Abstract
One of the most notable trends in the U.S. housing market in the recent decades is the increasing house price growth (HPG) synchronization across states. Using four decades of data, we provide novel evidence that the increasing HPG synchronization leads to higher business cycle alignment across U.S. states. One standard deviation increase in HPG synchronization is associated with a 15%, 12%, and 10% increase in the alignment of the states’ gross state product, employment, and income growth, respectively. The relation is stronger between states with similar banking development and in non-tradable sectors. Supporting both the collateral and direct wealth effect channels, we find more aligned house-secured borrowing activities and consumption growth between states with more synchronized house price growth. Results also hold at the MSA level and are robust to various endogeneity controls, including a Bartik-type instrument. Overall, our findings suggest that the housing market integration can lead to amplified business cycles associated with an increased systemic economic risk at the country level.
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Notes
As we discuss in detail in Sect. 2.2, we measure HPG synchronization (business cycle alignment) as the negative value of the absolute difference in annual growth rates of House Price Index (GSP, income, or employment) across U.S. state pairs. In Sect. 5, we show that our results are robust to alternative synchronization measures.
Studies show that house price fluctuations have a direct effect on the wealth and consumption of households (Aladangady, 2017; Gan, 2007; Mian & Sufi, 2011; Mian et al., 2013), a firm’s financial capacity (Barro, 1976; Hart & Moore, 1994; Stiglitz & Weiss, 1981), and regional employment and output (Adelino et al., 2015; Corradin & Popov, 2015; Loutskina & Strahan, 2015; Mian & Sufi, 2014; Schmalz et al., 2017).
State income includes wages, proprietors' income, dividends, interest, rents, and other income received by each state's residents. Our results are robust if we use wage to replace income in our analysis.
These results are available in an earlier version: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3790586.
For ease of exposition, we use the term “alignment” instead of “synchronization” for these outcome variables to make a distinction between them and the key independent variable (i.e., HPG synchronization) in our discussion.
These results are available in an earlier version: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3790586.
The banking integration variable has a value of zero for about 75% of our sample observations, leading to a small sample average (0.027). These statistics are consistent with those reported in other studies such as Michalski and Ors (2012) and Landier et al. (2017).The amount of zeros is largely driven by the fact that banks are segmented before the deregulation of interstate banking in 1994.
Specifically, our classification of non-tradable sectors is obtained from the Online Appendix Table A2 of Mian and Sufi (2014). Non-tradable sectors include retail trade, accommodation and food services, and hospitals and residential care facilities. We download the industry-level output data from the BEA and calculate annual growth rates after excluding non-tradable sectors.
To ensure that our results are not driven by the boom and bust of the real estate industry itself, we remove the real estate industry (and the related construction industry) from our sample. We continue to find a positive and significant relation between HPG synchronization and business cycle alignment after this exclusion.
It is beyond the scope of our paper to identify the primitive factors that lead to the states’ differential responses to national shocks after controlling for economic fundamentals. Nevertheless, we propose a few potential factors based on the literature. First, residents in different states may have different tastes for owning a house, which can lead to the states’ having different sensitivities to nation-wide house price shocks (Sinai, 2013). Second, difference in expectations and the degree of disagreement about future long-run growth prospects in the housing market may also cause the states to react differently to aggregate housing price shocks (Nathanson & Zwick, 2018). Both factors need not to be related to economic fundamentals.
Our results are robust if we use the MSA-level elasticity of land supply developed by Saiz (2010) as an alternative IV and conduct our analyses at the MSA level. Related discussions and results are in an earlier version: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3790586.
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Acknowledgements
We thank the editor, an anonymous referee, and Elizabeth Berger, Don Chance, Zhiguo He, Joseph Mason, Haitao Mo, Lawrence F Pohlman, Wei-ling Song, Chongyu Wang, Junbo Wang, Xinxin Wang, Han Xia, Miaomiao Yu, and seminar participants at the Paris Financial Management Conference, Georgia Institute of Technology, and Louisiana State University for helpful comments. The authors assume responsibility for any errors.
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Eun, C., Wang, L. & Zhang, T. House Price Growth Synchronization and Business Cycle Alignment. J Real Estate Finan Econ 65, 675–710 (2022). https://doi.org/10.1007/s11146-021-09849-x
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DOI: https://doi.org/10.1007/s11146-021-09849-x
Keywords
- House price growth synchronization
- Business cycle alignment
- Housing collateral
- Consumption growth
- Banking integration and development