EXPRESS: An LLM-Generated Explainable Property Valuation System with Neighbor Imputation

The demand for property valuation has attracted significant attention from sellers, buyers, and customers applying for loans. Reviews of existing approaches have revealed shortcomings in terms of not being able to handle missing value situations, as well as lacking interpretability, which means they cannot be used in real-world applications. To address these challenges, we propose an LLM-Generated EXplainable PRopErty valuation SyStem with neighbor imputation called EXPRESS, which provides the customizable missing value imputation technique, and addresses the opaqueness of prediction by providing the feature-wise explanation generated by LLM. The dynamic nearest neighbor search finds similar properties depending on different application scenarios by property configuration set by users (e.g., house age as criteria for the house in rural areas, and locations for buildings in urban areas). Motivated by the human appraisal procedure, we generate feature-wise explanations to provide users with a more intuitive understanding of the prediction results.