Predicting Aggregate and State-Level US House Price Volatility: The Role of Sentiment

Authors

  • Rangan Gupta Department of Economics, University of Pretoria, Pretoria, 0002
  • Chi Keung Marco Lau Huddersfield Business School, University of Huddersfield, Huddersfield, HD1 3DH
  • Wendy Nyakabawo Department of Economics, University of Pretoria, Pretoria, 0002

DOI:

https://doi.org/10.6000/1929-7092.2020.09.05

Keywords:

Housing sentiment, housing market returns and volatility, higher-order nonparametric causality-in-quantiles test, overall and regional US economy.

Abstract

This paper examines the predictive ability of housing-related sentiment on housing market volatility for 50 states, District of Columbia, and the aggregate US economy, based on quarterly data covering 1975:3 and 2017:3. Given that existing studies have already shown housing sentiment to predict movements in aggregate and state-level housing returns, we use a k-th order causality-in-quantiles test for our purpose, since this methodology allows us to test for predictability for both housing returns and volatility simultaneously. In addition, this test being a data-driven approach accommodates the existing nonlinearity (as detected by formal tests) between volatility and sentiment, besides providing causality over the entire conditional distribution of (returns and) volatility. Our results show that barring 5 states (Connecticut, Georgia, Indiana, Iowa, and Nebraska), housing sentiment is observed to predict volatility barring the extreme ends of the conditional distribution. As far as returns are concerned, except for California, predictability is observed for all of the remaining 51 cases.

