Abstract
This paper studies the dynamic relationship among house prices, income and interest rates in 15 OECD countries. We find that any disequilibrium in the long-run cointegrating relationship among these variables is corrected by the subsequent movement in house prices in most of these countries. This error-correction property of house prices implies that most of the variations in house prices are transitory, as compared to the movements in income and interest rates that are permanent, suggesting that the short-run movements in house prices are independent of the movements in income and interest rates. The results suggest that only the permanent movement in house prices, income and interest rates are associated with each other. We also find that the correlation in house price cycles across different OECD countries has changed over time with the highest correlation during the boom period of 1998–2005.
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Notes
Our sample includes Australia, Belgium, Canada, Denmark, Finland, France, Ireland, Italy, Netherlands, New Zealand, Norway, Spain, Sweden, the U.K. and the U.S..
These countries are Belgium, Canada, Denmark, France, Ireland, Italy, Norway, Spain, the U.K. and the U.S..
Zimmer (2015) studies the correlation in housing prices between four major U.S. cities and confirms that housing prices do, indeed, exhibit correlations that change over time.
A more detailed description of the data can be found in Mack and Martínez-García (2011) and we acknowledge the use of this data set.
The OECD database describes the short-term interest rate as either the three month interbank offer rate or the rate associated with Treasury bills, Certificates of Deposit or comparable instruments, each of three month maturity. One can also possibly argue that the long-term interest rates are more appropriate in capturing the dynamic relationship with the house prices and income. Hence, as a robustness check, we also test our results using long-term interest rates. These results are reported in the Appendix B and we find that there is no qualitative change in the results.
The “hats” represent estimated value in rest of the paper.
The Johansen test suggests one cointegrating vector in this system of three variables for most of the countries. Consequently, while understanding the role of permanent and transitory components we would have 3−1=2 permanent innovation and one transitory innovation.
No adjustment is necessary for the possibility that the explanatory variables and error terms could be correlated. The generated regressors do not pose a problem since the estimated β ′ s are superconsistent i.e. the true parameter converges to the true values at rate T rather than \(\sqrt {T}\) as in OLS (Stock and Watson 1988).
Among the group of OECD countries, in case of Germany and Switzerland we didn’t find evidence of cointegration and the sign of the coefficients were inconsistent with the economic theory. Hence, these two countries are dropped from the sample.
In an issue “The Economist” wrote that, “by slashing interest rates (by more than the Taylor rule prescribed) the Fed encouraged a house-price boom”.
Apart from the important role of government in the housing markets, the mortgage products are found to have varying degree of sophistication in its characteristics like interest rate determination, loan amortization, penalties clauses, final maturity options, et cetera (Lea 2010; Scanlon et al. 2004; Girouard et al. 2006).
The results do not change qualitatively and to a large extent even quantitatively if alternative criteria like Schwartz information criterion or Hannan-Quinn criterion are adopted instead.
The half-life is computed as l n(0.5)/l n(1−|α h|) where |α h| represents the estimated coefficient on the lagged cointegrating residual \(\hat {\beta }^{\prime } Y_{t-1}\).
Not imposing the cointegrating restrictions can lead to identification problem with the permanent and the transitory components of the model (Gonzalo and Granger 1995).
Different methods have been proposed in the literature for performing trend and cycle decomposition. Beveridge and Nelson (1981) decomposition is a natural choice to perform trend and cycle decomposition in the present example since it utilizes the long-run cointegrating relationship in performing the decomposition. Moreover, as shown by Morley et al. (2003), the restrictions imposed by Beveridge-Nelson decomposition tend to be supported more by the data than an alternative trend-cycle method like an unobserved component model.
The actual and trend for every variable as well as cycle of non-significant variables in the error correction process are not presented to maintain brevity. These results are available upon request.
Some merit to this argument of slower adjusted of housing supply can also be found in Andrews et al. (2011).
Also see Holly and Jones (1997).
Scanlon et al. (2008) study the mortgage product innovation in advanced economies and report that these changes are prominent particularly in Denmark, Netherlands, and the U.K..
In the context of the U.S. Zimmer (2015) finds that the correlation in house price across four major U.S. cites has changed over time with correlation strengthening during the recent financial turmoil.
Splitting the whole sample into these periods may rather seem arbitrary. However, it has been chosen to reveal the changes in the housing market dynamics among these nations. Also, it better captures the nature of the recent episode of global boom-bust cycle in the housing markets.
These results support the recent views expressed by the Fed Chair Janet L. Yellen. While presenting the semiannual monetary policy report to the congress before the Committee on Banking, Housing, and Urban Affairs on July 16, 2104 the Fed chair testified that “while prices of real estate, equities, and corporate bonds have risen appreciably and valuation metrics have increased, they remain generally in line with historical norms.”
