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The Role of People’s Expectation in the Recent US Housing Boom and Bust

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Abstract

This paper investigates how an important driver of the recent housing boom and bust, people’s expectation, influences housing asset returns. Specifically, it extends the volatility feedback model to study the relationship between housing volatility and asset returns during 19632007. The analysis considers two alternative breakpoints, 1984Q1 and 1999Q1, in order to distinguish the permanent structural break from temporary Markov-switching volatility. The novelty of this study lies in its insightful investigations into the recent U.S. housing boom and bust in the post-1999 period in four dimensions. First, the significantly negative volatility feedback effect in the housing market suggests a positive relationship between housing volatility and expected asset returns, and highly supports the important role of people’s expectations in the recent housing boom and bust. Second, the high-volatility regimes of the housing market delivered by this study indicate a strong association between housing cycles and business cycles, as well as a remarkable uncertainty in the U.S. housing market after the recession 2001. Third, the violated fundamental which refers to the broken negative relationship between housing volatility and realized asset returns during 2001–2004 implies the possible presence of a housing bubble during this period. Finally, volatility feedback anticipates the recent bubble-like housing market dynamics because high volatility during 2002–2003 implies low realized returns in the early housing-boom stage (2002–2003), as well as high expected returns in the second stage of the housing boom (2004–2005).

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Notes

  1. For example, Mills (1989) discusses the efficiency of capital stock allocation and divides real capital returns into two types—returns to housing and to non-housing capital. Recently, Cannon et al. (2006) investigate asset pricing using a cross-sectional approach of risk and returns across the U.S. stock market and the metropolitan housing market at the ZIP code level. Lustig and Nieuwerburgh (2006) as well as Piazzesi et al. (2007) use CCAPM (Consumption-based Capital Asset Pricing Model) to address the role of the housing asset in the equity premium dynamics.

  2. Glaeser and Gyourko (2007) suggest that the housing price is predictable due to predictability of wages and construction. This finding of the housing dynamics supports the implication of their “rational expectation” model. Besides, Glaeser et al. (2008) classify the housing boom and bust into two types—an exogenous irrational bubble and an endogenous self-reinforcing bubble with adaptive expectations of irrational buyers, and argue that the latter bubble results from self-sustaining over-optimism.

  3. Davis and Palumbo (2008) argue that the housing market is demand-driven between 1998 and 2004. They propose that both appreciation and volatility of home prices are even more likely to be determined by demand-side factors currently than before due to the sharp price rise of the residential land. Piazzesi and Schneider (2009) also analyze the housing market from the demand side. They establish a search model to discuss the dominant role of a small number of optimistic traders on house prices during the housing boom. Sommervoll et al. (2010) establish a housing market model with interactions among heterogeneous agents to address the link between adaptive expectations and housing market cycles.

  4. Roche (2001) applies the framework of Schaller and van Norden (2002) to model the housing market by assuming the existence of two states—a high variance (bad) state and a low variance (good) state. Recently, Ceron and Suarez (2006) discuss the relationship between housing price volatility and the growth rate, applying a two-state Markov-switching model to examine housing price dynamics in fourteen developed countries between 1970 and 2003. The common latent two-state variable and the country-specific component collectively give insights into the change in volatility of the housing markets across cold and hot states. They find that the volatility is larger during cold phases, which is associated with low housing market growth.

  5. For example, Stock and Watson (2008) use a factor model with different specifications to examine when the instability occurs. They suggest a single breakpoint in 1984Q1 which is associated with the “Great Moderation of output” in accord with the previous literature. In addition, Kim et al. (2005a, b), and Kim et al. (2007a, b) use 1984Q1 as the breakpoint based on Kim and Nelson (1999a, b), and McConnell and Perez-Quiros (2000).

  6. We need a more confirmative research to explore the value of the average ratio of the quarterly housing price to the sum of the quarterly housing price and the quarterly housing rent, ρ. Although this is beyond the focus of this study, the study adopts robust tests by trying different values of ρ (by its definition, also set close to one), and the tests deliver qualitatively the same empirical results. Thus, the robust tests support that the choice of ρ does not have a significant impact on our analysis.

  7. Primarily because they are quite subject to various supply shocks, prices of food and energy are so volatile and non-persistent that they are not good proxies to reflect the relative changes in price levels in the macro-economy. Therefore, the core CPI, CPI for all urban consumers: all items less food and energy, is used in order to represent the aggregate price dynamics in a more appropriate fashion than CPI including the two items.

  8. Since the service flow payments from housing assets are relatively constant compared to the capital gain return, the capital gains approximate the variation in the overall total housing return quite well.

