Using stochastic frontier models to mitigate omitted variable bias in hedonic pricing models: A case study for air quality in Bogotá, Colombia

Abstract

Hedonic pricing models use property value differentials to value changes in environmental quality. If unmeasured quality attributes of residential properties are correlated with an environmental quality measure of interest, conventional methods for estimating implicit prices will be biased. Because many unmeasured quality measures tend to be asymmetrically distributed across properties, it may be possible to mitigate this bias by estimating a heteroskedastic frontier regression model. This approach is demonstrated for a hedonic price function that values air quality in Bogotá, Colombia.