The rapid growth of consumer-to-consumer luxury resale platforms has raised important questions about how price premiums influence follow-up sales. While existing research has explored the relationship between price premiums and consumer demand using analytical or theoretical approaches, empirical research on this topic has been impeded by the limited availability of transaction-level data and the absence of an appropriate modeling framework. This study addresses this important research gap through a combination of (1) transaction-level data from Korea's largest luxury resale platform, (2) a representation learning model that compresses product-related information, and (3) a machine learning–based causal inference model that enables nonlinear estimation of the causal effect. The results reveal that higher price premiums generally lead to increased consumer demand, and vice versa. However, this relationship reverses when the price premiums exceed a certain threshold. The study contributes to the luxury consumption behavior literature and provides actionable insights for platform owners and consumers.
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