Abstract
This paper examines the importance of house prices on the US business cycle since the mid-1970s. The point of departure is to construct and estimate a baseline Markov-switching common factor model in which the co-movement of several individual economic series and the inherently different behavior of the economy across expansion/recession phases are accommodated. The importance of house price variables is then evaluated by comparing the results of the baseline model and those of the extended models that have been augmented with the house price variables. Two strands of extended models are considered: one in which house prices directly affect the individual macroeconomic series (and thereby the business cycle) and another in which house prices affect the probabilities of transitions between expansion/recession phases. The results for the first extension support that only house price decreases have nontrivial effects on the movements in macroindicators (and consequently on the business cycle), while house price increases do not. In the second extension, we find strong evidence that changes in house prices significantly affect the transition of the US economy between the recession–expansion phases. It is also reaffirmed that the influence of house price decreases is more important than that of the increases. Finally, the above results are generally robust to using a different data frequency, a subsample of the data excluding the recent episode of housing boom–bust, replacing house price with housing permits, and incorporating financial variables as additional driver of the business cycle.
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Notes
Iacoviello (2005) further notes that because higher price level and house prices bring forth higher borrowing capacities of homeowners (and lower the real burden of their nominal debt obligation), the higher aggregate demand accompanying house price increases can lead to amplified increases in output.
A few recent work, such as Iacoviello and Neri (2010), also introduce the explicit role of housing supply and the spillover from the housing market to the whole economy over the business cycle.
The variance of \(\upnu _{\mathrm{t}}\) is fixed at unity for identifying \({\upgamma }\)’s.
We also considered a different specification for HP-M type models in which the control variable \(Z_{t}\) was incorporated into Eq. \((2')\) so that house price movements could directly affect the evolution of the common factor growth. The results (available from the authors upon request) are not qualitatively different from those discussed in Sect. 3.
Although the four indicators are available from January 1975 onwards, the span of the demeaned series is shortened to match the time structure of the control variable \(Z_{t}\) in the extended models.
The complete results for the baseline model are reported in the “Appendix.”
Another house price index with frequency and length comparable to the FMHPI is the median sales prices of new homes; this index is available from the US Census Bureau. We did not use this index because it exhibits too much noise and seasonal variation, even after seasonal adjustment.
Leamer (2007) argues that while the housing sector is an important channel through which business cycle fluctuations can propagate, volume such as sales and residential investment, rather than house prices, is what actually matters for business cycles.
The correlation between \(Z_t^0\) and the estimated p(t) is 0.774, much smaller than that between \(Z_t^0\) and the estimated q(t).
Using a similar set of models, Cho and Kim (2013) compare the importance of house price and housing permits for the UK business cycle. Their findings are: Housing permits have significant effects on the business cycle, while house price does not, and asymmetric decreases in house price significantly affect the UK business cycle more than those in permits.
To reduce the dimensionality of data, we construct the first two principal components of the above 4 financial variables and take the three-month backward moving average of the two principal components. Then, we choose the lag orders of the two components where the cross-correlations with the growth in the national composite coincident index (obtained from the FRED database) are largest, with the maximum lag order allowed is 12 months. The lag orders thus determined are 7 months for the first principal component and 12 months for the second one.
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Acknowledgments
Jan R. Kim acknowledges the research support from the Hankuk University of Foreign Studies. Keunsuk Chung acknowledges the research support by the 2011 Research Fund (Grant 1.100038) of Ulsan National Institute of Science and Technology (UNIST).
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Kim, J.R., Chung, K. The role of house price in the US business cycle. Empir Econ 51, 71–92 (2016). https://doi.org/10.1007/s00181-015-1001-4
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DOI: https://doi.org/10.1007/s00181-015-1001-4