Competition Law and Hospitality Dynamic Pricing Algorithms

Introduction

Hospitality dynamic pricing algorithms are software systems used by hotels, resorts, casinos, online travel agencies (OTAs), and hotel-management platforms to change room prices automatically in response to variables such as:

• occupancy and remaining inventory;
• booking velocity;
• historical demand;
• competitor prices;
• seasonality;
• events and holidays;
• cancellation rates;
• customer-search behaviour;
• length of stay;
• room type;
• distribution channel; and
• sometimes geographically or individually targeted demand information.

Dynamic pricing is not inherently anti-competitive. A hotel may independently use its own demand, capacity and historical data to adjust prices. The competition-law problem arises where an algorithm becomes a mechanism through which competitors coordinate prices, exchange competitively sensitive information, suppress discounting, or indirectly eliminate independent pricing decisions.

The modern legal question is therefore not simply:

“Did the algorithm raise prices?”

It is:

“Did the algorithm replace independent competitive decision-making with coordinated decision-making?”

This distinction has become particularly important in hotel-room pricing. In 2026, the U.S. Third Circuit's decision in Cornish-Adebiyi v. Caesars Entertainment allowed a hotel algorithmic price-fixing claim to proceed, while the Ninth Circuit's earlier Gibson v. Cendyn decision reached a substantially more defendant-friendly conclusion on different pleadings. These cases demonstrate the rapidly developing law of algorithmic hospitality pricing.

I. Meaning of Hospitality Dynamic Pricing Algorithms

A conventional hotel pricing system might operate as follows:

Demand data → Revenue-management system → Recommended room rate → Hotel manager → Consumer price

A more sophisticated system may operate:

Hotel A data + Hotel B data + Hotel C data + market data → Common algorithm → Individual recommendations → Hotel prices

The second model creates substantially greater competition-law risk.

Example

Suppose five competing hotels provide a common software provider with:

• current room rates;
• occupancy;
• expected vacancies;
• future availability;
• discounts;
• cancellation information; and
• pricing strategies.

The software processes the information and recommends:

Hotel A: ₹15,000
Hotel B: ₹15,500
Hotel C: ₹14,800
Hotel D: ₹15,200
Hotel E: ₹15,100

If the hotels independently decide whether to accept those recommendations using genuinely independent information, the legal position is different from a situation where:

1. competitors agree to provide confidential data;
2. the algorithm uses competitors' non-public data;
3. the software recommends higher prices to all participants;
4. hotels are expected to follow the recommendations; and
5. the system discourages deviations.

The latter may constitute algorithmic coordination or hub-and-spoke price fixing.

II. Competition-Law Issues Created by Dynamic Pricing

1. Algorithmic price fixing

The most serious concern is price fixing through software.

Traditional price fixing might involve:

Hotel A + Hotel B + Hotel C → agreement → common price.

Algorithmic price fixing can instead look like:

Hotel A → common platform ← Hotel B

Hotel C → common algorithm ← Hotel D

↓

Coordinated pricing outcome

The absence of a traditional meeting or telephone conversation does not necessarily eliminate the possibility of an unlawful agreement.

The U.S. FTC and DOJ expressly stated in the hotel algorithm litigation that competitors cannot lawfully cooperate to set prices merely because the cooperation occurs through an algorithm rather than human employees. They also emphasized that an agreement to use shared pricing recommendations may be unlawful even when hotels retain some discretion to override the recommendation.

III. Hub-and-Spoke Theory

Dynamic pricing algorithms particularly raise the hub-and-spoke conspiracy issue.

Structure

Hotel A
↘
Hotel B → Algorithm Provider ← Hotel C
↗
Hotel D

The algorithm provider is the hub and the competing hotels are the spokes.

A traditional hub-and-spoke conspiracy generally requires:

1. a relationship between each spoke and the hub; and
2. some form of horizontal coordination or "rim" connecting the competing spokes.

The difficult question is whether the hotels' common adoption of a pricing system, combined with knowledge of its operation and expected pricing effects, is sufficient to establish that horizontal element.

This issue is central to Cornish-Adebiyi and Gibson.

IV. Six Major Case Laws

1. Cornish-Adebiyi v. Caesars Entertainment Inc. — Third Circuit, 2026

This is presently one of the most important hospitality algorithmic-pricing cases.

