Achieving Fairness and Accuracy in Regressive Property Taxation

Regressivity in property taxation, or the disproportionate overassessment of lower-valued properties compared to higher-valued ones, results in an unfair taxation burden for Americans living in poverty. To address regressivity and enhance both the accuracy and fairness of property assessments, we introduce a scalable property valuation model called the $K$-segment model. Our study formulates a mathematical framework for the $K$-segment model, which divides a single model into $K$ segments and employs submodels for each segment. Smoothing methods are incorporated to balance and smooth the multiple submodels within the overall model. To assess the fairness of our proposed model, we introduce two innovative fairness measures for property evaluation and taxation, focusing on group-level fairness and extreme sales price portions where unfairness typically arises. Compared to the model employed currently in practice, our study demonstrates that the $K$-segment model effectively improves fairness based on the proposed measures. Furthermore, we investigate the accuracy--fairness trade-off in property assessments and illustrate how the $K$-segment model balances high accuracy with fairness for all properties. Our work uncovers the practical impacts of the $K$-segment models in addressing regressivity in property taxation, offering a tangible solution for policymakers and property owners. By implementing this model, we pave the way for a fairer taxation system, ensuring a more equitable distribution of tax burdens.