EXPRESS: Neither a Picasso Nor a Da Vinci: An Examination of Novice Artwork Pricing with Multi-Modal Data

Abstract

Novice art pricing is an understudied domain. Novice artists operate as microenterprises, making crucial price-setting decisions. Research shows that newcomers often risk overpricing or underpricing their work, and existing online tools offer basic, cost-based pricing advice. Using a three-study framework, we examine novice art pricing on Etsy, where artwork listings include structured data, images, and textual descriptions. We first analyze how these inputs relate to final selling prices using a hedonic regression on structured data, followed by a multimodal fusion deep learning (MMF-DL) model that integrates structured, visual, and textual features. Our results show that features related to artist authenticity (e.g., certificates), customer service (e.g., shipping, returns, personalization), and art style (e.g., genre) are important price predictors. Thus, novice art sold on online platforms exhibits some features typical of mature art markets (e.g., authenticity and reputation) but emphasize customer-focused services. Finally, using a Cox proportional hazards model, we show that, while higher artist reputation is associated with faster sales, discounting correlates with longer time on market. These associations suggest the importance of price setting. From these insights, we develop a price recommender application that predicts both selling prices and time-to-sale, offering practical guidance for newcomer artists and online platforms.