Housing rental terms: algorithmic devices.

Senator Pérez's legislation targeting algorithmic rental pricing practices would prohibit landlords from using software that processes non-public competitor data to set residential rental terms. The bill bars both the sale of such algorithms to multiple users in the same market and the adoption of rental terms based on algorithmic recommendations derived from non-public competitor information.

The measure establishes specific enforcement mechanisms, allowing the Attorney General, city attorneys, and county counsel to pursue civil actions with penalties up to $1,000 per violation. Individual tenants harmed by violations may also seek damages and injunctive relief, with courts required to award attorney fees to prevailing plaintiffs. Each month a violation persists and each affected residential property constitutes a separate violation under the law.

The bill defines non-public competitor data as information about actual rental amounts, occupancy rates, and lease dates obtained through private channels from multiple competitors. It explicitly exempts publicly available information, including rental listings, government registries, census data, and industry reports. The measure also carves out exceptions for algorithms using data more than one year old and products used to establish rent limits for affordable housing programs.