller who selectively cuts prices, because that undermines the stability of the conspiracy. But they do not have an incentive to retaliate against a seller who takes advantage of an opportunity to selectively raise prices.[9]

So all told, the incentives for using bespoke pricing are all too likely to skew in the direction of higher prices. Informed consumer choice is the engine that drives competition; because consumers won’t be as informed, and thus will have little or no agency in the supposed competitive benefits, they are more apt to be taken advantage of than to benefit.

Regardless, this justification is likely to be too abstract to appeal to the typical consumer. When consumers learn that companies are using bespoke pricing, they will assume that they are being subjected to it, without their knowledge or consent, and being taken advantage of. They are apt to react viscerally, to regard it as invasive of their privacy, and offensive to their sense of fairness. When they are told that it’s the “invisible hand”[10] at work, they are likely to conclude that the invisible hand is picking their pocket.

Consumers will see data-charged bespoke pricing as a fundamental reversal of the way the marketplace has worked, to their benefit, for 150 years, since the arrival of the price tag.

Possible enforcement action or legislation

Bespoke pricing has caught the attention of the Federal Trade Commission. The FTC has launched a study of the practice and its effects under its section 6(b) authority, requesting information from eight data intermediary companies that offer pricing products and services that incorporate data about consumers’ characteristics and behavior.[11] The results of the study will inform the FTC, Congress, and other policymakers, and the public, about the practice and its effects. And it will help the FTC determine whether the practice is a violation of the FTC Act and, if so, in what circumstances, or if new rulemaking or legislation is warranted to prohibit or rein it in.

The practice has also been the subject of hearings in the Senate Committee on Banking, Housing, and Urban Affairs, with follow-up letters to Walmart and Amazon inquiring about their use of pricing algorithms.[12] 

Stronger privacy laws 

Enacting stronger data privacy laws, which has been a top priority for CDT since our founding three decades ago,[13] would have many benefits for consumers, among them dramatically curtailing the potential for bespoke pricing.[14] Restricting sellers’ access to consumers’ personal data as a basis for setting prices would limit sellers’ intimate knowledge of their customers, and would confine targeting only to broad categories of consumers, based on factors like geography, timing of purchase, and quantity. With sufficiently strong data privacy protections, we could restore some anonymity to buyer identities, like price tags in a brick-and-mortar store offered.

Restoring anonymity through purchasing agents

An interesting approach to consider, in addition to or as an alternative to legal action or new lawmaking, is using technology to restore anonymity and put power back in the consumer’s hands, by means of one or more intermediaries who could act as a purchasing agent for the consumer. The seller would have no information about the prospective buyer beyond the fact that the buyer is choosing to use the intermediary.

To be most effective, an organization acting as purchasing agent would be able to guarantee – and willing to be held accountable for – preserving the consumer’s anonymity. This should include, for example, that the purchasing agent not ask for, collect, or retain any data beyond what is necessary and proportionate for acting as the consumer’s agent, including for ensuring that the product is delivered at the agreed price and terms.[15] The data should also be carefully protected against access by any third party, as well as by anyone working at the organization not involved in arranging the specific purchase.

Ideally, this would mean eliminating the use of any third parties to process the data. And for further protection, the consumer’s request, and all subsequent communications, would be encrypted.

One good option for a purchasing agent could be an independent, non-profit consumer organization with the capability and integrity to maximize reliability and trustworthiness. Using a non-profit could keep the fee the agent would charge to a nominal amount, close to the amount needed to cover the amortized costs of setting up the service and the expenses of running it.

Consumer Reports is one example of the kind of organization that could be a suitable candidate for establishing such an intermediary purchasing agent. CR has the credibility of a strong brand with consumers, as well as significant resources. And it has experience with two principal components of this approach, anonymous purchasing and agency. Its experience with anonymous purchasing goes back over many decades of purchasing the products it tests in the marketplace, without disclosing that CR is the one purchasing them. More recently, CR has established the Permission Slip,[16] under which a consumer can appoint CR as their authorized agent to contact any number of companies to request that they stop sharing that consumer’s personal data, or that they delete it altogether. The infrastructure for an authorized purchasing agent service would be similar in scale to Permission Slip, matching a consumer with multiple companies.

