Retrieval-Enhanced Real Estate Appraisal

arXiv cs.LG / 3/16/2026

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Key Points

The authors propose learning a selection policy for comparables in the Sales Comparison Approach, integrated with a hybrid vector-geographical retrieval module and jointly trained with an estimation component.
The approach enables models to use fewer comparables and parameters while achieving performance close to state-of-the-art methods.
Experiments on five datasets spanning the United States, Brazil, and France demonstrate the method's generalizability across diverse markets.
The work highlights the potential of retrieval-enhanced modeling to adapt real estate valuation models to different datasets and workflows.

Abstract

The Sales Comparison Approach (SCA) is one of the most popular when it comes to real estate appraisal. Used as a reference in real estate expertise and as one of the major types of Automatic Valuation Models (AVM), it recently gained popularity within machine learning methods. The performance of models able to use data represented as sets and graphs made it possible to adapt this methodology efficiently, yielding substantial results. SCA relies on taking past transactions (comparables) as references, selected according to their similarity with the target property's sale. In this study, we focus on the selection of these comparables for real estate appraisal. We demonstrate that the selection of comparables used in many state-of-the-art algorithms can be significantly improved by learning a selection policy instead of imposing it. Our method relies on a hybrid vector-geographical retrieval module capable of adapting to different datasets and optimized jointly with an estimation module. We further show that the use of carefully selected comparables makes it possible to build models that require fewer comparables and fewer parameters with performance close to state-of-the-art models. All our evaluations are made on five datasets which span areas in the United States, Brazil, and France.