Journal of Service Research and Studies (서비스연구)

The Society of service Science (서비스사이언스학회)
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A Study on the Prediction Models of Used Car Prices for Domestic Brands Using Machine Learning

머신러닝을 활용한 브랜드별 국내 중고차 가격 예측 모델에 관한 연구

  • Seungjun Yim (Department of Business Administration, Hong-ik University) ;
  • Joungho Lee (School of Business, Konkuk University) ;
  • Choonho Ryu (Department of Business Administration, Hong-Ik University)
  • Received : 2023.07.10
  • Accepted : 2023.08.08
  • Published : 2023.09.30
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Abstract

The domestic used car market continues to grow along with the used car online platform service. The used car online platform service discloses vehicle specifications, accident history, inspection history, and detailed options to service consumers. Most of the preceding studies were predictions of used car prices using vehicle specifications and some options for vehicles. As a result of the study, it was confirmed that there was a nonlinear relationship between used car prices and some specification variables. Accordingly, the researchers tried to solve the nonlinear problem by executing a Machine Learning model. In common, the Regression based Machine Learning model had the advantage of knowing the actual influence and direction of variables, but there was a disadvantage of low Cost Function figures compared to the Decision Tree based Machine Learning model. This study attempted to predict used car prices of six domestic brands by utilizing both vehicle specifications and vehicle options. Through this, we tried to collect the advantages of the two types of Machine Learning models. To this end, we sequentially conducted a regression based Machine Learning model and a decision tree based Machine Learning model. As a result of the analysis, the practical influence and direction of each brand variable, and the best tree based Machine Learning model were selected. The implications of this study are as follows. It will help buyers and sellers who use used car online platform services to predict approximate used car prices. And it is hoped that it will help solve the problem caused by information inequality among users of the used car online platform service.

국내 중고차 시장은 지속적으로 성장하고 있으며, 이와 동시에 중고차 온라인 플랫폼 서비스 역시 함께 매년 시장 점유율을 확대하고 있다. 중고차 온라인 플랫폼 서비스는 차량의 제원, 점검 이력, 사고 내역, 그리고 세부 옵션 등을 서비스 이용자에게 제공하고 있다. 대부분의 기존 연구는 차량의 제원과 차량의 일부 옵션을 활용한 중고차 가격의 예측이었으며, 중고차 가격과 일부 제원 변수 간 비선형 관계임을 확인하였다. 이에 따라 연구자들은 이러한 비선형 문제를 해결하기 위해 머신러닝(Machine Learning) 모델의 실행을 제안하였으며, 그 결과 회귀(Regression) 기반 머신러닝 모델은 변수의 실질적인 영향력과 방향성을 알 수 있는 장점이 존재하였으나, 트리(Decision Tree) 기반 머신러닝 모델에 비해 비용함수 수치가 저조한 단점이 존재하였다. 본 연구는 국내 브랜드를 대상으로 차량의 제원과 차량의 옵션, 총 70여 개의 변수를 모두 활용하여 회귀 기반 머신러닝 모델과 트리 기반 머신러닝 모델을 순차적으로 실행하여 두 유형의 머신러닝 모델의 장점을 취합하고자 하였다. 이를 통해 브랜드별 변수의 실질적 영향력과 방향성을 확인한 후 브랜드별 가장 우수한 트리 기반 머신러닝 모델을 선정하였다. 본 연구의 시사점은 다음과 같다. 중고차 온라인 플랫폼 서비스를 이용하는 구매자와 판매자가 전반적인 중고차 가격 예측을 지원할 수 있다. 이에 따라 중고차 온라인 플랫폼 서비스 이용자 간 정보의 비대칭으로 인한 문제 해결 역시 지원이 가능할 것으로 기대한다.

