A tale of two shocks: The dynamics of international real estate markets

• Date: 01 January 2020
• DOI: http://doi.org/10.1002/ijfe.1725
• Author: Ranadeva Jayasekera,Amanda Dahlström,Stelios Bekiros,Oskar Ege,Gazi Salah Uddin
• Published date: 01 January 2020

RESEARCH ARTICLE

A tale of two shocks: The dynamics of international real

estate markets

Stelios Bekiros

1

| Amanda Dahlström

2

| Gazi Salah Uddin

3

| Oskar Ege

3

|

Ranadeva Jayasekera

4

1

Department of Economics, European

University Institute, Villa La Fonte, Via

delle Fontanelle, 18, Florence I‐50014,

Italy

2

Transportstyrelsen, Norrköping 601 73,

Sweden

3

Department of Management and

Engineering, Linköping University,

Linköping SE‐581 83, Sweden

4

Trinity Business School, Trinity College

Dublin, University of Dublin, College

Green, Dublin 2, Ireland

Correspondence

Stelios Bekiros, Department of Economics,

European University Institute, Villa La

Fonte, Via delle Fontanelle, 18, Florence

I‐50014, Italy.

Email: stelios.bekiros@eui.eu

Funding information

Jan Wallanders and Tom Hedelius

Foundation

Abstract

We examine the major potential drivers of five international housing markets

utilizing a quantile regression approach. In particular, we investigate property

market dynamics during three variant market environments, namely, under

downward (bearish), normal (median), and upward (bullish) trending condi-

tions. Monthly data series for the United States, United Kingdom, Australia,

Singapore, and Hong Kong are analysed, in an attempt to quantify uncertainty

and detect trading patterns for the largest securitized real estate markets. We

find that the stock market volatility, measured by the “pushing factor”

VIX

S&P500

, provides agents with the most reliable and efficient information in

terms of predicting market returns during bear market conditions, whereas

“pulling factors”such as money supply, treasury yields, and unemployment

explain the main stylized facts, incorporating contagion and diverse endoge-

nous and exogenous shocks. Our work provides a richer understanding on

comovements in house prices, allowing policy makers to anticipate shocks in

global markets in a timely manner.

KEYWORDS

financial crises,housing market, quantile regression, uncertainty

JEL CLASSIFICATION

C15; C32; C58; G10; G17

1|INTRODUCTION

Following its origin at the U.S. sub‐prime crisis, vigorous

research has been carried out characterizing the value

and impact of the contagion in the worldwide property

market. The sub‐prime market crisis was a niche but

was followed by a growing part of the U.S. market, which

imploded after a period of falling residential prices. The

implosion spread through different channels until it had

evolved into the Global Financial Crisis of 2008

(Eichengreen, Mody, Nedeljkovic, & Sarno, 2012; Kang

& Liu, 2014). The spark for the sub‐prime market collapse

came in large from a lack of understanding of what fol-

lows large swings in property prices, with the assumption

hitherto being that property returns in different U.S.

states would exhibit less dependence during extreme

events. This was exactly the opposite of what happened

(Zimmer, 2015) as strong downward pressure in property

prices during 2007 spread among most U.S. states, coin-

cided with the increased of the level of market

integration.

There is plenty of evidence that property markets are

increasingly integrated, not only within countries but also

across borders (Kang, Uddin, Ahmed, & Yoon, 2018).

Received: 30 December 2017 Revised: 28 November 2018 Accepted: 21 March 2019

DOI: 10.1002/ijfe.1725

Int J Fin Econ. 2020;25:3–27. © 2019 John Wiley & Sons, Ltd.wileyonlinelibrary.com/journal/ijfe 3

This synchronization has been widely studied in recent

years, but the factors driving its dynamics are yet to be

properly understood (Hui & Chan, 2013; Liow, 2010;

