Predicting Property Tax Classifications: An Empirical Study Using Multiple Machine Learning Algorithms on U.S. State-Level Data

DOI

10.2991/978-94-6463-811-0_36How to use a DOI?

Keywords

Property Tax Classification; Machine Learning; Tax Policy Analysis; Real Estate Valuation

Abstract

This study presents a comprehensive analysis of property tax classification using machine learning approaches applied to the 2024 U.S. Property Tax Roll dataset. The research employs four different machine learning algorithms - XGBoost, Random Forest, Support Vector Machine (SVM), and Logistic Regression - to predict and analyze property classifications across American states. To address the challenge of imbalanced data distribution in property classes, we implement the SMOTE technique for data balancing. The experimental results demonstrate that the XGBoost algorithm achieves superior performance with an accuracy of 0.901, significantly outperforming other models across multiple evaluation metrics. The study reveals strong correlations between total assessment values and tax exemptions (correlation coefficient 0.98), providing insights into the relationship between property valuation and tax policy implementation. The findings have important implications for both tax administrators and policymakers, offering a data-driven approach to property tax classification and assessment.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Jiawei Tian
AU  - Chujun Yin
AU  - Meijia Wang
AU  - Hongji Li
AU  - Haifeng Xu
PY  - 2025
DA  - 2025/08/14
TI  - Predicting Property Tax Classifications: An Empirical Study Using Multiple Machine Learning Algorithms on U.S. State-Level Data
BT  - Proceedings of the 2025 5th International Conference on Enterprise Management and Economic Development (ICEMED 2025)
PB  - Atlantis Press
SP  - 339
EP  - 347
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-811-0_36
DO  - 10.2991/978-94-6463-811-0_36
ID  - Tian2025
ER  -