We construct a neural network algorithm that generates price predictions for art at auction, relying on both visual and nonvisual object characteristics. We find that higher automated valuations relative to auction house presale 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.

中文翻译：

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有偏见的拍卖师

我们构建了一种神经网络算法，可以根据视觉和非视觉对象的特征为拍卖中的艺术品生成价格预测。我们发现，相对于拍卖行预售估值而言，更高的自动估值与更高的价格与估值比率和更低的买入率相关，表明估值的信息效率低下。对于分散程度较低且平均价格较低的艺术家，机器学习的相对贡献较高。此外，我们表明拍卖师的预测错误在艺术家和拍卖行层面都存在，因此可以使用过去错误的信息直接预测自己。