Amsterdam house price ripple effects in The Netherlands

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.

PurposeVarious empirical studies have demonstrated that house prices in different geographic regions have a tendency to co‐move. But these studies have focused on developed economies in the west. The purpose of this paper is to test this hypothesis in the case of three major urban areas in the rapidly developing economy of Malaysia, namely Klang Valley, Penang and Johor.Design/methodology/approachUsing Pesaran et al.'s bounds testing approach to cointegration and Granger non‐causality analysis, the short‐run and long‐run dynamics of regional house prices are analysed.FindingsFirst, house prices in all three regions appear to be cointegrated. Second, there is evidence of short‐run bi‐directional causality between house prices in all regions. Third, long‐run house price movements in Johor are not Granger‐caused by house prices in Klang Valley and Penang. This observation could be rationalised in the light of the argument that Johor house values may be more closely aligned with activities in the Singaporean economy, given the region's geographic proximity to Singapore.Practical implicationsThe findings have several practical implications for housing investors who intend to optimise investment decisions on housing purchases. The understanding of the nature of regional house dynamics could also enrich the government's knowledge of how the local housing markets work and enable the design and implementation of relevant housing policies.Originality/valueThis is the first known study that establishes stylised facts of lead‐lag relationships for regional house prices, while also providing short‐run and long‐run estimates of regional house price interactions for Malaysia.

PurposeThe purpose of this paper is to examine the house market in Malaysia from 2002 to 2015. Specifically, the macroeconomic determinants on the house price and house demand are investigated.Design/methodology/approachStructural Vector Autoregressive Regression was adopted to estimate the unexpected changes in both house demand (residential transaction volume) and prices based on economic theoretical reasoning that consider shock from macroeconomic determinants.FindingsThe transaction volume and real house prices respond to most of the macroeconomic shocks. While the impact of real gross domestic product (GDP) on house prices appears to be stronger and longer in comparison to other macroeconomic shocks, a 60 per cent change in house prices can be explained by real GDP regardless of whether it is in the short run or the long run. The studies also reveal that a positive effective exchange rate plays an important role when demonstrating the transaction volume. Moreover, monetary liquidity plays a major role in justifying the transaction volume. This implies that mortgage lending may have an impact on housing demand. Meanwhile, movements of house prices cannot be explained by the demand in quantity. This signifies that supply has a strong influence in determining the price.Research limitations/implicationsThis study has implications on policymakers of which the interest rate as a cooling measure might not be effective in the short run. The interest rate has very little impact on housing prices. Furthermore, policymakers should address the concerns on speculations, as the results reveal that monetary liquidity and the exchange rate have a strong impact on the housing demand.Originality/valueThis study seeks to provide answers regarding the recent upsurge of Malaysian housing prices. Besides focusing on the house price changes, this study addresses the role of transaction volume while evaluating the house market, as housing prices are usually downwards rigid. Since the price and transaction volume are both related to the transaction activity, this study is significant and could be a good reflection on the actual demand behaviour in the residential market.

This empirical study tests the ripple effect and the long-run convergence associated with the dynamics of U.S. regional housing prices using the ARDL bounds testing approach and seasonally-adjusted monthly data from 1991:1 to 2010:12 for the nine U.S. census regions. The results support the presence of the ripple effect across regional housing markets and the long-run convergence of regional housing prices. However, the results reveal variation in the degree to which changes in regional housing prices differ across different regions in both the short run and long run. The speed of adjustment toward long-run equilibrium also varies across regions as well.

