Committee considers ban on algorithmic rent‑setting coordination; industry and advocates sharply divided

Lawmakers on the House Housing Committee heard competing views on SB 5,469, a bill that would prohibit algorithmic or automated coordination used to recommend rental prices and that would authorize enforcement by the Attorney General under the Consumer Protection Act and a private right of action.

Serena Dolly, committee staff, summarized the bill as a prohibition on “coordination” defined to include collection of rental prices, price changes, supply levels, occupancy rates, and lease dates from two or more landlords and using automated analysis to recommend pricing to multiple landlords. The staff summary said the bill excludes publication of rent estimates based solely on publicly available information.

Tenant advocates and antitrust groups pressed the committee to act. Michelle Thomas of the Washington Low Income Housing Alliance testified that algorithmic pricing products have “been scandalized across the nation” and argued they “are leaving renter households victim to price fixing between landlords.” Lee Hepner, senior legal counsel at the American Economic Liberties Project, cited an early White House study in testimony saying such algorithms were estimated to cost Americans billions monthly and urged lawmakers to codify prohibitions now rather than wait for slower federal litigation.

Industry witnesses, including Mike Semco, vice president and legal counsel at RealPage, said the product is market research and pricing suggestion software that does not compel landlords to accept recommendations. Semco told the committee the platform primarily uses a property’s own data and publicly available asking rents and aggregates anonymized executed lease data into market benchmarks; he said, “We do not set rents. Our customers set the rents.” He argued the bill’s drafting could ban ordinary industry analysis or even ‘‘math’’ used across sectors and suggested narrower prohibitions or rules addressing specific incentives that would encourage customers to accept recommendations.

Committee members pressed industry witnesses on how external data are weighted, whether the anonymized aggregated data could be used to facilitate collusion by large owners or property managers, and what narrow policy fixes (for example, limits on incentives, transparency requirements, or prohibitions on pricing‑based termination of customers) would address the risk without banning standard market analysis. Several members asked for follow‑up evidence and suggested drafting changes to preserve legitimate listing services and MLS functions while targeting anti‑competitive conduct.

Public testimony included tenants describing steep rent increases they attribute to algorithmic pricing. The committee concluded with a request for additional materials (federal litigation summaries, suggested amendments to preserve MLS listings and public datasets, and options that address incentives), and placed SB 5,469 on the upcoming exec calendar for further action.