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Machines and Masterpieces: Predicting Prices in the Art Auction Market

Abstract : We assess the accuracy and usefulness of machine-learning valuations in illiquid real asset markets. We apply neural networks to data on one million painting auctions to price artworks using non-visual and visual characteristics. Our out-of-sample automated valuations predict auction prices dramatically better than standard hedonic regressions. The discrepancies with pre-sale estimates provided by auction house experts correlate with sale outcomes: the more aggressive the auctioneer’s pre-sale estimate relative to our valuation, the higher the probability of an unsuccessful auction and the lower the post-acquisition return. Finally, machine learning can detect predictability in auctioneers’ “prediction errors”.
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Preprints, Working Papers, ...
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https://hal-hec.archives-ouvertes.fr/hal-02896049
Contributor : Antoine Haldemann <>
Submitted on : Friday, July 10, 2020 - 12:10:25 PM
Last modification on : Saturday, July 11, 2020 - 3:51:41 AM

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Mathieu Aubry, Roman Kraeussl, Gustavo Manso, Christophe Spaenjers. Machines and Masterpieces: Predicting Prices in the Art Auction Market. 2020. ⟨hal-02896049⟩

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