Zoning for housing supply: modelling the asymmetric welfare costs of errors in demand forecasts

The task of zoning sufficient new housing capacity to provide for future demand for housing is often one that falls on local councils, who typically base such zoning decisions on population forecasts. However, predictions of future population growth (and thus housing demand) are inherently uncertain, introducing a risk of ‘regulatory error’ in setting zoning capacity. In this paper, I explain why the welfare costs of this error are likely to be asymmetric: the welfare cost from underestimating demand and zoning ‘too little’ is likely materially greater than the welfare cost of overestimating demand and zoning ‘too much’. I develop a model of the welfare cost asymmetry for zoning, and using Monte Carlo simulation and illustrative housing supply and demand parameters, show how the asymmetry in welfare costs can justify the application of a margin to uplift forecast demand, to mitigate the risk of relatively larger welfare costs from under-zoning.