Deep Learning for Art Market Valuation

TL;DR

This paper explores how deep learning, especially multi-modal models combining images and tabular data, can enhance art market valuation, particularly for first-time sales where traditional data is limited.

Contribution

It introduces multi-modal deep learning models that incorporate visual content into art valuation, highlighting their effectiveness for new artworks without historical transaction data.

Findings

Visual embeddings contribute meaningfully to valuation.

Deep models outperform classical methods for first-time sales.

Interpretability shows models focus on stylistic features.

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.