Urban Studies. Empirical Modelling of Regional House Prices and the Ripple Effect

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1 Urban Studies Empirical Modelling of Regional House Prices and the Ripple Effect Chien-Chiang Lee and Mei-Se Chien Urban Stud : 2029 originally published online 17 January 2011 DOI: / The online version of this article can be found at: Published by: On behalf of: Urban Studies Journal Foundation Additional services and information for Urban Studies can be found at: Alerts: Subscriptions: Reprints: Permissions: Citations: >> Version of Record - Jul 14, 2011 OnlineFirst Version of Record - Jan 17, 2011 What is This? Downloaded from usj.sagepub.com at NATL KAOHSIUNG NORMAL UNIV on June 30, 2014

2 48(10) , August 2011 Empirical Modelling of Regional House Prices and the Ripple Effect Chien-Chiang Lee and Mei-Se Chien [Paper first received, October 2009; in final form, July 2010] Abstract This paper investigates the stationarity properties and long-run relationship of Taiwan s regional house prices from 1993Q1 to 2009Q2. It applies the recent unit-root test of the panel seemingly unrelated regressions augmented Dickey Fuller (SURADF) test developed by Breuer et al. The empirical results illustrate that Taiwan s regional house prices are a mixture of stationary and non-stationary processes, showing that the stationarity properties of these prices are dependent on the structure and properties of the various regions. Secondly, the findings of the cointegration test provide substantive evidence for a long-run relationship among all regions except Taipei City, implying a diffusion of regional house prices among each regional market except Taipei City. Finally, the results of the weak exogeneity test indicate that uni-directional causality relationships exist for three regions Taipei County, Taoyuan-Hsinchu and Tainan- Kaohsiung towards Taichung. The regional house price efficiently diffuses itself among these regions, caused by a much smaller public housing sector and the pre-sale system in Taiwan. 1. Introduction Both researchers and government policy-makers have shown increasing interest and investigation over the past several years on the issue of regional house prices. This is principally due to the marked changes in asset prices, which have considerable influence on housing affordability. The Taiwan context offers a substantial challenge for empirical models of regional house prices, because geographical and regional economic conditions differ greatly in Taiwan, which are reflected in the value of housing prices. According to the 2006 census by Taiwan s Ministry of Interior, the average homeownership rate is over 87 per cent, which is one of the highest in the world. Because there are Chien-Chiang Lee is in the Department of Finance, National Sun Yat-Sen University, Kaohsiung, Taiwan, Kaohsiung, 804, Taiwan. cclee@cm.nsysu.edu.tw. Mei-Se Chien is in the Department of Finance, National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan. cms@cc.kuas.edu.tw Print/ X Online 2010 Urban Studies Journal Limited DOI: /

3 2030 CHIEN-CHIANG LEE AND MEI-SE CHIEN few investment instruments and housing s relevant opportunity cost is better than other assets in Taiwan, the nation s housing market offers a beneficial investment opportunity (Chen et al., 2007). To date, there are very few empirical studies that have investigated the interrelationship between regional house prices for Taiwan. We employ regional data controls for such national characteristics and focus on specific aspects of the economic and financial systems. We intend to see whether there is a pattern to Taiwan s housing market performance. As to the housing policy in Taiwan, the public housing sector is much smaller than the ownership sector and thus private housing developers have more freedom and face less competition from the state sector. More importantly, while home prices are more responsive to regional economic and demographic shocks instead of national shocks, a highlight at the regional level enables us to compare the dynamics in housing markets across regions (Kim and Bhattacharya, 2009). The behaviour of regional house prices has established a bright area of research in recent years (Holmes and Grimes, 2008). As to empirical studies on Taiwan s house prices, little attention is paid to examining the diffusion of regional house prices in different areas. Some studies have investigated housing prices and quality (Huang, 1999), some have examined the effects of economic variables on housing prices (Hsueh and Chen, 1998) and still others have applied the structural time-series model to examine housing prices (Chen, 2003). All of these papers did not investigate the spatial diffusions among Taiwan s regional house prices. Conversely, Chien (2010) only focuses on the ripple effect in regional/national house price ratios, but not the long-run equilibrium and lead lag relationship among regional house prices in different areas. Some studies in the literature have focused on housing dynamics through different theoretical approaches, including neighbourhood change, filtering, search, equity effects and urban growth. Empirical investigations of these models are uncommon due to the complexity of the models or a shortage of data. Some have studied the interdependence between housing and financial assets, such as Jud and Winkler (2002) and Riddel (2004), while another important line of empirical research refers to whether the disparities in housing prices occur irregularly or whether a ripple effect exists within regional house markets in the long run. The existing literature always discusses this subject as a convergence or divergence in regional house prices. Alexander and Barrow (1994) state that regional house prices are not considered by economic theory to present a common trend over time, but rather the migration of households from economic changes within regions causes the possibility of convergence in regional house prices. Meen (1999) also indicates that convergence exists if long-run equilibrium relationships occur between the regional house markets. Because housing is an immobile asset, property can cause regional house price variations to be persistent or show non-stationarity (Ashworth and Parker, 1997). Seeking explanations of the structure of regional house prices within the UK, some works apply Engle and Granger (1987) or Johansen (1988) cointegration tests to investigate the notion of a causal link between different regional house prices, but the conclusions drawn from these relative studies are diverse. Using standard house price models on regional data, MacDonald and Taylor (1993) and Alexander and Barrow (1994) demonstrate a long-run relationship between regional house prices, but within only either the north or the south of Britain. This finding prompted a weak segmentation of north/south housing markets in the UK. Conversely, using the error correction model (ECM), Ashworth and Parker (1997) cast doubt that different UK regions can adjust to housing price shocks together.

