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Monetary Policy, Term Structure and Asset Return: Comparing REIT, Housing and Stock

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Abstract

This paper confirms that a regime-switching model out-performs a linear VAR model in terms of understanding the system dynamics of asset returns. Impulse responses of REIT returns to either the federal funds rate or the interest rate spread are much larger initially but less persistent. Furthermore, the term structure acts as an amplifier of the impulse response for REIT return, a stabilizer for the housing counterpart under some regime, and, perhaps surprisingly, almost no role for the stock return. In contrast, GDP growth has very marginal effect in the impulse response for all assets.

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Notes

  1. Among others, see Cochrane (2001, 2005), Goodhart and Hofmann (2007), Chang et al. (2010), and the reference therein.

  2. By regulation, REITs are required to invest at least 75% of their assets in real estate and pay the minimum 90% of their taxable earnings as dividends (Chan et al. 2003).

  3. This statement has been confirmed by the data of the U.S. as well as other advanced countries. Among others, see Campbell (1987), Chen (1991), Fama (1990), Ferson (1989), Plosser and Rouwenhorst (1994), Estrella and Mishkin (1997), Estrella and Hardouvelis (1991), and the reference therein. For a review of the more recent literature, see Estrella (2005), Estrella and Trubin (2006), among others.

  4. In the literature of term structure, a lot of efforts have been devoted to verify the “expectation hypothesis.” However, Collin-Dufresne (2004) shows that there are several versions of the expectation hypothesis and they are not consistent with one another. Thus, the explicit formulation of the expectation may matter to the final empirical result.

  5. Clearly, it is beyond the scope of this paper to review this large literature. See King and Watson (1994, 1997) and the reference therein.

  6. Since Hamilton (1989), there is a large literature on applying regime-switching process in economics and finance research. For a review of the literature, see Hamilton (1994), among others.

  7. According to Datastream, the total US stock market capitalization (“TOTMKUS” which comprises of top 80% of companies in US) is around 15,519.84 billion at the end of 2007. Thus, the total market capitalization of REITs in 2007 accounts for around 2% of the total US stock market capitalization. See also Chan et al. (2003).

  8. Among others, see Ong et al. (2008) and the reference therein.

  9. For instance, see Ciochetti et al. (2002) for empirical evidence. Wang et al. (1995) find that REIT stock with higher percentages of institutional investors tend to perform better.

  10. Throughout this paper, we use nominal return. More discussion on this will be presented in the data section.

  11. Among others, see Bond and Patel (2003) for further analysis of the higher moment of real estate return.

  12. For example, the impact of policy rate changes on the equity market affects the expected level of future dividends of the firms which can be paid out as dividends for REITs; however, real estate related assets returns should be responding to long-term rate more than to short-term rate, via the influence on general economic activity that feeds through to the demand in the underlying real estate market.

  13. It includes the Hamilton (1989) regime switching model, and the test on the stationarity test for regime-switching model developed by Francq and Zakoian (2001).

  14. Some recent studies of housing market also use nominal prices and returns instead of the real ones, including Himmelberg et al. (2005) and Hott and Monnin (2008), among others.

  15. In spite of this, we will introduce more variables in the analysis in some later sections.

  16. The idea that some markets can adjust faster than other markets is not new. See Arrow and Hahn (1971) for a review of the earlier theoretical literature. In addition, asset prices may be more forward-looking than the macroeconomic variables. We will come back to this point in some later sections. See also Dornbusch (1976) for an illustration in the context of an open economy.

    For a survey of the sluggish adjustment in the goods market and the labor market, see Taylor (1999), among others.

  17. Treasury securities are also useful because they are not subject to significant credit risk premiums that may change with maturity and over time.

  18. The 3-month secondary market T-bill rate provided by the Federal Reserve System is on a discount basis. We follow Estrella and Trubin (2006) by converting the 3-month discount rate (r d) to a bond-equivalent rate (r): \(r=\frac{365\times r^{d}/100}{360-91\times r^{d}/100}\times 100\).

