Deep Learning for Art Market Valuation
This research addresses valuation challenges in the art market, particularly for first-time sales, offering insights for academic and practical applications, though it is incremental as it builds on existing deep learning methods.
The study tackled the problem of art market valuation by incorporating visual content into predictive models, finding that multi-modal deep learning provides a distinct and economically meaningful contribution for fresh-to-market works where historical data is lacking.
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.