4 MODELLING HOUSE PRICES 2031 If a ripple effect or convergence is present, then the ratio is stationary between each regional price and the national house price (see Meen, 1999; Cook, 2005; Holmes and Grimes, 2008). Employing the augmented Dickey Fuller (ADF; Dickey and Fuller, 1979) unit-root test, the empirical results of Meen (1999) do not support stationarity in the regional national house price ratios for the UK. After revising the model with spatial correlations in housing prices, Meen (1999) achieves the reverse conclusion. Using the threshold regression model and allowing for the possibility of an asymmetrical adjustment process about the stationary attractor, Cook (2003) supports the stationarity of regional house price ratios in the UK. Cook (2005) applies a joint application of two unit-root tests the DF-GLS test of Elliot et al. (1996) and the KPSS test of Kwiatkowski et al. (1992) and the empirical results confirm the existence of stationarity in regional house price ratios throughout the UK. Few research studies investigate the spillover of housing price changes within neighbouring areas. Clapp et al. (1995) find evidence of spatial diffusion for housing price changes between neighbouring towns in Connecticut and around San Francisco, but there is none across non-neighboring towns. The empirical results of Dolde and Tirtiroglu (1997) also support the same results using GARCH-M models. Others investigate the ripple effect of a housing sub-market within a city. Ho et al. (2008) examine spatial ripple effects across different housing quality tiers within Hong Kong for the period 1987 to 2004, and the Granger causality tests show the argument that domino effects within a single housing market occur in response to external shocks. The main purpose of this paper is to investigate the stationarity properties and long-run relationship of Taiwan s regional house prices with quarterly data over 1993Q1 to 2009Q2. To examine the stationarity of these prices, we apply the newest panel seemingly unrelated regressions augmented Dickey Fuller (Panel SURADF) test developed by Breuer et al. (2001), which allows us to account for possible cross-sectional effects and to identify how many and which regional house prices within the panel contain a unit root (nonstationary). We proceed by measuring the half-lives and the corresponding confidence intervals when regional house price stationarity is confirmed. Thirdly, for regional house prices in which the series are integrated of degree one (I(1)), we use the cointegration method to evaluate whether these regional house prices are cointegrated or segmented, indicating market integration (convergence) or segmentation (divergence) in the prices (MacDonald and Taylor, 1993). Finally, we implement the weak exogeneity test to investigate the causality relationships and examine the ripple effects among different regional house prices. The remainder of the paper proceeds as follows. Section 2 outlines and discusses Taiwan s housing market and regional economic development. Section 3 describes the methodology. Section 4 presents the empirical findings and section 5 summarises the conclusions that are drawn. 2. Taiwan s Housing Market and Regional Economic Development Real estate is enormously important in Taiwan due to people s belief in the traditional idea of land is wealth. According to the 2006 census of Taiwan s Ministry of Interior, the average homeownership rate is over 87 per cent, which is one of the highest in the world. Moreover, the average housing unit vacancy rate is 17.6 per cent, which is far above the average of 3 5 per cent in other countries. Taiwan s space consumption reached 42 square metres per person in In terms of living space, Taiwan has surpassed many advanced countries for instance, the UK, Singapore and Japan (Yip and Chang, 2003; Department of Statistics, 2000).

5 2032 CHIEN-CHIANG LEE AND MEI-SE CHIEN With a population of 23 million and an area of square kilometres, Taiwan is one of the most densely populated areas in east Asia. Taiwan s population migration to urban areas has been considerable since the 1970s. The main metropolitan areas are shown in Figure 1 and are introduced as follows. First, the northern region includes Taipei City the capital and most important economic centre and its two surrounding areas of Taipei County and Taoyuan-Hsinchu. Taipei City residents amounted to 2.6 million in 2009 and there Taipei City Taipei County Taoyuan- Hsinchuu Taichung Tainan-Kaohsiung km Figure 1. The main metropolitan areas in Taiwan.

