European Business Schools Librarian's Group
HEC Research Papers Series,
HEC Paris
No 1332: Machines and Masterpieces: Predicting Prices in the Art Auction Market
Mathieu Aubry, Roman Kräussl, Gustavo Manso and Christophe Spaenjers ()
Additional contact information
Mathieu Aubry: Ecole Nationale des Ponts et Chaussées (ENPC)
Roman Kräussl: Bayes Business School (formerly Cass); Hoover Institution, Stanford University; Centre for Economic Policy Research (CEPR)
Gustavo Manso: University of California, Berkeley - Haas School of Business
Christophe Spaenjers: HEC Paris
Abstract: 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 price-to-estimate ratios and lower buy-in rates, pointing to estimates’ informational inefficiency. The relative contribution of machine learning is higher for artists with less dispersed and lower average prices. Furthermore, we show that auctioneers’ prediction errors are persistent both at the artist and at the auction house level, and hence directly predictable themselves using information on past errors.
Keywords: art; auctions; experts; asset valuation; biases; machine learning; computer vision
46 pages, March 20, 2019
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