Algorithm-Driven Hedonic Real Estate Pricing – An Explainable AI Approach

Tarasov Stanislav () and Dessoulavy-Śliwiński Bartłomiej ()
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Tarasov Stanislav: Faculty of Economic Sciences, University of Warsaw, Długa 44/50, 00-241 Warsaw, Poland
Dessoulavy-Śliwiński Bartłomiej: Department of Management and Information Technology, Faculty of Economic Sciences, University of Warsaw, Długa 44/50, 00-241 Warsaw, Poland

Real Estate Management and Valuation, 2025, vol. 33, issue 1, 22-34

Abstract: Data-driven machine learning algorithms triggered a fundamental change in hedonic real estate pricing. However, their adaptive nonparametric structure makes inference and out-ofsample prediction challenging. This study introduces an explainable approach to interpreting machine learning predictions, which has not been done before in the local market context. Specifically, Random Forest and Extreme Gradient Boosting models are developed for residential real estate price prediction in Warsaw in 2021 on 10,827 property transactions. Model-agnostic Explainable Artificial Intelligence (XAI) methods are then used to investigate the black box decision making. The results show the practicability of applying XAI frameworks in the real estate market context to decode the rationale behind data-driven algorithms. Information about the relationships between input variables is extracted in greater detail. Accurate, reliable and transparent real estate valuation support tools can offer substantial advantages to participants in the real estate market, including banks, insurers, pension and sovereign wealth funds, as well public authorities and private individuals.

Keywords: residential real estate; machine learning; explainable artificial intelligence; mass appraisal; automated valuation models (search for similar items in EconPapers)
JEL-codes: C14 C18 R39 (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:remava:v:33:y:2025:i:1:p:22-34:n:1003

DOI: 10.2478/remav-2025-0003

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