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How Effective are Policy Interventions in a Spatially-Embedded International Real Estate Market?

  • Kun DuanEmail author
  • Tapas Mishra
  • Mamata Parhi
  • Simon Wolfe
Article
  • 397 Downloads

Abstract

We introduce the role of ‘space’ in analyzing the effect of macroeconomic policy interventions on cross-country housing price movements. We build an empirically testable analytical model and test our theoretical predictions for a panel of European countries over the period 1985–2015. Our aim is to demonstrate that while macroeconomic policy exerts a significant impact on international housing markets, the magnitudes of such impacts may be overestimated in the absence of spatial frictions. To test our hypotheses, we employ a spatial dynamic panel method and quantify intra- and inter-country differences of the effects of macroeconomic policy interventions on spatially interdependent housing markets. Endogeneity issues arise in our estimation, which we ameliorate by employing the spatial Durbin model for panel data. Following this approach, we include spatial, temporal and spatio-temporal lags for the identification purpose. We show that a spatially-embedded model produces relatively smaller and correct signs for macroeconomic variables in contrast to the traditional non-spatial model. It is concluded that empirical estimates from the traditional model are consistently over-estimated. These have significant policy implications for the exact role of macroeconomic interventions in explaining housing price movements. A battery of robustness tests and evaluations of predictive performance confirm our results.

Keywords

Housing price variations Macroeconomic adjustments Spatial frictions Real estate market Spatial dynamic panel regression Estimation bias 

JEL Classification

C33 E32 E60 E62 R03 R31 

Notes

Acknowledgments

We sincerely thank Professor C.F. Sirmans, the editor and an anonymous referee for many helpful suggestions, which have significantly improved the quality of the paper. We also thank the seminar participants at the Southampton Business School (University of Southampton), Bristol Business School (University of the West England), and Portsmouth Business School (University of Portsmouth) for many helpful comments. Special thanks are due to Philip Arestis, Mauro Costantini, Laura Costanzo and Richard Werner for offering many insightful comments on an earlier draft of this research. We, however, are solely responsible for possible errors and omissions.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Kun Duan
    • 1
    Email author
  • Tapas Mishra
    • 1
  • Mamata Parhi
    • 2
  • Simon Wolfe
    • 1
  1. 1.Southampton Business SchoolUniversity of SouthamptonHighfield CampusUK
  2. 2.Department of Business and ManagementUniversity of RoehamptonLondonUK

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