  19. Among others, see Sims (1980) for more discussion on these issues and the potential biases that could arise if single equation approach is adopted instead of the VAR method.

  20. One may be tempted to remove the data prior to 1985Q2 and thus focus on a single-regime case. First, we did not know the high volatility regime is concentrated in one period (1978Q3–1985Q1). Second, the early period (1975Q2–1978Q2) still belongs to the regime 2. If we remove all the data before 1985Q2, the estimation of the regime 2 parameters will become less precise, some may even be mis-labelled as insignificant. Perhaps more importantly, as we will see in the next section, the high-volatility regime for housing return is very different from that of the REIT. Thus, it may still be wise to use the full sample to estimate the regime-switching model, rather than to artificially cut off some earlier periods and estimate a linear VAR model.

  21. Among others, Goodfriend and King (2005) and Goodfriend (2007) provides a summary of the history of monetary policy during that period.

  22. The value of variance of the federal funds rate is 2.343 (\(\sigma _{1}^{2}\) ) in regime 1 and 0.111 (\(\sigma _{1}^{2}\times \lambda _{1}^{2}\left( 2\right) \)). For the spread, they are 1.050 in regime 1 and 0.088 in regime 2. Finally, the housing market returns is 0.464 (\(\sigma _{3}^{2}\)) in regime 1 and 0.390 ((\(\sigma _{3}^{2}\times \lambda _{3}^{2}\left( 2\right) \)) in regime 2.

  23. Clearly, it is beyond the scope of this paper to review the literature on the 1987 stock market crash. Among others, see Schwert (1990).

  24. Throughout this paper, it is assumed that the dynamic system always stays in the same regime when the impulse response exercise is conducted. The results in the previous sections show that for both REIT and housing returns, the regimes are very persistent. Thus, this assumption may not be as strong as it seems.

  25. Among others, see Dornbusch (1976) for an illustration.

  26. For example, Rosenberg and Maurer (2008) confirm term spread to be a leading indicator of recession, and the expectations component, rather than term premium, of the term spread is important in explaining the role of term spread in predicting recession. Laurent (1988) uses the yield curve as an indicator of monetary policy, and finds it statistically associated with the subsequent pace of output growth. Estrella and Hardouvelis (1991) find that the yield curve performed well in predicting aggregate GNP, consumption, investment, and recessions.

  27. There is a large empirical literature behind this fact. Among others, see Cochrane (2001, 2005) for a review.

  28. Notice that real GDP is non-stationary. Thus, we follow the literature to use the real GDP growth as the additional variable in our regression. Due to the space limit, here we present only figures of identified regimes and impulse responses. The estimated results of parameter values for these two models are omitted to save space. They are available upon request. Furthermore, a caveat for estimating the model with housing market returns (FFR, SPR, GDP, HRET) is that the AIC determines the model to have two lags, as in the three-variate model; however, with two lags the total number of parameters to be estimated amounts to 94. We therefore allows only one lag while estimating this model.

  29. The results will be available upon request.

  30. It does not mean that GDP is unimportant. For instance, Telmer and Zin (2002) find that in a dynamic general equilibrium model with incomplete financial market, pricing kernels that are simple functions of equilibrium prices (or returns), provide good proxies for ‘actual’ pricing kernels that are typically higher dimensional functions of disaggregate information. Thus, a structural asset pricing model with a strong theoretical base can be consistent with reduced-form specifications which, in practice, tend to ‘perform’ better.

  31. Among others, see Emiris (2006) and Leung and Teo (2008), and the reference therein.

References

  • Akerlof, G. (1970). The market for lemons: Qualitative uncertainty and the market mechanism. Quarterly Journal of Economics, 84, 488–500.

    Article  Google Scholar 

  • Arrow, K., & Hahn, F. (1971). General competitive analysis. New York: North-Holland.

