Machines and Masterpieces: Predicting Prices in the Art Auction Market - Archive ouverte HAL Access content directly
Preprints, Working Papers, ... Year :

Machines and Masterpieces: Predicting Prices in the Art Auction Market

(1) , , , (2)
1
2
Mathieu Aubry
  • Function : Author
  • PersonId : 964162
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”.
Not file

Dates and versions

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

Licence

Copyright

Identifiers

Cite

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

Altmetric

Share

Gmail Facebook Twitter LinkedIn More