To the extent that bespoke pricing can actually benefit consumers, and can further the pro-competitive objective of expanding the market, by offering a lower price to consumers who need it to afford to purchase, those consumers can still obtain that benefit by checking prices both ways and comparing them.

Sellers might say they need to charge higher prices to some consumers in order to lower the price to other consumers and still obtain the same overall revenue. But in a marketplace where competition is functioning effectively, a seller will still have incentives to identify prospective buyers who would pay a lower price that would still be profitable to the seller.

Conclusion

Bespoke pricing is likely coming soon, to sellers near you – if it has not already arrived. The technological capability is here, and the incentive to use it will be irresistible.[17] We need to come to terms with how to confront it on the consumer side, so that the online marketplace works for consumers as promised when its arrival was heralded. The FTC investigation and the Senate Banking inquiry are good places to start.

[1] G. Slover & H. Babinski, Is Artificial Intelligence a New Gateway to Anticompetitive Collusion, Center for Democracy & Technology, Oct. 2, 2023, https://cdt.org/insights/is-artificial-intelligence-a-new-gateway-to-anticompetitive-collusion/.
[2] D. Dayen, One Person One Price, The American Prospect, June 4, 2024, https://prospect.org/economy/2024-06-04-one-person-one-price/. An analogous concern has arisen regarding companies’ use of workers’ personal data to discriminate in what they are paid. See, e.g., Veena Dubal, On Algorithmic Wage Discrimination, 123 Columbia Law Rev. 1929 (2023), https://www.jstor.org/stable/27264954.
[3] See FTC Issues Orders to Eight Companies Seeking Information on Surveillance Pricing, July 23, 2024 (concurring statement of Commissioner Ferguson), https://www.ftc.gov/system/files/ftc_gov/pdf/surveillance-pricing-6b-ferguson-concurrence.pdf.
[4] See Federal Trade Commission, Behind the FTC’s Inquiry into Surveillance Pricing Practices, July 23, 2024, https://www.ftc.gov/policy/advocacy-research/tech-at-ftc/2024/07/behind-ftcs-inquiry-surveillance-pricing-practices.
[5]  J. Glorfeld, John Wanamaker Makes a Sale, Cosmos, May 2, 2021, https://cosmosmagazine.com/people/john-wanamaker-makes-a-sale/; B. Wallheimer, Are You Ready for Personalized Pricing?, Chicago Booth, Feb. 26, 2018, https://www.chicagobooth.edu/review/are-you-ready-personalized-pricing#.
[6]  See Slover & Babinski, n. 1; The New Invisible Hand? The Impact of Algorithms on Competition and Consumer Rights, Senate Comm. on the Judiciary, Subcomm. on Competition Policy, Antitrust, and Consumer Rights, Dec. 13, 2023, https://www.judiciary.senate.gov/committee-activity/hearings/the-new-invisible-hand-the-impact-of-algorithms-on-competition-and-consumer-rights.
[7] See C. Dilmegani, Ultimate Guide to Dynamic Pricing in 2024: Roadmap & Vendors, AIMultiple, Jan. 3, 2024, https://research.aimultiple.com/dynamic-pricing/.
[8]  Slover & Babinski, n. 1.
[9]  Id.
[10]  Adam Smith, The Wealth of Nations, 1776.
[11] FTC Issues Orders to Eight Companies Seeking Information on Surveillance Pricing, July 23, 2024, https://www.ftc.gov/news-events/news/press-releases/2024/07/ftc-issues-orders-eight-companies-seeking-information-surveillance-pricing.
[12]  Press release, Brown Demands Answers on Amazon and Walmart’s Use of So-Called “Dynamic Pricing”, May 9, 2024, https://www.banking.senate.gov/newsroom/majority/brown-demands-answers-on-amazon-and-walmarts-use-of-so-called-dynamic-pricing.
[13]  See, e.g., Testimony of Deirdre Mulligan before the Senate Committee on Commerce, Science and Transportation Subcommittee on Communications, Sept. 23, 1998, https://cdt.org/insights/testimony-of-deirdre-mulligan-before-the-senate-committee-on-commerce-science-and-transportation-subcommittee-on-communications/. CDT has also long been emphasizing that data minimization is a key privacy protection. See, e.g., https://cdt.org/insights/report-why-collection-matters-surveillance-as-a-de-facto-privacy-harm/ and https://cdt.org/insights/states-are-letting-us-down-on-privacy/.
[14]  See T. Noble, To Fight Surveillance Pricing, We Need Privacy First, Electronic Frontier Federation, Aug. 5, 2024, https://www.eff.org/deeplinks/2024/08/fight-surveillance-pricing-we-need-privacy-first.
[15] At some point after the purchase price and terms are agreed to, the seller will need a mailing or email address for the buyer to deliver the product or service.
[16]  K. Waddell, How to Take Back Control of Online Data With Apps Like Consumer Reports’ Permission Slip, Consumer Reports, Oct. 3, 2023, https://www.consumerreports.org/electronics/privacy/take-control-of-online-data-with-apps-a5151057853/.
[17]  See, e.g., D. Dayen, The Emerging Danger of Surveillance Pricing, Jacobin, July 9, 2024, https://jacobin.com/2024/07/surveillance-personalized-pricing-data-collection.

