Pixels to Prices: Visual Traits, Market Cycles, and the Economics of NFT Valuation

This paper studies how visual traits and market cycles shape prices in NFT markets. Using 94,039 transactions from 26 major generative Ethereum collections, the analysis extracts 196 machine-quantified image features (covering color, composition, palette structure, geometry, texture, and deep learning embeddings), then applies a three-stage filter process to identify stable predictors for hedonic regression. A static mixed-effects model shows that market sentiment and transparent, interpretable image traits have significant and independent pricing power: higher focal saturation, compositional concentration, and curvature are rewarded, while clutter, heavy line work, and dispersed palettes are discounted; deep embeddings add limited incremental value conditional on explicit traits. To assess state dependence, the study estimates a Bayesian dynamic mixed-effects panel with cycle effects and time-varying coefficients for a salient image attribute (Composition Focus - Saturation). Collection-level heterogeneity ("brand premia") is absorbed by random effects. The time-varying coefficients exhibit regime sensitivity, with stronger premia in expansionary phases and weaker or negative loadings in downturns, while grand-mean effects remain small on average. Overall, NFT prices reflect both observable digital product characteristics and market regimes, and the framework offers a cycle-aware tool for asset pricing, platform strategy, and market design in digital art markets.