We study how deep learning can improve valuation in the art market by incorporating the visual content of artworks into predictive models. Using a large repeated-sales dataset from major auction houses, we benchmark classical hedonic regressions and tree-based methods against modern deep architectures, including multi-modal models that fuse tabular and image data. We find that while artist identity and prior transaction history dominate overall predictive power, visual embeddings provide a distinct and economically meaningful contribution for fresh-to-market works where historical anchors are absent. Interpretability analyses using Grad-CAM and embedding visualizations show that models attend to compositional and stylistic cues. Our findings demonstrate that multi-modal deep learning delivers significant value precisely when valuation is hardest, namely first-time sales, and thus offers new insights for both academic research and practice in art market valuation.

翻译：本研究探讨了如何通过将艺术品的视觉内容纳入预测模型，利用深度学习改进艺术品市场的估值。基于从主要拍卖行收集的大型重复交易数据集，我们将经典的享乐回归和基于树的方法与现代深度架构（包括融合表格数据和图像数据的多模态模型）进行了基准比较。研究发现，虽然艺术家身份和先前的交易历史在整体预测能力中占主导地位，但对于缺乏历史锚点的首次上市作品，视觉嵌入提供了独特且具有经济意义的贡献。使用Grad-CAM和嵌入可视化进行的可解释性分析表明，模型关注的是构图和风格线索。我们的研究结果表明，多模态深度学习恰恰在估值最为困难（即首次销售）时提供了显著价值，从而为艺术品市场估值的学术研究和实践提供了新的见解。