Biased auctioneers

  • 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.

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Author:Mathieu AubryORCiD, Roman KräusslORCiDGND, Gustavo MansoORCiD, Christophe SpaenjersORCiD
Series (Serial Number):CFS working paper series (No. 692)
Publisher:Center for Financial Studies
Place of publication:Frankfurt, M.
Document Type:Working Paper
Year of Completion:2022
Year of first Publication:2022
Publishing Institution:Universitätsbibliothek Johann Christian Senckenberg
Release Date:2023/02/27
Tag:art; asset valuation; auctions; biases; computer vision; computer visionbiases; experts; machine learning
Volume:This version: January 6, 2022
Page Number:46
Institutes:Wirtschaftswissenschaften / Wirtschaftswissenschaften
Wissenschaftliche Zentren und koordinierte Programme / Center for Financial Studies (CFS)
Dewey Decimal Classification:3 Sozialwissenschaften / 33 Wirtschaft / 330 Wirtschaft
JEL-Classification:C Mathematical and Quantitative Methods / C5 Econometric Modeling / C50 General
D Microeconomics / D4 Market Structure and Pricing / D44 Auctions
G Financial Economics / G1 General Financial Markets / G12 Asset Pricing; Trading volume; Bond Interest Rates
Z Other Special Topics / Z1 Cultural Economics; Economic Sociology; Economic Anthropology / Z11 Economics of the Arts and Literature
Licence (German):License LogoDeutsches Urheberrecht