Traditional art pricing models often lack fine measurements of painting
content. This paper proposes a new content measurement: the Shannon information
quantity measured by the singular value decomposition (SVD) entropy of the
painting image. Using a large sample of artworks' auction records and images,
we show that the SVD entropy positively affects the sales price at 1%
significance level. Compared to the other commonly adopted content variables,
the SVD entropy has advantages in variable significance, sample robustness as
well as model fit. Considering the convenient availability of digital painting
images and the straightforward calculation algorithm of this measurement, we
expect its wide application in future research