Casino-hotel guests alleged that competing Atlantic City casino hotels used Cendyn's Rainmaker pricing software to coordinate hotel-room prices.

The allegations included:

• hotels supplying non-public pricing and occupancy information;
• the common algorithm processing competitor information;
• algorithmically generated room-rate recommendations;
• recommendations being incorporated into hotel systems;
• hotels allegedly accepting recommendations approximately 90% of the time; and
• restrictions or disincentives concerning overrides.

The Third Circuit reversed dismissal and held that the allegations were sufficient at the pleading stage to support an inference of a conspiracy to fix prices.

The significance is substantial: the court recognized that algorithmic coordination may provide the mechanism through which competitors achieve price coordination even without conventional direct communications.

Principle

A common algorithm can potentially serve as the mechanism for horizontal price coordination.

The case does not mean that every hotel using the same revenue-management software violates competition law. The allegations concerning information sharing, common pricing recommendations, implementation and compliance were particularly important.

2. Gibson v. Cendyn Group, LLC — Ninth Circuit, 2025

This case involved hotel guests who alleged that Las Vegas Strip hotels used Cendyn's pricing software and thereby charged artificially inflated prices.

The Ninth Circuit affirmed dismissal.

The court distinguished between:

• independent hotels separately licensing the same software; and
• an actual agreement among competitors to restrain competition.

The court concluded that merely showing that competing hotels independently purchased the same pricing software, followed by higher prices, was insufficient to establish a Section 1 restraint.

Principle

Common use of pricing software + higher prices ≠ automatically an antitrust conspiracy.

There must be an adequate causal and legal connection between the challenged agreements and an anticompetitive restraint.

Importance

Gibson is especially important because it prevents competition law from treating ordinary adoption of sophisticated revenue-management software as inherently unlawful.

3. RealPage Algorithmic Pricing Litigation — U.S. DOJ, 2024–2025

Although involving residential rental housing rather than hotels, the RealPage litigation is highly relevant to hospitality algorithms because the underlying mechanism is remarkably similar.

Competing landlords allegedly supplied confidential pricing and occupancy information to RealPage. The algorithm then used the information to generate pricing recommendations.

The DOJ alleged violations of Sections 1 and 2 of the Sherman Act.

The theory was essentially:

Competitors' confidential data → common algorithm → pricing recommendations → reduced independent competition

The DOJ emphasized that antitrust law applies even where the coordination mechanism is an algorithm rather than a human agreement.

The DOJ later imposed significant restrictions through a 2025 RealPage consent judgment, including restrictions concerning the use of current competitive pricing information and monitoring requirements.

Hospitality relevance

The analogy is powerful:

4. Duffy v. Yardi Systems

Duffy v. Yardi Systems is another important algorithmic pricing case concerning the use of competitor information by revenue-management software.

The allegations concerned competing landlords using algorithmic pricing technology to generate rental recommendations from market information.

A U.S. district court allowed significant algorithmic price-fixing allegations to proceed at the pleading stage.

The case is relevant because it illustrates the proposition that an algorithm may facilitate a traditional antitrust theory rather than create a completely new category of competition law.

Principle

The fact that competitors technically make the final pricing decision does not necessarily eliminate liability where the surrounding arrangement allegedly removes meaningful independent pricing.

This reasoning is particularly relevant to hotels that argue:

"The algorithm only recommends the price; management makes the final decision."

That fact is important, but not necessarily dispositive.

5. HRS–Hotel Reservation Service — German Bundeskartellamt

The German competition authority's proceedings against HRS concerned hotel booking and most-favoured-nation/rate-parity clauses.

HRS required hotels to maintain price parity, preventing them from offering lower room rates through alternative channels.

The competition concern was that such clauses could restrict hotels' freedom to determine prices across distribution channels and reinforce the market power of the intermediary.

The German competition authority treated the restrictive clause as a serious competition concern.

Principle

Competition law can be concerned not only with an algorithm directly fixing prices but also with contractual mechanisms that constrain independent hotel pricing.

This becomes particularly relevant when dynamic pricing algorithms operate together with:

• OTA rate-parity clauses;
• minimum-price provisions;
• restrictions on direct-booking discounts;
• parity requirements; and
• automated monitoring of hotel prices.

