In the last decade, new technologies have led to a boom in real-time pricing. I analyze the most salient example, surge pricing in ride hailing. Using data from Uber, I develop an empirical model of spatial equilibrium to measure the welfare effects of surge pricing. The model is composed of demand, supply, and a matching technology. It allows for temporal and spatial heterogeneity as well as randomness in supply and demand. I find that, relative to a counterfactual with uniform pricing, surge pricing increases total welfare by 1.59% of gross revenue. Welfare effects differ substantially across sides of the market: rider surplus increases by 5.25% of gross revenue, whereas driver surplus and platform profits decrease by 1.81% and 1.77% of gross revenue, respectively. Riders at all income levels benefit, while disparities in driver surplus are magnified.