MSA-level estimates of a housing supply schedule must offer a solution to the twin problems of simultaneity and stationarity that plague the time series data for local housing prices and stock. An Error Correction Model (ECM) is shown to provide a solution to stationarity, but not simultaneity. A Vector Error Correction Model (VECM) is suggested to handle both the stationarity and endogeneity problems. Such models also nicely distinguish between (very) long run elasticities and a variety of short term impacts. We estimate these models separately for 68 US MSA using quarterly data on housing prices and residential construction permits since 1980. The results provide long run supply elasticity estimates for each market that are better bounded than previous panel-based attempts and also correspond with much conventional thought. We find these elasticities are well explained by geographic and regulatory barriers, and that inelastic markets exhibit greater price volatility over the last two decades. Using the models’ short run dynamics we make several forecasts of prices over the next decade. In current dollars, some MSA will still not recover to recent peak (2007) house price levels by 2022, while others should exceed it by as much as 70%.The authors are indebted to the MIT Center for Real Estate, and CBRE. They remain respnosible for all results and conclusions derived there from
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