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Read the full letter.

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CDT has been supporting laws, in the states and federally, that would require an equipment manufacturer to give independent repair providers access to the documentation, parts, and tools they need, at costs and terms that are equivalent to the most favorable costs and terms it gives to its authorized repair providers and to itself.

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A recent feature article in the December edition of Competition Policy International’s Antitrust Chronicle affirms the importance of ensuring that consumers have a right to choose where to get their digital electronic equipment repaired.

The right to repair products you own has been an inherent incident of ownership for many centuries, and manufacturers should not be permitted to exploit new technological advances to deny it, or to deny the right of independent repair shops to compete to offer repair services.

Efforts are underway to secure these rights, in Congress, in federal agencies, and in state legislatures across the country.

Read a PDF of the full article.

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It references our blog post on artificial intelligence as a new gateway to collusion, a principal focus of the hearing.

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Read the full letter here.

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Roughly 85 percent of adults in the United States interact with the Internet on a daily basis.[1] Commerce over the Internet has in many ways made the lives of Americans easier, more convenient, and streamlined. But has it also opened the door for companies to utilize new and innovative technology to take advantage of their customers, suppliers, and workers by engaging in collusive price fixing? And if so, what can be done about it?

Antitrust scholars have been raising this question for several years,[2] but the new innovations in artificial intelligence are bringing renewed attention to it.

Under the U.S. antitrust laws, unlawful collusion – specifically, price fixing, the form of collusion we focus on here – encompasses any agreement among competing companies to set prices at inflated levels.[3] The law condemns collusion because it subverts the free market and denies consumers the benefits of prices determined by competition, where companies honestly compete against each other to win customers by offering more attractive products and services at more affordable prices.[4] The antitrust laws have traditionally drawn a distinction, for a mix of policy and practicality reasons, between price-fixing agreements and what is referred to as “conscious parallelism.” The distinction lies in that the latter can actually constitute honest competition, with companies separately and independently monitoring each other’s prices in order to look for opportunities to gain an advantage over their competitors and attract new customers.

The former, in contrast, is an agreement to avoid honest competition. Antitrust enforcers, and courts, have recognized that conscious parallelism is not without its problems. Companies can monitor each other’s prices in order to see how high they can inflate their own prices, and this can result in prices that are higher than if vigorous competition were taking place. Yet, enforcers and courts have concluded that it is impossible, as a practical matter, to identify and stop conscious parallelism that inflates prices without risking interfering with honest competitive responses to normal price and value fluctuations of goods and services.[5] So they look for indicators that the price monitoring and adjustments are not independent but rather are mutual, intended coordination.