    Google Scholar 

  • Beneveniste, L., Capozza, D., & Seguin, P. (2001). The value of liquidity. Real Estate Economics, 29, 633–660.

    Article  Google Scholar 

  • Bernanke, B., & Gertler, M. (2001). Should central banks respond to movements in asset prices? American Economic Review, 91, 253–257.

    Article  Google Scholar 

  • Bollen, N., Gray, S., & Whaley, R. (2000). Regime switching in foreign exchange rates: Evidence from currency option prices. Journal of Econometrics, 94(1–2), 239–276.

    Article  Google Scholar 

  • Bond, S. A., & Patel, K. (2003). The conditional distribution of real estate returns: Are higher moments time varying? Journal of Real Estate Finance and Economics, 26(2–3), 319–339.

    Article  Google Scholar 

  • Bredin, D., O’Reilly, G., & Stevenson, S. (2007). Monetary shocks and REIT returns. Journal Real Estate Finance and Economics, 35, 315–331.

    Article  Google Scholar 

  • Cai, J. (1994). A Markov model of switching-regime ARCH. Journal of Business and Economic Statistics, 12, 309–316.

    Article  Google Scholar 

  • Campbell, J. Y. (1987). Stock returns and the term structure. Journal of Financial Economics, 18(2), 373–99.

    Article  Google Scholar 

  • Capozza, D., & Seguin, P. (1998). Managerial style and firm value. Real Estate Economics, 26, 131–150.

    Article  Google Scholar 

  • Capozza, D., & Seguin, P. (1999). Focus, transparency, and value: The REIT evidence. Real Estate Economics, 27, 587–619.

    Article  Google Scholar 

  • Chan, K. C., Hendershott, P. H., & Sanders, A. B. (1990). Risk and return on real estate: Evidence from equity REITs. American Real Estate and Urban Economics Association Journal, 18, 431–452.

    Article  Google Scholar 

  • Chan, S. H., Erickson, J., & Wang, K. (2003). Real estate investment trusts: Structure, performance, and investment opportunities. Oxford: Oxford University Press.

    Google Scholar 

  • Chan, S. H., Leung, W. K., & Wang, K. (2005). Change in REIT structure and stock performance: Evidence from the Monday Stock Anomaly. Real Estate Economics, 33(1), 89–120.

    Article  Google Scholar 

  • Chang, K. L., Chen, N. K., & Leung, C. K. Y. (2010). Monetary policy, asset return dynamics and the general equilibrium effect. In D. Rosch & H. Scheule (Eds.), Model risk in financial crises - Challenges and solution for financial risk models. London: Risk Books and Journals (in press).

    Google Scholar 

  • Chen, N.-F. (1991). Financial investment opportunities and the macroeconomy. Journal of Finance, 46(2), 529–54.

    Article  Google Scholar 

  • Cheung, Y. W., & Erlandsson, U. G. (2005). Exchange rates and Markov switching dynamics. Journal of Business and Economic Statistics, 23(3), 314–320.

    Article  Google Scholar 

  • Christiano, L. J., Eichenbaum, M., & Evans, C. L. (1999). Monetary policy shocks: What have we learned and to what end? In J. B. Taylor & M. Woodford (Eds.), Handbook of macroeconomics (Vol. 1A). Amsterdam: Elsevier.

    Google Scholar 

  • Ciochetti, B., Craft, T., & Shilling, J. (2002). Institutional investor’ preferences for REIT stocks. Real Estate Economics, 30(4), 567–593.

    Article  Google Scholar 

  • Cochrane, J. (2001). Asset pricing. Princeton: Princeton University Press.

    Google Scholar 

  • Cochrane, J. (2005). Financial markets and the real economy. NBER Working Paper 11193.

  • Collin-Dufresne, P. (2004). Affine term structure models. Mimeo, UC Berkeley.

  • Dornbusch, R. (1976). Expectations and exchange rate dynamics. Journal of Political Economy, 84, 1161–1176.

