The Role of Spatial Demand on Outlet Location and Pricing

In this paper we consider the problem of outlet pricing and location in the context of unobserved spatial demand. Our analysis constitutes a scenario wherein capacity-constrained firms set prices conditioned on their location, demand and costs. This enables firms to develop maps of latent demand patterns across the market in which they compete. The analysis further suggests locations for additional outlets and the resulting equilibrium effect on profits and prices. Using Bayesian spatial statistics, we apply our model to seven years of data regarding apartment location and prices in Roanoke, Virginia. We find spatial covariation in demand to be material in outlet choice; the 95% spatial decay in demand extends 7.5 miles in a region measuring slightly over 9.5 miles. We further find that capacity constraints can cost complexes upwards of $193 per apartment. As predicted, price elasticities and costs are biased downward when spatial covariance in demand is ignored. Using our analysis to suggest locations for entry, we find that a proper accounting of spatial effects and capacity constraints leads to an entry recommendation that improves profitability by 66% over a model that ignores these effects