  9. The results using other breakpoints are available upon request. The results using the other years around 1999 which represents the breakpoint of the most recent housing boom, such as 1998 or 2000, are statistically the same as those using 1999 in this study. The robustness of the empirical results in this study is discussed in “Model 4: Volatility Feedback Effect Due to Full Revelation”.

  10. The recession during 1973–1975 was caused by the oil shock from the supply side, so it is not surprising that it cannot be captured by the volatility feedback model which spotlights the role of people’s expectations from the demand side.

References

  • Agnello, L., & Schuknecht, L. (2011). Booms and busts in housing markets: determinants and implications. Journal of Housing Economics. doi:10.1016/j.jhe.2011.04.001.

  • Bai, J., & Perron, P. (1998). Testing for and estimation of multiple structural changes. Econometrica, 66, 47–79.

    Article  Google Scholar 

  • Campbell, J. Y., & Hentschel, L. (1992). No news is good news: an asymmetric model of changing volatility in stock returns. Journal of Financial Economics, 31, 281–318.

    Article  Google Scholar 

  • Campbell, S. D., Davis, M. A., Gallin, J., & Martin, R. F. (2009). What moves housing markets: a variance decomposition of the rent-price ratio. Journal of Urban Economics, 66, 90–102.

    Article  Google Scholar 

  • Campbell, J. Y., & Shiller, R. (1998). The dividend-price ratio and expectations of future dividends and discount factors. Review of Financial Studies, 1, 195–227.

    Google Scholar 

  • Cannon, S., Miller, N. G., & Pandher, G. S. (2006). Risk and return in the U.S. housing market: a cross-sectional asset-pricing Approach. Real Estate Economics, 34, 519–552.

    Article  Google Scholar 

  • Ceron, J., & Suarez, J. (2006). Hot and cold housing market: international evidence. CEMFI Working Paper No. 0603.

  • Davis, A. M., & Palumbo, G. M. (2008). The price of residential land in large U.S. cities. Journal of Urban Economics, 63, 352–384.

    Article  Google Scholar 

  • Gallin, J. (2006). The long-run relationship between house prices and income: evidence from local housing markets. Real Estate Economics, 34, 417–438.

    Article  Google Scholar 

  • Glaeser, E. L., & Gyourko, J. (2007). Housing dynamics. Harvard Institute of Economic Research Discussion Paper No. 2137.

  • Glaeser, E. L., Gyourko, J., & Saiz, A. (2008). Symposium: mortgages and the housing crash: housing supply and housing boom and busts. Journal of Urban Economics, 64, 198–217.

    Article  Google Scholar 

  • Goodman, A. C., & Thibodeau, T. G. (2008). Where are the speculative bubbles in US housing markets? Journal of Housing Economics, 17, 117–137.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Hansen, B. E. (2001). The new econometrics of structural change: dating breaks in U.S. labor productivity. Journal of Economic Perspectives, 15, 117–128.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Hwang, M., Quigley, J. M., & Son, J. Y. (2006). The dividend pricing model: new evidence from the Korean housing market. Journal of Real Estate Finance and Economics, 32, 205–228.

    Article  Google Scholar 

  • Kim, C. J., & Nelson, C. R. (1999a). Friedman’s plucking model of business fluctuations: tests and estimates of permanent and transitory components. Journal of Money, Credit and Banking, 31, 317–34.

    Article  Google Scholar 

  • Kim, C. J., & Nelson, C. R. (1999b). Has the U.S. economy become more stable? A Bayesian based approach based on a Markov switching model of the business cycle. Review of Economics and Statistics, 81, 608–616.

    Article  Google Scholar 

  • Kim, C. J., Morley, J. C., & Nelson, C. R. (2004). Is there a positive relationship between stock market volatility and the equity premium? Journal of Money, Credit and Banking, 36, 339–360.

    Article  Google Scholar 

  • Kim, C. J., Morley, J. C., & Piger, J. (2005a). Nonlinearity and the permanent effects of recessions. Journal of Applied Econometrics, 20, 291–309.

    Article  Google Scholar 

  • Kim, C. J., Morley, J. C., & Nelson, C. R. (2005b). The structural break in the equity premium. Journal of Business and Economic Statistics, 23, 181–191.

    Article  Google Scholar 

  • Kim, C. J., Piger, J., & Startz, R. (2007a). The dynamic relationship between permanent and transitory components of U.S. business cycle. Journal of Money, Credit and Banking, 39, 187–204.

    Article  Google Scholar 

  • Kim, J. W., Leatham, D. J., & Bessler, D. A. (2007b). REIT’s dynamics under structural change with unknown break points. Journal of Housing Economics, 16, 37–58.

