Latest action: Read twice and referred to the Committee on Banking, Housing, and Urban Affairs.
Referred to one or more standing committees for review, hearings, and possible markup.
Specific committees not parsed from action data.
The committee may hold hearings, amend the bill, or let it die without action.
Next: Reported
Full chamber floor — Speaker / Majority Leader controls scheduling
Creates federal program to track and map housing discrimination patterns across the United States.
The problem
Housing discrimination remains difficult to detect and measure systematically. Lack of comprehensive data on discrimination patterns prevents effective enforcement and policy responses.
The proposal
Establishes federal mapping initiative to collect, analyze, and publicly share data on housing discrimination complaints and patterns by region.
The impact
Affects fair housing advocates, renters, homebuyers, real estate industry, and federal agencies enforcing housing laws by improving discrimination detection and accountability.
Mapping discrimination patterns helps identify problem areas quickly. Authorities can target enforcement efforts where discrimination is most common. Data makes hidden discrimination more visible.
Renters and homebuyers get transparency about discrimination risks in different areas. Families can make safer housing choices with better information available.
Federal agencies gain tools to find and prosecute housing discrimination. Data-driven approach makes enforcement more efficient and effective.
Comprehensive data informs lawmakers about where problems exist. Allows targeted solutions rather than generic policies.
Collecting and mapping discrimination complaints may expose personal information. Questions arise about who accesses the data and how it's protected.
Building a national mapping system requires significant federal spending. Ongoing maintenance and updates add continuing costs to government budget.
Real estate agents or landlords in mapped high-discrimination areas may face stigma. Businesses worry about reputational harm from data inclusion.
Mapping only shows reported complaints, not actual discrimination. Many victims never file complaints, so data may be incomplete.
Generated by AI from the bill text. Not a prediction and not legal or policy advice. The percentage is the model's own rating of each item — self-reported, and not a measure of how accurate it is.
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Individual contributions, grouped by employer · FEC
Where their individual donors work · 2026 cycle
Money from people who work there, not from the company. Not tied to this bill.
Itemized individual contributions via FEC, grouped by the employer each contributor self-reported. Companies and unions cannot contribute to federal candidates from their treasuries, so these totals are money from people who work at an employer — never from the organization itself. They are campaign contributions covering a full election cycle and are not connected to this bill.
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