Spatial Dependence in Asset Pricing Models

Abstract

When analyzing asset prices in isolation, the classical asset pricing models only account for the time-series variation of the asset with the factors. However, valuable information would be lost if some cross-sectional dependence exists across the assets. We extend the factor model in Fama and French (2012) to account for spatial dependence across returns and estimate a spatial factor model. We model the spatial linkages using a measure of physical distance between the properties of listed real estate companies. We find that the spatial factor model is not rejected and the spatial parameter is significant. The spatial factor model performs better than the factor model, substantially improving the model fit. Proximity across the property holdings of real estate companies can predict higher return correlation across the firms, controlling for size, book-to-market, and momentum characteristics.