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Machines and Masterpieces: Predicting Prices in the Art Auction Market

Author

Listed:
  • Mathieu Aubry

    (LIGM - Laboratoire d'Informatique Gaspard-Monge - ENPC - École des Ponts ParisTech - CNRS - Centre National de la Recherche Scientifique - Université Gustave Eiffel)

  • Roman Kraeussl
  • Gustavo Manso
  • Christophe Spaenjers

    (Department of Economics - Tilburg University [Netherlands])

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".

Suggested Citation

  • Mathieu Aubry & Roman Kraeussl & Gustavo Manso & Christophe Spaenjers, 2020. "Machines and Masterpieces: Predicting Prices in the Art Auction Market," Working Papers hal-02896049, HAL.
    • Handle: RePEc:hal:wpaper:hal-02896049
    DOI: 10.2139/ssrn.3347175
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    Cited by:

    1. Ewelina Plachimowicz & Piotr Wójcik, 2022. "What makes Punks worthy? Valuation of Non-Fungible Tokens based on the CryptoPunks collection using the hedonic pricing method," Working Papers 2022-27, Faculty of Economic Sciences, University of Warsaw.

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