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

Arbeitspapier

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: CFS Working Paper Series ; No. 635

Classification and Topics

• Classification: Wirtschaft ; Econometric Modeling: General ; Auctions ; Asset Pricing; Trading Volume; Bond Interest Rates ; Cultural Economics: Economics of the Arts and Literature
• Subject: asset valuation ; auctions ; experts ; big data ; machine learning ; computer vision ; art

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Aubry, Mathieu ; Kräussl, Roman ; Manso, Gustavo ; Spaenjers, Christophe
• Event: Veröffentlichung
• (who): Goethe University Frankfurt, Center for Financial Studies (CFS)
• (where): Frankfurt a. M.
• (when): 2019

Further information

• Handle: http://hdl.handle.net/10419/206414
• URN: urn:nbn:de:hebis:30:3-515969
• Last update: 10.03.2025, 11:43 AM CET