{"ID":2824713,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.23078","arxiv_id":"2512.23078","title":"Deep Learning for Art Market Valuation","abstract":"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.","short_abstract":"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-...","url_abs":"https://arxiv.org/abs/2512.23078","url_pdf":"https://arxiv.org/pdf/2512.23078v1","authors":"[\"Jianping Mei\",\"Michael Moses\",\"Jan Waelty\",\"Yucheng Yang\"]","published":"2025-12-28T21:04:09Z","proceeding":"q-fin.GN","tasks":"[\"q-fin.GN\",\"cs.AI\",\"cs.CV\",\"cs.LG\",\"econ.GN\"]","methods":"[]","has_code":false}