House price diffusions across three urban areas in Malaysia

Recent developments in the analysis of cointegration in the presence of asymmetric adjustment are extended and applied to data on regional house prices in the UK. This extension is found to have a dramatic impact upon the results derived. In contrast to recent studies employing standard methods, allowance for the possibility of asymmetric behaviour results in the detection of a large number of long‐run relationships between house prices in different regions. A consistent pattern of asymmetric adjustment is observed, with reversion to equilibrium occurring more rapidly (slowly) when house prices in the South of England decrease (increase) relative to other regions. While the results derived support the existence of a ripple effect underlying the observed movements in regional house prices, the extent of cointegration uncovered casts doubt upon the recently proposed notion of weak segmentation in the UK housing market.

Purpose: This paper examines the existence of the ripple effect from Amsterdam to the housing markets of other regions in the Netherlands. It identifies which regional housing markets are influenced by house price movements in Amsterdam. Design/methodology/approach: The paper considers the ripple effect as a lead-lag effect and a long-run convergence between the Amsterdam and regional house prices. Using the real house prices for second-hand owner-occupied dwellings from 1995q1 to 2016q2, the paper adopts the Toda-Yamamoto Granger Causality approach to study the lead-lag effects. It uses the ARDL-Bounds cointegration techniques to examine the long-run convergence between the regional and the Amsterdam house prices. The paper controls for house price fundamentals to eliminate possible confounding effects of common shocks. Findings: The cumulative evidence suggests that Amsterdam house prices have influence on (or ripple to) all the Dutch regions, except one. In particular, the Granger Causality test concludes that a lead-lag effect of house prices exists from Amsterdam to all the regions, apart from Zeeland. The cointegration test shows evidence of a long-convergence between Amsterdam house prices and six regions: Friesland, Groningen, Limburg, Overijssel, Utrecht and Zuid-Holland. Research limitations/implications: The paper adopts an econometric approach to examine the Amsterdam ripple effect. More sophisticated economic models that consider the asymmetric properties of house prices and the patterns of interregional socio-economic activities into the modelling approach are recommended for further investigation. Originality/value: This paper focuses on the Netherlands for which the ripple effect has not yet been researched to our knowledge. Given the substantial wealth effects associated with house price changes that may shape economic activity through consumption, evidence for ripples may be helpful to policy makers for uncovering trends that have implications for the entire economy. Moreover, our analysis controls for common house price fundamentals which most previous papers ignored.

Purpose: This paper examines the existence of the ripple effect from Amsterdam to the housing markets of other regions in the Netherlands. It identifies which regional housing markets are influenced by house price movements in Amsterdam. Design/methodology/approach: The paper considers the ripple effect as a lead-lag effect and a long-run convergence between the Amsterdam and regional house prices. Using the real house prices for second-hand owner-occupied dwellings from 1995q1 to 2016q2, the paper adopts the Toda-Yamamoto Granger Causality approach to study the lead-lag effects. It uses the ARDL-Bounds cointegration techniques to examine the long-run convergence between the regional and the Amsterdam house prices. The paper controls for house price fundamentals to eliminate possible confounding effects of common shocks. Findings: The cumulative evidence suggests that Amsterdam house prices have influence on (or ripple to) all the Dutch regions, except one. In particular, the Granger Causality test concludes that a lead-lag effect of house prices exists from Amsterdam to all the regions, apart from Zeeland. The cointegration test shows evidence of a long-convergence between Amsterdam house prices and six regions: Friesland, Groningen, Limburg, Overijssel, Utrecht and Zuid-Holland. Research limitations/implications: The paper adopts an econometric approach to examine the Amsterdam ripple effect. More sophisticated economic models that consider the asymmetric properties of house prices and the patterns of interregional socio-economic activities into the modelling approach are recommended for further investigation. Originality/value: This paper focuses on the Netherlands for which the ripple effect has not yet been researched to our knowledge. Given the substantial wealth effects associated with house price changes that may shape economic activity through consumption, evidence for ripples may be helpful to policy makers for uncovering trends that have implications for the entire economy. Moreover, our analysis controls for common house price fundamentals which most previous papers ignored.