    Article  Google Scholar 

  • Driffill, J., & Sola, M. (1998). Intrinsic bubbles and regime-switching. Journal of Monetary Economics, 42(2), 357–373.

    Article  Google Scholar 

  • Duan, J. C., Popova, I., & Ritchken, P. (2002). Option pricing under Regime switching. Quantitative Finance, 2(2), 116–132.

    Article  Google Scholar 

  • Emiris, M. (2006). The term structure of interest rates in a DSGE model. Mimeo, National Bank of Belgium.

  • Estrella, A. (2005). Why does the yield curve predict output and inflation? Economic Journal, 115, 722–744.

    Article  Google Scholar 

  • Estrella, A., & Hardouvelis, G. (1991). The term structure as a predictor of real economic activity. Journal of Finance, 46(2), 555–576.

    Article  Google Scholar 

  • Estrella, A., & Mishkin, F. (1997). The predictive power of the term structure of interest rates in Europe and the United States: Implications for the European Central Bank. European Economic Review, 41, 1375–1401.

    Article  Google Scholar 

  • Estrella, A., & Trubin, M. R. (2006). The yield curve as a leading indicator: Some practical issues. Federal Reserve Bank of New York. Current Issues in Economics and Finance, 12(5).

  • Fama, E. (1990). Term-structure forecasts of interest rates, inflation, and real returns. Journal of Monetary Economics, 25, 59–76.

    Article  Google Scholar 

  • Fama, E., & French, K. (1989). Business conditions and expected returns on stocks and bonds. Journal of Financial Economics, 25, 23–49.

    Article  Google Scholar 

  • Ferson, W. E. (1989). Changes in expected security returns, risk, and level of interest rates. Journal of Finance, 44, 1191–1217.

    Article  Google Scholar 

  • Francq, C., & Zakoian, J. M. (2001). Stationary of multivariate Markov-switching ARMA models. Journal of Econometrics, 10, 339–364.

    Article  Google Scholar 

  • Froot, K., & Obstfeld, M. (1991). Exchange-rate dynamics under stochastic regime shifts: A unified approach. Journal of International Economics, 31(3–4), 203–229.

    Article  Google Scholar 

  • Glascock, J., Lu, C., & So, R. (2002a). Further evidence on the integration of REIT, bond, and stock returns. Journal of Real Estate Finance and Economics, 20, 177–194.

    Article  Google Scholar 

  • Glascock, J., Lu, C., & So, R. (2002b). REIT returns and inflation: Perverse or reverse causality effects? Journal of Real Estate Finance and Economics, 24, 301–317.

    Article  Google Scholar 

  • Goodfriend, M. (2007). How the world achieved consensus on monetary policy. Journal of Economic Perspectives, 21(4), 47–68.

    Article  Google Scholar 

  • Goodfriend, M., & King, R. (2005). The incredible Volcker disinflation. Journal of Monetary Economics, 52(5), 981–1015.

    Article  Google Scholar 

  • Goodhart, C. (2001). What weight should be given to asset prices in the measurement of inflation? Economic Journal, 111, 335–356.

    Article  Google Scholar 

  • Goodhart, C., & Hofmann, B. (2007). House prices and the macroeconomy: Implications for banking and price stability. Oxford: Oxford University Press.

    Google Scholar 

  • Hamilton, J. D. (1989). A new approach to the economic analysis of nonstationary time series and the business cycle. Econometrica, 57(2), 357–384.

    Article  Google Scholar 

  • Hamilton, J. D. (1994). Time series analysis. Princeton: Princeton University Press.

    Google Scholar 

  • Hansen, A. T., & Poulsen, R. (2000). A simple regime switching term structure model. Finance and Stochastics, 4(4), 409–429.

    Article  Google Scholar 

  • He, L. T., Webb, J. R., & Myer, F. C. N. (2003). Interest rate sensitivities of REIT returns. International Real Estate Review, 6, 1–21.

