Paper Info
Title
Towards Explainable Real Estate Valuation via Evolutionary Algorithms
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.
Year
DOI
Venue
2022
10.1145/3512290.3528801
PROCEEDINGS OF THE 2022 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE (GECCO'22)
Keywords
DocType
Citations 
Evolutionary algorithms, case-based reasoning, neural networks, real estate valuation
Conference
0
PageRank 
References 
Authors
0.34
0
9
Name
Order
Citations
PageRank
Sebastian Angrick100.34
Ben Bals200.34
Niko Hastrich300.34
Maximilian Kleissl400.34
Jonas Schmidt500.34
Vanja Doskoc600.34
Louise Molitor714.08
Tobias Friedrich82110.66
Maximilian Katzmann900.34