A Novel Reinforcement Learning-Based Feature Selection with Scope Loss and Evolutionary Optimization for Accurate Real Estate Tax Forecasting

Abstract

Accurate forecasting of real estate taxes is essential for property owners and government agencies. It affects financial strategies and public revenue generation. Conventional deep learning approaches for real estate tax often struggle to select appropriate features and are highly sensitive to changes in hyperparameters. To address these challenges, this paper proposes a real estate tax model that uses reinforcement learning to guide feature selection. The reinforcement learning component constantly updates the selection of important features based on data interactions. It helps focus on the most relevant features and prevents overfitting. The reinforcement learning model incorporates a scope loss function that balances learning from current data with exploring new patterns. This balance helps preserve the accuracy and generalizability of the model. Additionally, the artificial bee colony algorithm enhances the flexibility and efficiency of the model by optimizing hyperparameter settings. The model was evaluated using several real estate datasets from Kaggle. These datasets cover Bucharest, California, Helsinki, King County, and Saudi Arabia. The results demonstrate that the proposed model outperforms traditional prediction tools. Across all datasets, it achieves mean absolute percentage error values ranging from 0.216 to 1.081. This strong performance demonstrates the potential of the model for use across various markets. It can improve economic assessments and planning. Code is publicly available at https://github.com/Mahyar-ramezankhani/Tax.