Predicting Art Price using Machine Learning and Statistical Models

The art market is comprised of complex cultural, economic, and social characteristics and has recently received a lot of attention. Art transactions are carried out in a variety of ways, of which art auctions account for a large portion. We collected auction data on Post-War and Contemporary Art artists born after 1920 from Artnet and implemented an art price prediction model. In addition to the hedonic price model commonly used in existing studies, price prediction performance was compared using various machine learning models such as regression model, random effects model, regularization model, tree-based model, and SVM. To compare various machine learning models, RMSE and MAE were compared using two cross-validation methods. As a result, the nonlinear random effects model using author information showed the good performance. The impact of explanatory variables on price was analyzed using the optimal model. In addition, we were able to obtain price curves of various patterns according to the passage of time for each author. Among machine learning models that do not use author information, Bagging showed the best performance. As a result of measuring the importance of variables through Bagging, it was found that the size of the work had the most influence in predicting the price, followed by the artist's year of birth, the method of painting, the material with which the painting was painted, and the year of production.