Machine learning, human experts, and the valuation of real assets - Mathieu Aubry; Roman Kräussl; Gustavo Manso; Christophe Spaenjers (Goethe University Frankfurt, Center for Financial Studies (CFS), 2019)
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.
urn:nbn:de:hebis:30:3-515969
Keywords: C50; D44; G12; Z11; 330; asset valuation; auctions; experts; big data; machine learning; computer vision; art