Previous Issue
Volume 1, September
 
 

Real Estate, Volume 1, Issue 3 (December 2024) – 3 articles

  • Issues are regarded as officially published after their release is announced to the table of contents alert mailing list.
  • You may sign up for e-mail alerts to receive table of contents of newly released issues.
  • PDF is the official format for papers published in both, html and pdf forms. To view the papers in pdf format, click on the "PDF Full-text" link, and use the free Adobe Reader to open them.
Order results
Result details
Select all
Export citation of selected articles as:
15 pages, 2547 KiB  
Article
Variation in Property Valuations Conducted by Artificial Intelligence in Japan: A Viewpoint of User’s Perspective
by Akira Ota and Masaaki Uto
Real Estate 2024, 1(3), 252-266; https://doi.org/10.3390/realestate1030013 - 1 Nov 2024
Viewed by 541
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 variations in the AI property valuations among these services is not [...] Read more.
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 variations in the AI property valuations among these services is not clear. This study focuses on five services and uses a sample of 4295 valuations for 859 condominium units in six popular residential areas in Tokyo. (1) Multiple comparison tests of the AI property valuations among the services are conducted to confirm their statistical significance and to examine the extent of the variations. (2) The business models of each service are compared to examine the factors contributing to these variations. The results showed that the average variation in the AI property valuations was 10.6%, which was larger than the variations observed in traditional property valuations. It was also found that the valuation groups, categorized as high or low, varied based on the business models of the service providers. These results indicate that it is necessary to promote the healthy development of AI property valuation by establishing guidelines, such as requiring the AI property valuation services to ensure fair prices or disclosing their algorithms and data. Full article
Show Figures

Figure 1

23 pages, 2226 KiB  
Article
Property Valuation in Latvia and Brazil: A Multifaceted Approach Integrating Algorithm, Geographic Information System, Fuzzy Logic, and Civil Engineering Insights
by Vladimir Surgelas, Vivita Puķīte and Irina Arhipova
Real Estate 2024, 1(3), 229-251; https://doi.org/10.3390/realestate1030012 - 21 Oct 2024
Viewed by 476
Abstract
This study aimed to predict residential apartment prices in Latvia and Brazil using algorithms from machine learning, fuzzy logic, and civil engineering principles, with a focus on overcoming multicollinearity challenges. To explore the market dynamics, we conducted four initial experiments in the central [...] Read more.
This study aimed to predict residential apartment prices in Latvia and Brazil using algorithms from machine learning, fuzzy logic, and civil engineering principles, with a focus on overcoming multicollinearity challenges. To explore the market dynamics, we conducted four initial experiments in the central regions of Riga and Jelgava (Latvia), as well as São Paulo and Niterói (Brazil). Data were collected from real estate advertisements, supplemented by civil engineering inspections, and analyzed following international valuation standards. The research integrated human decision-making behavior with machine learning and the Apriori algorithm. Our methodology followed five key stages: data collection, data preparation for association rule mining, the generation of association rules, fuzzy logic analysis, and the interpretation of model accuracy. The proposed method achieved a mean absolute percentage error (MAPE) that ranged from 5% to 7%, indicating strong alignment with market trends. These findings offer valuable insights for decision making in urban development, particularly in optimizing renovation priorities and promoting sustainable growth. Full article
Show Figures

Figure 1

17 pages, 529 KiB  
Article
Crypto Herf: Utilizing the Herfindahl Index to Assess Cryptocurrency Investment Preference
by G. Jason Goddard, Todd A. Parrish and David M. Church
Real Estate 2024, 1(3), 212-228; https://doi.org/10.3390/realestate1030011 - 1 Oct 2024
Viewed by 638
Abstract
This paper utilizes the Herfindahl Index to assess university business major student investment preferences regarding cryptocurrency. This paper seeks to determine which cryptocurrency investment options are most desirable and, more importantly, ascertain the reasons for said investments. This paper reviews the real estate-based [...] Read more.
This paper utilizes the Herfindahl Index to assess university business major student investment preferences regarding cryptocurrency. This paper seeks to determine which cryptocurrency investment options are most desirable and, more importantly, ascertain the reasons for said investments. This paper reviews the real estate-based currency of the French Revolution in order to provide historical lineage for the popularity of cryptocurrency investment today. Full article
Show Figures

Figure 1

Previous Issue
Back to TopTop