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Time-Varying and Spatial Herding Behavior in the US Housing Market: Evidence from Direct Housing Prices

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Abstract

This paper investigates herding behavior in the US residential housing market. The sample period is 1975 M01 to 2015 M06. The study utilizes the housing price index of each of the 50 states and Washington DC to form nine census region-based markets, or portfolios and then employs switching and quantile regressions to examine the spatial and time-varying disparities of housing return dispersions and investors’ herding behavior. The study finds that the degree of herding varies across regimes, regions and conditional distributions. The regime-specific herd formation may be partially originated by extreme housing market conditions, bull and bear housing market conditions, uncertainty in national financial markets, economic recessions and uncertainty of economic policies. The bull housing markets exhibits stronger effects on return dispersion than down markets, which is consistent with the “flight-to-safety” consensus behavior of investors. The study also finds that positive and negative linear and nonlinear returns magnify dispersions in an asymmetric manner. The increase in co-movement and interdependence of state and regional-level housing markets returns among geographically diverse states and regions offer little hope of successful geographical portfolio diversification strategies for U.S housing market investors. Moreover, time-invariant modeling may yield incorrect inferences regarding herd formation in regional housing markets.

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Notes

  1. Market participants can be demarcated into informed and uninformed participants. Informed participants may comprise professional real estate market analysts, real estate surveyors/appraisers, professional real estate traders and real estate investment trusts-REITs fund managers. They possess very accurate information and their participation in the housing market equals revelation of private signals. Uninformed participants consist of non-professional real estate market investors such as households who invest their savings in the housing market and individuals who buy homes for speculative purposes. This group possesses low-quality or noisy information.

  2. These may include hedgers, speculators, arbitrageurs, institutional and retail investors, Equity fund managers, Financial Analysts stock dealers.

  3. See studies by Christie and Huang (1995), Chang et al. (2000), Gleason et al. (2004), Demirer and Kutan (2006), Tan et al.(2008), Goodfellow et al. (2009), Chiang et al. (2010), Chiang and Zheng (2010) and Economou et al. (2011), inter alia.

  4. Studies by Harrison (2010) and Foldvary (1997) show that during economic recession, the prices of land are lowest. The reduction in interest rate (through monetary policy interventions) to spur economic expansion reduces the cost of borrowing, increases demand for land (and of course the price of land) and residential houses. The upshot of this trend is rapid burgeoning of construction and economic activities.

  5. See also the influential paper by Carlino and DeFina (1998) which elaborates why regional housing markets respond differently to monetary shocks.

  6. See http://www.freddiemac.com/finance/fmhpi/archive.html.

  7. Although it may be argued that the formation of equally-weighted portfolios or regions may skew our results due to different sizes of state-level housing markets, we could not find a reliable weighting variable. Past studies such as Chiang and Zheng (2010) have used equally weighted economic sectors in the study of country-level herding behavior in the stock market.

  8. In this study, our markets refer to the nine regions for the purpose of investigating the herd formation behavior in the US housing market.

  9. For each structural break regression, the estimations are carried out using Newey and West (1987) heteroskedasticity and autocorrelation consistent (HAC) standard errors due to strong correlation among state returns and substantial variation in dispersions. We also use 15 % trimming of the observations in testing for the breaks.

  10. See Koenker (2005) for derivation of and details on quantile regressions.

  11. CCK (2000) use 1 % (99 %) and 5 % (95 %) return distributions to define lower (upper) tails. We also used 1 and 99 % returns distributions and our results remain qualitatively similar.

  12. Zero in this case means that no herding coefficient in the lower tails is statistically significant (See MAT and PAC regions) again reinforcing the dominance of herding behavior in the upper over lower tails of return distributions.

  13. We could also have used the S&P 500 volatility index (VIX) as a measure of uncertainty but the data are available from 1990 while our data run from 1975. To ensure that our results are not spurious, we tested for unit root of the ANFCI using ADF test. The test statistic was −3.679 against a critical value of −3.472 at 1 % significant level. Therefore, ANFCI is stationary.

  14. ANFCI is constructed by Chicago Fed. It is normalized to have a mean of zero. Unlike the national financial condition index (NFCI), ANFCI isolates a component of financial conditions uncorrelated with economic conditions to provide an update on financial conditions relative to current economic conditions. See https://www.chicagofed.org/research/data/nfci/background for additional details

  15. To spur revival of the housing market, the U.S government, through its various organs, took unprecedented steps and intervened in the housing market by injecting liquidity (additional capital) into Fannie Mae and Freddie Mac in 2008, creating a home-buyer tax credit program in 2009 and 2010, launching the Home Affordable Modification Program (HAMP) and implementing the Home Affordable Refinancing Program (HARP). Both HAMP and HARP were set up in March 2009. Even after taking these drastic steps, policy uncertainty remained high and the housing market failed to respond favorably.

  16. To ensure that our results are not spurious, we tested for unit root of the EPU index using ADF test. The test statistic was −4.547 against a critical value of −3.472 at 1 % significant level. We reject the null of unit root.

  17. See www.PolicyUncertainty.com for details and data. EPU is also available for Germany, France, Italy, Spain and Europe.

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Acknowledgments

We are greatly indebted to Professor Emeritus John M. Dunaway for reading our manuscript, providing some valuable insights and redacting the original manuscript.

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Authors and Affiliations

  1. Stetson School of Business and Economics, Mercer University, 1501 Mercer University Dr, Macon, GA, 31207, USA

    Geoffrey M. Ngene & Daniel P. Sohn

  2. Department of Economics and Finance, University of New Orleans, New Orleans, LA, 70148, USA

    M. Kabir Hassan

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  1. Geoffrey M. Ngene
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Correspondence to Geoffrey M. Ngene.

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Ngene, G.M., Sohn, D.P. & Hassan, M.K. Time-Varying and Spatial Herding Behavior in the US Housing Market: Evidence from Direct Housing Prices. J Real Estate Finan Econ 54, 482–514 (2017). https://doi.org/10.1007/s11146-016-9552-5

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