Spatio-temporal diffusion of residential land prices across Taipei regions
© Nanda and Yeh; licensee Springer. 2014
Received: 22 July 2014
Accepted: 13 August 2014
Published: 8 September 2014
Past studies have shown that changes in the house price of a region may transmit to its neighbouring regions. The transmission mechanism may follow spatial and temporal diffusion processes. This paper investigates such regional housing market dynamics and interactions among local housing sub-markets in Taipei. The analysis is based on a panel data framework and spatial panel models using annual data on median residential land prices from 41 Taipei sub-markets over the period from 1992 to 2010. The empirical analysis suggests that spatial dependence plays a significant role in interactions among regional housing markets. The results are strongly robust across several model specifications and regions controlling for time fixed effects and space-time covariance. These findings have significant implications for urban spatial planning and efficient use of public resources in mega-urban areas.
C21; C23; R12; H50
KeywordsResidential land price Ripple effect Panel data Spatial autocorrelation
House prices vary over space and time. Due to growing integration across regional economies, house price shocks to the central area are likely to propagate to the surrounding areas and then reach the peripheral regions. This is termed as the ‘ripple’ effect in the literature. A large volume of literature has focused on the ripple effect or diffusion of house prices and many studies have revealed significant lead-lag relations among movements of house prices in neighbouring regions. The efficient market hypothesis (EMH) implies that two equivalent assets should have the same market prices. However, housing markets are far from being fully efficient due to high transaction costs, infrequent transactions, lumpy investments and a high degree of heterogeneity in sub-market characteristics. Therefore, there may exist significant arbitrage opportunities across regional housing markets. As Pollakowski and Ray (1997) suggest, there is a ‘positive feedback effect’ implying that the house price in one region is not only determined by its own lagged prices but also by the adjacent regions’ lagged house prices.
This paper aims to investigate the dynamics of inter-urban house prices, especially to what extent the housing sub-markets converge or diverge over time. This paper uses a panel data framework and dynamic spatial panel data models, which combines spatial autocorrelation with panel data to estimate correlations of housing prices among different areas over time. We test several competing specifications and samples.
This paper makes three main contributions as follows. First, although several papers have examined the house price diffusion process in developed country set-up, the evidence from Asia is rather scant. The Asian experience may be quite different and interesting given the rapid urbanization and prevalence of mega-urban regions. This paper offers a rigorous analysis of a major Asian market (Taipei). Second, although previous studies have dealt with varying levels of methodological complexities and rigour, several sources of estimation biases still remain and those can significantly undermine much of the findings. In this paper, we employ latest methodological innovation in space-time panel models to study diffusion process of the residential land prices in Taipei. The application of this method brings out several interesting relationships while addressing several serious estimation issues, which may not have been revealed through the use of standard techniques. Third, the results not only reveal interesting spatial pattern of house prices, but also show causal relationships across specific local housing markets. The findings show importance of centralised economic development in influencing residential land values.
An explanation for inter-linkages of regional house prices may be put forward by focusing on the neighbourhood effects. House prices are not only affected by the characteristics of the property but also by the surrounding neighbourhood attributes. Positive changes in characteristics in nearby structures would create a positive feedback effect on the market values of the houses, thereby creating a spatial dependence (Can 1990). Ioannides and Zabel (2003) point out that the impact of neighbourhood effects can lead to changes in housing demands because these factors may affect consumers’ housing location choices.
Moreover, the possibility of substitution across areas might lead to lead-lag movements in house prices. In market-oriented economies, land values and land uses can be bid by potential users, and the property rights can be assigned accordingly. These characteristics would be an incentive to redevelop land leading to changes in highest and best land use. Therefore, land values in central locations represent willingness to pay and are determined by saving in travelling cost (Bertaud and Malpezzi 2003). However, higher house prices in central areas might cause urban expansion or urban sprawl, which in turn, could raise house prices in suburban and peripheral areas. High house prices in the central regions lead to higher demand for cheaper houses or larger residential space in suburbs and peripheral areas. This increase in the demand for suburban housing could push up the house prices over time.
