Machines and Masterpieces: Predicting Prices in the Art Auction Market

Mathieu Aubry (), Roman Kraeussl, Gustavo Manso and Christophe Spaenjers
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Mathieu Aubry: LIGM - Laboratoire d'Informatique Gaspard-Monge - ENPC - École nationale des ponts et chaussées - CNRS - Centre National de la Recherche Scientifique - Université Gustave Eiffel
Authors registered in the RePEc Author Service: Roman Kräussl

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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".

Keywords: asset valuation; auctions; experts; big data; machine learning (search for similar items in EconPapers)
Date: 2020-07-10
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Working Paper: Machines and Masterpieces: Predicting Prices in the Art Auction Market (2019)
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Persistent link: https://EconPapers.repec.org/RePEc:hal:wpaper:hal-02896049

DOI: 10.2139/ssrn.3347175

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