Estimating U.S. housing price network connectedness: Evidence from dynamic Elastic Net, Lasso, and ridge vector autoregressive models

Abstract

This paper investigates the dynamic connectedness of random shocks to housing prices between the 50 U.S. states and the District of Columbia. The paper implements a standard vector autoregressive (VAR) model as well as three VAR models with shrinkage effects — Elastic Net, Lasso, and Ridge VAR models. The transmission of real housing return shocks on a regional basis flows from Southern states to the other three regions, whereas the Northeast receives those shocks. The West receives shocks from the South and transmits shocks to the Midwest and the Northeast. Finally, the Midwest transmits shocks to the Northeast and receives shocks from the South and the West. Our results have important implications for policymakers and investors. To the extent that the housing market affects the business cycle, the Federal Reserve can monitor housing market movements in the net transmitter states to gather information about the beginnings of the housing market cycle. Moreover, the determination of which states or regions function as the main transmitter of shocks provides information to investors on acquiring housing assets in these markets rather than the ones that are more susceptible to such shocks as net receivers.