Hotel rate-parity arrangements have generated extensive European competition-law analysis.

6. Expedia / Hotel Rate-Parity Proceedings — European Competition Authorities

European competition authorities have examined hotel distribution arrangements involving OTAs and contractual restrictions on hotel pricing.

The underlying concern is that a dominant or powerful OTA may prevent hotels from offering different prices through competing distribution channels.

For dynamic pricing, this creates a significant interaction:

Algorithmic pricing + OTA market power + rate parity

may potentially reduce the ability of hotels to compete through:

• direct discounts;
• loyalty pricing;
• alternative booking platforms;
• mobile-only pricing;
• last-minute promotions; and
• differentiated distribution costs.

Principle

A hotel may technically have an algorithm capable of changing prices, but competition can still be weakened if contractual restrictions prevent the hotel from actually exercising that pricing freedom.

V. Additional Important Case: FTC/DOJ v. Hotel Algorithmic Pricing Theory

The 2024 FTC/DOJ statement in Cornish-Adebiyi deserves separate attention even though it was not itself a final merits judgment.

The agencies made two particularly important propositions.

First

A plaintiff does not necessarily need to identify a traditional direct communication between competitors if the alleged arrangement involving the algorithm provider can establish concerted action.

Second

Hotels cannot avoid antitrust law by saying:

"We only agreed to use the algorithm's recommendation."

The agencies specifically stated that shared pricing recommendations can raise Section 1 concerns even where individual participants retain some discretion.

VI. Dynamic Pricing Versus Algorithmic Collusion

The distinction can be summarized as follows:

VII. When Does Algorithmic Pricing Become Illegal?

There is no simple rule that:

Algorithm = illegal.

Instead, competition authorities and courts examine the architecture and commercial operation of the system.

Important factors include:

1. Source of data

Is the algorithm trained on:

• the hotel's own historical data; or
• competitors' confidential, current data?

The second presents substantially greater risk.

2. Nature of information

Risk increases where the system receives:

• current prices;
• future prices;
• occupancy;
• vacancies;
• inventory;
• discounts;
• promotional plans;
• cancellation data; and
• strategic pricing intentions.

3. Degree of price autonomy

A hotel genuinely free to reject the algorithmic recommendation is in a different position from a hotel that is commercially or contractually expected to follow it.

4. Override mechanisms

An override function is useful but not automatically sufficient.

If the algorithm:

• penalizes overrides;
• requires managerial justification;
• scores hotels on compliance;
• restricts deviations; or
• makes deviations commercially impractical,

the apparent independence may be questioned.

This allegation was significant in Cornish-Adebiyi.

5. Algorithm transparency

Competition authorities may ask:

• What data does the algorithm ingest?
• Whose data is included?
• How frequently is it updated?
• Does it use competitor-specific data?
• Does it identify individual competitors?
• Does it recommend prices above competitive levels?
• Does it monitor compliance?
• Does it punish deviation?

VIII. Relevant Competition-Law Doctrines

A. Agreement between competitors

The central issue under many antitrust regimes is whether competing hotels have entered into an agreement or coordinated conduct.

The agreement does not necessarily have to be a written contract saying:

"We agree to fix room prices."

Coordination can potentially arise from the structure and operation of the commercial relationship.

B. Hub-and-spoke conspiracy

The algorithm provider may constitute the hub.

Hotels constitute the spokes.

The critical issue becomes whether there is sufficient evidence of a horizontal relationship between the spokes.

C. Exchange of competitively sensitive information

Even where direct price fixing cannot be established, systematic exchange of current, confidential information may itself create competition concerns.

Particularly sensitive information includes:

• future prices;
• planned promotions;
• occupancy forecasts;
• room availability;
• expected demand;
• individual hotel revenue targets.

D. Concerted practices

Competition law in jurisdictions following the EU model may capture forms of coordinated behaviour that fall short of a conventional contractual agreement.

This is especially relevant where algorithmic systems create repeated interaction and rapid price alignment.

IX. EU Competition-Law Perspective

Article 101 TFEU is relevant where algorithmic arrangements involve coordination between independent hospitality businesses.

