Hedonic Models and Pre-Auction Estimates: Abstract Art Revisited

Abstract

We investigate the predictive power of hedonic models compared to that of pre-auction estimates in the context of art auctions. We use a panel data consisting of abstract paintings and a methodology that employs the estimates as instrumental variables in the framework of a hedonic regression model. The results suggest that hedonic models have no better predictive power than that of the estimates. Pre-auction estimates appear to fully account for the available public information on works of art.