Estimation Residential Real Estate Price Considering Spatial Variables using Machine Learning in GIS: A Case Study in Chon Buri, Thailand

This research investigates the effectiveness of machine learning (ML) techniques in predicting residential real estate prices, with a specific focus on integrating spatial variables within a Geographic Information System (GIS) framework. A case study in Chon Buri, Thailand, is utilized to compare the predictive accuracy of the Forest-based Classification and Regression (FBCR) algorithm, a GIS-specific ML tool, against established algorithms such as XGBoost (XGB) and Random Forest (RF). The methodology includes data collection through web scraping, rigorous preprocessing using the Interquartile Range (IQR) method for outlier detection, and spatial data integration via ArcGIS Pro. Model performance is evaluated using R², Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). The results demonstrate that FBCR outperforms both RF and XGBoost in predicting real estate prices, evidenced by a higher R² value of 0.6412, lower RMSE of 1.529, and lower MAE of 1.131 on the testing dataset. This superior performance highlights FBCR's ability to effectively capture and model the complex spatial relationships that influence property prices. The study underscores the potential of GIS-integrated ML tools in enhancing the accuracy and reliability of real estate valuation, providing valuable insights for urban planning and property market analysis. Key Words: residential real estate, predict price, machine learning, forest-based classification and regression (FBCR), geographic information system (GIS), spatial integration.