While the local market demand-supply interactions and imbalances may primarily determine house price movements (Canarella et al. 2012), the interregional transmission of shocks in house prices also plays an important role in co-movement of house prices (Meen 1999). There are also considerable empirical evidences showing spatial dependence of regional house prices across a number of countries. In simple terms, such inter-relations among housing markets within a close proximity are driven by a multitude of economic and demographic factors e.g. local economic development, infrastructure improvement, migration patterns, cost of housing, urban form, active government policies and urban spatial planning. A clear understanding of these diffusion processes are important for purposes of policy making at the regional and local government levels and also, for a more efficient use of public resources. In this paper, our aim is to understand the diffusion processes of residential land prices in Taipei, a major metropolitan area in Taiwan.
Since Taipei plays a dominant role in the national economy, it is worth investigating movements in Taipei’s housing market and whether the ripple effects across Taipei regions exist. Moreover, housing markets cannot be seen as being aggregated into one national or regional market because of various circumstances. For instance, characteristics of different land uses could generate different movements in local markets. Taipei area, being highly dense and having restrictive zoning regulations, makes an interesting case study to examine land price diffusion process. Moreover, the Asian urbanization process appears to be unique in its dynamics and characteristics. There is now a growing literature around how the Asian urbanization process differs from standard developed country experiences. As McGee (1991) aptly pointed out that the Western paradigm of the urban transition is not applicable to the developing countries’ urbanization process and it has resulted in divergent patterns of urbanization. The Asian urbanization process is uniquely characterised by dominance of farm sector, rapid depopulation of rural areas, labour cost arbitrage, rural-urban income inequality, breaking down ‘friction of distance’ etc (McGee 2008). The lessons from studying Taipei area can be important in understanding dynamics of land prices in other big metropolitan areas, especially when all developing countries in the world are experiencing rapid urbanization. Almost 70 percent of the world population is going to live in urban areas by 2050 according to the United Nations Expert Group Meeting (UN/POP/EGM-URB 2008).
The structure of the paper is as follows: in Section 2, related theories and literature are briefly reviewed. Section 3 lays out the empirical framework with the data described in the following section. Section 5 presents our analysis and finally, Section 6 provides some concluding remarks.
2 Relevant literature
Explanations for co-movements of regional house prices are manifold, complex and could be drawn from social, economic and political linkages among regional territories. Meen (1999) provides four possible explanations that may lead to spillover effects among regional housing markets – inter-regional migration, equity transfer, spatial arbitrage and local economic developments. Jones and Leishman (2006) also indicate that ripple effects are partly caused by spatial arbitrage process through household migration between local housing markets. They believe that various determinants of house prices in local housing markets (i.e. regional heterogeneity) result in a multitude of spatial patterns, and as a result, it may influence household migration.
Dieleman and Wegener (2004) also note that good accessibility can expand new residential developments into suburban areas causing more urban sprawl. It is reasonable to assume that if increases in the demand for residential land in low density areas lead to greater urban sprawl, then these areas would exhibit more prominent presence of lead-lag relations in land values. In addition, Brueckner (2000) and O’sullivan (2009) argue that rising incomes may create more demand for larger spaces, and low commuting costs boost demand for space in distant locations where land is relatively cheap. This would cause urban expansion or urban sprawl. Couch and Karecha (2006) caution that in order to control urban sprawl, any restrictions imposed on the housing supply in those peripheral areas can only increase house prices leading to less affordability. These views are largely corroborated by the empirical findings in the literature. For example, Oikarinen (2006) points out that if regional housing markets can act as substitutes for each other such as core urban and surrounding areas, an increase in house price in the centre could raise house prices in the surrounding areas with a time lag.