PurposeThis paper aims to examine the existence of the ripple effect from Amsterdam to the housing markets of other regions in The Netherlands. It identifies which regional housing markets are influenced by house price movements in Amsterdam.Design/methodology/approachThe paper considers the ripple effect as a lead-lag effect and a long-run convergence between the Amsterdam and regional house prices. Using the real house prices for second-hand owner-occupied dwellings from 1995q1 to 2016q2, the paper adopts the Toda–Yamamoto Granger Causality approach to study the lead-lag effects. It uses the autoregressive distributed lags (ARDL)-Bounds cointegration techniques to examine the long-run convergence between the regional and the Amsterdam house prices. The paper controls for house price fundamentals to eliminate possible confounding effects of common shocks.FindingsThe cumulative evidence suggests that Amsterdam house prices have influence on (or ripple to) all the Dutch regions, except one. In particular, the Granger Causality test concludes that a lead-lag effect of house prices exists from Amsterdam to all the regions, apart from Zeeland. The cointegration test shows evidence of a long-convergence between Amsterdam house prices and six regions: Friesland, Groningen, Limburg, Overijssel, Utrecht and Zuid-Holland.Research limitations/implicationsThe paper adopts an econometric approach to examine the Amsterdam ripple effect. More sophisticated economic models that consider the asymmetric properties of house prices and the patterns of interregional socio-economic activities into the modelling approach are recommended for further investigation.Originality/valueThis paper focuses on The Netherlands for which the ripple effect has not yet been researched to the authors’ knowledge. Given the substantial wealth effects associated with house price changes that may shape economic activity through consumption, evidence for ripples may be helpful to policy makers for uncovering trends that have implications for the entire economy. Moreover, the analysis controls for common house price fundamentals which most previous papers ignored.

PurposeThis paper aims to propose that a Neave-Worthington Match Test for Ordered Alternatives is a simple, non-parametric test that can be used to consider Gibrat’s law. Whether the law, that states that the proportional rate of growth is independent of absolute size, is supported by regional house price growth rates is considered. The Match Test is further used to test the applicability of beta-convergence and dual economy models to a house price context.Design/methodology/approachThe Match Test relates an actual rank order with an expected one. Gibrat’s law implies house price growth rates are independent of the absolute price levels. Beta-convergence posits that growth rates are inversely related to the initial price level. With a divergent system, there is a direct relationship between size-order and growth rates. As such, the Match Test is used to test alternative models of size-growth relationship.FindingsRather than convergence, there is a tendency to diverge across the UK, but not in Eire. That said, the size of growth shocks is related to price level on the upswing of a price cycle, but not in the down. Assigning the high-priced regions of the two islands into core and the rest into a periphery, total matching is dominated by the capital cities’ growth. The sigma-convergence observed in British house prices is likely to be associated with slower beta-divergence, not a convergent system. The law of Gibrat is not found to apply in a regional house price context.Research limitations/implicationsThis work only covers two countries and nineteen regions. Gibrat’s law in regional house prices may be better examined using a multi-country analysis.Practical implicationsAs the law of Gibrat is not found to apply in a regional house price context and core-regions appearing to dislocated, this has interesting implications for growth trend analysis and the claim of cointegration, which should be explored further. In particular, the level-growth relationship in the cyclical price upswing points to a ratcheting of differentials between high and low house price regions. The common trends in the long run may result from corrective periodic crashes. Not an ideal mechanism for policymakers.Originality/valueTo the best of the author’s knowledge, this paper makes a novel use of the Neave-Worthington test in the realm of regional convergence-divergence and in the first consideration of the law of Gibrat in a house price context across two countries.

The Ripple Effect and the Linguistic Border in Belgium: A Country Divided?

