Embedding-Enhanced Real Estate Valuation in Non-Metropolitan Sweden
Publicerad
Författare
Typ
Examensarbete för masterexamen
Master's Thesis
Master's Thesis
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
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.
Beskrivning
Ämne/nyckelord
Automated Valuation Model, real-estate appraisal, neural embeddings, gradient boosting, SHAP interpretability, non-metropolitan housing.