The Pricing Regulation Trend Every Hotel Tech Leader Should Understand

New legislation in California has put revenue management algorithms under formal regulatory scrutiny — and the industry should pay attention. California's AB 325, effective January 1, 2026, prohibits any pricing algorithm used by two or more parties that ingests competitor data to set or influence prices. Under the current reading of the statute, there is no carveout for publicly available rate information.Change 4 Your system's pricing algorithm cannot use comp set rate data in its recommendations.

For FLYR Hospitality customers, this is a model approach question we resolved before it became a regulatory one.Change 2 Our algorithm has never been built that way — not in response to legislation, but because of how we believe revenue science should work.

The Regulatory Landscape

AB 325 is not an isolated event. It reflects a convergence already well underway across major markets.

In Europe, the European Commission confirmed multiple active investigations into suspected algorithmic pricing violations in July 2025. Its acting Director General of DG Competition described "a large-scale exercise underway" at the 2026 ABA Antitrust Spring Meeting. The legal basis already exists: under Article 101 of the Treaty on the Functioning of the European Union, anticompetitive conduct can be pursued even without an express agreement between parties — meaning algorithms that reduce pricing uncertainty between competitors are already within the scope of conduct competition authorities have investigated under Article 101.Change 5

In the UK, the Competition and Markets Authority described algorithmic pricing as an "area of focus and concern" in September 2025 and has been running a dynamic pricing review across sectors since late 2024. It has had legal authority over pricing algorithm conduct since 2016. Canada's Competition Bureau released a discussion paper on algorithmic coordination risks in June 2025. An October 2025 OECD report found broad convergence across all G7 jurisdictions in how competition authorities are approaching the issue.

The direction of travel is clear. What regulators are zeroing in on is not pricing software broadly — it's the data that feeds the algorithm.

How FLYR Hospitality Is Built

FLYR Hospitality's revenue optimization algorithm operates independently for each property. Each hotel's model is trained and runs on that hotel's own data:

• Reservation and stay data from the hotel's connected Property Management System — proprietary operational data, not shared with or derived from any other property
• Event-based, non-pricing demand signals covering public holidays, local events, and major conferences — these inform demand forecasting and contain no pricing data from any source
• Market data — rate shopping feeds, STR benchmarking, competitive set information — can be displayed within the FLYR Hospitality platform, giving revenue managers full visibility into their competitive environment. That data is not ingested by the algorithm and plays no role in generating pricing recommendations. The separation is by design.

Revenue managers can input strategic influences that guide how the algorithm explores pricing decisions. Those parameters reflect the revenue manager's commercial judgment, not market rate data.Change 3 The algorithm handles optimization; the revenue manager defines the strategy.

Why Property-Led Algorithms Produce Better Revenue Science

Algorithms that anchor on competitor pricing signals are, by construction, reactive. They generate recommendations in response to what others are doing — not from a precise understanding of what drives demand for a specific property. And when competing properties have different cost structures, positioning, or demand drivers, their rates are often poor signals for what your property should charge.

FLYR Hospitality's models are trained on the demand patterns, booking windows, length-of-stay behavior, and segment mix specific to each hotel. Hotels with differentiated positioning or distinct demand drivers tend to see the widest gap between what a property-led model recommends and what a comp-set-reactive one would produce. The result is a pricing strategy built around what your property can actually achieve — not what your neighbors are doing.

Rate intelligence has a legitimate role in revenue management. Knowing where your competitive set is positioned is useful context. But when a pricing algorithm generates recommendations structurally dependent on competitor data, it is optimizing for market position rather than property-specific revenue maximization.Change 6 Those are not the same thing.

What This Means for Your RMS Decision

Regulatory scrutiny of algorithmic pricing will expand. But the more important question for hotel commercial leaders isn't legal — it's commercial: how confident is your RMS vendor that their system can deliver superior revenue outcomes without rate shopping data? If they can't answer that with conviction, you already have your answer.

For FLYR Hospitality customers, the answer hasn't changed.