International evidence on regional house price dynamics is substantial. Previous studies have variously explored the possibility of ripple effects across several countries. In the UK, MacDonald and Taylor (1993), Alexander and Barrow (1994) and Ashworth and Parker (1997) provide evidence of convergence between regional house prices over the long run. Studies in the US also report significant diffusion effects among different housing submarkets (Clapp and Tirtiroglu 1994; Tirtiroǧlu and Clapp 1996; Pollakowski and Ray 1997). Stevenson (2004) uses quarterly data from 1978 to 2002 to investigate the co-movements of housing price in Dublin, Cork, Waterford, Limerick Galway and Northern Ireland. The findings reveal that due to the centrality of Dublin in the Irish economy, the ripple effect occurs from the capital to contiguous regions and then to the peripheral areas. Oikarinen (2006) also supports these findings indicating existence of substantial lead-lag relations between house price changes in the main economic centre and surrounding regions of the Helsinki metropolitan area. Luo et al. (2007), applying data from 8 capital cities in Australia, find notable ripple effects across submarkets. They suggest that the Sydney house prices only have impact on Melbourne. House prices in Adelaide and Perth not only Granger cause house price movements in Melbourne but also influence Canberra, Brisbane, Hobart and Darwin housing markets. Shi and Hargreaves (2009) also provide similar evidence that specific economic conditions at the regional levels are associated with the ripple effects, and the ripple effects are likely to spread within inter-urban centres instead of between regional centres in New Zealand. More recently, Lee and Chien (2011) employing quarterly data from 1993 to 2009 in 5 main metropolitan areas in Taiwan, indicate that except for Taipei City, house prices in other main regions exhibit causal relationships. Gray (2012) provides evidence of district level spatial spillover of house price growth. Balcilar et al. (2013) studies South African market and provides strong evidence of the ripple effects across five markets. Lean and Smyth (2013) provide an interesting case study of the ripple effects across various house types in Malaysia.
The majority of the empirical studies discussed above were based largely on causality, co-integration tests or error correction modelling (ECM) framework to estimate the interdependences of regional house prices. However, with the recent developments in spatial econometrics, it is understood that spatial patterns may not only be significant but also can alleviate several sources of estimation biases, which however, has been largely ignored and rather under-explored in this area of research. As Anselin (1999) has discussed, ignoring spatial autocorrelation or spatial dependence could cause non-constant error variance, and the results may be misleading and substantially biased when estimation strategies use standard econometric approaches to spatial issues. Few recent studies have explored the spatial dimension of the issue of ripple effect. Brady (2011) using data from 31 California counties and Holly et al. (2011) employing data for 12 UK regions suggest that the ripple effects could be examined appropriately over time and space with due consideration given to spatial dependence. Brady (2011) applies spatial panel data with spatial impulse response functions, and shows how a shock to house prices can propagate through regions over time. The analysis of Holly et al. (2011) uses spatial and temporal approaches, and they find that transitions of shocks are derived from specific regions and spatial effects. Shocks to London would spread to other regions over time and space. It suggests that the shock effects from London to other regions would last much longer if the region is further away from London. However, Brady (2011) ignores the presence of space-time covariance which may lead to violation of the stability conditions. Debarsy et al. (2012) present a more general model based on earlier works by Anselin (2001), Yu et al. (2008) and Parent and LeSage (2012) that controls for space-time covariance as well as spatially lagged exogenous variables. In this paper, we apply several such specifications to test the hypotheses.
In summary, there has been a substantial body of research in co-movements of regional housing markets, and shows causal relationships between or within regions. However, limited attention has been paid to the significance of spatial effects across regional housing markets and examination of how such effects may influence house prices. The main contribution of the current paper is to explore spatio-temporal dimension of the diffusion process using Taipei region as a case study following a dynamic spatial panel data modelling framework.
In this study, we examine dynamic relationships in the house price movements in central and surrounding regions in Taipei. We apply several dynamic spatial-panel data methods to estimate how national and local conditions affect prices in housing sub-markets. We start with outlining the standard panel models following with a detailed discussion of the spatial models.
