TY  - UNPD
A1  - Aubry, Mathieu
A1  - Kräussl, Roman
A1  - Manso, Gustavo
A1  - Spaenjers, Christophe
T1  - Machine learning, human experts, and the valuation of real assets
T2  - Center for Financial Studies (Frankfurt am Main): CFS working paper series ; No. 635
N2  - 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.
T3  - CFS working paper series - 635 
KW  - asset valuation
KW  - auctions
KW  - experts
KW  - big data
KW  - machine learning
KW  - computer vision
KW  - art
Y1  - 2019
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/51596
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-515969
UR  - https://ssrn.com/abstract=3478851
IS  - June 23, 2019
PB  - Center for Financial Studies
CY  - Frankfurt, M.
ER  -