Potential concerns include:

Article 101(1)

• price fixing;
• exchange of commercially sensitive information;
• market allocation;
• restrictions on output;
• restrictive distribution arrangements.

Article 101(3)

An algorithmic arrangement might theoretically be defensible if it creates efficiencies benefiting consumers and satisfies the relevant exemption conditions.

For example, software that:

• improves inventory utilisation;
• reduces forecasting costs;
• reduces empty rooms; and
• produces genuine consumer benefits

is not automatically prohibited.

But efficiencies cannot simply be asserted. The overall arrangement must satisfy the applicable legal requirements.

X. Indian Competition-Law Perspective

In India, the principal framework is the Competition Act, 2002.

Dynamic pricing algorithms may implicate:

Section 3

Anti-competitive agreements.

Particularly relevant is Section 3(3), where competitors coordinate with respect to:

• prices;
• purchase or sale conditions;
• output;
• markets; or
• other competitive parameters.

Section 4

Abuse of dominant position may become relevant where a dominant OTA, hotel chain or technology intermediary uses its market power to impose restrictive pricing conditions.

Section 5

Algorithmic pricing may also become relevant in combinations involving large hotel platforms, OTAs or technology providers where consolidation affects competition.

XI. Hotel OTA Rate Parity and Dynamic Pricing

One of the most important practical issues is the combination of:

Dynamic pricing + OTA dominance + rate parity

Suppose a hotel wants:

• ₹10,000 on its own website;
• ₹9,500 for loyalty members;
• ₹9,200 on a smaller OTA.

An OTA's parity obligation may prevent the hotel from offering these differentiated prices.

Now add an algorithm that monitors all hotel prices.

The competition concern becomes considerably more complicated because the technology can automatically detect and discourage deviations.

Thus, an algorithm may transform a contractual restriction into a continuous enforcement mechanism.

XII. Individualised or Surveillance Pricing

Another emerging issue is personalised pricing.

A hotel algorithm could potentially consider:

• user's location;
• browsing history;
• device;
• booking history;
• loyalty status;
• time of search;
• demand urgency;
• previous purchasing behaviour.

This raises two distinct legal questions.

Competition question

Does the practice reduce competition or exploit market power?

Consumer-protection/data question

Is the consumer being deceptively or unfairly charged based upon personal information?

Therefore, competition law may overlap with:

• consumer protection;
• privacy law;
• data protection;
• unfair commercial practices.

The increasing regulatory scrutiny of personalised or surveillance pricing demonstrates that algorithmic price differentiation is broader than conventional dynamic pricing.

XIII. Algorithms and Tacit Collusion

A particularly difficult theoretical problem is tacit algorithmic collusion.

Suppose:

Hotel A's algorithm sees Hotel B's price.

Hotel A raises its price.

Hotel B's algorithm sees Hotel A's price.

Hotel B raises its price.

The process repeats.

Eventually:

A ₹10,000 → B ₹10,200 → A ₹10,400 → B ₹10,600

No hotel explicitly communicates with the other.

Is that illegal?

Not necessarily.

Competition law traditionally distinguishes between:

independent parallel conduct

and

concerted conduct involving an agreement or coordination.

Therefore, simply observing competitors' prices and independently responding to them does not automatically establish unlawful price fixing.

The risk increases substantially where competitors intentionally design or adopt a system to facilitate coordination.

XIV. Why Hospitality Markets Are Particularly Vulnerable

Hotels have several characteristics that make algorithmic coordination especially significant.

1. Highly perishable inventory

An unsold room tonight cannot ordinarily be sold tomorrow as the same inventory.

2. Repeated interaction

Hotels compete against the same competitors every day.

3. High transparency

OTA platforms make competitor prices extremely visible.

4. Centralised technology

A relatively small number of revenue-management providers can serve many hotels.

5. Rapid price changes

Algorithms can alter prices much faster than human managers.

6. Geographic concentration

In markets such as Las Vegas, Atlantic City, resort destinations or major event cities, a relatively small number of hotels may compete intensely for the same consumers.

XV. Competition-Law Risk Matrix

XVI. Compliance Framework for Hotels

Hotels using dynamic pricing systems should establish an algorithmic competition-compliance programme.

