Towards Explainable Real Estate Valuation via Evolutionary Algorithms

TL;DR

This paper enhances case-based reasoning for real estate valuation by applying evolutionary algorithms to improve similarity functions, achieving accuracy comparable to deep neural networks while maintaining interpretability.

Contribution

It introduces a novel method of using evolutionary algorithms to optimize similarity functions in CBR, balancing accuracy and explainability in real estate valuation.

Findings

EA-optimized similarity functions outperform standard CBR.

EA-enhanced CBR matches deep neural network accuracy.

Deep neural networks are only as accurate as traditional CBR methods.

Abstract

Human lives are increasingly influenced by algorithms, which therefore need to meet higher standards not only in accuracy but also with respect to explainability. This is especially true for high-stakes areas such as real estate valuation. Unfortunately, the methods applied there often exhibit a trade-off between accuracy and explainability. One explainable approach is case-based reasoning (CBR), where each decision is supported by specific previous cases. However, such methods can be wanting in accuracy. The unexplainable machine learning approaches are often observed to provide higher accuracy but are not scrutable in their decision-making. In this paper, we apply evolutionary algorithms (EAs) to CBR predictors in order to improve their performance. In particular, we deploy EAs to the similarity functions (used in CBR to find comparable cases), which are fitted to the data set at hand. As a consequence, we achieve higher accuracy than state-of-the-art deep neural networks (DNNs), while keeping interpretability and explainability. These results stem from our empirical evaluation on a large data set of real estate offers where we compare known similarity functions, their EA-improved counterparts, and DNNs. Surprisingly, DNNs are only on par with standard CBR techniques. However, using EA-learned similarity functions does yield an improved performance.