Text-augmented house valuation: using embedding vectors and SHAP values

Abstract

With the recent advent of large language models, text data can now be readily processed and analyzed across various sectors; traditionally, this was not the case, and text data had proven challenging to analyze. The housing market uses and features a vast amount of textual data, such as property descriptions and neighborhood trend reports, but has yet to fully exploit this potential. This study has two objectives: first, to enhance house valuation accuracy by integrating text information into valuation models; and second, to interpret how text information contributes to improved performance. We convert the environmental descriptions of each house into embedding vectors, which are then incorporated into regression and extreme gradient boosting (XGB) models to estimate house prices. Additionally, we interpret the XGB model’s results using Shapley Additive explanations (SHAP) values. The embedding vectors significantly improved the performances of both the regression and XGB models, and the embedding vector values aligned well with the semantic meaning of the environmental descriptions of each house. This study contributes to the literature by deepening our understanding of embedding vectors in the context of house valuations and housing markets.