Policy Briefing: How Changing Local Data Policies Can Reduce Barriers to Housing

Posted by Urban Institute on November 10, 2026

For most property owners and landlords, screening potential renters is a routine part of the application process, particularly in tight housing markets. More than 90 percent (PDF) of landlords report using tenant screening software, and the number of companies offering reports has increased to meet this demand.

As people’s data—including credit scores, criminal and eviction history, and income history—become more available, and as companies try to set their screening reports apart, many companies are developing algorithmic models that assign tenant risk scores or recommendations. Early studies suggest that landlords often defer to these scores or recommendations, creating high barriers for people who have old, low-severity records, such as misdemeanors or nonconvictions, or inaccurate records on their screening reports.

Little public evidence shows that risk scores accurately predict rental outcomes, and even less provides a clear mechanism for how certain histories are linked to lease violations. On top of this, these risk scores are unregulated and appear to include incorrect, outdated, or minor records that can prevent applicants from accessing housing.

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