Recent movements in stock and house prices have led to an examination of the presence of bubbles. Whilst, there is extensive research on stock price data, there is relatively less for house prices. This paper uses a present‐value model for house prices to test for the presence of bubbles. The results support the presence of a non‐fundamental component within UK national and regional house prices. In particular, for the majority of series considered, evidence is presented of linear non‐stationarity within the fundamental present‐value relationship, and of non‐linear stationarity, implying the presence of a non‐fundamental, or bubble, component. Furthermore, evidence is presented that prices adjust quicker when they are below fundamental equilibrium, than when they are above fundamental equilibrium, i.e. there is downward price stickiness. These results support the hypothesis that house price dynamics can be characterised by price‐to‐price momentum. Finally, forecast evidence suggests that real prices are likely to adjust downwards and converge with fundamental value.

Understanding the impact of changes in interest rates on house prices is important for managing house price bubbles and ensuring housing affordability. This paper investigates the effect of interest rates on regional house price to income measures based on a non-linear smooth transition VAR model of inter-regional house price dynamics. To minimize the impact of housing mix changes on estimated effects, we apply the model to an Australian dataset of regional hedonic house price indices that account for both changes in housing mix and quality over time. The empirical analysis provides evidence that house price to income ratios depend non-linearly on interest rates, and moreover that there is an interest rate ‘transition point’ below which a house price bubble is probable. We investigate the implications for monetary policy of stable and unstable house price regimes and propose a housing lending rate lower bound that achieves long-run house price stability in the presence of regime uncertainty. To check the generality of the result, we also apply the model to aggregate Australian and US data.

Being different from past research of regional housing prices, this paper employs smooth transition regression model, derived in Terasvirta (1998), to investigate ripple effects among four regional house prices in Taiwan. The aim of this paper is to test whether a smooth transition regression model, which is capable of capturing this non-linear behaviour, can show a better characterisation of regional housing prices than a linear model. This empirical analysis applies the four regional house prices of Taiwan, including the capital in Taiwan, Taipei City, and its suburban area, New Taipei City, and the other two mega cities of Taichung City and Kaohsiung City, from the first quarter of 1998 to the second quarter of 2011. Using the changing rate of housing price of Taipei City to be the threshold variable, the empirical results of the smooth transition regression model show that the ripple effect exists between housing prices of New Taipei City and Taipei City, while there is no ripple effect between housing prices of New Taipei City, Taichung City and Kaohsiung City. Besides, this paper has presented evidence of a non-linear relationship between housing prices of New Taipei City and Taipei City. When the changing rate of housing price in Taipei City is lower than 15.02, increasing housing price of Taipei City will make the hosing price of New Taipei City rise. Inversely, if it is higher than 15.02, increasing housing price of Taipei City will make the hosing price of New Taipei City decrease.

Using system clustering method to group China’s provinces into 3 new groups according to their housing prices, then establishing a state-space model and applying the Kalman filter calculation, we made a comparative analysis of the influences of different types of monetary policy instruments towards different regional housing prices. The empirical results show that both the quantitative instruments represented by M2 and the pricing instruments represented by real interest rate have increasing influences on different regional housing prices, but the former influence is much stronger than the latter. The influential differences of quantitative instruments to regional housing prices are much greater. It means the higher the regional housing price is, the greater the influence is. Therefore, the central bank shall optimize the combination of monetary policy instruments according to the above characteristics of different types of monetary policy instruments in order to acquire the regulatory target of real estate market.

This paper investigates the long-run convergence of regional house prices in the UK. Using a variety of econometric methods, existing studies have failed to reach a consensus on whether or not regional house prices are cointegrated and exhibit long-run constancy relative to each other. We propose the application of a new test that combines principal components analysis with unit root testing to throw new light on the regional convergence debate. Using mix-adjusted quarterly house price data for 1973-2005, we find that existing unit root and cointegration methodologies indicate the presence of multiple stochastic trends with, at best, very weak evidence of long-run convergence. However, testing for the stationarity of the largest principal component based on regional house price differentials suggests that all UK regional house prices are driven by a single common stochastic trend and can be regarded as exhibiting strong convergence in the long-run. Further analysis suggests there is a high degree of persistence in regional house price differentials.

