Predicting Aggregate and State-Level US House Price Volatility: The Role of Sentiment
Rangan Gupta (),
Chi Lau () and
Wendy Nyakabawo ()
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Chi Lau: Huddersfield Business School, University of Huddersfield, Huddersfield, United Kingdom
Wendy Nyakabawo: Department of Economics, University of Pretoria, Pretoria, South Africa
No 201866, Working Papers from University of Pretoria, Department of Economics
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 2014:3. 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.
Keywords: Housing sentiment; housing market returns and volatility; higher-order nonparametric causality-in-quantiles test; overall and regional US economy (search for similar items in EconPapers)
JEL-codes: C22 C32 C53 E7 R3 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-rmg and nep-ure
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