Machine learning, human experts, and the valuation of real assets

Mathieu Aubry, Roman Kräussl, Gustavo Manso and Christophe Spaenjers

No 635, CFS Working Paper Series from Center for Financial Studies (CFS)

Abstract: We study the accuracy and usefulness of automated (i.e., machine-generated) valuations for illiquid and heterogeneous real assets. We assemble a database of 1.1 million paintings auctioned between 2008 and 2015. We use a popular machine-learning technique - neural networks - to develop a pricing algorithm based on both non-visual and visual artwork characteristics. Our out-of-sample valuations predict auction prices dramatically better than valuations based on a standard hedonic pricing model. Moreover, they help explaining price levels and sale probabilities even after conditioning on auctioneers' pre-sale estimates. Machine learning is particularly helpful for assets that are associated with high price uncertainty. It can also correct human experts' systematic biases in expectations formation - and identify ex ante situations in which such biases are likely to arise.

Keywords: asset valuation; auctions; experts; big data; machine learning; computer vision; art (search for similar items in EconPapers)
JEL-codes: C50 D44 G12 Z11 (search for similar items in EconPapers)
Date: 2019
New Economics Papers: this item is included in nep-big, nep-cmp, nep-des and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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Persistent link: https://EconPapers.repec.org/RePEc:zbw:cfswop:635

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