Affiliations: [a] Department of Industrial Engineering, Seoul National University of Science & Technology, Nowon-gu, Seoul, Korea | [b] Department of Data Science, Seoul National University of Science & Technology, Nowon-gu, Seoul, Korea
Correspondence:
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Corresponding author: Kyoungok Kim, Department of Industrial Engineering, Seoul National University of Science & Technology, 232 Gongreungno, Nowon-gu, Seoul, 01811, Korea. Tel.: +82 2 970 7286; E-mail: [email protected].
Abstract: Art markets globally have grown, making artwork an investment of note. Precise valuation is pivotal for optimal returns. We introduce a two-step model with a two-level regressor, utilizing extreme gradient boosting (XGBoost) for accurate artwork price prediction. The model encompasses a price-class classifier and regressors for individual categories. This captures diverse factor influences, combining predictions to reduce misclassification risks. Visual features further enhance accuracy through the second-step two-level regressor. Experiments on Korean art auction data demonstrate the superiority of our two-step model with the two-level regressor over one-step and two-step alternatives, as well as the hedonic pricing model. While visual features affected one- and two-step models’ training, they boosted performance when integrated into the second-level decision tree, reducing first-level residuals. This emphasizes the two-level regressor’s efficacy in incorporating visual elements for artwork valuation. Our study highlights the potential of our approach in the field of artwork valuation.
Keywords: Korean art market, art investment, XGBoost, two-step model, price prediction
