Social Signals Predict Contemporary Art Prices Better Than Visual Features, Particularly In Emerging Markets

This article discusses the use of computer vision and pattern recognition algorithms to analyze visual features of artwork and predict its price. The authors use principal component analysis and the XGBoost algorithm to reduce the number of features and successfully predict the price of artwork. They also test the ability of these visual features to predict other characteristics of the artwork and artist.