Achieving Fairness and Accuracy in Regressive Property Taxation
arxiv(2023)
摘要
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.
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