Interpretable Machine Learning for Real Estate Valuation: A Case Study with Small Data

Abstract

This study explores residential property valuation in Seville, Spain, using interpretable machine learning techniques on a small dataset of 1701 sales ads of apartments collected online. Unlike conventional approaches that rely on large datasets, our research addresses the unique challenges of small data samples while maintaining model interpretability. We compare traditional hedonic linear regression with Random Forest algorithms. The results provide actionable insights for real estate stakeholders in medium-sized urban markets, bridging the gap between econometric tradition and machine learning innovation.