Neighborhood Change, Housing Market Dynamics, and Racial Inequality in Property Taxes

Persistent racial wealth inequality in the United States is closely tied to differences in homeownership rates and property values. Racial inequality in property taxes contribute to this inequality yet remain an essential source of revenue for local governments.

The author of this dissertation investigates the mechanisms and extent of racial inequality in property tax burdens. The study combines national quantitative analyses with interviews from county assessors in Washington state and uses administrative datasets, including Zillow's ZTrax, mortgage application data sourced from the Home Mortgage Disclosure Act, and the American Community Survey. To analyze properties from a wider variety of regions, the author imputes homeowner race using RAND's Bayesian Improved First name–Surname Geocoding algorithm. The author also compares models that use imputed race with those that use self-reported race to clarify the strengths and limitations of each method.

By integrating these methods with census tract–level socioeconomic variables, the author estimates racial gaps in property assessment ratios. The author examines how sales volume, gentrification, and other neighborhood characteristics affect assessment equity for Black, Latino, Asian Pacific Islander, and American Indian or Alaska Native homeowners relative to White homeowners.

The key findings show significant racial disparities in property tax assessments: Minority homeowners experience higher relative tax burdens in areas with lower housing market activity. In census tracts with high sales volumes, these gaps are smaller or, in some cases, statistically insignificant for certain minority groups. This suggests that increased transaction data and market transparency can help reduce assessment inequities.