(Un)Expected Housing Price Changes: Identifying the Drivers of Small Business Finance

(Un)expected housing price changes: Identifying the drivers of small business finance

An alternative perspective is provided on the existence of a ripple effect in the UK housing market. In contrast to previous studies, the analysis involves consideration of information on the changes in house prices to which the hypothesis of house price diffusion posited by the ripple effect relates, rather than their levels. In an examination of changes in house prices in London relative to other regions of the UK, directional forecasting methods are employed to establish the extent of the relationship between geographical proximity and comovement across the three month window provided by quarterly data. Consequently, the analysis provides a direct examination of the ripple effect which refers to changes in prices rather than the convergence of levels which has become a feature of the empirical literature. The literature is extended further by both the application of dating techniques to perform the analysis across cycles and phases of cycles (recovery and recessionary periods) in the UK housing market, and the use of data from two alternative house price index providers. Striking results in support of the presence of a ripple effect are noted, particularly for the less commonly considered Halifax price index where the most significant results for comovement with London are exhibited by its contiguous regions. In addition, the cyclical subsamples considered indicate comovement to be greater during upturns, rather than downturns in the market. This is consistent with previous research showing London to correct – that is, exhibit differing behaviour to other regions – during downturns.

China has been undergoing significant social and economic structural changes since launching its policy of economic reform and opening up in 1978. This has involved a transformation from a centrally planned economy, where there is no role for the market, to a market-oriented economy in which market principles play a major role. During the last four decades, great achievements have been made in terms of economic growth and social well-being. To name a few indicators: the Gross Domestic Product (GDP) of the country increased from USD 189.65 billion in 1980 to USD 10.866 trillion in 2015, positioning China as the second largest economy in the world, with an average annual growth rate over 10%. Meanwhile, poverty levels have greatly improved. The poverty headcount ratio at USD 1.90 a day (2011 PPP) has decreased dramatically, from 42.15% in 1981 to 10.68% in 2013. The rapid economic growth, combined with the reform of the Hukou registration system, has also accelerated the migration flow from rural areas to urban areas. The population living in urban China in 2015 reached 763 million, making the urbanisation level of 55.61%, almost three times that in 1980. With the rapid growth of the urban population, the welfare-based public housing provision system founded in the central planning era could no longer meet the increasing housing demand of urban residents. Thus, in 1994, comprehensive housing reforms were implemented, aiming to privatize the public housing sector and promote a housing allocation system based on market principles. The milestone of housing reform occurred in 1998, when the government completely suspended the traditional housing allocation system, making the housing market the only way to access housing services (Wang et al. 2012). The emergence of the private urban housing market spurred both housing transactions and prices. In 1998, the housing area traded on the market was approximately 108 million square metres on an average transaction price of 1854 yuan/m2. These two figures were nearly ten and three times higher in 2014, soaring to 1.05 billion square metres and 5933 yuan/m2, respectively. At the regional level, rapid economic development has been accompanied by increasing inequality. Soon after the launch of the economic reforms, some coastal regions, Guangdong and Zhejiang in Eastern China, for example, grew quickly, due to the influx of foreign direct investment (FDI), advanced technologies and equipment, and favourable policies of the central government. The ‘core’ position of these regions in the national economy was further enhanced through a self-reinforcing process (Anderson 2012, p.127), shaping a core-periphery economic structure in China. In 1980, the regional gross product of Eastern China accounted for 43.69% of total GDP in China, while in 2014 this ratio increased to 51.16%, reflecting the polarization of economic activities. Reflecting the distribution of economic activities, the inequality in the cost of housing between regions is also striking. In 2014, the average sale price in 35 main cities in mainland China was approximately 8599 yuan/m2, with the standard error also high, at 4651 yuan/m2, making the coefficient of variance 0.54, thus indicating a high degree of heterogeneity across this city-level housing market. The left panel of Figure 1.1 shows the spatial distribution of average house prices. It is apparent that the prices in the coastal cities of Eastern China are generally greater than the prices of inland cities. However, the picture of house price dynamics is a little different. From 2002 to 2014, the rapid growth in house prices, on average 11.38% per year, seems to be anational phenomenon and there is very little variance between the annual growth rates in different cities; the coefficient of variance is only 0.18, much lower than that of the house price level. Perhaps the most prominent spatial pattern of house price growth rate is that the northeastern cities experienced the lowest price appreciation during the period 2002-2014. This dissertation is fundamentally concerned with the spatial patterns of house prices and their dynamics across cities in China. Although literature on the Chinese housing market has been emerging in recent years, little is known about the spatial interaction of regional housing markets. The following four chapters will be dedicated to responding to questions concerning the emerging market: Why is there a core-periphery structure in the distribution of interurban house prices? To what extent are the house price developments across cities similar? How do house price dynamics in one city affect the house price changes in other cities? The investigation of the spatial dimension of the Chinese housing market has been always hampered by the quality of the data, especially when analysing house price dynamics. This situation has inspired the pursuit of research to construct house price indexes that reflect the house price changes as accurately as possible. In line with a key theme of this study, particular a

