Mass Valuation of Real Estate Using GIS-based Nominal Valuation and Machine Learning Methods

Geographic Information Systems (GIS) and Machine Learning methods are widely used in mass real estate valuation practices. Focusing on the physical attributes of properties, locational criteria are insufficiently used during the price prediction process. Whereas, locational criteria like proximity to important places, sea or forest views, flat topography are some of the spatial factors that extremely affect the real estate value. In this study, a hybrid approach is developed by integrating GIS and Machine Learning for automated mass valuation of residential properties in Turkey and the United Kingdom. GIS-based Nominal Valuation Method was applied to produce a land value map by carrying out proximity, terrain, and visibility analyses. Besides, ensemble regression methods like XGBoost, CatBoost, LightGBM, and Random Forest are built for price prediction. Spatial criteria scores obtained from GIS analyses were included in the price prediction data for feature enrichment purpose. Results showed that adding locational factors to the real estate price data increased the prediction accuracy dramatically. It also demonstrated that Random Forest was the most successful regression model compared to other ensemble methods.