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

Rangan Gupta,Chi Keung Marco Lau,Wendy Nyakabawo

doi:10.6000/1929-7092.2020.09.05

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.

Highlights

The housing market plays an important role in the economy of the United States (US), since it constitutes a significant share of many households’ asset holding and net worth
We aim to extend the literature on housing market volatility by analyzing whether housing market sentiment drives variation in housing returns by drawing on the findings of recent studies related to the equity markets, which tend to show that investor and corporate manager sentiments predicts volatility of stock markets (Bekiros et al, 2016; Balcilar et al, 2018a, b; Gupta, 2018) in line with “noise traders” theory3, whereby market agents tend to make overly optimistic or pessimistic judgments and choices
Given the existing evidence that housing sentiment can predict returns, we use the k-th order causality-inquantiles test of Balcilar et al, (2017) for our purpose, since this methodology allows us to test for predictability for both housing returns and volatility simultaneously

Summary

Introduction

The housing market plays an important role in the economy of the United States (US), since it constitutes a significant share of many households’ asset holding and net worth. A growing number of studies have attempted to model and predict volatility (using univariate models and with econometric frameworks including wide array of factors) at the aggregate and regional (state and metropolitan statistical areas (MSAs)-levels) of the US (see for example, Dolde and Tirtiroglu (2002), Miller and Peng (2006), Miles (2008), Zhou and Haurin (2010), Li (2012), Barros et al, (2015), Ajmi et al, (2014), Engsted and Pedersen (2014), Bork and Møller (2015), Fairchild et al, (2015), André et al, (2017), Chen (2017), Nyakabawo et al, (forthcoming)) These studies highlight the role of information in macroeconomic, financial, and economic uncertainty related variables in predicting US housing market volatility. We use the housing sentiment index developed by Bork et al, (2017), which is constructed based on household responses to questions regarding house buying conditions from the consumer survey of the University of Michigan, to predict volatility of the aggregate US housing market, the 50 states, as well as that of the District of Colombia

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Conclusion