IDEAS home Printed from https://ideas.repec.org/p/ebg/heccah/1332.html
   My bibliography  Save this paper

Machines and Masterpieces: Predicting Prices in the Art Auction Market

Author

Listed:
  • Aubry, Mathieu

    (Ecole Nationale des Ponts et Chaussées (ENPC))

  • Kräussl, Roman

    (Bayes Business School (formerly Cass); Hoover Institution, Stanford University; Centre for Economic Policy Research (CEPR))

  • Manso, Gustavo

    (University of California, Berkeley - Haas School of Business)

  • Spaenjers, Christophe

    (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.

Suggested Citation

  • Aubry, Mathieu & Kräussl, Roman & Manso, Gustavo & Spaenjers, Christophe, 2019. "Machines and Masterpieces: Predicting Prices in the Art Auction Market," HEC Research Papers Series 1332, HEC Paris.
    • Handle: RePEc:ebg:heccah:1332
    DOI: 10.2139/ssrn.3347175
    as

    Download full text from publisher

    File URL: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3347175
    File Function: Full text
    Download Restriction: no
    File URL: https://libkey.io/10.2139/ssrn.3347175?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Other versions of this item:

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    More about this item

    Keywords

    art; auctions; experts; asset valuation; biases; machine learning; computer vision;
    All these keywords.

    JEL classification:

    • C50 - Mathematical and Quantitative Methods - - Econometric Modeling - - - General
    • D44 - Microeconomics - - Market Structure, Pricing, and Design - - - Auctions
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • Z11 - Other Special Topics - - Cultural Economics - - - Economics of the Arts and Literature

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ebg:heccah:1332. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Antoine Haldemann (email available below). General contact details of provider: https://edirc.repec.org/data/hecpafr.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.