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

C-Tier
Journal: Economic Modeling
Year: 2025
Volume: 152
Issue: C

Authors (2)

Score contribution per author:

0.503 = (α=2.01 / 2 authors) × 0.5x C-tier

α: calibrated so average coauthorship-adjusted count equals average raw count

Abstract

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.

Technical Details

RePEc Handle
repec:eee:ecmode:v:152:y:2025:i:c:s0264999325002524
Journal Field
General
Author Count
2
Added to Database
2026-01-25