States are racing to rein in the growing use of artificial intelligence and computer algorithms to set prices, with New York leading the charge. On Dec. 15, a major amendment to New York’s antitrust law took effect, banning the use of certain algorithmic rent-setting tools for residential units under Section 340-b of the state’s General Business Law.
This follows New York’s November implementation of a first-in-the-nation disclosure law, Section 349-a of the General Business Law, that requires merchants to tell consumers when individualized prices are set using algorithms trained on personal data. State lawmakers are also considering S.B. 7033, which would outlaw algorithmic price discrimination based on protected characteristics like race and age.
These measures arrive amid a torrent of litigation challenging the use of algorithmic pricing software in contexts ranging from apartment rents to healthcare reimbursements, and as more companies adopt pricing systems that tailor offers to individual consumers.[1]
By pushing these laws, New York and other states seek to fill perceived gaps in the Sherman Antitrust Act and Robinson-Patman Act, the primary federal antitrust statutes prohibiting price-fixing and price discrimination.
Industry pushback has been swift: Multiple lawsuits are challenging the New York laws on constitutional and other grounds. The outcomes could determine not only the future of these statutes but the viability of similar regulations nationwide.
For businesses, this means both immediate new compliance burdens and continued uncertainty over how and whether these rules will ultimately be enforced.
The Algorithmic Rent-Setting Ban: Section 340-b
Section 340-b, enacted in October and effective Dec. 15, is the first state law barring the use of algorithms to set rents.[2]
The statute deems it an unlawful agreement in violation of New York’s Donnelly Act “for a residential rental property owner or manager to knowingly or with reckless disregard set or adjust rental prices, lease renewal terms, occupancy levels, or other lease terms and conditions” based on recommendations from algorithmic pricing software that performs a “coordinating function.”[3]
Notably, the law represents a potential expansion of liability beyond the federal Sherman Act, which requires proof of a horizontal agreement between competitors or potential competitors for a per se violation of the law. In contrast, the New York law deems the unilateral use of algorithmic pricing software to set or adjust rents and lease terms to constitute an unlawful agreement, even without a meeting of the minds between the landlords, provided the software performs a coordinating function.[4]
Further, the statute bars setting or adjusting rates using an algorithm, meaning a landlord could potentially violate the statute even if it does not reflexively adopt the recommended rent.[5] For example, landlords might arguably violate the statute by using algorithmic output as a default benchmark then adjusting the benchmark up or down to determine the actual rent.
This possibility represents a departure from some prior federal algorithmic pricing decisions, which dismissed complaints in part because the alleged conduct demonstrated that the defendants sometimes deviated from the algorithm-recommended price.[6]
Section 340-b is limited to algorithmic pricing software that performs a coordinating function. But the term “coordinating function” is defined broadly to include collecting past and present data on price and other factors from two or more competing property owners or managers, processing that data, and recommending rental prices or lease terms.[7]
The definition does not distinguish between algorithms that use confidential information or publicly available information. This too represents a meaningful departure from federal cases, which have generally treated algorithmic pricing based on public information as less problematic.[8]
Additionally, the statute does not require landlords to know they are using an offending pricing software. Instead, it is enough that they act with “reckless disregard.”[9] It is thus prudent for landlords to understand the mechanics of their software, as a lack of knowledge will not be a viable defense.
Landlords should also consider that software may fall within the covered definition even if coordinating rent prices is not its primary function. For example, one of the law’s sponsors indicated that the law could possibly apply to situations where “mom-and-pop style landlords” use ChatGPT or other AI tools to analyze data available on the internet to help them set rents.[10] While it is unclear whether courts would enforce the law in such a circumstance, landlords of any size should be aware of the potential risk.
In addition to prohibiting the use of certain software, the law makes it illegal for any person or entity to facilitate an agreement to not compete between residential rental properties, “including by operating or licensing” algorithmic pricing software that performs a coordinating function.[11] This provision plainly targets companies that make pricing software, but it may sweep in other actors who could be deemed to have facilitated an agreement.
It is unclear whether the statute requires proof of a predicate agreement to not compete for facilitator liability to attach, or if merely operating or licensing the offending software is enough. Given that the statute treats the use of such software to set rents as an unlawful agreement, a cautious approach is to assume that operating or licensing software used in that manner is sufficient to trigger the statute.
