Machines and Masterpieces: Predicting Prices in the Art Auction Market

Mathieu Aubry, Roman Kräussl, Gustavo Manso and Christophe Spaenjers ()
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Mathieu Aubry: Ecole Nationale des Ponts et Chaussées (ENPC)
Gustavo Manso: University of California, Berkeley - Haas School of Business
Christophe Spaenjers: HEC Paris

No 1332, HEC Research Papers Series from 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 (search for similar items in EconPapers)
JEL-codes: C50 D44 G12 Z11 (search for similar items in EconPapers)
Pages: 46 pages
Date: 2019-03-20
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Related works:
Working Paper: Machines and Masterpieces: Predicting Prices in the Art Auction Market (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:ebg:heccah:1332

DOI: 10.2139/ssrn.3347175

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