Optimal city zoning and Big Data

The purpose of this paper is to characterize the optimal design of a residential zone in a linear town by a welfare-maximizing regulator when firms might know to some extent the position of the customers/citizens in the city. Information might have different degrees of imperfectness. A micro-founded theoretical approach is adopted throughout the paper. The main findings are the following: a peripheral zoning is more likely to arise when information is scarcely precise, whereas central zoning is more likely when information is highly precise. Moreover, peripheral zoning is more likely the greater is the bias of the regulator toward the consumer surplus. The main policy implication is the following: public authorities should implement city zoning by taking into account the amount of data at the disposal of the firms.