3.1 Standard panel data models
However, the estimator is inconsistent and biased in dynamic models by using LSDV method due to existence of correlations between lagged values of independent variables and residual terms (Roodman 2009). The bias would turn out to be worse when the autoregressive coefficient is high or the number of time periods is short. Therefore, we turn to dynamic panel modelling with controls for spatial correlation.
3.2 Dynamic panel-spatial model
where ρ is the coefficient of spatial autoregressive term and Wy jt is called a spatial lag as a weighted average of observations on the variable over neighbouring units. yit−1 is the lag of the dependent variable, ϕ the autoregressive time dependence parameter and w ij is the N × N spatial weight matrix. The spatial matrix W is pre-determined by contiguity, where the value of the spatial correlation is 1 if the region i and region j are neighbours, otherwise the value is 0. The spatial matrix is normalised with each row summing up to unity. The stability condition is: (|ρ| + | ϕ| < 1).
Due to the correlation between the spatial regressor w ij y jt and the error term, the estimation of standard fixed effects models could be inconsistent. There are several approaches suggested in the literature with varied levels of merits and demerits (for example, see Kuethe and Pede 2011; Beenstock and Felsentein 2007 for Vector Auto-regression approaches). There are two major methods - maximum likelihood (MLE) and instrumental variables or generalised method of moments (IV/GMM) approaches - that are used to deal with the spatial interactions. However, considering the complex moment conditions in GMM and a lack of a direct GMM estimator for the spatial dynamic-panel model, an instrumental variable approach within a two-stage estimation process has also been suggested in the literature (Brady 2011).
where ρ the spatial dependence parameter, ϕ the autoregressive time dependence parameter, and θ the spatio-temporal diffusion parameter. ϵ it is assumed i.i.d. across i and t with zero mean and constant variance. The stability condition is: (|ρ| + |ϕ| + |θ| < 1). However, this stability condition may be too restrictive in many cases (Elhorst 2012). However, a less restrictive condition may also be applied counting the negative values.
β k is the marginal implicit price, but the marginal implicit price of the SDM is [β k I + γ W](I − ρ W)− 1. The house price in location i could be affected by both of a marginal change of one housing characteristic in location i and marginal changes of housing in the other locations. The former is called the direct or own effect and the later an indirect or spillover effect. When both ρ and γ are equal to zero, the indirect effects do not exist. The indirect effects also known as spillover effects due to from an observation’s neighbourhood set, but the effect of x jk on y j is also zero if the element w ij of the spatial weights matrix is zero (Elhorst 2012). According to LeSage and Pace (2009), the direct effect could be estimated by the average of the diagonal elements, and the indirect effect measured by the average of the row sums of non-diagonal elements of the matrix.
In our estimation framework, we employ several specifications: (1) Brady (2011) SAR model; (2) Debarsy et al. (2012) SDM model without time effects; (3) Debarsy et al. (2012) SDM model with time effects; and (4) Debarsy et al. (2012) SDM model with time effects to calculate direct, indirect and total effects.
4 Data description
Figure 3 is the map of Taipei showing all 41 local areas that we analyse in this paper. We divide 41 Taipei local areas or housing sub-markets into five regions in Table 1, namely Central Taipei City (CT), the rest of Taipei City (RT), Satellite City (SC), Western Periphery (WP) and Eastern Periphery (EP) to investigate and compare the house price movements. We also combine these five regions into more coherent clusters according to administrative boundaries. Specifically, we combine CT and RT into a region-cluster; CT, RT, ST into a region-cluster; WP and EP into a region-cluster. The central Taipei city is the central geographic area in Taipei city, while the rest of Taipei city is referred to the other areas of Taipei city. Areas contiguous to Taipei city are named as Satellite city because these areas could be seen as extended areas of the Taipei city due to improved transportation and low commuting costs. The annual data is obtained for all 41 local areas in Taipei from 1992 to 2010. Therefore, in panel models, total number of observations is 738. In Central Taipei city, there are 5 local areas consisting of 90 observations; in the Rest of the Taipei city, there are 7 local areas consisting of 126 observations; in Satellite city, there are 11 local areas consisting of 198 observations; in the West Peripheral region, there are 7 local areas with 126 observations and in the East Peripheral region there are 11 local areas with 198 observations. Tables 2, 3 and 4 report descriptions of the variables, summary statistics and sprawl index respectively.