This grey area between independent price monitoring in the interest of honest competition and orchestrated price coordination is also referred to as “tacit collusion” – recognizing that it has the same adverse effect on competition as intentional, “express” collusion, even though it is not treated as unlawful under the antitrust laws.[6]

The use of computer algorithms – and increasingly, their use in more sophisticated artificial intelligence – to manage companies’ determination of optimum pricing has re-opened the questions around tacit collusion. Is it technologically feasible for an algorithm to engineer inflated prices by tacit collusion? Does tacit collusion become easier and more likely with the aid of an algorithm? Where might it occur? How would it be detected by antitrust enforcers? Could current antitrust law be applied and adapted to better address the resulting harms to competition and consumers?

An algorithm can perform at light speed the component operations involved in determining optimum pricing – monitoring the prices of all competitors, and the purchases made at those prices, at various locations throughout a territory; calculating the effects of various changes in price; and adjusting accordingly. Because of this, the means of collusion are far more powerful, and the potential scale of harm is exponentially greater. Furthermore, the coordinated movements can be more subtle individually when they can occur multiple times every millisecond; this also makes them harder to detect.

In the past, collusive price coordination, whether express or tacit, has been shown to be easier to accomplish, and therefore more likely to occur, in markets where the following are true:

• the companies selling are relatively few, and the barriers to new entry by other companies are relatively high, due to high initial investment costs or other reasons, so consumers have few choices;
• the product or service is homogeneous, meaning the product or service offered by one company is essentially the same as the product or service offered by the other companies, so it is easier for the companies to converge on a price; and
• sales tend to be frequent and regular, and the price is transparent, so it is easier for the companies to monitor for changes.

These market characteristics make it easier for companies to coordinate their collusion, and easier for them to enforce the collusive agreement by facilitating the detection of any deviation from the agreed-upon inflated price by a company seeking to sneak an advantage over the others.[7] A classic example of a market susceptible to collusion is retail sales of gasoline. Tech markets that exhibit these characteristics include virtual private networks (VPNs), online ride-hailing, digital advertising, and cloud storage, among others. (And with the growth of e-commerce, the market for any product or service can be a tech market.)

An algorithm could be a powerful tool in aid of a price-fixing agreement, by making it easier to monitor the marketplace, to calculate the inflated price to which all companies will agree, to detect when a company is not adhering to the agreed price, and to determine and impose an effective “punishment” in response.[8]

Some of the market characteristics noted above may not be as necessary for algorithmic collusion, thanks to the light-speed monitoring and adjustments that algorithms are capable of. For example, the products and services may not have to be as homogeneous, or priced exactly the same, as long as they are similar enough that consumers see them as reasonable substitutes for each other. An algorithm can more easily take into account variations and assign appropriate price differentials that still result in prices being inflated above their competitive market levels.

But if the use of an algorithm could facilitate coordinated pricing, it could also make it easier for enforcers to detect and prove it. Proving unlawful price fixing requires evidence of mutual anticompetitive intent – of a de facto agreement – a “meeting of the minds,” a mutually communicated understanding, a “conscious commitment to a common scheme.”[9] This evidence can be circumstantial, but if circumstantial evidence is relied upon, it cannot be equally consistent with conscious parallelism; it must suggest the existence of a de facto agreement.[10] This circumstantial evidence is likely to be present in a similar fashion whether or not an algorithm is used to facilitate the agreement.

But the use of an algorithm could make it easier for enforcers and courts to confidently ascribe anticompetitive intent to interactions that they previously had to give the benefit of the doubt and accept as procompetitive or benign – as mere conscious parallelism. An algorithm can provide a window into the mind of the programmer, almost like a diary entry or a “smoking gun” email communication – if enforcers know what to look for.

