Skip to main content

Advertisement

Log in

House Price Growth Synchronization and Business Cycle Alignment

  • Published:
The Journal of Real Estate Finance and Economics Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Notes

  1. For example, Mian and Sufi (2011), Liu et al. (2013), Adelino et al. (2015), Corradin and Popov (2015), Loutskina and Strahan (2015), Mian et al. (2015), (2017), and Schmalz et al. (2017).

  2. Several recent studies also document the increasing house price co-movement using different data or periods. Examples include Cotter et al. (2011), Hirata et al. (2012), Kallberg et al. (2014), and Landier et al. (2017).

  3. 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.

  4. 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).

  5. 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.

  6. See Calhoun (1996) and Cotter et al. (2015) for a detailed discussion on the quality and coverage of the HPI index.

  7. These results are available in an earlier version: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3790586.

  8. 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.

  9. These results are available in an earlier version: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3790586.

  10. 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.

  11. 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.

  12. 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.

  13. 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.

  14. 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.

References

  • Adelino, M., Schoar, A., & Severino, F. (2015). House prices, collateral, and self-employment. Journal of Financial Economics, 117, 288–306.

    Article  Google Scholar 

  • Adelino, M., Ma, S., & Robinson, D. (2017). Firm age, investment opportunities, and job creation. Journal of Finance, 72, 999–1038.

    Article  Google Scholar 

  • Aladangady, A. (2017). Housing wealth and consumption: Evidence from geographically-linked microdata. American Economic Review, 107, 3415–3416.

    Article  Google Scholar 

  • Allen, F., Babus, A., & Carletti, E. (2012). Asset commonality, debt maturity and systemic risk. Journal of Financial Economics, 104, 519–534.

    Article  Google Scholar 

  • Aoki, K., Proudman, J., & Vlieghe, G. (2004). House prices, consumption, and monetary policy: A financial accelerator approach. Journal of Financial Intermediation, 13, 414–435.

    Article  Google Scholar 

  • Attanasio, O., Blow, L., Hamilton, R., & Leicester, A. (2009). Booms and busts: Consumption, expectations and house prices in the UK. Economica, 76, 20–50.

    Article  Google Scholar 

  • Barro, R. J. (1976). Rational expectations and the role of monetary policy. Journal of Monetary Economics, 2, 1–32.

    Article  Google Scholar 

  • Barro, R. J., & Sala-i-Martin, X. (1992). Convergence. Journal of Political Economy, 100, 223–251.

    Article  Google Scholar 

  • Bartik, T. (1991). Who benefits from state and local economic development policies? W.E. Upjohn Institute.

    Book  Google Scholar 

  • Bernanke, B., & Gertler, M. (1989). Agency costs, net worth, and business fluctuations. American Economic Review, 79, 14–31.

    Google Scholar 

  • Bernanke, B., Gertler, M., & Gilchrist, S. (1999). The financial accelerator in a quantitative business cycle framework. Handbook of Macroeconomics, 1, 1341–1393.

    Article  Google Scholar 

  • Bernstein, A. (2018). Negative equity, household debt overhang and labor supply. Journal of Finance, forthcoming.

  • Bertrand, M., Duflo, E., & Mullainathan, S. (2004). How much should we trust differences-in-differences estimates? Quarterly Journal of Economics, 119, 249–275.

    Article  Google Scholar 

  • Blanchard, O., & Katz, L. (1992). Regional evolutions. Brookings Papers on Economic Activity, 23, 1–75.

    Article  Google Scholar 

  • Brown, J., & Matsa, D. A. (2020). Locked in by leverage: Job search during the housing crisis. Journal of Financial Economics, 136, 623–648.

    Article  Google Scholar 

  • Calhoun, C. A. (1996). OFHEO house price indexes: HPI technical description. Office of Federal Housing Enterprise Oversight.

    Google Scholar 

  • Campbell, J. Y., & Cocco, J. F. (2007). How do house prices affect consumption? Evidence from micro data. Journal of Monetary Economics, 54, 591–621.

    Article  Google Scholar 

  • Canova, F. (1998). Detrending and business cycles facts. Journal of Monetary Economics, 41, 475–512.

    Article  Google Scholar 

  • Canova, F. (1999). Does detrending matter for the determination of the reference cycle and the selection of turning points? Economic Journal, 109, 126–150.

