Utilizing Machine Learning for Accurate Property Valuation: A Regression Model Analysis

Abstract

This research investigated the application of machine learning (ML) regression models to improve property valuation accuracy, addressing limitations of traditional methods. The study applied Random Forest (RF) and Support Vector Regression (SVR) models to a dataset of 59,180 property records from the Luanshya Municipal Council. Key features such as LAND_VALUE, MARKET_VALUE, and IMPROVEMENT_VALUE were used as inputs. The models' performance was evaluated using the Data, Reasoning, and Usefulness (DRU) Evaluation Framework. Results showed that both RF and SVR outperformed traditional methods, with RF achieving an R² of 0.9995. Machine Learning models demonstrated potential for more accurate property valuations, enabling fairer tax assessments, reduced manual effort, and improved urban planning decisions. Future research should address data quality and model explainability challenges.