Deep Learning for Art Market Valuation

• URL: http://arxiv.org/abs/2512.23078v1
• Date: Sun, 28 Dec 2025 21:04:09 GMT
• Title: Deep Learning for Art Market Valuation
• Abstract summary: We study how deep learning can improve valuation in the art market by incorporating the visual content of artworks into predictive models.<n>Using a large repeated-sales dataset from major auction houses, we benchmark classical hedonic regressions and tree-based methods.<n>We find that while artist identity and prior transaction history dominate overall predictive power, visual embeddings provide a distinct and economically meaningful contribution.
• Score: 5.05381284813208
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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