Retrieval-Enhanced Real Estate Appraisal

TL;DR

This paper introduces a learned selection policy for comparable property retrieval in real estate appraisal, improving model efficiency and performance across diverse datasets by optimizing the selection process.

Contribution

It proposes a hybrid retrieval method that learns to select better comparables, reducing data and parameter requirements while maintaining high accuracy.

Findings

Improved selection of comparables enhances model performance.

Fewer comparables are needed for accurate appraisal.

Method performs well across datasets from multiple countries.

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.