Modeling the time varying volatility of housing returns: Further evidence from the U.S. metropolitan condominium markets

• Author: Nicholas Apergis,James E. Payne
• Date: 01 January 2020
• Published date: 01 January 2020
• DOI: http://doi.org/10.1002/rfe.1063

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wileyonlinelibrary.com/journal/rfe Rev Financ Econ. 2020;38:24–33.

© 2019 University of New Orleans

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INTRODUCTION

The 2007–2008 global financial crisis certainly highlighted the importance of the housing market to the macroeconomy through

its impact on household wealth, the financial system, and the real estate and construction sectors. The collapse in housing prices

as a result also brought to the forefront the uncertainty associated with the volatility in housing prices and the transmission of

such volatility to other sectors of the economy. Indeed, the leverage effect attached to declining house prices contributes to an

increase in the debt to home equity ratio, thereby increasing the risk exposure associated with home ownership.1

Hence, the

greater house price volatility, the greater is the probability of negative home equity and mortgage foreclosure losses. As the

probability of large losses are greater in highly volatile periods than standard mean‐variance models would predict, the appro-

priate modeling of volatility is relevant for real‐estate portfolio management.

The presence of autoregressive conditional heteroskedasticity (i.e., ARCH effects) in condominium returns would indicate

there is a much higher risk of large losses for returns with volatility clustering during such volatile periods than standard mean–

variance analysis would predict. Thus, investors employing Value‐at‐Risk (VAR) models would be remiss to not identify and

model volatility clustering as any failure to do so could potentially lead to suboptimal portfolio management for investors in

metropolitan condominium markets. Moreover, the presence of volatility clustering in the metropolitan condominium markets

could have an impact on the local economy via wealth effects and the transmission of volatility shocks to mortgage markets

(i.e., mortgage backed bonds and insurance).

Received: 27 January 2019

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Revised: 19 March 2019

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Accepted: 24 March 2019

DOI: 10.1002/rfe.1063

ORIGINAL ARTICLE

Modeling the time varying volatility of housing returns: Further

evidence from the U.S. metropolitan condominium markets

NicholasApergis1,2

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James E.Payne3

1Department of Economics,University of

Piraeus, Piraeus, Greece

2University of Derby, Derby, UK

3Dean and Paul L. Foster and Alejandra

de la Vega Foster Distinguished Chair

in International Business,Department

of Economics and Finance,College of

Business Administration,University of

Texas at El Paso, El Paso, Texas

Correspondence

James E. Payne, Dean and Paul L.

Foster and Alejandra de la Vega Foster

Distinguished Chair in International

Business, Department of Economicsand

Finance, College of Business

Administration, University of Texas at El

Paso, El Paso, Texas 79968.

Email: jpayne2@utep.edu

Abstract

This study extends the literature on modeling the volatility of housing returns to the

case of condominium returns for five major U.S. metropolitan areas (Boston,

Chicago, Los Angeles, New York, and San Francisco). Through the estimation of

ARMA models for the respective condominium returns, we find volatility clustering

of the residuals. The results from an ARMA‐TGARCH‐M model reveal the absence

of asymmetry in the conditional variance. Dummy variables associated with the

housing market collapse unique to each metropolitan area were statistically insignifi-

cant in the conditional variance equation, but negative and statistically significant in

the mean equation. Condominium markets in Los Angeles and San Francisco exhibit

the greatest persistence to volatility shocks.

KEYWORDS

condominium returns, GARCH models, time‐varying volatility, U.S. metropolitan areas

JEL CLASSIFICATION

R30; C22