DOI QR코드

DOI QR Code

인공지능 (AI) 기반 섹터별 부동산 수익률 결정 모델 연구- 글로벌 5개 도시를 중심으로 (서울, 뉴욕, 런던, 파리, 도쿄) -

A Study on AI-Based Real Estate Rate of Return Decision Models of 5 Sectors for 5 Global Cities: Seoul, New York, London, Paris and Tokyo

  • 이원부 (동국대학교 핀테크블록체인학과 인공지능 전공) ;
  • 이지수 (동국대학교 핀테크블록체인학과 인공지능 전공) ;
  • 김민상 (주식회사 위펀딩)
  • 투고 : 2024.06.03
  • 심사 : 2024.07.03
  • 발행 : 2024.09.30

초록

Purpose: This study aims to provide useful information to real estate investors by developing a profit determination model using artificial intelligence. The model analyzes the real estate markets of six selected cities from multiple perspectives, incorporating characteristics of the real estate market, economic indicators, and policies to determine potential profits. Methods: Data on real estate markets, economic indicators, and policies for five cities were collected and cleaned. The data was then normalized and split into training and testing sets. An AI model was developed using machine learning algorithms and trained with this data. The model was applied to the six cities, and its accuracy was evaluated using metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R-squared by comparing predicted profits to actual outcomes. Results: The profit determination model was successfully applied to the real estate markets of six cities, showing high accuracy and predictability in profit forecasts. The study provided valuable insights for real estate investors, demonstrating the model's utility for informed investment decisions. Conclusion: The study identified areas for future improvement, suggesting the integration of diverse data sources and advanced machine learning techniques to enhance predictive capabilities.

키워드

과제정보

본 연구는 본 논문은 동국대학교와 주식회사 위펀딩, WRC, Morgan Stanley CI (Commercial Index), Westone의 지원으로 수행된 연구결과 중 일부임을 밝히며 지원에 감사드립니다.

참고문헌

  1. Arik, Sercan O., and Tomas Pfister. 2019. TabNet: Attentive Interpretable Tabular Learning. arXiv preprint arXiv:1908.07442. 
  2. Bae, Woo-Soon. 2019. Status of Proptech Companies and Challenges for Future Development. Land:21-26. 
  3. Bin, Jae-Ik. 2014. Analysis of Global Real Estate Market Cycles through Cases of Major European Countries and Implications. Issue Focus 2014(1):2-25. 
  4. Cho, Young-Im. 2019. Real Estate Industry Revitalization Strategies through the Integration of 4th Industrial Revolution Technologies such as Blockchain, IoT, and Real Estate Information. Real Estate Focus 115: 166-181. 
  5. Jaewoong Won, KIM YURIANNA, & Byounghoon Hwang. 2020. Use of Artificial Intelligence Techniques to Assess Architectural Aesthetics and Estimate the Rent Price of Office Buildings in Seoul. Journal of Appraisal Studies 19(1):5-26. 
  6. Jung, Su-Jin. 2018. Analysis of Growth Cases of U.S. Real Estate Platform Companies and Implications. Industry Technology Research Center. 
  7. KIM SUN JU, Jang Hee Soon. 2020. The Utilization and Influence of Fourth Industrial Revolution Technology in Appraisal Work. Journal of Real Estate Analysis 6(2):83-102. 
  8. Kim, Kwang-Seok. 2013. Global Real Estate Trends: Insights through Comparison of Major Countries' Real Estate Markets. Construction Economy 2013:106-120. 
  9. Kim, Kyung-Hoon, Yang Da-Young, and Kang Eun-Jung. 2019. Global Real Estate Price Analysis Utilizing Big Data. Korea Institute for International Economic Policy. 
  10. Lee, HyunJun, Shin, SeongYoun, & Yoon, YoungSik. 2021. Korean Academic Society of Business Administration. Korea Business Review 25(2):107-133, 1226-4997. 
  11. Legislative Policy Research Institute. 2021. Research on the Status and Improvement Directions of the Domestic and International Proptech Industry. 
  12. M. Lienhard and M. Wörnlein. 2019. A Deep Learning Approach to Real Estate Valuation with Automated Valuation Models (AVMs). 
  13. Ministry of Land, Infrastructure and Transport Statistics. Statistics Nuri. http://stat.molit.go.kr. 
  14. NAK HYUN JUNG, Taeyeon Oh, & Kang Hee Kim. 2023. A Study on AI-based Composite Supplementary Index for Complementing the Composite Index of Business Indicators 
  15. NAK HYUN JUNG, Taeyeon Oh, & Kang Hee Kim. 2023. A Study on DRL-based Efficient Asset Allocation Model for Economic Cycle-based Portfolio Optimization. 
  16. P. Bao, D. K. Tse, and X. Li. 2018. Predicting Residential Real Estate Prices Using Machine Learning Techniques 
  17. Park Kwang-dong. 2020. A Study on the Change of Real Estate Registration by Blockchain. Ilkam Real Estate Law Review 20(0):139-160. 
  18. R. M. Kolte, and R. B. Ahire. 2018. Predicting Real Estate Prices using Random Forest Regression. 
  19. S. B. Shukla, S. S. Wagh, and S. K. Patil. 2020. Real Estate Price Forecasting using Machine Learning Techniques: A Comprehensive Review. 
  20. S. Fan, J. Zhang, and X. Du. 2019. A Hybrid Machine Learning Model for Real Estate Price Prediction. 
  21. S. Kavulya, S. P. Karumuri, and V. K. Agarwal. 2020. Predicting Real Estate Prices Using Convolutional Neural Networks.