Bayesian analysis of Hong Kong's housing price dynamics

• DOI: http://doi.org/10.1111/1468-0106.12232
• Date: 01 August 2017
• Author: Michael Cheng,Ken Wong,Tommy Wu
• Published date: 01 August 2017

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SPECIAL ISSUE ARTICLE

Bayesian analysis of Hong Kong's housing price

dynamics

Tommy Wu | Michael Cheng | Ken Wong

Research Department, Hong Kong

Monetary Authority, Hong Kong

Correspondence

Ken Wong, Research Department,

Hong Kong Monetary Authority, 55/F,

Two International Finance Centre, 8

Finance Street, Central, Hong Kong.

Email: kcswong@hkma.gov.hk

Abstract

This paper uses a Bayesian vector‐autoregressive model

with sign restrictions to estimate the underlying drivers

of Hong Kong's housing price dynamics in the short

run. While existing studies are useful in analysing

housing valuation, little attention has been paid to the

short‐run dynamics. In contrast, the present paper

identifies short‐run drivers of housing prices using

structural identification with theoretical underpin-

nings. We find that among the shocks that we have

identified, bank lending shock and housing supply

shock were the main factors affecting Hong Kong's

housing prices. Low mortgage rates were another key

factor that led to the significant increase in housing

prices after the global financial crisis.

1|INTRODUCTION

Many economies experienced housing booms in the post‐global financial crisis (GFC) period.

Some prime examples are the real estate markets in London, Vancouver, Sydney and Hong Kong.

The surge in housing prices has attracted the attention of and created concerns among academics,

market participants and policy‐makers given the overheating risks in the housing market. As

such, many economies, particularly those in Asia, including Singapore and Hong Kong, have

introduced macro‐prudential measures to safeguard banking stability against risks stemming

from the housing market. As boom–bust cycles in housing prices would pose risks not only to

financial stability but also to the price stability of an economy, it is important to have a good grasp

of housing price dynamics to facilitate pre‐emptive policy‐making.

In this paper, we focus on the drivers of Hong Kong's housing price dynamics. The case of

Hong Kong is of particular interest due to the city's free market economy and full capital account

convertibility. Many studies have analyzed Hong Kong's property market, in light of its salient

Received: 26 January 2017 Accepted: 20 April 2017

DOI: 10.1111/1468-0106.12232

312 © 2017 John Wiley & Sons Australia, Ltd Pac Econ Rev. 2017;22:312–331.wileyonlinelibrary.com/journal/paer

features and importance to the economy (e.g. Hui, Cheung, & Pang, 2010; Chang, Chen, &

Leung, 2013; Leung and Tang, 2015a).

1

Nevertheless, most of these studies focus either on the

transmission of financial market fluctuations to the housing market, or on the micro features

using transaction‐level data. The present paper aims to fill the void by focusing on the macro

drivers of housing prices.

It is common to analyze housing price dynamics using the error correction framework

(e.g. Malpezzi, 1999; Capozza, Hendershott, & Mack, 2004; Beenstock & Felsenstein, 2010;

Leung, 2014).

2

In the case of Hong Kong, several studies (e.g. Leung, Chow, & Han, 2008; Craig

and Hua; 2011; Chung, 2012) have estimated error correction models (ECM) with a focus on the

long‐run determinants of housing prices, such as demographics, housing supply and other

fundamental variables. While useful in analysing housing valuation, the literature has paid

little attention to revealing the short‐run drivers of housing prices. The present paper adds to

the previous studies by identifying the role of different factors in affecting housing prices in the

short run.

We use a Bayesian vector autoregression (BVAR) model with sign restrictions to study the Hong

Kong housing market. The approach we take is similar to that in Towbin and Weber (2015), and we

modify the model specifications to suit the case of Hong Kong. We find that among the shocks that

we have identified, bank lending shocks and housing supply shocks were the main drivers of the

short‐run housing price dynamics in our sample period between 1996 and 2016. Low mortgage

rates were another key factor leading to the significant increase in housing prices after the GFC.

The rest of the paper is organized as follows. Section 2 discusses Hong Kong's housing market

development since the Asian financial crisis. Section 3 presents the BVAR model and discusses

the sign restrictions for shock identification. Section 4 presents the empirical results. Section 5

concludes and discusses policy implications.

2|HONG KONG'S HOUSING MARKET DEVELOPMENT

After a downturn between the Asian financial crisis in 1998 and the SARS outbreak in 2003,

Hong Kong's housing market has been on a long rally of more than 12 years. Hong Kong's

housing prices have increased nearly fourfold between its trough in 2003 and its peak in 2015,

despite occasional declines (Figure 1).

Several factors are likely to have contributed to the robust growth of housing prices in

Hong Kong. On the demand side, the solid growth in household income over the past decade

and the steady growth in the number of households have continued to drive housing need

(Figures 2 and 3).

1

Leung and Tang (2015a) find that the initial public offerings of Chinese firms in the Hong Kong stock market and Hong

Kong's housing market can improve the prediction of each other, pointing to the role of market sentiment as the driving

force. The regime‐switching models of Chang et al. (2013) indicate that the unexpected shock of US stock returns had the

most significant effect on HK asset returns and GDP. Hui et al. (2010) use a hierarchical Bayesian approach to value Hong

Kong's residential properties with reference to its micro features, and find that their model can outperform other valua-

tion methods that are based on average price‐per‐square‐feet or expert assessments.

2

Malpezzi (1999) proposes an ECM featuring housing price‐to‐income ratio and finds that the model can match the state‐

level of US data well. Capozza et al. (2004) proposes an ECM featuring some long‐run equilibrium housing prices and

find support for their model from US data. Leung (2014) builds a DSGE model that can produce reduced‐form dynamics

consistent with the ECM proposed by Malpezzi (1999) and Capozza et al. (2004). Beenstock and Felsenstein (2010) intro-

duce the spatial element into ECM of regional housing prices in Israel.

WU ET AL.313

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