    Google Scholar 

  • Himmelberg, C., Mayer, C., & Sinai, T. (2005). Assessing high house prices: Bubbles, fundamentals and misperceptions. Journal of Economic Perspective, 19, 67–92.

    Article  Google Scholar 

  • Hott, C., & Monnin, P. (2008). Fundamental real estate prices: An empirical estimation with international data. Journal of Real Estate financial Economics, 36, 427–450.

    Article  Google Scholar 

  • King, R., & Watson, M. (1994). The post-war U.S. Phillips curve: A revisionist econometric history. Carnegie-Rochester Conference Series on Public Policy, 41, 157–219.

    Article  Google Scholar 

  • King, R., & Watson, M. (1997). Testing long-run neutrality. Federal Reserve Bank of Richmond Economic Quarterly, 83, 69–101.

    Google Scholar 

  • Laurent, R. D. (1988). An interest-based indicator of monetary policy. In Economic perspective, federal reserve bank of Chicago, 3–14 January/February.

  • Leeper, E. M., Sims, C. A., & Zha, T. (1996). What does monetary policy do? Brookings Papers on Economic Activity, 2, 1–63.

    Article  Google Scholar 

  • Leung, C. K. Y., & Teo, W. L. (2008). The Wall Street concerns might be right: Equilibrium correlations of asset price do change with monetary policy. Paper presented at the Asian Real Estate Society meeting.

  • Ling, D., & Naranjo, A. (1997). Economic risk factors and commercial real estate returns. Journal of Real Estate Finance and Economics, 15(3), 283–307.

    Google Scholar 

  • Liu, C., & Mei, J. (1992). The predictability of returns on equity REITs and their co-movement with other assets. Journal of Real Estate Finance and Economics, 5, 401–418.

    Google Scholar 

  • Lizieri, C., & Satchell, S. (1997). Property company performance and real interest rates: A regime switching approach. Journal of Property Research, 14, 85–97.

    Article  Google Scholar 

  • McCue, T. E., & Kling, J. L. (1994). Real estate returns and the macroeconomy: Some empirical evidence from real estate investment trust data 1972–1991. Journal of Real Estate Research, 9(3), 277–287.

    Google Scholar 

  • Mishkin, F. (1990). What does the term structure tell us about future inflation? Journal of Monetary Economics, 25, 77–95.

    Article  Google Scholar 

  • Mueller, G., & Pauley, K. (1995). The effect of interest-rate movements on real estate investment trusts. Journal of Real Estate Research, 10(5), 319–325.

    Google Scholar 

  • Ong, S. E., Ooi, J., & Kawaguchi, Y. (2008). Seasoned equity issuance by Asian REITs. Paper presented at the DePaul University REIT symposium.

  • Ott, S., Riddiough, T., & Yi, H. C. (2005). Finance, investment and investment performance: Evidence from the REIT sector. Real Estate Economics, 33(1), 203–235.

    Article  Google Scholar 

  • Plosser, C., & Rouwenhorst, K. G. (1994). International term structures and real economic growth. Journal of Monetary Economics, 33, 133–155.

    Article  Google Scholar 

  • Rosenberg, J. V., & Maurer, S. (2008). Signal or noise? Implications of the term premium for recession forecasting. FRBNY Economic Policy Review (forthcoming).

  • Sargent, T., Williams, N., & Zha, T. (2006). Shocks and government beliefs: The rise and fall of American inflation. American Economic Review, 96, 1193–1224.

    Article  Google Scholar 

  • Schwert, G. W. (1990). Stock volatility and the Crash of ’87. Review of Financial Studies, 3(1), 77–102.

    Article  Google Scholar 

  • Simpson, M. W., Ramchander, S., & Webb, J. R. (2007). The asymmetric response of equity REIT returns to inflation. Journal of Real Estate Finance and Economics, 34(4), 513–529.

    Article  Google Scholar 

  • Sims, C. (1980). Macroeconomics and reality. Econometrica, 48(1), 1–48.