Step 1 — Data mapping

Identify every data source entering the algorithm.

Step 2 — Classify information

Separate:

• internal data;
• public data;
• aggregated data;
• historical data;
• competitor-specific data; and
• current confidential competitor information.

Step 3 — Restrict sensitive inputs

Current competitor-specific pricing and strategic information should receive particular scrutiny.

Step 4 — Preserve independent decision-making

Hotel management should retain genuine authority to determine prices independently.

Step 5 — Document overrides

Hotels should document legitimate reasons for rejecting or modifying algorithmic recommendations.

Step 6 — Audit the algorithm

Regularly examine whether the algorithm:

• systematically raises prices;
• suppresses discounts;
• produces unusually parallel prices;
• uses competitor-sensitive information;
• penalises deviation.

Step 7 — Contractual safeguards

Contracts with algorithm providers should address:

• permissible data;
• competitor information;
• confidentiality;
• use of aggregated information;
• compliance obligations;
• audit rights;
• algorithm modifications.

XVII. Key Evidentiary Indicators

In litigation, the following evidence could be particularly important:

1. software-provider contracts;
2. algorithm documentation;
3. source-code or model documentation where discoverable;
4. data dictionaries;
5. API records;
6. hotel communications with the software provider;
7. competitor-data sharing arrangements;
8. override statistics;
9. pricing recommendation histories;
10. actual hotel prices;
11. internal compliance policies;
12. training materials;
13. communications among competing hotels;
14. evidence concerning algorithm adoption; and
15. evidence showing whether hotels independently deviated from recommendations.

The 90% acceptance allegation in Cornish-Adebiyi illustrates why implementation behaviour may matter as much as the formal terms of the software contract.

XVIII. Important Distinction: Common Software Is Not Automatically Illegal

This is perhaps the most important lesson from the developing case law.

Scenario A

Ten hotels independently purchase the same revenue-management software.

The software uses:

• each hotel's own data;
• public market information; and
• independently generated forecasts.

Hotels remain free to reject recommendations.

Risk: relatively low.

Scenario B

Ten hotels provide the same software provider with:

• current room rates;
• future pricing plans;
• occupancy;
• inventory;
• discounts.

The software combines the information and gives each hotel recommendations based partly upon its competitors' confidential information.

Hotels overwhelmingly follow the recommendations.

Risk: substantially higher.

That distinction explains why Gibson and Cornish-Adebiyi can produce different outcomes despite involving similar Cendyn-related technology.

XIX. Six-Case Comparative Summary

XX. Overall Legal Position

The emerging competition-law rule can be expressed as follows:

Dynamic pricing is generally a legitimate business practice; algorithmic coordination among competitors is not.

The key distinction is independent optimisation versus coordinated optimisation.

A hotel may lawfully say:

"Our algorithm tells us demand is high, so we will increase our room price."

The competition-law risk becomes much greater where the commercial arrangement effectively says:

"All competing hotels feed their confidential pricing information into one system, the system recommends coordinated prices, and the hotels are expected to follow those recommendations."

The latest U.S. hospitality cases make this distinction particularly important. Gibson demonstrates that independent adoption of common software does not by itself establish an unlawful restraint, while Cornish-Adebiyi shows that sufficiently detailed allegations concerning shared non-public information, common algorithmic recommendations and implementation may be enough to survive dismissal.

Conclusion

Hospitality dynamic pricing algorithms sit at the intersection of revenue management and competition law.

The technology itself is not the problem. Algorithms can produce substantial efficiencies by forecasting demand, reducing unsold inventory and allowing hotels to respond quickly to changing market conditions.

The competition-law danger arises when the algorithm:

• aggregates competitors' confidential information;
• facilitates common pricing;
• reduces independent decision-making;
• discourages competitive undercutting;
• enforces adherence to common recommendations; or
• is combined with restrictive OTA arrangements.

Accordingly, regulators and courts are increasingly likely to examine how the algorithm works, what information it receives, who controls it, how hotels use its recommendations, and whether competitors remain genuinely independent.

The most important modern proposition is therefore:

An algorithm is not a legal shield. If human competitors could not lawfully agree to coordinate their prices, they cannot necessarily achieve the same result merely by delegating the coordination to software.