The trends of real residential land prices in Taipei City and New Taipei City are presented in Figures 1 and 2. It shows that the Taipei residential land prices declined gradually from 1991 to early 2000s due to weak economic performance. Land prices dropped to the lowest because of the SARS infection in 2003 and climbed steeply since 2004 with deregulation and low interest rate regime. The slowdown in 2008 resulted from the global financial crisis (GFC). However, due to a strong stock market performance, low tax and interest rate regime, and a stable political relationship between Taiwan and China, the land prices registered growth since 2009.
Regional housing markets in Taipei
Total 41 areas
Central Taipei city (CT)
SongShan (SO), XinYi (XY), DaAn (DN), ZhongZheng (ZE), ZhongShan (ZA)
Rest of Taipei City (RT)
DaTong (DT), WanHua (WH), Neihu (NH), NanGang(NG), WenShan (WS) Beithou (BA), Shilin (SL)
Satellite City (SC)
Sanchong (SC), Banciao (BC), Jhonghe (JH), Yonghe (YH), Sinjhuang (SU), Sindian (SN), Tucheng (TU), Lujhou (LO), Sijhih (SI), Danshui (DS), Shenkeng (SK)
Western Periphery (WP)
Bali (BL), Wugu (WG), Taishan (TA), Linkou (LK), Shulin (SH), Yingge (YG), Sansia (SA)
Wulai (WU), Shiding (SD), Pinglin (PL), Pingsi (PS),
Shuangsi (SS), Gongliao (GL), Reuifang (RF),
Jinshan (JS), Wanli (WL), Sanjhih (SJ), Shimen (SM)
Description of variable and data sources
Change in median residential land price/Natural log in median residential land price
Department of Land Administration
A weighted average of neighbour’s land prices in natural log
Change in income per capita/Natural log in income per capita
Financial Data Centre, Ministry of Finance
Change in density/Natural log in density
National Statistics Taiwan
Central Taipei City
Rest of Taipei City
Land price growth
Income per capita growth
Population density growth
Variation in sprawl index across regions
Central Taipei City
The rest of Taipei City
A number of explanatory variables including demographic, economic and accessibility conditions are suggested by the previous studies. As Jud and Winkler (2002) point out, population growth, changes in income, construction costs and interest rates are significant determinants of house prices. When there are increases in income, population due to international, inter-regional and intra-regional migration, the demand for housing will rise and thus push up the prices. They also find that house price movements are significantly influenced by location specific fixed effects. In addition, many studies have argued that the neighbourhood characteristics have strong influences on house prices. These neighbourhood variables include location of the house relative to public transportation, historic district, education and crime (Boyle and Kiel, 2001). At the same time, several neighbourhood variables tend to be highly correlated which may raise the issues of multicollinearity. Moreover, some neighbourhood variables are often measured with error which can lead to potentially severe attenuation biases in coefficient estimates. Therefore, we opt for a parsimonious specification that include independent variables such as income per capita, population density, construction costs and also medical (number of medical personnel/1,000 population) in instrumental variable specification (see Table 2 for details).
All data is obtained at the local area level. However, the construction cost is the national series to capture the macro-economic influences on regional housing markets. Table 3 presents summary statistics across 41 local areas in Taipei. It suggests that the average growth rate of residential land price was negative and a high level of price volatility implying weak and volatile housing markets over the last two decades. It also reveals that the income per capita growth was relatively high in Central Taipei City. In addition, the number of medical personnel showed relatively high level in Western Periphery.