As to the new law’s geographic and temporal scope, Section 340-b adds a new provision to New York’s existing antitrust statute, the Donnelly Act. By its terms, the Donnelly Act applies to restraints on “business, trade or commerce or in the furnishing of any service in this state,” meaning there must be a nexus between the alleged conspiracy and injury to competition in New York.[12] The new law further specifies that the property owners and managers subject to the ban are individuals and entities that own or manage residential rental units in New York state.[13]
Although the law does not indicate whether it applies retroactively or prospectively, New York courts generally hold that “amendments are presumed to have prospective application unless the Legislature’s preference for retroactivity is explicitly stated or clearly indicated” or the amendment is remedial in nature.[14]
Violations of the new law may result in civil penalties of up to $1 million, as well as potential criminal prosecution.[15] The Donnelly Act authorizes suits by the government and provides a private right of action, meaning this new section could create a basis for private plaintiffs to sue.[16] Parties suing under Section 340-b may be entitled to treble damages and attorney fees.
A Developing Challenge to Section 340-b
RealPage Inc., a company that provides pricing software to rental properties and recently entered a consent decree resolving a federal antitrust investigation, sued New York’s attorney general on Nov. 26 to block enforcement of Section 340-b.[17]
In its complaint, RealPage argues the statute violates the First Amendment by restricting lawful advice and analysis, imposing severe penalties, and discriminating based on viewpoint. The company seeks declaratory and injunctive relief, claiming the law is overbroad, unsupported by evidence and a threat to routine data-driven tools.
While RealPage’s preliminary injunction motion is still pending, the action’s outcome will have a large impact on the viability of Section 340-b and potentially other algorithmic rent-setting bans elsewhere.
Regulating Personalized Algorithmic Pricing: Section 349-a and S.B. 7033
In addition to legislative efforts aimed at restricting the use of algorithmic tools in rent-setting, New York has enacted and is considering further measures to regulate algorithms that leverage consumers’ personal data for individualized pricing. These legislative developments stem from heightened scrutiny of personalized pricing practices that have emerged in response to the proliferation of data-driven systems, particularly in industries like e-commerce, where algorithms dynamically adjust prices in real time based on consumer profiles and behavioral data.
Notably, New York’s Algorithmic Pricing Disclosure Act, codified at Section 349-a of the General Business Law, went into effect in November. The statute requires merchants to disclose when a published price has been set by an algorithm using a consumer’s personal data.[18]
The law recently survived a challenge brought by the National Retail Federation alleging that the disclosure requirement violates the First Amendment’s prohibition on compelled speech, though that ruling is under appeal.[19]
A second bill, New York S.B. 7033, the Preventing Algorithmic Pricing Discrimination Act, is currently under committee review. If enacted, this bill would prohibit the use of protected class data to set prices for goods or services that differ from those offered to other consumers. Protected class data encompasses information identifying legally protected characteristics, such as ethnicity, nationality, age, disability, sex or sexual orientation.
Unlike the Robinson-Patman Act, S.B. 7033 would regulate price discrimination in direct-to-consumer transactions and apply to both goods and services, thereby expanding protections to areas previously outside the reach of federal law.
Takeaways for Businesses
New York Property Owners and Managers
Property owners and managers within the state should:
- 임대료, 점유율 및 기타 임대 조건 설정을 위한 알고리즘 가격 책정 소프트웨어 또는 시스템의 사용을 재평가하십시오;
- 소프트웨어의 작동 방식을 이해하십시오. 해당 소프트웨어의 데이터 소스, 처리 방법 및 출력을 파악하여 제340-b조에 따른 조정 기능을 수행하는지 여부를 판단하십시오; 그리고
- 제3자 가격 책정 도구의 허용 및 금지된 사용 방법에 대해 팀을 교육하고 관리하십시오.
뉴욕 부동산 소유주에게 알고리즘 소프트웨어를 제공하는 기업들
부동산을 임대하는 이들에게 알고리즘 소프트웨어를 제공하는 업체는 다음을 준수해야 합니다:
- 제340-b조에 따라 조정 기능을 수행하는 것으로 합리적으로 분류될 수 없음을 확인하기 위해 제품을 감사한다;
- 감사 결과를 검토하고, 특히 경계선 사례에서 규정 준수를 확인하십시오;
- 잠재적 중개자 책임을 방어하기 위해 데이터 출처, 모델 버전 및 준수 감사 결과를 포함한 조사 결과를 상세히 문서화하고;
- 제340-b조에 해당될 가능성이 있는 고객 사용을 방지하거나 표시하는 규정 준수 기능을 구현하고, 고객이 의도치 않은 위반을 피할 수 있도록 명확한 지침 및 교육 자료를 제공하십시오.
