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Preprints, Working Papers, ... Year : 2020

Machines and Masterpieces: Predicting Prices in the Art Auction Market

Mathieu Aubry
Roman Kraeussl
  • Function : Author
Gustavo Manso
  • Function : Author

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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hal-02896049 , version 1 (10-07-2020)

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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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