References

Ajmi, A.H., Babalos, V., Economou, F. and Gupta, R. (2014). Real estate market and uncertainty shocks: A novel variance causality approach. Frontiers in Finance and Economics, 2(2), 56-85.
Andersen T.G., and Bollerslev T. (1998). Answering the skeptics: yes, standard volatility models do provide accurate forecasts. International Economic Review, 39(4), 885-905.
https://doi.org/10.2307/2527343
André, C., Bonga-Bonga, L. Gupta, R., and Mwamba, J.W.M. (2017) Economic Policy Uncertainty, US Real Housing Returns and their Volatility: A Nonparametric Approach. Journal of Real Estate Research, 39(4), 493-513.
André, C., Gupta, R., and Muteba Mwamba, J.W. (2018). Are Housing Price Cycles Asymmetric? Evidence from the US States and Metropolitan Areas. International Journal of Strategic Property Management.
https://doi.org/10.3846/ijspm.2019.6361
Balcilar, M., Bekiros, S., and Gupta, R. (2017). The role of news-based uncertainty indices in predicting oil markets: a hybrid nonparametric quantile causality method. Empirical Economics, 53(3), 879-889.
https://doi.org/10.1007/s00181-016-1150-0
Balcilar, M., Demirer, R., Gupta, R., and Wohar, M.E. (2018b). Differences of opinion and stock market volatility: evidence from a nonparametric causality-in-quantiles approach. Journal of Economics and Finance, 42(2), 339-351.
https://doi.org/10.1007/s12197-017-9404-z
Balcilar, M., Gupta, R. and Kyei, C. (2018a). Predicting Stock Returns and Volatility with Investor Sentiment Indices: A Reconsideration using a Nonparametric Causality-in- quantiles test. Bulletin of Economic Research, 70(1), 74-87.
https://doi.org/10.1111/boer.12119
Balcilar, M., Gupta, R., Miller, S.M. (2015). The Out-of-Sample Forecasting Performance of Non-Linear Models of Regional Housing Prices in the US. Applied Economics, 47(22), 2259-2277.
https://doi.org/10.1080/00036846.2015.1005814
Balcilar, M., Gupta, R., Pierdzioch, C., and Wohar, M.E. (2018c). Terror Attacks and Stock-Market Fluctuations: Evidence Based on a Nonparametric Causality-in-Quantiles Test for the G7 Countries. European Journal of Finance, 24(4), 333-346.
https://doi.org/10.1080/1351847X.2016.1239586
Barros, C.P., Gil-Alana, L.A., and Payne, J.E. (2015). Modeling the Long Memory Behavior in U.S. Housing Price Volatility. Journal of Housing Research, 24(1), 87-106.
Bekiros, S., Gupta, R., and Kyei, C. (2016). A nonlinear approach for predicting stock returns and volatility with the use of investor sentiment indices. Applied Economics, 48(31), 2895-2898.
https://doi.org/10.1080/00036846.2015.1130793
Black, F. (1986). Noise. Journal of Finance, 41, 529–43.
https://doi.org/10.1111/j.1540-6261.1986.tb04513.x
Bonaccolto, G., Caporin, M., and Gupta, R. (2018). The dynamic impact of uncertainty in causing and forecasting the distribution of oil returns and risk. Physica A: Statistical Mechanics and its Applications, 507 (1), 446-469.
https://doi.org/10.1016/j.physa.2018.05.061
Bork, L., and Møller, S.V. (2015). Forecasting house prices in the 50 states using Dynamic Model Averaging and Dynamic Model Selection. International Journal of Forecasting, 31(1), 63-78.
https://doi.org/10.1016/j.ijforecast.2014.05.005
Bork, L., Møller, S.V., and Pedersen, T.Q. (Forthcoming). A New index of housing sentiment. Management Science.
Brock, W., Dechert, D., Scheinkman, J. and LeBaron, B. (1996). A test for independence based on the correlation dimension. Econometric Reviews, 15, 197–235.
https://doi.org/10.1080/07474939608800353
Campbell, J. Y. and Kyle, A. S. (1993). Smart money, noise trading, and stock price behaviour. Review of Economic Studies, 60, 1–34.
https://doi.org/10.2307/2297810
Case, K.E. Quigley, J.M., and Shiller, R.J. (2013). Wealth Effects Revisited 1975-2012. Critical Finance Review, 2(1), 101-128.
https://doi.org/10.1561/104.00000009
Chen, H. (2017). Real Estate Transfer Taxes and Housing Price Volatility in the United States. International real Estate Review, 20(2), 207 – 219.
De Long, J.B., Shleifer, A., Summers, L.G. and Waldman, R.J. (1990). Noise trader risk in financial markets. Journal of Political Economy, 98, 703–38.
https://doi.org/10.1086/261703
DeLong, J., Shleifer, A., Summers, H. and Waldmann, R. (1991). The survival of noise traders in financial markets. Journal of Business, 64, 1–19.
https://doi.org/10.1086/296523
Diks, C. G. H., and Panchenko, V. (2005). A note on the Hiemstra-Jones test for Granger noncausality. Studies in Nonlinear Dynamics and Econometrics, 9(2), 1-7.
https://doi.org/10.2202/1558-3708.1234
Diks, C. G. H., and Panchenko, V. (2006). A new statistic and practical guidelines for nonparametric Granger causality testing. Journal of Economic Dynamics and Control, 30(9-10), 1647-1669.
https://doi.org/10.1016/j.jedc.2005.08.008
Dolde, W., and Tirtiroglue, D. (2002). Housing Price Volatility Chan- ges and Their Effects. Real Estate Economics, 30(1), 41-66.
https://doi.org/10.1111/1540-6229.00029
Engsted, T., and Pedersen, T.Q. (2014). Housing market volatility in the OECD area: Evidence from VAR based return decompositions. Journal of Macroeconomics, 42, 91-103.
https://doi.org/10.1016/j.jmacro.2014.07.005
Fairchild, J., Ma, J., and Wu, S. (2015). Understanding Housing Market Volatility. Journal of Money Credit and Banking, 47(7), 1309-1337.
https://doi.org/10.1111/jmcb.12246
Gupta, R. (2018). Manager Sentiment and Stock Market Volatility. Working Paper No. 201853, University of Pretoria, Department of Economics.
Henderson, J.V. and Ioannides, Y. (1987). Owner Occupancy: Consumption vs. Investment Demand. Journal of Urban Economics, 21(2), 228–41.
https://doi.org/10.1016/0094-1190(87)90016-7
Hiemstra, C., and Jones, J. D. (1994). Testing for linear and nonlinear Granger causality in the stock price-volume relation. Journal of Finance, 49 1639–1664.
https://doi.org/10.1111/j.1540-6261.1994.tb04776.x
Jeong, K., Härdle, W.K., and Song, S. (2012). A consistent nonparametric test for causality in quantile. Econometric Theory, 28(4), 861-887.
https://doi.org/10.1017/S0266466611000685
Li, K-W. (2012). A study on the volatility forecast of the US housing market in the 2008 crisis. Applied Financial Economics, 22(22), 1869-1880.
https://doi.org/10.1080/09603107.2012.687096
Lo, A. (2004). The Adaptive Market Hypothesis: Market Efficiency from an Evolutionary Perspective. Journal of Portfolio Management, 30(5), 15–29.
https://doi.org/10.3905/jpm.2004.442611
Miles,W. (2008). Volatility Clustering in U.S. Home Prices. Journal of Real Estate Research, 30, 73-90.
Miller, N. and Peng, L. (2006). Exploring Metropolitan Housing Price Volatility. Journal of Real Estate Finance and Economics, 33(1), 5–18.
https://doi.org/10.1007/s11146-006-8271-8
Ngene, G., Sohn, D., and Hassan, M.K. (2017). Time-Varying and Spatial Herding Behavior in the U.S. Housing Market: Evidence from Direct Housing Prices. Journal of Real Estate Finance and Economics, 54(4), 482-514.
https://doi.org/10.1007/s11146-016-9552-5
Nishiyama, Y., Hitomi, K., Kawasaki, Y., and Jeong, K. (2011). A consistent nonparametric test for nonlinear causality - Specification in time series regression. Journal of Econometrics, 165, 112-127.
https://doi.org/10.1016/j.jeconom.2011.05.010
Nyakabawo, W., Gupta, R., and Marfatia, H.A. (Forthcoming). High-Frequency Impact of Monetary Policy and Macroeconomic Surprises on US MSAs and Aggregate US Housing Returns and Volatility: A GJR-GARCH Approach. Advances in Decision Sciences.
Plakandaras, V., Gupta, R., Gogas, P., and Papadimitriou, T. (2015). Forecasting the U.S. Real House Price Index. Economic Modelling, 45(1), 259-267.
https://doi.org/10.1016/j.econmod.2014.10.050
Rapach, D. E., and Zhou, G. (2013). Forecasting stock returns, Handbook of Economic Forecasting, Volume 2A, Graham Elliott and Allan Timmermann (Eds.), Amsterdam: Elsevier, 328–383.
https://doi.org/10.1016/B978-0-444-53683-9.00006-2

Shefrin, H. and Statman, M. (1994). Behavioral capital asset pricing theory. The Journal of Financial and Quantitative Analysis, 29, 323–49.
https://doi.org/10.2307/2331334
Shiller, R. (1998). Macro Markets: Creating Institutions for Managing Society’s Largest Economic Risks. New York, NY: Oxford University Press.
https://doi.org/10.1093/0198294182.001.0001
Soo, C.K. (2018). Quantifying Sentiment with News Media across Local Housing Markets. The Review of Financial Studies, 31(10), 3689–3719.
https://doi.org/10.1093/rfs/hhy036
Zhou, Y., and Haurin, D.R. (2010). On the Determinants of House Value Volatility. The Journal of Real Estate Research, 32(4), 377-396.

Downloads

Published

2020-01-29

Issue

Section

Articles