Housing Markets in China and Policy Implications: Comovement or Ripple Effect

• Published date: 01 November 2014
• Author: Shu‐hen Chiang
• DOI: http://doi.org/10.1111/cwe.12094
• Date: 01 November 2014

103

China & World Economy / 103–120, Vol. 22, No. 6, 2014

©2014 Institute of World Economics and Politics, Chinese Academy of Social Sciences

Housing Markets in China and Policy Implications:

Comovement or Ripple Effect

Shu-hen Chiang *

Abstract

The overheated housing market has recently become a top priority of the Chinese authorities

and whether the ripple effect exists is key to understanding this housing issue. The present

paper uses a cointegration estimation technique for six first-tier Chinese cities during the

2003–2013 period to show that the comovements among housing prices in China are fully

reflected in a long-run equilibrium. Using the Toda–Yamamoto causality test, the ripple

effect is found to be characterized by a lead–lag relationship. More importantly, it is found

that Beijing is the main source of housing price appreciation in China, and should be

targeted as the regulatory object to efficiently resolve the troubles in this increasingly high

housing-price era.

Key words: causality test, housing prices, long-run equilibrium, ripple effect

JEL codes: O18, R30, R58

I. Introduction

The concept of “There is land, there is wealth” is deeply rooted in Chinese minds. Coinciding

with a lack of diversified investment instruments, spending on housing in China has grown

even more rapidly than economic growth since the late 1990s, on the back of a series of

housing privatization reforms (Chen et al., 2011b). Even with the emergence of the subprime

mortgage crisis in the USA and the subsequent global financial crisis, housing prices in

Chinese cities continued to increase sharply and repeatedly hit new highs. According to

Wu et al. (2012), China is experiencing much greater housing price appreciation than that

*Shu-hen Chiang, Associate Professor, Department of Finance, Chung-Yuan Christian University, Chug-li,

Taiwan. Email: shchiang@cycu.edu.tw. The author is indebted to Shang-hsien Chiang, his father and the

rest of his family for inspiring his research. The author also wishes to thank Pei-shan Chien for her

assistance. Finally, financial support was provided for this study by the Ministry of Science and Technology

in Taiwan (NSC 102-2410-H-033-030-MY2).

104 Chiang Shu-hen / 103–120, Vol. 22, No. 6, 2014

©2014 Institute of World Economics and Politics, Chinese Academy of Social Sciences

experienced during the US housing boom from 1995 to 2006. Therefore, China ’s housing

issue has received much domestic and global attention in recent research.

What is the “ripple effect”? Past studies have often failed to provide a clear definition

of the ripple effect. To clarify the concept of the ripple effect, we refer to a similar term,

“comovement,” which is widely applied in social science. Comovement can be classified

into two types: lead–lag and contemporaneous relationships. The ripple effect, occurring

in a lead–lag manner, begins with the spillover of housing prices in a specific region to

other regions and it eventually leads to comovement among local housing prices in the

long run. Because the existence of the ripple effect may be critical to housing policy in

China, it is important to consider whether regional housing price diffusion is occurring in

China. If the ripple effect exists in China, we may be able to identify the origin of local

housing price appreciation and control the real estate market in the source region, rather

than that of all cities or regions. This method, of applying “the right cure for the disease,”

could improve the efficiency of current housing price policies. In contrast, if pure

(contemporaneous) comovements among local housing markets are determined,

macroeconomic control of housing markets is justified.

As mentioned above, following two steps we examine six first-tier cities located in different

regions to study a unique Chinese housing phenomenon. First, we set up a vector error

correction model (VECM) and find one long-run equilibrium relationship, which is totally

manifest in the manner of the comovements among these cities. Second, the outcome of the

Toda–Yamamoto causality test reveals that Beijing, China’s center of political power, is the

main source of housing price diffusion; similar results are found from innovation accounting.

There is evidence in plenty to prove the existence of the ripple effect in China ’s housing

markets, and our results lead us to make certain policy suggestions: for instance, the authorities

should place special emphasis on how best to control housing prices in Beijing.

The remainder of this paper is organized as follows. Section II reviews research on

housing price diffusion and ripple effects. Section III outlines the dynamic framework of a

regional housing price diffusion model and an econometric method. In Section IV, the housing

price data for six mega cities in China are described and unit root tests are applied. Section V

first develops a cointegration estimation technique to detect the interaction among these six

cities in the long run and then uses the Toda–Yamamoto causality test to search for the origin

of housing price fluctuations. The relevant economic implications are discussed.

II. Literature Review

Over the past few decades, a considerable number of studies have been published on