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What Drives Housing Markets: Fundamentals or Bubbles?

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Abstract

This study applies the dynamic Gordon growth model which is in the circumstance of rational bubbles to decompose log price-rent ratio into three parts, i.e., rational bubbles, discounted expected future rent growth rates and discounted expected future returns. The latter two terms represent housing fundamentals. The magnitudes of the components of price-rent ratio’s variance are estimated to distinguish the relative impact of the three parts on housing prices. Using time series data from the housing markets in the four largest cities in China (1991:Q1–2011:Q1 for Shanghai, Guangzhou and Shenzhen; 1993:Q2–2011:Q1 for Beijing), this paper presents a number of empirical findings: (a) the variance of rational bubbles is much larger than the variance of price-rent ratio, and rational bubbles contribute more fluctuations directly to price-rent ratio than the expected returns or the expected rent growth rates do; (b) the covariance between rational bubbles and expected returns or expected rent growth rates is also large; (c) the positive covariance of rational bubbles and expected returns implies that high expected returns coexist with bubbles, which differs from previous findings that lower expected returns drive asset prices; (d) the negative covariance of rational bubbles and expected rent growth rates indicates that the larger the bubbles are, the lower the expected rent growth rates are; (e) the positive covariance of expected returns and expected rent growth rates reveals under-reaction of the housing markets to rents.

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Notes

  1. Brunnermeier and Julliard (2008) suggest that the mispricing provided by the dynamic Gordon growth model captures bubbles.

  2. These studies include the overall bond markets (Campbell and Ammer 1993), the interaction between global stock (Ammer and Mei 1996), the influence of monetary policy on stock returns (Patelis 1997), the individual stocks (Vuolteenaho 2002), intertemporal CAPM model (Campbell and Vuolteenaho 2004a), inflation’s impact on the stock markets (Bekaert and Engstrom 2010), and so on.

  3. DTZ, a UGL company and one of the five famous international real estate consultants, has 26,000 permanent employees and 47,000 personnel including contractors, operating across 208 offices in 52 countries throughout Europe, the Middle East, Africa, Asia and the Pacific and the Americas. See the company’s web site http://www.dtz-ugl.com/content/corporate-Information.

  4. This data is up to 2011 because of the discontinuity of DTZ index data. The study of China’s housing markets, and even the world housing markets is restricted to the time span of the data. The studies of China’s housing markets often use the data from the National Bureau of Statistics of China. This data series started from 1998 (“Dreger and Zhang 2011” is cited in text but not given in the reference list. Please provide details in the list or delete the citation from the text. Dreger and Zhang 2011; Wu, et al. 2012) while Chinese housing markets prices increased sharply at that time. However, the data series do not go through an overall cycle. Therefore, it is difficult to determine whether there is a bubble in the markets (Wu, et al. 2012). Fortunately, from Fig. 1 in this paper, we can see intuitively that our data series has experienced both the increase and the decrease, and go through an overall cycle.

  5. Campbell et al. (2009) find that the average rental yield (i.e., the house rent-price ratio) in a dozen of U.S. cities from 1975 to 2007 is 6.03 %, that is to say, the price-to-rent ratio is 16.58.

  6. We construct the benchmark model of VAR system which contains three state variables, the same as Brunnermeier and Julliard (2008) and Hayunga and Lung (2011). What’s more, their structural VAR system variables contained inflation, because they need to test the inflation illusion assumption in the housing markets.

  7. Hiebert and Sydow (2011) set up a VAR system which contains the growth rate of per capita disposable income as the macroeconomic state variable. Campbell et al. (2009) set up VAR system which contains growth rate of per capita disposable income, employment growth rate and population growth rate as the macroeconomic state variables. Engsted and Pedersen (2014) set up a VAR system which contains GDP growth, unemployment, and the spread between short-term and long-term interest rate as the macroeconomic state variables. In this paper, three macroeconomic variables data in the four cities are taken from the bureau of statistics web site in each city, but most of them are the annual data. In order to unify analysis, this paper refers to Campbell et al. (2009), assuming that the annual growth rate of per capita disposable income, the annual growth rate of employed population and the annual growth rate of population in the four quarters of the year is fixed.

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Acknowledgments

We thank Meijie Zhu, Mengsi Luo and two anonymous referees for very helpful comments.

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Authors and Affiliations

  1. College of Economics and Management, South China Agricultural University, Guangzhou, China

    Renhe Liu & Jiaqi Lv

  2. Department of Building and Real Estate, The Hong Kong Polytechnic University, Hong Kong, China

    Eddie Chi-man Hui

  3. Guiyang Central Sub-Branch, The People’s Bank of China, Beijing, China

    Yi Chen

Authors
  1. Renhe Liu
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  2. Eddie Chi-man Hui
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  3. Jiaqi Lv
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  4. Yi Chen
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Corresponding author

Correspondence to Jiaqi Lv.

Additional information

Renhe Liu acknowledges the financial support of the National Social Science Foundation of China (No. 14BJY186) and the Philosophy and Science Foundation of Guangdong Province of China (No. GD12CYJ07).

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Liu, R., Hui, E.Cm., Lv, J. et al. What Drives Housing Markets: Fundamentals or Bubbles?. J Real Estate Finan Econ 55, 395–415 (2017). https://doi.org/10.1007/s11146-016-9565-0

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