For example, an algorithm could be programmed to test where the sustainable maximum price is, by experimenting with incremental price increases to see if other companies follow. Or it could be programmed to promptly follow another company’s price increase, but to be slower in following another company’s price decrease. Or it could be programmed to retaliate against another company’s price decrease with an even greater price decrease of its own, targeted at the other company and the places where it has most of its sales – thereby not only erasing the other company’s opportunity for increased sales it hoped to achieve by its price-cutting but punishing it even further by taking away some of its existing customers.

These anticompetitive actions, if performed discreetly by humans, could be difficult for enforcers to detect, and even more difficult to ascribe intent to. However, an algorithm’s code can provide a roadmap into the mind of the human programmer. And if the programmer was acting as the agent of the company using the algorithm, or acting at its request, the intent revealed in the programming could be ascribed to the company. Evidence of an agreement is still needed to prove a case of explicit unlawful collusion. But if more than one company is using the same algorithm, or algorithms designed in conjunction with each other, or algorithms programmed to monitor each other, it may be easier to infer an agreement by showing that both companies share the same anticompetitive intent.

Along with examining the algorithms’ code and how companies are using them, enforcers can also look for traditional tell-tale indicators of possible collusion as cause for closer investigation. For example, prices that seem “stickier” in staying high despite changes in cost or consumer demand.[11] There might also be a pattern of price changes suggesting retaliation against a price-cutter, and subsequent harmonization could be circumstantial evidence of an agreement between the price-cutter and the retaliator that brings the penitent price-cutter back into the collusive fold.

But what if there is no indication that the company or the programmer had premeditated intent for the algorithm to facilitate collusion? What if the initial programming was ostensibly neutral, and the algorithm has “figured out” on its own (i.e., through “machine learning”) how to coordinate the company’s pricing with other companies in a way that leads to everyone’s prices settling at higher levels, and with higher profits for all participating companies, as a result of their not competing?

Can the current antitrust laws effectively address these new challenges? What adjustments to those laws might be useful?

As explained above, an important part of the reason tacit collusion has been accepted, or acquiesced in, is that it is so difficult to confidently judge the motive for what appears to be coordinated pricing. And having the algorithm available to examine could help clarify that motive, allowing enforcers to identify coding instructions that are inconsistent with pricing competitively. So if two or more companies selling similar products or services are using algorithms that are programmed to enable anticompetitive pricing, that can be evidence of, at minimum, a deliberate facilitating practice that foreseeably leads to inflated prices. That might be a rule-of-reason violation, or it might even give rise to a presumption of a per se price-fixing agreement. With machine learning, on the other hand – where the algorithm is given general instructions to optimize pricing and “learns” on its own to do so through coordination with other companies’ pricing – the companies could try to further distance themselves from the algorithm’s actions.

They could claim that they did not set out to program their algorithm to coordinate with competitors to keep prices inflated and that they are as surprised as anyone that their algorithm may have figured out on its own how to do so. However, even in this situation, the algorithm provides a useful window. Here it’s a window that also enables the company using it – or the programmer on the company’s request – to monitor and make follow-up assessments of how the algorithm is operating in practice. So enforcers and courts could make a similar presumption that holds companies legally accountable for setting their pricing algorithms loose on the marketplace with a “set it and forget it” blessing, and never following up on the algorithm’s functioning and capability.

In order for these situations to give rise to antitrust liability, enforcers and the courts would need to recognize that the traditional reasons that conscious parallelism had to be given the benefit of the doubt no longer apply. Today, it is possible to “read the mind” of an algorithm, so the company employing it can be held accountable when it fails to do so in order to keep the algorithm from engaging in coordination that leads to inflating prices above competitive levels. This higher enforcement sensitivity enabled by “investigating the algorithm” would still be entirely consistent with traditional antitrust principles – still deferring to companies acting independently in the free marketplace to decide how to competitively offer their products and services. Enforcers, and the courts, would need to accept that a commensurate adjustment in interpreting existing law is warranted, along with developing more technologically sophisticated analytical techniques.

