Analyzing price efficiency using machine learning generated price indices: The case of the Chilean used car market

This paper examines how the prices of newly imported cars affect the valuation of used vehicles in Chile’s secondary car market, offering a novel perspective on price efficiency within durable goods markets. Previous studies analyze substitution effects between new and used vehicles in the context of equilibrium models with demand-side heterogeneity. Leveraging a dataset of 2.7 million used car advertisements, we employ Machine Learning techniques to construct synthetic price indices, which serve as the foundation for an event study. Our findings reveal a prompt and statistically significant adjustment in used car prices, particularly among newer and higher-end models, even before the public release of import price data. These results suggest a high degree of informational efficiency in Chile’s used car market and are consistent with demand substitution effects between new and used cars and the incorporation of supply-side shocks by market participants into price valuations.