뉴욕주 내 알고리즘 소비자 가격 책정 활용 기업
뉴욕 주 내 알고리즘 가격 책정을 사용하는 기업들은 다음을 준수해야 합니다:
- 제349-a조에 따른 적용 범위를 결정하기 위해 가격 산정 알고리즘을 검토하십시오;
- 개인정보를 활용한 알고리즘으로 게시된 가격이 결정되는 경우 소비자에게 명확히 공개하고;
- 뉴욕 주 상원 법안 7033호의 진행 상황을 모니터링하십시오. 이 법안은 보호 대상 집단 데이터를 기반으로 한 알고리즘 가격 차별을 금지할 수 있습니다.
뉴욕 외 지역 기업
뉴욕 주 외 지역의 기업은 다음을 준수해야 합니다:
- 관련 관할권에서 알고리즘 가격 책정과 관련된 입법 동향 및 집행 조치를 추적하십시오. 캘리포니아주와 같은 다른 주에서도 유사한 규정을 제정했거나 검토 중이기 때문입니다.
폴리 앤 라드너 소속 변호사 리처드 리와 사바나 미라클, 그리고 법학 졸업생 한나 올베리가 본 기사에 기여하였으며, 본 기사는 원래 Law360 에 게재되었으며, 허가를 받아 재게재합니다.
[1] 예를 들어,In re: RealPage Inc., Rental Software Antitrust Litig. (No. II) , 709 F. Supp. 3d 478 (M.D. Tenn. 2023);In re: MultiPlan Health Ins. Provider Litig. , 789 F. Supp. 3d 614 (N.D. Ill. 2025).
[2] 수많은 도시와 지방 자치 단체가 자체적으로 알고리즘 기반 임대료 책정 금지 조치를 시행하고 있으며, 여기에는 샌프란시스코, 버클리, 산타모니카, 샌디에이고(캘리포니아); 저지시티, 호보켄(뉴저지); 시애틀, 킹 카운티(워싱턴); 필라델피아; 미니애폴리스; 프로비던스 등이 포함된다.
[3] N.Y. Gen. Bus. Law § 340-b(3).
[4] Id.
[5] 동일.
[6] 예를 들어,Gibson v. MGM Resorts Int’l 사건, No. 2:23-CV-00140-MMD-DJA, 2023 WL 7025996, at *3 (D. Nev. 2023년 10월 24일) (호텔 운영자가 권장 가격을 채택해야 한다는 주장을 제기하지 않은 것은 "소장의 치명적 결함"이라고 판시한 사례).
[7] N.Y. Gen. Bus. Law § 340-b(1)(c).
[8] 예를 들어,Dai v. SAS Inst. Inc. 사건 참조, No. 24-CV-02537-JSW, 2025 WL 2078835, at *5 (N.D. Cal. July 18, 2025) (사법부가 기밀 정보 교환을 합리적으로 추론할 수 있을 만큼 사실이 불충분하다는 이유로 기각함); Gibson v. Cendyn Grp. LLC, 148 F.4th 1069, 1083, n.8 (9th Cir. 2025) (원고가 소프트웨어 제공자가 "라이선스 보유자들 간에 각 경쟁 호텔의 기밀 정보를 공유했다"고 주장하지 않은 경우 기각을 확정한 사례).
[9] N.Y. Gen. Bus. Law § 340-b(3).
[10] 6-10-25 Session: Hearing on AO1417, N.Y. State Assemb. 161-63 (2025).
[11] N.Y. Gen. Bus. Law § 340-b(2).
[12] 동일 조항 § 340(1) 참조;Glob. Reinsurance Corp. U.S. Branch v. Equitas Ltd. , 969 N.E.2d 187, 196 (N.Y. 2012).
[13] 뉴욕주 일반상법 § 340-b(1)(d).
[14]In re: Gleason (Michael Vee Ltd.) , 749 N.E.2d 724, 726 (N.Y. 2001).
[15] N.Y. Gen. Bus. Law §§ 341, 342-a.
[16] Id. §§ 340(5).
[17] 소장 1면, RealPage Inc. v. James, No. 25-cv-9847 (S.D.N.Y. 2025).
[18] 참조: N.Y. Gen. Bus. Law § 349-a(2).
[19] 참조:Nat’l Retail Fed’n v. James , No. 25-CV-5500 (JSR), 2025 WL 2848212 (S.D.N.Y. Oct. 8, 2025).
