Embedding-Enhanced Real Estate Valuation in Non-Metropolitan Sweden

Abstract

Automated valuation of residential properties in sparsely populated regions poses unique challenges due to thin transaction volumes, diverse housing stock, and lim ited comparables. This thesis presents a hybrid modeling approach combining an embedding-based artificial neural network (ANN) with a LightGBM gradient boost ing machine to predict sale prices in six Swedish municipalities, focusing specifically on houses in non-metropolitan areas. The ANN learns dense representations of categorical and geographic features that capture latent spatial and socioeconomic patterns, while the GBM leverages both raw features and ANN embeddings to refine residual errors. Model interpretability is achieved via SHAP values and case studies of embedding dimensions, revealing that distance to regional centers, living area, property condition, and proximity to points of interest are key value drivers, even where market data are scarce. The hybrid model demonstrates competitive accu racy, particularly for mid-priced homes, and offers transparent explanations for each valuation. However, large errors persist for rare, high-end properties and extremely remote dwellings, reflecting fundamental data limitations. The results highlight how AI-driven valuation tools can complement traditional appraisal methods by provid ing rapid, interpretable estimates for routine cases and flagging high-uncertainty transactions for expert review.