Tradeoffs are Domain Dependent: Improving Accuracy and Fairness in Property Tax Assessments

2026-05-14

AI Summary

This study addresses systemic biases in U.S. property tax assessments that disproportionately burden low-value homeowners and exacerbate the regressivity of the tax system. Leveraging transaction data from 26 million properties across 95% of U.S. counties, the research integrates predictive modeling, large-scale empirical analysis, and domain-adapted fairness metrics to challenge the conventional assumption that accuracy and fairness in assessment systems are inherently at odds. The findings reveal a strong positive correlation between accuracy and fairness under current practices. Moreover, incorporating richer property characteristics alongside publicly available census data simultaneously enhances both dimensions, offering public-sector algorithmic systems a viable pathway to achieve greater efficiency and equity.

Abstract

Algorithmic fairness research often assumes a tradeoff between fairness and accuracy. Yet this tradeoff may not be universal. We test this assumption in the context of U.S. property tax assessment - a setting in which the output of predictive algorithms directly determines the distribution of tax obligations among homeowners. Currently, systematic assessment errors cause owners of lower-valued properties to face disproportionately high tax burdens, creating regressivity in the property tax system. Using data on 26 million property sales spanning 95% of U.S. counties, we conduct three complementary analyses. First, we find that assessment accuracy and fairness - measured using domain-relevant metrics - are strongly correlated across counties under status quo practices. Second, in simulated assessment models, we show that adding property features improves accuracy in most cases, and that when accuracy improves, fairness almost always improves as well. Third, we show that incorporating publicly available Census data into assessment models - a feasible reform in most counties - would significantly improve both accuracy and fairness relative to status quo assessments. Together, these results challenge the presumed universality of the fairness-accuracy tradeoff and demonstrate that well-designed modeling improvements can advance both fairness and accuracy in large-scale public sector systems.

Problem

• property tax assessment
• algorithmic fairness
• accuracy-fairness tradeoff
• tax regressivity
• predictive algorithms

Innovation

• fairness-accuracy tradeoff
• property tax assessment
• algorithmic fairness
• domain-dependent
• Census data integration