用大模型生成解释，解决房产估价中缺失数据和不透明问题
EXPRESS: An LLM-Generated Explainable Property Valuation System with Neighbor Imputation
- 基于用户设定条件动态找相似房源，补全缺失数据
- 通过大模型生成逐特征解释，让估价结果可理解
- 适合需要透明估价的贷款、买卖等实际场景
房产估价需求日益增长，但现有方法在处理缺失数据和缺乏可解释性方面存在不足，难以用于真实场景。为此，我们提出基于大模型生成解释的房产估价系统 EXPRESS，支持自定义缺失值填补，并通过大模型生成逐特征解释，提升预测透明度。系统根据用户设定的属性（如农村房屋以房龄为关键指标，城市建筑以位置为核心）动态搜索最近邻房源，模拟人工评估流程，使用户更直观理解估价依据。
原文摘要 · Abstract (English)
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.
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