City Research Online

Abstract

We construct a neural network algorithm that generates price predictions for art at auction, relying on both visual and nonvisual object characteristics. We find that higher automated valuations relative to auction house presale 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.

Publication Type:	Article
Publisher Keywords:	art, auctions, experts, asset valuation, biases, machine learning, computer vision
Subjects:	H Social Sciences > HD Industries. Land use. Labor H Social Sciences > HF Commerce Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments:	Bayes Business School Bayes Business School > Finance
SWORD Depositor:	Symplectic Administrator

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