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Appendices
Appendix A: State-Space Representation of Multivariate Beveridge-Nelson Trend and Cycle
Based on Morley (2002) we present a state-space representation of the multivariate Beveridge-Nelson trend and cycle. We have the following VECM equations
The Beveridge-Nelson cycle is defined as
where I t is the information available at time t. The state space representation of the above model is as follows:
where \( \gamma ^{h}_{10i} = \gamma ^{h}_{1i}-\beta _{1}\gamma ^{h}_{2i}-\beta _{2}\gamma ^{h}_{3i}, \gamma ^{\ast } = 1+\alpha ^{h}-\alpha ^{y}-\alpha ^{r},\) and e z t = e h t −β 1 e x t −β 2 e r t and the starred letters represent the mean adjusted variable. In matrix form the state space form can be written as
where eigenvalues of the matrix F are less than unity in modulus. Then the cycle of the i th component of vector \(Y^{\ast }_{t}\) can be written as (i,i)th element of the matrix \(-(F+F^{2}+F^{3}+ ---) \ast {\Delta } Y^{\ast }_{t}\) which is equivalent of (i,i)th element of matrix \(-F(I-F)^{-1} \ast {\Delta } Y^{\ast }_{t}\). The trend component is computed by subtracting the cyclical component from the corresponding variable.
Appendix B: Robustness Check - Long-Term Interest Rates
As a robustness check, we also test our results using long-term interest rates. The table below reports the cointegrating vector estimates and the t-value from DOLS (Panel A) and the adjustment coefficient from the VECM model (Panel B) in case of cointegrating relationship between real house prices, real personal income, and real long-term interest rates.
Country | Constant | t-value | RPDI | t-value | RINT | t-value |
---|---|---|---|---|---|---|
Panel A: DOLS: long-term interest rates | ||||||
Australia | −5.627 | −18.08 | 2.185 | 30.26 | 0.021 | 3.66 |
Belgium | −0.936 | −0.84 | 1.238 | 5.17 | −0.087 | −6.73 |
Canada | −4.657 | −14.30 | 2.015 | 28.31 | −0.003 | −0.66 |
Denmark | −7.872 | −2.01 | 2.699 | 3.20 | −0.003 | −0.09 |
Finland | −5.816 | −6.64 | 2.238 | 11.74 | 0.038 | 3.25 |
France | −3.409 | −6.25 | 1.744 | 14.21 | −0.040 | −4.79 |
Ireland | −4.113 | −13.06 | 1.877 | 24.37 | −0.021 | −4.53 |
Italy | −8.901 | −4.93 | 2.927 | 7.44 | 0.010 | 1.43 |
Netherlands | −7.089 | −5.65 | 2.534 | 9.39 | −0.047 | −2.82 |
New Zealand | −6.217 | −13.95 | 2.317 | 23.25 | 0.001 | 0.20 |
Norway | −0.716 | −0.37 | 1.198 | 2.93 | −0.016 | −0.41 |
Spain | −7.928 | −22.13 | 2.682 | 33.63 | 0.013 | 1.07 |
Sweden | −1.141 | −0.94 | 1.268 | 4.95 | −0.064 | −4.41 |
U.K. | −2.035 | −6.05 | 1.426 | 18.41 | −0.027 | −2.71 |
U.S. | 1.124 | 4.21 | 0.730 | 11.34 | −0.015 | −2.58 |
Panel B: VECM: Long-Term Interest Rates | ||||||
Australia | −0.009 | −0.44 | 0.033 | 1.99 | 0.782 | 1.02 |
Belgium | −0.042 | −3.68 | −0.006 | −1.19 | −0.535 | −1.26 |
Canada | −0.082 | −3.07 | −0.021 | −1.26 | 0.989 | 1.08 |
Denmark | −0.033 | −2.32 | 0.002 | 0.20 | −0.097 | −0.14 |
Finland | −0.048 | −1.62 | 0.029 | 2.17 | 3.918 | 3.71 |
France | −0.021 | −2.72 | −0.002 | −0.45 | −0.204 | −0.53 |
Ireland | −0.042 | −3.45 | −0.008 | −0.76 | 1.158 | 1.12 |
Italy | −0.020 | −1.81 | −0.001 | −0.12 | 2.191 | 2.54 |
Netherlands | −0.016 | −2.23 | 0.000 | 0.04 | −0.186 | −0.31 |
New Zealand | −0.019 | −1.31 | 0.033 | 3.58 | 0.730 | 0.69 |
Norway | −0.066 | −4.81 | −0.015 | −1.74 | −0.853 | −1.28 |
Spain | −0.048 | −3.11 | −0.003 | −0.44 | 0.656 | 0.86 |
Sweden | −0.035 | −2.38 | −0.018 | −2.04 | −3.510 | −3.12 |
U.K. | −0.024 | −2.03 | −0.006 | −0.76 | −0.926 | −1.92 |
U.S. | −0.031 | −3.06 | −0.003 | −0.34 | 0.559 | 0.84 |
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Kishor, N.K., Marfatia, H.A. The Dynamic Relationship Between Housing Prices and the Macroeconomy: Evidence from OECD Countries. J Real Estate Finan Econ 54, 237–268 (2017). https://doi.org/10.1007/s11146-015-9546-8
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DOI: https://doi.org/10.1007/s11146-015-9546-8