결정 트리 기반 학습 모형을 이용한 미술품 경매 가격 예측

Dongryul Jang ; Minjae Park

(First Author) Department of Culture and Arts Management, Graduate School, Hongik University dongryulj@naver.com
(Corresponding Author) College of Business Administration, Hongik University mjpark@hongik.ac.kr

Art Price Prediction Using Decision Tree-Based Machine Learning Methods

Copyright 2011 THE KOREAN ACADEMIC SOCIETY OF BUSINESS ADMINISTRATION
This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Recently, many researchers not only from industry but also from academia have interested in the art finance market. Although many people have studied growing art finance market, very few studies on the prediction model to evaluate the art price have been conducted. Therefore, in this study we have gathered a database of 12,105 paintings auctioned between 2009 and 2018 and have implemented decision tree-based machine learning algorithms (e.g., random forests, gradient boosting, XGboost) to develop prediction models for art prices and to improve the reliability for art experts’ estimates accurately. We have compared the prediction accuracy of the proposed approach based on root mean square error and mean absolute error. As a result of the analysis, we noticed that experts’ estimates from auction houses are more accurate only for high-priced artworks but overestimates low-priced artworks. On the other hand, the suggested prediction model’s accuracy considering decision tree-based model is better than the accuracy based on the parametric OLS model. Finally, we test the accuracy of prediction models considering expert evaluation to enhance the models’ predictive power. These results show that an integrated approach between expert appraisal systems and statistical models improves the prediction accuracy for artworks’ price.

Keywords:

art price, decision tree, extreme gradient boosting, gradient boosting, random forests

Acknowledgments

Minjae Park’s work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (NRF-2020R1F1A104823711).

A previous version of this paper won a third prize at the “18th Thesis Competition“ hosted by Statistics Korea.

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∙The author Dongryul Jang graduated from Hongik University with a master's degree in Arts and Cultural Management. His main interests are machine learning, big data modeling, and cultural industry. His paper related was selected as outstanding paper award in 'the 18th Statistics Korea Research Competition’.

∙The author Minjae Park is currently an associate professor at Business School, Hongik University, Seoul, Korea. His research interests include quality management, reliability modeling, maintenance policy, mean shift detection, optimization, and applied statistics.