6 MODELLING HOUSE PRICES 2033 are 3.9 million in Taipei County and 2.5 million in Taoyuan-Hsinchu. Next, Taichung is in the central region and Tainan-Kaohsiung is in the southern region respectively containing around 2.6 and 3.8 million residents. The expansion of these three main areas in Taiwan has been subject to economic development and changing global circumstances. In the 1960s and 1970s, Taiwan s spatial economy was characterised by a bipolar concentration, with one core of the southern area at Kaohsiung with Taiwan s largest seaport and home to petrochemical and heavy industries, and the core of the northern area the capital of Taipei, which has since become a highly diversified regional economy with strong political and corporate power (Lin and Liaw, 2000). At the same time, a smaller export processing zone (EPZ) near Taichung was established in 1969, leading to the industrialisation of that city. Although Taichung and Kaohsiung took advantage of fast industrialisation starting in the 1960s, the traditional manufacturing industries in these two areas began to lose their competitiveness in the 1990s. Conversely, at the same time Taipei City upgraded itself as the node of a high-technology knowledge centre with the status of a regional global city (Wang, 2003). In Taipei, there are many new jobs which are created from many corporate headquarters and advanced service industries. Geographical and regional economic conditions differ much in Taiwan, which is reflected in the value of housing prices. Table 1 shows the average house price in these areas. In the fourth quarter of 2008, Taipei City s average house price was NT$ and at least 50 per cent higher than the other areas, reflecting the relative economic gains of Taipei City, coupled with an inelastic housing supply. Kaohsiung s average house price was NT$ , the lowest in all regions, which also shows the loss of economic competition there being attributed to the shocks of economic restructuring and globalisation in the 1980s. As to Taiwan s housing policy, the public housing sector is much smaller than the ownership sector, providing rental and owneroccupied housing to approximately 8 per cent of all households only (Chiu, 2010). Because the housing policy is heavily skewed towards homeownership, applying subsidised mortgage loans came into play as an alternative to direct provisions and started in As the level of housing intervention is relatively low in Taiwan, private housing developers have more freedom and face less competition from the state sector (Yip and Chang, 2003). 1 In other words, Taiwan s government acts with a much smaller role in the housing sector, which makes the housing markets operate on free market principles. Table 1. Average house price and the ratio of house price to income Area Average house price (NT$) Ratio of house price to income (percentage) Taipei City Taipei County Taoyuan-Hsinchu Taichung Kaohsiung Taiwan area Source: Housing Demand Survey for Fourth Quarter 2008, the Institute for Physical Planning and Information.

7 2034 CHIEN-CHIANG LEE AND MEI-SE CHIEN Another special feature of Taiwan s housing market is the pre-sale system. Under the country s rapid economic development, the pre-sale system has been introduced to establish more efficient markets where private property can be created. Developers sell their property units before construction is started so as to get financing for their businesses and to reduce the risk of building something that remains empty. One characteristic feature of this system is its similarity to a forward or futures deal (Chang and Ward, 1993), which can improve the efficiency of the housing market. 3. Methodology 3.1 Panel Unit-root Tests Recent developments in panel unit-root testing include refinements made to the Levin et al. (2002) test (LLC), the Breitung (2000) test, the Im et al. (2003) test (IPS), the Fishertype ADF, the Phillips Perron test (see Choi, 2001 and Maddala and Wu, 1999) and the Hadri (2000) test. The LLC (2002) test is based on the ADF test, but in a panel setting the model is expressed as follows: y = α + β y + ρ y + e it i i it, 1 ij it, j it j= 1 (1) where, b i is restricted such that it is identical across regions; y it (i = 1, 2,..., N; t = 1, 2,..., T) is the house price series of panel member regions i in period t; p i is the number of lags in the ADF regression; and the error terms e it are assumed to be independently and normally distributed random variables for all i s and t s with zero means and finite unit specific variances, σ i 2. The IPS (2003) test relaxes the assumptions of the LLC (2002) test by allowing b of equation (1) to vary across units under the alternative p i hypothesis. It is also more general in the sense that it allows for heterogeneity in the autoregressive coefficients for all panel members. However, Breitung (2000) shows that the IPS test can suffer from a loss of power due to bias correction when individual specific trends are included. He proposes an alternative test without bias adjustments and shows that it has higher power than the IPS test. In order to have a more powerful test, Hadri (2000) argues that one should have stationarity under the null hypothesis, and it should be reversed in order to have a stronger power test. An alternative approach to the panel unit-root tests uses Fisher s (1933) results to derive tests that combine the p-values from individual unit-root tests. Maddala and Wu (1999) offer their alternative test based on the p-values of the separate Dickey Fuller tests for each of the N cross-section units. Breuer et al. (2001, 2002) develop a panel unit-root test that is based on the augmented Dickey Fuller (ADF) regression estimation in a seemingly unrelated regressions (SUR) framework and then test for an individual unit root within the panel members. This procedure has several advantages. First, these multivariate tests use the information content in the variance covariance matrix, so that the unrealistic assumption of crosssection independence made in the panel tests can be avoided. Secondly, the estimation tests also allow for an important degree of heterogeneity in the lag structure across the panel members, in that the lag order of the augmented test varies among the individuals and the autoregressive parameter also differs for every cross-section. Thirdly, the panel SURADF unit-root test allows us to identify how many and which members of the panel contain a unit root. The unit-root test of the panel SURADF for N regions and T time-periods is based on the system of ADF equations which can be represented as:

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