If enforcers and the courts are unwilling to take these interpretive steps, or if these interpretive steps prove ineffective, Congress should consider enacting legislation to clarify the law to better enable effective antitrust enforcement against collusion by algorithm, while holding to traditional antitrust principles.

Increased transparency, through required reporting of how algorithms are designed and used, could help facilitate the detection of collusion by algorithm.[12] (Proceeding with caution, mindful that increased transparency can be a double-edged sword, also potentially facilitating anticompetitive coordination among companies.) Enforcers could also educate themselves by using algorithmic models to simulate conditions conducive to algorithmic tacit collusion and run tests to determine if, when, and how it occurs.[13]

Unless enforcers and the courts act, or Congress does, algorithms have the potential to supercharge price coordination and to lead to widespread price hikes, aggrandizing company profits at the expense of consumers forced to pay more than they should.

[1] Andrew Perrin & Sara Atske, About Three-in-Ten U.S. Adults Say They are ‘Almost Constantly’ Online, Pew Rsch. Ctr. (Mar. 26, 2021), https://www.pewresearch.org/short-reads/2021/03/26/about-three-in-ten-u-s-adults-say-they-are-almost-constantly-online/.

[2] E.g., Ariel Ezrachi & Maurice E. Stucke, Artificial Intelligence & Collusion: When Computers Inhibit Competition, 2017 U. Ill. L. Rev. 1775 (2017); Federal Trade Commission, Hearings on Competition and Consumer Protection in the 21st Century, Hearing 7, Session 1, Algorithmic Collusion (Nov. 14, 2018), https://www.ftc.gov/media/71288.

[3] Collusion can also take place in the other direction, with companies as buyers, agreeing to keep the prices they pay for goods and services they buy, and the wages and benefits they pay their workers, at depressed levels. Platforms for commercial transactions are considered to be sellers of services to users on both sides of the platform. And there are other forms of collusion besides agreements directly about price. For example, companies can “allocate markets” as noncompete zones, by agreeing to sell to different sets of customers. Or they might agree to slow innovations and improvements in product and service quality to a pace that is more profitable for all of the colluding companies. All of these forms of collusion cause similar harm to competition and the free market. And they could all potentially be impacted by computer algorithms and artificial intelligence. Here, we focus on price-related collusion by companies acting as sellers of goods and services, which we will refer to as “price fixing.”)

[4] Price Fixing, Bid Rigging, and Market Allocation Schemes: What They Are and What to Look For, Dep’t. of Just. Antitrust Div. (2021), https://www.justice.gov/atr/file/810261/download.

[5] See, e.g., In re Text Messaging Antitrust Litig., 782 F.3d 867 (7th Cir. 2015).

[6] See Ariel Ezrachi & Maurice E. Stucke, Sustainable and Unchallenged Algorithmic Tacit Collusion, 17 Nw. J. Tech. & Intell. Prop. 217, 224 (2020).

[7] See Marc Ivaldi et al., The Economics of Tacit Collusion, Eur. Comm’n (March 2003), https://ec.europa.eu/competition/mergers/studies_reports/the_economics_of_tacit_collusion_en.pdf.

[8] See Ariel Ezrachi & Maurice E. Stucke, Algorithmic Collusion: Problems and Counter-Measures, at 3-4, Roundtable on Algorithms and Collusion, OECD, June 2017, https://one.oecd.org/document/DAF/COMP/WD%282017%2925/en/pdf.

[9] Monsanto Co. v. Spray-Rite Serv. Corp., 465 U.S. 752, 764, 768 (1984).

[10] See Ezrachi & Stucke, supra note 5, at 222-23.

[11] See Francisco Beneke & Mark-Oliver Mackenrodt, Remedies for Algorithmic Tacit Collusion, 9 J. of Antitrust Enf’t., at 161-62 (2021).

[12] Ezrachi & Stucke, supra note 2, at 1806-07.

[13] Ezrachi & Stucke, supra note 5, at 258.

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