The commercial residential industry has high added value and comprehensive economic benefit so the commercial residential industry is naturally a hot-spot issue. The core issue of the commercial housing is the price. This thesis conducts the descriptive statistic analysis of residential real estate prices, urban resident income and other relevant data in China’s 30 provinces (excluding Tibet) from 1998 to 2006. The change trend and the difference feature of both the residential real estate price and the urban resident income in those regions are revealed, which is expected to make a contribution to the macro-control in China’s real estate. In recent years, the real estate market in China is growing rapidly. On the one hand, it plays a vital role in both promoting the national economic growth and improving the living standards of urban residents. On the other hand, some problems in the development of China’s current real estate market have been fully exposed, such as the overheated investment, the unbalance in supply and demand, insufficient financing channels, soaring property prices and so on. In particular, the rapid growth in the housing price has brought challenges to the sound development in both China’s real estate market and the whole national economy and it has also become a hot-spot and difficult issue in the current academia. Such relevant research as whether the rapid growth of China’s housing prices has become disjointed with resident income seriously or not and what the rules of the changes in income and housing prices in China’s different regions are is realistically significant for guiding the micro-control in China’s real estate. I. Index Selection and Disposal of Comparability The samples selected in this thesis are composed of the fluctuating residential real estate prices in China’s 30 provinces (excluding Tibet) from 1998 to 2006, urban resident income and other relevant panel data that are from various years of China Statistical Yearbook. The data of the real estate prices adopts the real estate prices in urban areas. The income indexes adopt the annual per capita disposable income of urban residents. In order to remove the impacts of the price and make indexes of various types had comparability in time series, the disposal of comparability has been conducted in indexes of different types in the thesis and their present value has been turned into the value of the constant price, namely, on the basis of the constant price in 1998, the concrete calculation method is that the housing price is deflated by the housing sales price index and the disposable income is deflated by the consumer price index of urban residents. II. Analysis of Commercial Housing Price Variance among Provinces According to the average housing prices and their growth rates in provinces from 1998 to 2006, thirty provinces, cities and autonomous regions across the country can be divided into three types in accordance with the mean and the growth rate of their average housing prices. It’s found that the provinces, cities and autonomous regions of the three types also have common in geographic areas so they can be divided into such three regions as the eastern region, the central region and the western region on the basis of their geographic areas. For the regional division of the average housing price in China, Figure 1 compares the changes in the average housing prices in the central, western and eastern regions from 1998 to 2006. It’s found that the average housing price in the east is prominently higher than those in the western and International Conference on Education Technology, Management and Humanities Science (ETMHS 2015) © 2015. The authors Published by Atlantis Press 1100 central regions and the trend of its average housing prices is on the rise. In particular, the rising trend of the average housing prices is obvious after 2004. The changes in the average housing prices in eastern and western regions are comparatively similar. However, the average prices in the central region are rising slowly while for the western region, a small decline also appears in its slightly rising process. Besides, the average housing prices in the eastern region surpass those in the western region after 2004. III. Analysis of the Differences in the Income Change among Regions Figure 1: Changes in the housing prices in the central, eastern and western regions Figure 2: Changes in the income in the central, eastern and western regions For the regional division of the average housing price in China, Figure 2 compares the changes in the income in the central, western and eastern regions from 1998 to 2006. It’s found that the per capita income in the east is prominently higher than those in the western and central regions and its trend is uniformly on the rise.

