DOI QR코드

DOI QR Code

Common Maintenance Cost Estimation Model Using Random Forest for Multi-Family Housing

랜덤 포레스트 기반 공동주택 공용관리비 추산모델

  • 정진호 (서울시립대학교 대학원) ;
  • 김종협 (서울시립대학교 대학원) ;
  • 추재호 (서울시립대학교 대학원) ;
  • 이승훈 ((주)건축사사무소 건원엔지니어링) ;
  • 현창택 (서울시립대학교 건축학부)
  • Received : 2016.10.21
  • Accepted : 2017.03.24
  • Published : 2017.03.30

Abstract

As multi-family housing has been chosen as the most popular types of housing in Korea, the maintenance of these facilities has become a key issue for the lives of the residents. The problem concerning the estimation of common maintenances cost is a sensitive issue and one that is currently receiving a lot of interest from the residents. Factors affecting this issue are complex and diverse. Thus, it is difficult to verify if the results of given estimates are adequate or whether or not the costs are high in comparison to other similar facilities. So, based on the historical data of multi-family housing, this study suggests an estimation model for arriving at common maintenance costs by utilizing the random forest method - a technique used in data mining. The multi-family housing data used in the configuration of the model was collected from certain regions in Seoul and, based on this, the random forest method was used to analyze the factors that influence common maintenance costs in order to develop an estimation model. This model is notable as it differed from traditional statistical methods by utilizing data mining -the random forest method- to build an estimation model for common maintenance costs. As a result of the case verification, the random forest based estimation model in this study is considered useful, and it is expected that more precise estimates will be gradually achieved as the data accumulates due to the nature of data mining and machine learning techniques.

Keywords

Acknowledgement

Supported by : 국토교통과학기술진흥원

References

  1. Brieman, L. (2001). Random forests, Machine learning, 45(1), 5-32. https://doi.org/10.1023/A:1010933404324
  2. Ha, J. (2015). Random Forests for the class imbalanced data, Master Thesis, Yonsei University
  3. Han, J. Kamber, M. & Pei, J. (2012). DATA MINING Concepts and Techniques 3rd Edition, Elsevier Inc., 332.
  4. Her, M. (2013). A Study on Actual Analysis of Management Fee of Apartment Houses and Saving of Management Fee - Focusing on the apartment housing in Ulsan -, Doctorate Thesis, Youngsan University
  5. Hur, J. & Lee, J. (2011). An empirical study on the determinants of management fee of multi-family housing in Seoul, Journal of the Korean Urban Management Association, 24(2), 173-185.
  6. Hwang, J. (2006). Double Regression Tree Model for Statistical Matching, Doctorate Thesis, Chungnam National University
  7. Jung, J., Jung, H., Seo, H. & Kim, J. (2012). A Study on the Requirement Maintenance Factors in Apartment Housing for the Efficient Management of Residents, Journal of the Korean Housing Association, 24(2), 277-288.
  8. Jung, J., Kim, J., Seo, H. & Kim, J. (2013). A Study on the Maintenance Factors of Multi-Family Housing through a Comparison of the Awareness of Managers and Resident, Journal of the Korean Housing Association, 24(4), 19-27. https://doi.org/10.6107/JKHA.2013.24.4.019
  9. Kang, H. & Han, C. (2006). An Empirical analysis of the Effect of Variables on Maintenance Expenses of Public Rental Housing, Korea Journal of Construction Engineering and Management, 7(6), 185-192.
  10. Kang, L., Yoon, S., & Kwon, J. (2009). Efficient Evaluation Method of Operation Cost for Wastewater Treatment Plant by BTL Contract Type, Journal of the Korean Society of Civil Engineers D, 29(5D), 627-634.
  11. Kang, T. & Park, J. (2013). A Study on the Problem and Improvement Management Expenses of Multi-family Housing: Focused on Apartment, Korean International Accounting Review, 49, 405-430.
  12. Kim, S., Jin, R., Hyun, C. & Cho, C. (2013). Development of Operation & Maintenance Cost Estimating Model for Facility Management of Buildings, Journal of the Architectural Institute of Korea Structure & Construction, 29(1), 11-21. https://doi.org/10.5659/JAIK_SC.2013.29.1.11
  13. Kim, S. (2015). A Corporate Credit Ratings Model with Random Forests, Master Thesis, Kookmin University
  14. Kim, Y. (1998). Minor Cost Analysis and Modeling for Life Cycle Cost Predictions of Apartment Buildings, Journal of the Architectural Institute of Korea Structure & Construction, 14(10), 105-112.
  15. Kwon, A. (2013). Variable Selection Using Random Forest, Master Thesis, Inha University
  16. Lee, J. (2011). Determinant of apartment housing administrative expenses-Focusing on the apartment housing in Seoul-, Master Thesis, Chung-ang University
  17. Lee, J. (2016). A Study on the Determinants of Apartment Management Fees - Focused on Busan Metropolitan City, Master Thesis, Pusan National University
  18. Lee, K., Yang, J. & Chae, C. (2010). A Study on the Maintenance Cost Estimation Model of the Apartment Housing, Journal of the Korean Housing Association, 21(2), 59-67. https://doi.org/10.6107/JKHA.2010.21.2.059
  19. Patel, B. & Rana, K. (2014). A Survey on Decision Tree Algorithm, Journal of Management in Engineering, 27(2), 106-115. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000038
  20. Razagopalan, B. & Krovi, R. (2002). Benchmarking Data Mining Algorithms, Journal of Database Management, 13(1), 25-35. https://doi.org/10.4018/jdm.2002010103
  21. Seo, K. (1997). A Study concerning Building Managemental Improvement through the Analysis of Building Maintenance Cost, Master Thesis, Konkuk University
  22. Shin, H. (2016). Development of a Random Forest Model using a Data Pre-processing Method, Master Thesis, Sungkyunkwan University