In this article, the authors consider the problem of outlet pricing and location in the context of unobserved spatial demand. The analysis constitutes a scenario in which capacity-constrained firms set prices conditional 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 resultant equilibrium effect on profits and prices. Using Bayesian spatial statistics, the authors apply their model to seven years of data on apartment location and prices in Roanoke, Va. They find that spatial covariation in demand is material in outlet choice; the 95% spatial decay in demand extends 3.6 miles in a region that measures slightly more than 9.5 miles. They also find that capacity constraints can cost complexes upward of $100 per apartment. As they predict, price elasticities and costs are biased downward when spatial covariance in demand is ignored. Costs are biased upward when capacity constraints are ignored. Using the analysis to suggest locations for entry, the authors find that properly accounting for spatial effects and capacity constraints leads to an entry recommendation that improves profitability by 66% over a model that ignores these effects.
The effect of Medicaid expansion on use of opioid agonist treatment and the role of provider capacity constraints
