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Biased auctioneers
(2022)
We construct a neural network algorithm that generates price predictions for art at auction, relying on both visual and non-visual object characteristics. We find that higher automated valuations relative to auction house pre-sale estimates are associated with substantially higher price-to-estimate ratios and lower buy-in rates, pointing to estimates’ informational inefficiency. The relative contribution of machine learning is higher for artists with less dispersed and lower average prices. Furthermore, we show that auctioneers’ prediction errors are persistent both at the artist and at the auction house level, and hence directly predictable themselves using information on past errors.
David Rothenberg, a philosophy professor and Jazz musician, has been improvising with nonhuman animals for years, among his playing partners are birds and whales, known to be territorial animals. As Deleuze and Guattari propose that the origin of art is precisely the territorialising animal and more a function of nature than a specifically human cultural achievement, their concept of territory and rhythm offers a non-anthropocentric way of looking at these encounters. Rothenberg’s sonic experiments in resonance and interspecies interaction do not rely on language, thus I argue that the human and the nonhuman animals form a temporary joint territory via sonic rhythms and engage in a mutual becoming by forming a rhizome. His sound thinking practice thus also helps in decentralising further anthropocentric models of music and art.
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.