This paper investigates the convergence and ripple effects of regional house prices in China over 1998 to 2010 by using 35 main capital cities’ house price indexes, by developing some innovative econometrical approaches—the pairwise Granger causality tests and the panel regression models. One of this paper’s originalities is the first attempt to apply above approaches into the study of regional house prices in China; and the other originality is to add the distance factors into the house price convergence study. Generally, the empirical results suggest that there is some evidence of ripple effects but not convergence between regional house prices in China; and house price shocks in some core cities ‘ripple out’ to other cities, wherein Beijing, Hangzhou, Guangzhou and Shenzhen are original regions of nationwide house price swings. Keywords Convergence, Ripple Effects, Pairwise Granger Causality test, Panel Regression Model, Regional House Prices

PurposeThis paper aims to examine the linkage of regional housing markets between Taiwan and China as increasing economic integration.Design/methodology/approachTwo time-varying estimations of cointegration tests, Gregory and Hansen (1996) cointegration test with structural break and the recursive coefficients of cointegration (Hansen and Johansen, 1993) are applied to trace the possible dynamic linkage of cross-border regional housing prices between Taiwan and China.FindingsFirst, the estimating results of the long-run relationships show that increasing housing prices in Beijing and Shanghai decrease Taipei’s house prices, while Shenzhen and Chengdu have converse effects. The technologies’ levels of Taiwanese industries surrounding the cities in China will affect the direction of the linkage of regional housing prices between the two economies. Second, in light of causalities of these five housing prices’ changes, Beijing and Shanghai lead Taipei and Shanghai leads Chengdu, which, in turn, leads Shenzhen. Finally, the results of time-varying cointegration tests show that some critical economic and political incidents changed the linkages of housing prices between Taipei and the four cities in China.Originality/valueAlthough some empirical works examined the linkages between cross-border house prices in Europe and the USA, study has looked at the linkages of cross-border housing prices between Taiwan and China. This is an interesting topic insofar as house price integration has implications for wealth effects that feed into consumer expenditure in both Taiwan and China. The empirical evidence overall displays the existence of the integration of regional housing markets between Taiwan and China. For the longer-term future, increasing economic integration between China and other Asia countries will result in greater and more diversified cross-border housing markets and pools of investors.

House price indexes had become important economic indicators worldwide, since movements in house prices have been closely correlated with the economic cycle. In order to compute these kind of indexes it is imperative to produce reliable estimates of the average transaction price of houses, not only at the macrolevel (e.g. national and state level), but also at the microlevel (e.g. district, municipalities or further disaggregate regional level). In Portugal, there is a rapidly growing demand of such microlevel statistics since the beginning of the recent financial and economic crisis. The Portuguese Statistical Office provides a range of invaluable data at national level; however, this data cannot be used directly to produce reliable regional-level estimates due to small sample sizes. In this paper we employ small area estimation techniques to produce design and model-based estimates of average transaction price of houses for Portuguese regions with small sample sizes. Our results show that the model-based estimates based on spatial and temporal models are more accurate than the traditional direct design-based estimates. The use of these techniques allows the production of information at disaggregated regional levels that would not be available under the traditional direct estimation approaches. Furthermore, it is even possible to produce reliable model-based estimates for geographical areas without sample. The estimates are expected to provide invaluable information to policy-analysts and decision-making.

This paper investigates the long-run convergence of regional house prices in the UK. Existing studies have failed to reach a consensus on whether or not regional house prices exhibit long-run convergence with each other. The application is proposed of a new test involving unit root testing of the first principal component based on regional—national house price differentials. Using mix-adjusted quarterly data for 1973—2006, it is found that the first principal component is stationary. This suggests that all UK regional house prices are driven by a single common stochastic trend. Further analysis suggests that those regions that are more distant from London exhibit the highest degrees of persistence with respect to deviations in house price differentials.