Version 1
: Received: 30 July 2024 / Approved: 30 July 2024 / Online: 31 July 2024 (11:47:22 CEST)
How to cite:
Ota, A.; Uto, M. Variation of Property Valuations Conducted by Artificial Intelligence in Japan: A Viewpoint of User's Perspective. Preprints2024, 2024072502. https://doi.org/10.20944/preprints202407.2502.v1
Ota, A.; Uto, M. Variation of Property Valuations Conducted by Artificial Intelligence in Japan: A Viewpoint of User's Perspective. Preprints 2024, 2024072502. https://doi.org/10.20944/preprints202407.2502.v1
Ota, A.; Uto, M. Variation of Property Valuations Conducted by Artificial Intelligence in Japan: A Viewpoint of User's Perspective. Preprints2024, 2024072502. https://doi.org/10.20944/preprints202407.2502.v1
APA Style
Ota, A., & Uto, M. (2024). Variation of Property Valuations Conducted by Artificial Intelligence in Japan: A Viewpoint of User's Perspective. Preprints. https://doi.org/10.20944/preprints202407.2502.v1
Chicago/Turabian Style
Ota, A. and Masaaki Uto. 2024 "Variation of Property Valuations Conducted by Artificial Intelligence in Japan: A Viewpoint of User's Perspective" Preprints. https://doi.org/10.20944/preprints202407.2502.v1
Abstract
Property valuation services using artificial intelligence (AI) have been developed, with more than 20 services available in Japan. However, since their algorithms and training data are not publicly available, the extent of differences in the AI property valuations among these services is not clear. This study focuses on the five services handling a lot of properties and uses a sample of 4,295 property valuations for 859 condominium units in six popular residential areas in Tokyo. (1) Multiple comparison tests of the AI property valuations among each service are conducted to confirm their statistical significance and to examine the degree of the difference and variation. (2) The business models of each service are compared to examine the factors contributing to these differences and variations. The results showed that there was a variation of approximately 0.15 in the coefficient of variation between the high and low valuation groups, and the service providers in the high valuation group guided the users to their sales, while those in the low valuation group focused on attracting users as their business models. The contribution of this study is to indicate the possibility that the business models may lead to the differences in the AI property valuations.
Keywords
artificial intelligence; property valuation; real estate; variation; business model; appraisal value; artificial neural network
Subject
Social Sciences, Urban Studies and Planning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
