We construct a neural network algorithm that generates price predictions for art at auction, relying on both visual and non-visual object characteristics. We find that higher automated valuations relative to auction house pre-sale estimates are associated with substantially higher...

Aggregate art price patterns mask a lot of underlying variation — both in the time series and in the cross-section. We argue that, to increase our understanding of the market for aesthetics, it is helpful to take a micro perspective on the formation of art prices, and acknowledge that each...

Motivated by the fast growth of personal wealth in emerging economies like Russia, we investigate the investment performance of modern Russian art. A hedonic analysis of more than 50,000 art transactions results in a geometric average return of 3.97%, in real USD terms, between 1967 and 2007....