    Article  Google Scholar 

  • Taylor, J. B. (1999). Staggered price and wage setting in macroeconomics. In J. B. Taylor & M. Woodford (Eds.), Handbook of macroeconomics (Vol. 1B). New York: Elsevier.

    Google Scholar 

  • Telmer, C., & Zin, S. (2002). Prices as factors: Approximation aggregation with incomplete markets. Journal of Economic Dynamics and Control, 26, 1127–1157.

    Article  Google Scholar 

  • Tsatsaronis, K., & Zhu, H. (2004). What drives housing price dynamics: Cross-country evidence. BIS Quarterly Review, 65–78.

  • Wang, K., Erickson, J., Gau, G., & Chan, S. H. (1995). Market microstructure and real estate returns. Real Estate Economics, 23(1), 85–100.

    Article  Google Scholar 

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Acknowledgements

We are grateful to Shaun Bond, Wing Hong Chan, Jim Shilling, seminar participants of (alphabetical order) AREUEA meeting, Bank of International Settlements, City University of Hong Kong, DePaul University REIT symposium, and especially an anonymous referee, for many helpful comments and insights, and City University of Hong Kong, UGC Earmark Grant (Code: CityU 144709) for financial support. The usual disclaimer applies.

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  1. Department of Applied Economics, National Chiayi University, 580 Sinmin Road, Chiayi City, 60054, Taiwan

    Kuang-Liang Chang

  2. Department of Economics, National Taiwan University, 21 Shuchow Road, Taipei, 10021, Taiwan

    Nan-Kuang Chen

  3. Department of Economics and Finance, City University of Hong Kong, Kowloon Tong, Hong Kong, China

    Charles Ka Yui Leung

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Appendix

Appendix

This appendix attempts to address the concerns of some alternative interpretation of the model as well as the data.

First, it has been suggested to us that

But one story is: when the economy is booming, income are high, and households are more likely to be able to finance the purchase of housing. This raises the liquidity of housing. When housing is more liquid, many people are willing to be buyers and sellers. As a result, more transactions take place. But as more transactions take place, lenders charge lower interest rates... The lower interest rate spread causes even more buyers to enter the market, reinforcing the greater liquidity and causing prices to rise. But, as I understand it, a transmission mechanism of this sort is ruled out by the authors by the way in which the VAR is specified...

We are very grateful to this suggestion. Yet when we try to test this alternative hypothesis, we face some difficulty. We lack measures of the housing market liquidity as well as transaction volume of the housing market for the same sampling period. Thus, we can only look at a “reduced form” of the hypothesis, which is a negative correlation between the income (or GDP) and the term spread (SPR). Below is what we find:

Please allow us to explain. We first study the correlation between the GDP (i.e. GDP growth rate, as the level is non-stationary) and the term spread for the full sampling period. The correlation seems to be very small (0.152 as shown in the first row of Table 9). We then use our regime-switching VAR model to identify the periods in which the economy is in “regime 1” (i.e. the high volatility regime) and the periods in which the economy is in “regime 2.” We then divide the sample into two sub-samples: the “regime 1 sub-sample” and the “regime 2 sub-sample.” We compute the correlation between the two variables for each of these sub-samples. Again, the correlations are small (0.156 and 0.151).

Table 9 The correlation coefficient between GDP growth and SPR
Full size table

One can argue that while the (unconditional) correlation is low, the conditional correlation (or “partial correlation” can be high). To investigate such a possibility, we run a regression of GDP on the FFR and HRET first, and get the residual term u(t) as the “conditional GDP growth” (please see Table 10 for more details). We then compute the correlations between the “conditional GDP growth” and the SPR under the full sample, the regime 1 subsample and the regime 2 subsamples. The results are shown in the second row of Table 9. Again, the correlations are very low. Thus, given our very limited proxies, we have not been able to find evidence to support this alternative theory.

