data-driven pricing algorithmic collusion consumer protection

States Step Up to Curb “Smart” Pricing - What It Means for Consumers

Posted on November 21, 2025 at 09:49 PM

States Step Up to Curb “Smart” Pricing: What It Means for Consumers

How U.S. states are moving against data-driven price hikes and algorithmic rental algorithms

Even as the federal government appears to loosen its grip on algorithm regulation, U.S. states are quietly ramping up efforts to rein in pricing practices powered by data and algorithms—practices critics say are quietly driving up costs for consumers. According to a report by Reuters, at least 19 states are now considering or have already passed legislation to limit the use of third-party software to set prices for hotels, rentals and other services. ([Reuters][1])

The push-back on “smart” pricing systems

Here’s what’s going on:

  • States such as New York and California are leading the charge. New York passed a law in October to prohibit landlords from using algorithmic tools to collude on rental pricing. California’s law goes further, broadly banning algorithm-based collusion altogether. ([Reuters][1])
  • A key concern: retail and rental companies are using increasingly granular data—past purchases, browsing history, media consumption, even location—to tailor prices to what they believe you will pay. ([Reuters][1])
  • For example: investigative reporting has shown that some travel-booking sites quoted higher hotel rates to shoppers in San Francisco than to similar users in Kansas City. ([Reuters][1])
  • The regulatory motive: It’s no longer just about price transparency or collusion in the classic sense—it’s about pricing being set based on who you are and the data profile that you match. As one policy analyst put it: “What we are concerned about is different people paying different prices based on who a company thinks they are.” ([Reuters][1])

Why this matters

  • Consumer fairness: For many households already squeezed by inflation and rising costs, the idea that companies might charge them more because their data signals “you’ll pay more” is a fresh pain point.
  • Regulatory divergence: With the federal government apparently exploring a move to pre-empt state AI and algorithm laws (via executive order), states are doubling down. The battle between state and federal jurisdiction is becoming clear. ([Reuters][1])
  • Business impacts: Companies that rely heavily on dynamic pricing, algorithmic rental setting, and real-time bidding on data might face new compliance costs, transparency requirements or even bans in certain jurisdictions.
  • Broader implications for AI and data: This is less about just “algorithmic discrimination” or overt bias, and more about the economics of personalized pricing—embedding consumer profiling deep into pricing strategy. The regulatory lens is widening.

What to watch

  • The legislation moving through states: which ones will pass bills, what form they take (bans, disclosure obligations, restrictions on use of third-party software) and what sectors they cover (rentals, travel, retail, insurance, etc.).
  • How business models adapt: Will companies scale back personalized pricing? Will third-party pricing software face stricter scrutiny or certification requirements?
  • What the federal government does: If an executive order moves forward to pre-empt state laws on AI or algorithm regulation, the federal vs. state dynamic could shift dramatically.
  • Enforcement and litigation: Even if laws pass, how they are enforced—and whether consumer-advocacy groups bring lawsuits—will determine how real the impact becomes.

Bottom line

The era of “invisible” data-driven pricing is starting to raise visible regulatory alarms. For consumers, the promise is greater protection and transparency. For businesses that lean heavily on algorithmic flexibility in pricing, the field is about to change. States are making it clear: if you’re going to price based on you, you better be prepared to explain how—and justify why.

Glossary

  • Data-driven pricing: The practice of setting prices for goods or services based on large volumes of consumer data (such as browsing history, purchase history, location, and behaviour) combined with algorithms to tailor prices.
  • Algorithmic collusion: When pricing algorithms coordinate—explicitly or implicitly—across competitive entities to raise or stabilize prices without direct human agreement.
  • Third-party pricing software: Software tools developed by independent vendors that companies use (often alongside AI/ML models) to set or adjust pricing in real time by pulling in competitor data, consumer data and market signals.
  • Pre-emption: A legal doctrine under which a higher level of government (e.g., federal) overrides or limits the power of lower-level governments (e.g., states) to pass or enforce laws.
  • Personalised pricing: The pricing strategy where different consumers are offered different prices based on their individual data profile or behavioural signals, rather than uniform pricing.

Source: https://www.reuters.com/sustainability/boards-policy-regulation/us-states-take-aim-data-driven-pricing-ease-consumer-pain-2025-11-21/

[1]: https://www.reuters.com/sustainability/boards-policy-regulation/us-states-take-aim-data-driven-pricing-ease-consumer-pain-2025-11-21/ “US states take aim at data-driven pricing to ease consumer pain Reuters”
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