Measures of vertical inequality in assessments

Abstract

Standard measures of vertical inequality suggest that assessments are regressive in the sense that high-priced properties are often assessed at lower rates than low-priced properties. We show that some of this regressivity is due to the regression-based estimation procedures used by many jurisdictions to calculate assessments. A review of existing measures of assessment regressivity suggests that severe biases associated with regression-based procedures make them much less useful than the traditional price-related differential (PRD) as a measure of vertical inequality. To supplement existing measures of vertical inequality, we propose approaches using Gini coefficients, Suits index, and kernel density tests to provide information on the relationship between the assessment and sale price distributions. We compute the measures using data on sales prices and assessments for 48 large central city counties. The results suggest that the PRD remains a useful approach for measuring vertical inequality due to its simplicity and familiarity, while distribution-based procedures are helpful because they are not as sensitive to small numbers of very high-priced sales. Together, the approaches provide a more complete picture of how assessment rates vary across the full distribution of sales prices.