Table 10 The estimates results for simple regression
Full size table

Clearly, a key variable, the transaction volume variable, is missing. We would re-visit this issue in our ongoing research with a different dataset and hopefully we will be able to deliver a more satisfactory answer in the future.

Second, in terms of the identification restriction, it is the same one used in the monetary policy literature (among others, see the survey by Christiano et al. 1999). It actually allows the real estate return to affect the FFR and SPR with time lags. The only restriction is that the effect is one-directional contemporaneously. For less restrictive identification assumption, one needs to adopt the Bayesian methodology (see Leeper et al. 1996, for more elaborations). Our impression is that while some researchers welcome the Bayesian method, some seem to have reservations. Therefore, we attempt to pursue with the “classical econometrics” method (which may be less controversial) and hence inevitably adopt the currently used identification assumption.

Third, there is a suggestion concerning the financial intermediation.

Further, is the potential for adverse selection so much greater in the housing market relative to the REIT market, thereby explaining why house prices are less significantly but more persistently impacted by changes in monetary policy than are REIT returns?

This is a very interesting suggestion. Without the corresponding micro-level data, we are unable to make much progress for this hypothesis though. We only have a simple observation at this point, which is that adverse selection could lead a market to shrink, as in the case of “Lemon” in the classical paper of Akerlof (1970). Yet the mortgage market actually expands between year 2000 and 2005. Why did not the financial intermediations further ration the credit when the adverse selection problem became more severe? Thus, it seems that a more complete theory demands not only the adverse selection of the residential mortgage demand, but also the supply, i.e. the behavior of the financial intermediaries. We are currently working towards that direction. Again, we are very grateful to this inspiring insight.

The fourth concern is related to the recent crisis.

Given that the originate-to-securitize process had unintended consequences in the US housing market during the 2000–2005 period, do we really expect the effects of monetary policy to be same across the authors’ entire sample period?

Again, this is a very good point. Unfortunately, even when we restrict the attention to the single regime (i.e. linear) VAR model with 4 variables, we find that we need to estimate 96 parameters with 6 years of data (for the parameters in the dynamic equation as well as the variance-covariance matrix), which is insufficient! Thus, we extend the period to 2000–2008, which is barely enough for the estimation of a linear four-variate VAR model. Needless to say, since the model in the paper is a regime-switching VAR model with much longer time series, the results may not be directly comparable. Figure 21 provides a visualization of the impulse responses. In the case of REIT (the left hand side), it is clear that the 2000–2008 sub-sample are very different. For the “full sample” case, or the periods under regime 1, or those under regime 2, a positive innovation of FFR will lead to a drop in the REIT return. It seems natural as an increase in the interest rate tends to depress asset returns. In fact, it is also what happened to the housing return (HRET) and the stock return (SRET).

Fig. 21
figure 21

Impulse responses of asset return to innovations in FFR

Full size image

However, for the 2000–2008 sub-sample, the initial response is an increase in the REIT return, followed by a decrease, even when the GDP is controlled. In fact, it is clear that it is also the case for housing return (HRET) and stock return (SRET). So, the 2000–2008 period is indeed “abnormal.” How can this happen? At this point, the only explanation we can provide is a “signaling type story.” Assume that the central bank wants to prevent the market from “over-heating.” And assume that people believe that the central bank has some private information about the future economic growth. In that case, when an increase in the interest rate is a signal that the economy will grow even more in the future, and this stimulates even more investment in all assets, and lead to an increase in the returns. When the people discover that it is not the case, investors are disappointed and the asset returns over-shoot (in this case, drop below the steady state value).

Clearly, this “explanation” is very preliminary and we hope that our future research can address this issue in a more satisfactory manner.

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Chang, KL., Chen, NK. & Leung, C.K.Y. Monetary Policy, Term Structure and Asset Return: Comparing REIT, Housing and Stock. J Real Estate Finan Econ 43, 221–257 (2011). https://doi.org/10.1007/s11146-010-9241-8

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