Demystifying the Role of Aesthetics in NFT Pricing: Model, Analysis, and Insights

Abstract

The extreme and often seemingly irrational pricing of non-fungible tokens (NFTs) has inspired interest in identifying their valuation determinants. While visual features have been proposed as partial determinants for NFT price variation, previous studies often rely on non-interpretable models and fail to control for a critical confounder: NFT collection identity. In this paper, we analyze over 160,000 NFTs across 32 major collections to evaluate the explanatory power of visual features for NFT pricing. We show that visual models achieve high predictive performance only when collection label leakage is present, falsely inflating results by learning collection-specific price tiers. Once collection identity is controlled, we find that visual featuresinterpretable or deep-learning based-offer limited generalizability and weak predictive power across collections. However, for a subset of collections, visual features do exhibit modest withincollection predictability, a phenomenon we term photorelevance. We introduce a framework to quantify photorelevance and identify two collection-level characteristics-image contrast variance and price variance-as statistically significant predictors of it. Our findings highlight the methodological importance of addressing collection label leakage and suggest that aesthetic attributes play a limited and only localized role in NFT valuation. Code and data are publicly released to enable reproducibility and further research.