AI Tenant Screening

Tenant screening companies are consumer reporting agencies. They gather credit data from the major bureaus, eviction filings collected from court records, criminal records from court and commercial databases, and sex offender registries. Many then run that data through a scoring model and return a number or a simple recommendation such as accept, conditional or decline. The landlord often sees only the verdict, not the records behind it. The weak point is the data. Court records are collected in bulk and matched to applicants, and loose matching (on name alone, or name plus a partial birth date) attaches other people's records to applicants. Eviction filings are frequently reported without the outcome, so a case that was dismissed or settled looks the same as a judgment against the tenant. Regulators have acted on this. In 2018 RealPage agreed to pay a $3 million civil penalty to settle FTC charges that it did not use reasonable procedures to ensure accuracy. In 2023 TransUnion's rental screening businesses agreed to pay $15 million to settle a joint FTC and CFPB case that included inaccurate eviction reporting. Bias is the second problem. A model can discriminate without using race as an input if it leans on factors, such as criminal records or credit history, that fall unevenly across racial groups. Under the Fair Housing Act this is disparate impact liability. In a 2024 class action settlement in Massachusetts, SafeRent Solutions agreed to pay about $2.3 million and to change how it scores applicants who use housing vouchers, after plaintiffs argued its score disproportionately harmed Black and Hispanic voucher holders. Two misconceptions are common. The first is that buying a vendor's score moves the legal risk onto the vendor. It does not: the landlord makes the decision and owes applicants FCRA notices. The second is that an algorithm is neutral because it has no opinions. A model reproduces the patterns in its training data and its inputs.