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Price Determinant Factors of Artworks and Prediction Model Based on Machine Learning

작품 가격 추정을 위한 기계 학습 기법의 응용 및 가격 결정 요인 분석

  • Jang, Dongryul (Department of Culture and Arts Management, Graduate School, Hongik University) ;
  • Park, Minjae (College of Business Administration, Hongik University)
  • 장동률 (홍익대학교 일반대학원 문화예술경영학과) ;
  • 박민재 (홍익대학교 경영학과)
  • Received : 2019.09.01
  • Accepted : 2019.09.18
  • Published : 2019.12.31

Abstract

Purpose: The purpose of this study is to investigate the interaction effects between price determinants of artworks. We expand the methodology in art market by applying machine learning techniques to estimate the price of artworks and compare linear regression and machine learning in terms of prediction accuracy. Methods: Moderated regression analysis was performed to verify the interaction effects of artistic characteristics on price. The moderating effects were studied by confirming the significance level of the interaction terms of the derived regression equation. In order to derive price estimation model, we use multiple linear regression analysis, which is a parametric statistical technique, and k-nearest neighbor (kNN) regression, which is a nonparametric statistical technique in machine learning methods. Results: Mostly, the influences of the price determinants of art are different according to the auction types and the artist 's reputation. However, the auction type did not control the influence of the genre of the work on the price. As a result of the analysis, the kNN regression was superior to the linear regression analysis based on the prediction accuracy. Conclusion: It provides a theoretical basis for the complexity that exists between pricing determinant factors of artworks. In addition, the nonparametric models and machine learning techniques as well as existing parameter models are implemented to estimate the artworks' price.

Keywords

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