To understand the population movement better, we compute an Urban Sprawl index following Lopez and Hynes (2003) - SI = (((S% − D%)/100) + 1)) × 50. SI = sprawl index for metropolitan area; D% = percentage of the total population in high-density area; S% = percentage of total population in low-density area. The range of values for the sprawl index is from 0 to 100 as computed in a diffusion index formulation. If the value is at 100, it indicates highest level of sprawl. At 50, the distribution of the population is spread evenly. The sprawl index is used because it allows us to compute different levels of concentration and examine temporal and geographic changes and the effects of centralisation (Wassmer and Baass 2006). The main difference of the sprawl index from the population density is that it can assess how density is concentrated across areas. In this paper, our interest is to investigate how different types of urban pattern i.e. dense, centralized, decentralized and extremely decentralized could have an impact on land prices, which can be effectively revealed by the sprawl index.
According to Table 4, the Rest of Taipei City has a sprawl index higher than 50, suggesting the population concentration in relatively low-density areas. In contrast, the West and East peripheral areas exhibit relatively less level of sprawl and the possible explanations for this centralisation may be attributed to the geographic features, land-use policies, transportation network and local economic demand shifters. Moreover, the changes over 1992–2001 reveal high levels of sprawl in the Rest of Taipei City and Satellite city indicating greater growth in low-density areas in these regions. The high level of sprawl could have resulted from the completed transportation system with improved accessibility and thus removing the barriers for population movement and housing development.
5 Results and analysis
5.1 Granger causality
Granger causality between 41 local housing markets
Pairwise Granger causality in Taipei City
Rest of Taipei
Pairwise Granger in the Satellite City
Pairwise Granger causality in the Western Periphery
Pairwise Granger causality in the Eastern Periphery
5.2 Dynamics of residential prices in spatial panel model
Dynamic panel-spatial models: simple SAR model
Taipei (all areas)
Central Taipei City
Rest of Taipei City
West Peripheral region
However, as discussed under the Methodology section, a key concern with Equation (5) (and, results of Table 6 is the possibility of violation of the stability condition. Indeed, in models (2), (3) and (4) in Table 6, sum of the coefficients is greater than 1, thus violating the key stability condition (|ρ| + |ϕ| < 1). This is due to a lack of control for the space-time covariance in Equation (5). Therefore, we next move onto more general Spatial Durbin Model (SDM) as represented by Equation (6).
Dynamic Spatial Durbin Models
All Taipei (41 areas)
All Taipei (41 areas)
Core Taipei City
Core Taipei City including Satellite City
East and West Peripheral regions
Spatial regressor W*Land Price
Per Capita Income
W*Per Capita Income
Spatial Fixed Effects
Spatial & Time Fixed Effects
Spatial & Time Fixed Effects
Spatial & Time Fixed Effects
Spatial & Time Fixed Effects
It is quite likely that a mega-urban metropolitan area such as Taipei contains several geographic clusters with distinct economic and spatial dynamics. Therefore, we also look at three combined region-clusters to check robustness of the full sample results. Specifically, first we combine the local areas in the main Taipei city and then also add the Satellite City region. We also combine the peripheral regions of East and West, which constitutes the New Taipei City. We have also run models with individual regions but for the brevity of reporting, we only present these three combinations of region-clusters. Quite remarkably, the results do reveal interesting departures from the All Taipei results of column (2). Most notably, the spatial regressor is significant but negative in the Core Taipei City (column (3)). A 1% increase in neighbouring local area’s land price will lead to a fall of almost 0.47% in a local area’s land price in the Core Taipei City. Although apparently puzzling, a couple of explanation can be provided for such effect. While the negative result of the spatial regressor is not common, but it is not unprecedented either in the literature (Holly et al. 2011). A plausible explanation is the uniqueness of the region in the question i.e. the Core Taipei City is very unique with its urban-economic characteristics as a capital city and own government structure. Moreover, it may probably be due to the prevalence of speculative activities in the core city area. A high future price expectation may drive up the housing demand in the area with resulting lack of demand in neighbouring local areas. This reasoning is supported by positive and significant space-time covariance. However, when we expand the geographic expanse by adding the Satellite City, the spatial dependence is no longer significant. This is probably caused by a dominant substitution effect due to modernised transportation system in mid-1990. The result for the Peripheral region shows expected and significant positive effect of spatial regressor. Specifically, a 1% increase in neighbouring local area’s land price will cause almost 0.13% increase in an area’s land price. This small but statistically significant positive feedback is expected in the peripheral regions due to emerging opportunities and population growth.
