Deep Learning for Art Market Valuation

2025-12-28

AI Summary

Valuing debut artworks—those lacking historical transaction records—remains a fundamental challenge in art market analytics. To address this, we propose an interpretable multimodal deep learning framework that performs feature-level fusion of vision-based representations (extracted jointly by CNN and Transformer encoders) with structured metadata. Our work provides the first empirical evidence that visual content confers independent economic value to debut artworks. We employ Grad-CAM for interpretability, confirming that the model attends to intrinsic artistic attributes such as composition and stylistic features. Evaluated on a debut-auction subset—a “no-anchor” valuation scenario widely regarded as the most challenging—the framework achieves a 19.3% improvement in valuation accuracy over conventional methods. By simultaneously ensuring high predictive performance and economically grounded interpretability, our approach establishes a novel paradigm for intelligent art valuation.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Multimodal Learning
• Humans and AI: Learning Human Values and Preferences

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Web Mining and Content Analysis: Large pretrained models with web data
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

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.

Problem

• Incorporates visual content into art valuation models
• Compares deep learning with classical methods for prediction
• Focuses on first-time sales where historical data is absent

Innovation

• Multi-modal deep learning fuses tabular and image data
• Visual embeddings provide distinct contribution for fresh-to-market works
• Models attend to compositional and stylistic cues via interpretability analyses