    Article  Google Scholar 

  • Chaney, T., Sraer, D., & Thesmar, D. (2012). The collateral channel: How real estate shocks affect corporate investment. American Economic Review, 102, 2381–2409.

    Article  Google Scholar 

  • Chu, Y., Deng, S., & Xia, C. (2020). Bank geographic diversification and systemic risk. Review of Financial Studies, 33, 4811–4838.

    Article  Google Scholar 

  • Corradin, S., & Popov, A. (2015). House prices, home equity borrowing, and entrepreneurship. Review of Financial Studies, 28, 2399–2428.

    Article  Google Scholar 

  • Cotter, J., Gabriel, S., & Roll, R. (2011). Integration and contagion in US housing markets. University of California Los Angeles working paper.

  • Cotter, J., Gabriel, S., & Roll, R. (2015). Can housing risk be diversified? A cautionary tale from the housing boom and bust. Review of Financial Studies, 28, 913–936.

    Article  Google Scholar 

  • Donaldson, J., Piacentino, G., & Thakor, A. (2019). Household debt overhang and unemployment. Journal of Finance, 74, 1473–1502.

    Article  Google Scholar 

  • Fung, M. K. (2009). Financial development and economic growth: Convergence or divergence? Journal of International Money and Finance, 28, 56–67.

    Article  Google Scholar 

  • Gan, J. (2007). Collateral, debt capacity, and corporate investment: Evidence from a natural experiment. Journal of Financial Economics, 85, 709–734.

    Article  Google Scholar 

  • Guerrieri, V., & Lorenzoni, G. (2017). Credit crises, precautionary savings, and the liquidity trap. Quarterly Journal of Economics, 132, 1427–1467.

    Article  Google Scholar 

  • Guren, A., McKay, A., Nakamura, E., & Steinsson, J. (2021). Housing wealth effects: The long view. Review of Economic Studies, 88(2), 669–707.

    Article  Google Scholar 

  • Hall, R. (2011). The long slump. American Economic Review, 101, 431–469.

    Article  Google Scholar 

  • Hart, O., & Moore, J. (1994). A theory of debt based on the inalienability of human capital. Quarterly Journal of Economics, 109, 841–879.

    Article  Google Scholar 

  • Hirata, H., Kose, M. A., Otrok, C., & Terrones, M. E. (2012). Global house price fluctuations: Synchronization and determinants. Working paper no. 18362, National Bureau of Economic Research.

  • Hoesli, M., Kadilli, A., & Reka, K. (2017). Commonality in liquidity and real estate securities. Journal of Real Estate Finance and Economics, 55, 65–105.

    Article  Google Scholar 

  • Iacoviello, M. (2005). House prices, borrowing constraints, and monetary policy in the business cycle. American Economic Review, 95, 739–764.

    Article  Google Scholar 

  • Ibragimov, R., Jaffee, D., & Walden, J. (2011). Diversification disasters. Journal of Financial Economics, 99, 333–348.

    Article  Google Scholar 

  • Kalemli-Ozcan, S., Papaioannou, E., & Peydro, J. (2013). Financial regulation, financial globalization, and the synchronization of economic activity. Journal of Finance, 68, 1179–1228.

    Article  Google Scholar 

  • Kallberg, J. G., Liu, C. H., & Pasquariello, P. (2014). On the price comovement of US residential real estate markets. Real Estate Economics, 42, 71–108.

    Article  Google Scholar 

  • Kiyotaki, N., & Moore, J. (1997). Credit cycles. Journal of Political Economy, 105, 211–248.

    Article  Google Scholar 

  • Landier, A., Sraer, D., & Thesmar, D. (2017). Banking integration and house price co-movement. Journal of Financial Economics, 42, 1–25.

    Article  Google Scholar 

  • Liu, Z., Wang, P., & Zha, T. (2013). Land-price dynamics and macroeconomic fluctuations. Econometrica, 81, 1147–1184.

    Article  Google Scholar 

  • Loutskina, E., & Strahan, P. E. (2015). Financial integration, housing, and economic volatility. Journal of Financial Economics, 115, 25–41.

    Article  Google Scholar 

  • Melzer, B. (2017). Mortgage debt overhang: Reduced investment by homeowners at risk of default. Journal of Finance, 72, 575–612.