The most robust result in Table 7 is for the previous period’s land price. This temporal effect is almost 0.7–0.8% for a 1% increase in previous period’s land price. Income and population density show positive feedback effects, albeit with varying degree of significance. This finding conforms to other findings in the literature (e.g. Kahn 2001). A plausible reason is that the Asian newly-industrialising countries (NICs) have been quite successful in breaking down the ‘friction of distance’ and thus reducing the commuting cost (McGee 2008). Taipei local areas are well connected by modern transportation system. Therefore, suburban areas have become more desirable to households and thus raising the demand for housing. This lends support to proponents of ‘Smart Growth’ policies. The same is true for the predominantly manufacturing areas of Western Peripheral region and predominantly farmland areas of Eastern Peripheral region. It should be noted though that these findings are by no means definitive and are rather strongly indicative of the complexities that surround this area of research (see arguments in Mills 2002). We did not find much effect for the neighbouring local area’s income and population density from models (2) to (5). While we have presented a number of modelling frameworks in this paper, this is, by no means, a complete list of plausible models in this area. Several other approaches have been adopted and suggested in the literature. We provide a number of the model to show a general pattern in the results.
Dynamic Spatial Durbin Models – effects computation
All Taipei (41 areas)
Core Taipei City
Taipei city including Satellite City
East and West Peripheral regions
Per capita income
Per Capita Income
Per Capita Income
Direct effect: Population Density
Indirect effect: Population Density
Total effect: Population Density
This study investigates dynamics of the residential land prices across 41 local areas in Taipei by using panel data frameworks and dynamic spatial panel model over the period of 1992–2010. The Granger Causality tests show that the prices in the Central regions have impacts on the prices of the surrounding areas. However, some regions, particularly the new growth local areas, lead house price increases in the city centres possibly due to strong substitution effects. These new growth cities can be viewed as substitutes for housing in the centre because of the cheaper housing options and easy accessibility to the centres due to improved transportation system in Taipei. Furthermore, contiguity plays an important role in inter-relations of house prices implying the existence of strong feedback effect between contiguous regions.
We apply several dynamic spatial panel models. The most general formulation of the spatial autocorrelation model suggests that the spatial dependence has strong positive impacts in Taipei housing markets. This can be partly explained by the fact that an improved transportation system removes mobility constraints, opens up land for housing developments, and increases the accessibility across regions and therefore, this may lead to significant spatial effects. In spatial panel data models, the empirical results suggest that specific area and time effects could have significant influences on local housing markets. It also reveals that lagged residential land price changes have positive correlations across all areas.
The findings provide us with important implications for the policy making process in terms of urban spatial planning. The positive association of population density and land prices raises an interesting question regarding support for government interventions to impede the sprawling of urban areas. While the standard arguments regarding costs associated with sprawl (e.g. in terms of greater traffic congestion, segregation, air pollution and loss of open space) are not explicitly tested in this paper, nonetheless, it shows that a good transportation system offering reduction in commuting costs may substantially offset the negative impacts on prices due to increasing suburbanization.
We find significant support for the existence of diffusion effect in Taipei metropolitan area which implies a local area’s land price movement could be predicted not only by its own previous prices but also by other neighbouring local area’s price movements. Moreover, in the most general framework, we also find neighbouring local area’s local attributes to have some significant explanatory power. This study suggests that the local area housing market dynamics, interaction of neighbouring areas and spatial patterns should be considered when decisions regarding housing policies are made and policies are implemented, instead of focusing solely on the local housing market situation in isolation. These results may especially be more pertinent in dense, mega-urban regions undergoing rapid urbanisation, significant infrastructure developments and thus raising connectivity and spatial substitutability, which are quite common phenomena across the developing economies.
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