House Price Prediction in Andalusia: A Comparative Study of Hedonic and Machine Learning Approaches Using Online Data - Gutiérrez, Emiliano (2026)
This study explores housing price prediction in six major cities of Andalusia using a combination of traditional and machine learning models. Online property listings were compiled to build a detailed dataset incorporating structural, socioeconomic, and locational variables. A traditional hedonic model was estimated using Ordinary Least Squares (OLS), and its performance was compared with that of Random Forest, K-Nearest Neighbours, and XGBoost. Results show that linear models tend to underperform, highlighting the relevance of non-linear approaches in capturing the complex dynamics of the housing market. To enhance interpretability, SHapley Additive exPlanations (SHAP) values were applied to the XGBoost model, enabling the identification of the most influential predictors in each city. While structural variables remained important across all models, socioeconomic and locational features exhibited varying effects depending on the city. These findings underscore the potential of combining rich data sources with interpretable machine learning techniques for urban housing market analysis.