    Article  Google Scholar 

  • Lai, R. N., & Van Order, R. (2010). Momentum and house price growth in the U.S.: anatomy of a Bubble. Real Estate Economics, 38, 753–773.

    Article  Google Scholar 

  • Lustig, H., & Nieuwerburgh, S. V. (2006). Can housing collateral explain long-run swings in asset returns? NBER Working Paper No. W12766.

  • McConnell, M. M., & Perez-Quiros, G. (2000). Output fluctuations in the United States: what has changed since the early 1980s? The American Economic Review, 90, 1464–1476.

    Article  Google Scholar 

  • Meese, R., & Wallace, N. (1990). Determinants of residential housing prices in the bay area 1970–1988: effects of fundamental economic factors or speculative bubbles. In Proceedings from Federal Reserve Bank of San Francisco. San Francisco, CA: Federal Reserve Bank of San Francisco.

  • Mikhed, V., & Zemcík, P. (2007). Testing for bubbles in housing markets: a panel data approach. Journal of Real Estate Finance and Economics, 38, 366–386.

    Article  Google Scholar 

  • Mills, S. E. (1989). Social returns to housing and other fixed capital. Real Estate Economics, 7, 197–211.

    Article  Google Scholar 

  • Piazzesi, M., & Schneider, M. (2009). Momentum traders in the housing market: survey evidence and a search model. The American Economic Review, 99, 406–411.

    Article  Google Scholar 

  • Piazzesi, M., Schneider, M., & Tuzel, S. (2007). Housing, consumption, and asset pricing. Journal of Financial Economics, 83, 531–569.

    Article  Google Scholar 

  • Roche, M. J. (2001). The rise in house prices in Dublin: bubble, fad or just fundamentals. Economic Modelling, 18, 281–295.

    Article  Google Scholar 

  • Schaller, H., & Van Norden, S. (2002). Fads or bubbles. Empirical Economics, 27, 335–362.

    Article  Google Scholar 

  • Shiller, R. J. (2005). Irrational Exuberance (2nd ed.). Princeton, NJ: Princeton University Press.

    Google Scholar 

  • Shiller, R. J. (2006). Long-term perspectives on the current boom in home prices. Economists' Voice, 3, Article 4.

    Google Scholar 

  • Shiller, R. J. (2008). Understanding recent trends in house prices and homeownership. In Jackson Hole Conference Series(Ed.), Housing, Housing Finance and Monetary Policy (pp. 85–123). Kansas City, MO: Federal Reserve Bank of Kansas City.

  • Smith, M. H., & Smith, G. (2006). Bubble, bubble, where’s the housing bubble? Brookings Papers on Economic Activity, 1, 1–50.

    Article  Google Scholar 

  • Sommervoll, D. E., Borgersen, T. A., & Wennemo, T. (2010). Endogenous housing market cycles. Journal of Banking and Finance, 34, 557–567.

    Article  Google Scholar 

  • Stiglitz, J. E. (1990). Symposium on bubbles. Journal of Economic Perspective, 4, 13–18.

    Article  Google Scholar 

  • Stock, J. H., & Watson, M. W. (2008). Forecasting in dynamic factor models subject to structural instability. In J. Castle & N. Shephard (Eds.), The Methodology and Practice of Econometrics, A Festschrift in Honour of Professor David F. Hendry, Oxford: Oxford University Press.

    Google Scholar 

  • Turner, C. M., Richard, S., & Nelson, C. R. (1989). A Markov model of heteroskedasticity, risk, and learning in the stock market. Journal of Financial Economics, 25, 3–22.

    Article  Google Scholar 

  • Vargas-Silva, C. (2008). Monetary policy and the US housing market: a VAR analysis imposing sign restrictions. Journal of Macroeconomics, 30, 977–990.

    Article  Google Scholar 

  • Wheaton, W. C., & Nechayev, G. (2008). The 1998–2005 housing ‘bubble’ and the current ‘correction’: what’s different this time. Journal of Real Estate Research, 30, 1–26.

    Google Scholar 

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Acknowledgements

I thank the anonymous referee for the helpful comments which have greatly improved the paper. And, I gratefully acknowledge insightful suggestions from Marcelle Chauvet. Also, I thank James Morley for his suggestions as this paper was presented in 17th International Conference on Computing in Economics and Finance held in San Francisco, U.S. during June 29 to June 1, 2011.

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  1. College of Management (Finance), Yuan Ze University, 135 Yuan-Tung Road, Chung-Li, Taiwan, Republic of China

    MeiChi Huang

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Huang, M. The Role of People’s Expectation in the Recent US Housing Boom and Bust. J Real Estate Finan Econ 46, 452–479 (2013). https://doi.org/10.1007/s11146-011-9341-0

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