Neural Network Software Valuation of Fine Art

Given the uniqueness of fine art objects and uncertainties in demand (at auctions), can investors in paintings get accurate estimates of market values of holdings and potential acquisitions? In their March 2019 paper entitled “Machines and Masterpieces: Predicting Prices in the Art Auction Market”, Mathieu Aubry, Roman Kräussl, Gustavo Manso and Christophe Spaenjers compares accuracies of value estimates for paintings based on: (1) a linear hedonic regression (factor model), (2) neural network software and (3) auction houses. For the first two, they employ 985,188 auctions of paintings during 2008–2014 for in-sample training and 104,404 auctions of paintings during the first half of 2015 for out-of-sample testing. Neural network software inputs include information about artists and paintings (year of creation, materials, size, title and markings), and images of the paintings. Using information about artists/paintings and images and auction house estimates and sales prices for the specified 1,089,592 paintings by about 125,000 artists offered through 372 auction houses during January 2008 through June 2015, they find that:

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