    Article  Google Scholar 

  • Mian, A., & Sufi, A. (2011). House prices, home equity based borrowing, and the U.S. household leverage crisis. American Economic Review, 101, 2132–2156.

    Article  Google Scholar 

  • Mian, A., & Sufi, A. (2014). What explains the 2007–2009 drop in employment? Econometrica, 82, 2197–2223.

    Article  Google Scholar 

  • Mian, A., Rao, K., & Sufi, A. (2013). Household balance sheets, consumption, and the economic slump. Quarterly Journal of Economics, 128, 1687–1726.

    Article  Google Scholar 

  • Mian, A., Sufi, A., & Trebbi, F. (2015). Foreclosures, house prices, and the real economy. Journal of Finance, 70, 2587–2634.

    Article  Google Scholar 

  • Mian, A., Sufi, A., & Verner, E. (2017). Household debt and business cycle worldwide. Quarterly Journal of Economics, 132, 1755–1817.

    Article  Google Scholar 

  • Mian, A., Sufi, A., & Verner, E. (2020). How does credit supply expansion affect the real economy? The productive capacity and household demand channels. Journal of Finance, 75, 949–994.

    Article  Google Scholar 

  • Michalski, T., & Ors, E. (2012). (Interstate) banking and (interstate) trade: Does real integration follow financial integration? Journal of Financial Economics, 104, 89–117.

    Article  Google Scholar 

  • Monacelli, T. (2009). New Keynesian models, durable goods, and collateral constraints. Journal of Monetary Economics, 56, 242–254.

    Article  Google Scholar 

  • Morgan, D. P., Rime, B., & Strahan, P. E. (2004). Bank integration and state business cycles. Quarterly Journal of Economics, 119, 1555–1584.

    Article  Google Scholar 

  • Myers, S. (1977). Determinants of corporate borrowing. Journal of Financial Economics, 5, 147–175.

    Article  Google Scholar 

  • Nathanson, C. G., & Zwick, E. (2018). Arrested development: Theory and evidence of supply side speculation in the housing market. Journal of Finance, 73, 2587–2633.

    Article  Google Scholar 

  • Palmer, C. (2015). Why did so many subprime borrowers default during the crisis: Loose credit or plummeting prices? UC Berkeley working paper.

  • Saiz, A. (2010). The geographic determinants of house prices. Quarterly Journal of Economics, 100, 379–406.

    Google Scholar 

  • Schmalz, M., Sraer, D., & Thesmar, D. (2017). Housing collateral and entrepreneurship. Journal of Finance, 72, 99–132.

    Article  Google Scholar 

  • Sinai, T. (2013). House price moments in boom-bust cycles. In E. L. Glaeser & T. Sinai (Eds.), Housing and the financial crisis (pp. 19–68). University of Chicago Press.

    Chapter  Google Scholar 

  • Staiger, D., & Stock, J. (1997). Instrumental variables regression with weak instruments. Econometrica, 65, 557–586.

    Article  Google Scholar 

  • Stein, J. C. (1995). Prices and trading volume in the housing market: A model with down-payment effects. Quarterly Journal of Economics, 110, 379–406.

    Article  Google Scholar 

  • Stiglitz, J., & Weiss, A. (1981). Credit rationing in markets with imperfect information. The American Economic Review, 71, 393–410.

    Google Scholar 

  • Stock, J. H., & Yogo, M. (2005). Testing for weak instruments in linear IV regression. In D. W. K. Andrews & J. H. Stock (Eds.), Identification and inference for econometric models: A Festschrift in Honor of Thomas J. Rothenberg. Cambridge University Press.

    Google Scholar 

  • Stock, J., Wright, J., & Yogo, M. (2002). A survey of weak instruments and weak identification in generalized method of moments. Journal of Business & Economic Statistics, 20, 518–529.

    Article  Google Scholar 

  • Wang, C., Cohen, J., & Glascock, J. (2019). Geographically overlapping real estate assets, liquidity spillovers, and liquidity multiplier effects. Journal of Real Estate Finance and Economics, forthcoming.

Download references

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.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Cheol Eun.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix

Appendix

Table 8 Variable definitions

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11146-021-09849-x

Keywords

JEL Classification

Navigation