Human-Machine Synergy in Real Estate Similarity Concept

Renigier-Biłozor Małgorzata () and Janowski Artur ()
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Renigier-Biłozor Małgorzata: Faculty of Geoengineering, University of Warmia and Mazury in Olsztyn, Michała Oczapowskiego 2, 10-719 Olsztyn, Poland
Janowski Artur: Institute of Geodesy and Construction, Faculty of Geoengineering, University of Warmia and Mazury in Olsztyn, ul. Michała Oczapowskiego 2, 10-719 Olsztyn, Poland

Real Estate Management and Valuation, 2024, vol. 32, issue 2, 13-30

Abstract: The issue of similarity in the real estate market is a widely recognized aspect of analysis, yet it remains underexplored in scientific research. This study aims to address this gap by introducing the concept of a Property Cognitive Information System (PCIS), which offers an innovative approach to analyzing similarity in the real estate market. The PCIS introduces non-classical and alternative solutions, departing from the conventional data analysis practices commonly employed in the real estate market. Moreover, the study delves into the integration of artificial intelligence (AI) in the PCIS. The paper highlights the value added by the PCIS, specifically discussing the validity of using automatic ML-based solutions to objectify the results of synergistic data processing in the real estate market. Furthermore, the article establishes a set of essential assumptions and recommendations that contribute to a well-defined and interpretable notion of similarity in the context of human-machine analyses. By exploring the intricacies of similarity in the real estate market through the innovative PCIS and AI-based solutions, this research seeks to broaden the understanding and applicability of data analysis techniques in this domain.

Keywords: human-machine similarity analysis; real estate market; Property Cognitive Information System (PCIS); artificial intelligence; synergistic data processing (search for similar items in EconPapers)
JEL-codes: C45 C60 D91 R30 (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:remava:v:32:y:2024:i:2:p:13-30:n:1002

DOI: 10.2478/remav-2024-0010

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