Artificial intelligence in real estate valuation and its impact on efficiency and effectiveness

Marius Müller and Carsten Lausberg

ERES from European Real Estate Society (ERES)

Abstract: Artificial Intelligence (AI) is becoming an integral part of everyday processes in the real estate industry. In real estate valuation, AI has long been used for automated valuations, but so far rarely for manual valuations. One reason for the reluctant adoption is the complex and know-ledge-intensive process that requires the careful evaluation of numerous factors. This paper investigates for condominiums whether AI can improve manual real estate valuations by redu-cing time and enhancing accuracy. To address this question, we first provide a comprehensive review of the current literature on AI applications in real estate valuation and discuss the po-tential advantages and drawbacks of integrating AI into valuation practices. Then we present the results of an experiment in which the 28 participants were asked to appraise an apartment using either an AI-supported tool or a conventional Excel-based tool. Performance indicators show that the AI tool significantly reduces time and inter-valuer variability, while valuation ac-curacy is largely unaffected. The insights gained from this analysis contribute to understanding the practical applicability of AI in real estate valuation and highlight opportunities for further research and industry adoption.

Keywords: Appraisal; Artificial Intelligence; Real Estate Valuation; valuation accuracy (search for similar items in EconPapers)
JEL-codes: R3 (search for similar items in EconPapers)
Date: 2025-01-01
New Economics Papers: this item is included in nep-ain and nep-exp
References: Add references at CitEc
Citations:

Downloads: (external link)
https://eres.architexturez.net/doc/oai-eres-id-eres2025-216 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:arz:wpaper:eres2025_216

Access Statistics for this paper

More papers in ERES from European Real Estate Society (ERES) Contact information at EDIRC.
Bibliographic data for series maintained by Architexturez Imprints ().