Image Content, Complexity, and the Market Value of Art

This paper presents an approach to measuring the complexity and content of art images that is based on information theory and can be replicated using widely-available analytic tools. The approach is combined with other machine learning algorithms to produce image content measurements for a sample of over 313,000 works offered for sale at auction over the past four decades. The work was produced by 1090 artists employing a variety of styles and using a variety of media and support. Drawing on approaches from economics, mathematics, computer science and psychology, models are estimated to measure the association of image complexity and other image characteristics with the auction price for which the painting was sold. The results support the hypothesis that art buyers have a preference for image complexity and are willing to pay for it. A one standard error increase in the entropy of the image is estimated to be associated with an increased market value of 138%, other factors held equal. We also examine and estimate the impact of faces, likelihood of the image containing racy or adult content, and other content measures. While these don't have as large an estimated impact as image complexity, many of them have large impacts that suggest such measures should be more widely applied in understanding the determinants of the market values of art.