Zhou, 2010). It is found that comovement increases dur-

ing times of high volatility, especially during bearish mar-

kets (Hatemi‐J, Roca, & Al‐Shayeb, 2014; Hui & Chan,

2013; Michayluk, Wilson, & Zurbruegg, 2006; Zimmer,

2015). Since the introduction of real estate investment

trusts in the 1990s, there has been a surge in property

investments as investors have been able to enjoy

favourable tax conditions, while also maintaining liquid-

ity. These trusts allow investors to speculate on property

prices without having to hold physical property. There

has been an expectation that property investment may

act as a diversification tool vis‐à‐vis stock market portfo-

lios under normal economic times. However, this premise

has been challenged as the effects of the global financial

crisis become better understood along with the applica-

tion of improved statistical models to available data. Even

if we assume the global financial crisis as a once‐in‐a‐

lifetime event, a recession caused by a housing crisis still

leads to a deeper and more prolonged drop in GDP than a

recession sprung from a normal business cycle downturn

(Aßmann, Boysen‐Hogrefe, & Jannsen, 2013). The above

evidence in corroboration with an increasing interna-

tional property market synchronization requires a thor-

ough investigation of the causes and dynamics of falling

property prices. This is the subject of the present study.

In the past decade, a plethora of new evidence

emerged particularly vis‐à‐vis the integration among dif-

ferent markets. It has become clear that the comovements

of international markets have been increasing and that

there are strong asymmetric responses in the return vola-

tilities, all the more so in negative market conditions.

However, despite the apparent presence and importance

of asymmetries in volatility spillovers, few articles explore

the driving factors that explain the fluctuations of prop-

erty returns. Most studies use the real estate investment

trusts as a proxy for property prices, applying one of the

five general methodologies commonly used to study

comovements and market integrations, namely, causality,

copulas, GARCH modelling, ordinary least square (OLS)

significance, and bootstrapping. Liow (2008) used simple

Johansen linear co‐integration and Granger causality

tests to investigate the long‐and short‐run relationships

between the United States, United Kingdom, and eight

Asian real estate markets, before, during, and after the

Asian financial crisis. They found that integration

between these markets increased during the Asian finan-

cial crisis and this market integration was persistent in

the following decade. Zhou (2010) and Liow (2010)

arrived at similar conclusions and demonstrated increas-

ing integration between international property markets.

Zhou (2010) used a wavelet analysis that also captured

the direction of causality shifts depending on the fre-

quency and time observed. Liow (2010) utilized a

dynamic conditional correlation model to detect the

increasing integration between international securitized

real estate markets and stock markets. However, this lat-

ter cross‐asset integration is weaker than the integration

between international stock markets.

The conditional volatility of real estate returns has

been also studied extensively, for example, by Michayluk

et al. (2006), Liow (2007), Ho, Huynh, and Jacho‐Chávez

(2015), and Zimmer (2015). Using an asymmetric

dynamic conditional correlation model, Michayluk et al.

(2006) find that positive and negative news have a differ-

ent impact upon market returns. Zimmer (2015) and Ho

et al. (2015) reached the same conclusion using two cop-

ula models, vine‐and non‐parametric copula, whereas

Liow (2007) detected the same behaviour along with clus-

tering, predictability, and strong persistence in the condi-

tional volatility of regional and world real estate security

markets. Zimmer (2015) looked at the U.S. regional mar-

kets and found a clear pattern of asymmetric returns that

was previously believed to show little to no comovement.

During the bearish trend of the global financial crisis,

these markets all fell simultaneously. Utilizing OLS,

Kallberg, Liu, and Pasquariello (2014) established that

excess correlation is not as important factor as believed

when explaining increasing market integration among

different U.S. regions. Milunovich and Trück (2013)

detected a similar degree of dependence among different

property markets, both during crisis and noncrisis periods

via an EGARCH model. Liow (2012) identified increased

correlation and covariance during the global financial cri-

sis in the Asian‐securitized real estate markets. Liow

(2010) concluded that international market links have

been increasing over time but that integration between

stock markets has moved further than the one for real

estate market. Arestis and Gonzales‐Martinez (2016)

investigated how housing prices are affected by current

account balances, mortgage rates, and disposable income.

Aßmann et al. (2013) do not find evidence that a boom–

bust cycle in the construction sector contributes to a

housing crisis nor that a drop in private consumption

affected the costs of a housing crisis. They believed that

this cost has an asymmetric shape due to differences in

behaviour during rising versus falling house prices.

Lastly, Huang, Wu, Liu, and Wu (2016) demonstrated

that mortgage spreads and VIX are informative in

predicting real estate investment trust tail dependencies.

The purpose of this paper is to explore whether pulling

(internal) or pushing (external) factors best explain the

changes in conditional property market returns during

bearish (downward), median (normal), and bullish

4BEKIROS ET AL.