Keywords

References

  1. Arawomo, D. F., and Osigwe, A. C. (2016), Nexus of fuel consumption, car features and car prices: Evidence from major institutions in Ibadan, Renewable and Sustainable Energy Reviews, 59, 1220-1228  https://doi.org/10.1016/j.rser.2016.01.036
  2. BALCE, A. O. (2016), Factors Affecting Prices in an Used Car E-Market, Journal of Internet Applications and Management, 7(2), 5-20  https://doi.org/10.5505/iuyd.2016.30974
  3. Chaudhary, L., Sharma, S., and Sajwan, M. (2022), Comparative Analysis of Supervised Machine Learning Algorithm, Available at SSRN 4143890.
  4. Chen, C., Hao, L., and Xu, C. (2017), Comparative analysis of used car price evaluation models, In AIP Conference Proceedings, 1839(1), 020165 
  5. Gegic, E., Isakovic, B., Keco, D., Masetic, Z., and Kevric, J. (2019), Car price prediction using machine learning techniques, TEM Journal. 8(1), 113. 
  6. Khan, Z. (2022), Used Car Price Evaluation using three Different Variants of Linear Regression, International Journal of Computational and Innovative Sciences, 1(1) 
  7. Kim, C. (2022), The Problems of the Used Car Market in Korea and the Entry of Large Enterprises, KIET Industrial Economic Policy and Issues, 65-69
  8. Kim, H., and Lee, M. (2021), Research Trends in Machine Learning Applications in Marketing, Korean Journal of Marketing, Vol.36, No.1, pp.1-25
  9. Kwon, C. (2022), Python Machine Learning Complete Guide, Wikibooks
  10. Lasek, A., and Wyszynski, K. (2019), Determinants of used car prices-The Black Volkswagen Golf case, University of Warsaw Faculty of Economic Science, Warsaw, January 2019, 1-16 
  11. Lee, J. (2022), Discover the potential of the domestic used car market, Eugene Investment & Securities Automotive Issues, 1-36
  12. Lee, D., and Shin, M. (2023), Prediction of Dormant Customer in the Card Industry, Journal of Service Research and Studies, 13(2), 99-113
  13. Lee, S. (2022), 2022 Copyright industry issues in the next-generation digital enviroment, Trade and Industry Statistics Team KOREA COPYRIGHT COMMISSION, 14-37
  14. Meng, S. M., Liu, L. J., Kuritsyn, M., and Pechnikov, V. (2019), Price Determinants on Used Car Auction in Taiwan, International Journal of Asian Social Science, 9(1), 48-58  https://doi.org/10.18488/journal.1.2019.91.48.58
  15. Nasiboglu, R., and Akdogan, A. (2020), Estimation of the second hand car prices from data eXtreamcted via web scraping techniques, Journal of Modern Technology and Engineering, 5(2), 157-166 
  16. Ramampiandra, E. C., Scheidegger, A., Wydler, J., and Schuwirth, N. (2023), A comparison of machine learning and statistical species distribution models: Quantifying overfitting supports model interpretation, Ecological Modeling, 481, 110353 
  17. Samuel, A. L. (1959), Machine learning, The Technology Review, 62(1), 42-45 
  18. Shanti, N., Assi, A., Shakhshir, H., and Salman, A. (2021), Machine Learning Powered Mobile App for Predicting Used Car Prices, In 2021 3rd International. Conference on Big-data Service and Intelligent Computation, Xiamen, China, November 19-21, 2021, 52-60 
  19. Staartjes, V. E., Regli, L., and Serra, C. (Eds.). (2022), Machine learning in clinical neuroscience: Foundations and application, Springer. 
  20. Venkatasubbu, P., and Ganesh, M. (2019), Used Cars Price Prediction using Supervised Learning Techniques, Int. J. Eng. Adv. Technol(IJEAT), 9(1S3) 
  21. Yadav, A., Kumar, E., and Yadav, P. K. (2021), Object detection and used car price predicting analysis system (UCPAS) using machine learning technique, Linguistics and Culture Review. 5(S2), pp.1131-1147.  https://doi.org/10.21744/lingcure.v5nS2.1660
  22. Yang, J. (2020), Current Status and Challenges of the Domestic Market for Used Cars, Industrial trends, 49
  23. Yennimar, Y., Kelvin, K., Suwandi, S., and Amir, A. (2022), Comparison Analysis of SVM Algorithm with Linear Regression in Predicting used Car Prices, Journal Mantik. 5(4), 2720-2028