1. Introduction International transmission of house price changes appears to be a natural corollary of an increasingly internationalized and interdependent financial environment. In addition, parallel movements in borrowing conditions and macroeconomic fundamentals are expected to strengthen the tendency of international house prices to comove. This should be particularly true in the case of the euro-zone, where the currency is common and monetary policy is conducted by the European Central Bank on behalf of all members. Since the mid-1990s, in particular, the housing prices of major European economies have been strongly increasing and this increase has been largely associated with high growth rates experienced over the last decade. However, recently such comovement has been blamed for triggering the latest global financial crisis. Housing as a non-traded good is not easily substituted among different countries. Over the last decade it has been claimed that major European countries' housing markets have been overvalued and that housing spillover effects appeared not only within an economy but also across economies. Two main differences stand out in the behavior of real assets like residential housing as opposed to financial markets assets like shares and bonds. First it is possible to use the information included in housing prices to make returns in excess of a buy and hold strategy i.e the efficient market hypothesis does not hold and second the housing prices are less flexible downwards compared with stocks. So the risk-return profile of an asset like residential housing makes it more attractive to investors. This paper sets out to investigate the factors underlying the apparent comovement of housing prices of the largest Euro zone economies. Specifically, we will examine first the relative importance of local factors (income, interest and stock prices) in explaining house price movements on a national level. Doing that, we differentiate between pre- and post-Euro periods and distinguish between economic expansions and contractions. We then look at the existence of spillover effects of shocks from both German monetary policy and volatility in its home housing market to the real house prices of the other countries in the Euro zone. Finally, we investigate the impact of global shocks such as emanating from changes in the US monetary policy on the volatility of each country's housing market. Using monthly data from DSI Statistical Bases for 1990(1)-2009(4), we investigate first whether there is overvaluation in real house prices across seven Euro zone economies. The economies examined namely Austria, Finland, France, Germany, Italy, the Netherlands, and Spain, constitute the core of the Euro zone, and are responsible for 90% of the zone's GDP and making up the second largest economy in the world after the US. Next, we concentrate on the impact of the adoption of the common currency on real house prices movements. We conduct the analysis using country-specific macroeconomic variables and then extend it by adding foreign-specific macro variables to each country's model. The empirical analysis includes cointegration analysis and VAR specifications. The rest of the paper is organized as follows: A brief survey of related literature is conducted in section 2. Section 3 presents stylized facts from each country's housing market. Section 4 outlines the methodology employed in the paper and includes the data description and variable selection. Section 5 presents and discusses the empirical findings. Concluding remarks are made in section 6. 2. Review of the Literature A number of studies have indicated comovement of house price changes, mainly attributed to synchronization of monetary policy, financial liberalization, integration of international financial markets, as well as global business cycle linkages (see, for example, Helbling and Terrones, 2003; Tsatsaronis and Zhu, 2004; Scanlon et al. …

The structural differences and the dynamics in prices on the second-hand market for family houses in large (Stockholm, Gothenburg and Malmö), medium-sized, small and industrial cities and sparsely populated areas are analysed in this paper. The basic house price data set used in the analysis consists of constant quality monthly price indices. The sample starts in January 1981 and ends in July 1997. The real price changes in house prices for all seven regions display a high degree of autocorrelation, and the correlograms reveal a mean reverting pattern. The Granger causality test indicates that the real price changes for the Stockholm area 'Granger cause' the price changes in the other areas. Thus, the real price change in the Stockholm area has a ripple effect on the six other areas. Both bivariate and multivariate Granger tests indicate information content in a number of macroeconomic variables versus the real price changes for the Stockholm area and the entire country. A simple VAR model was estimated with the price changes for family houses in the Stockholm area, a proxy for consumption growth and the change in the rate in the unemployment rate as endogenous variables and a number of exogenous macro variables. Experiments with impulse response functions show that a shock in the change in the rate of unemployment has a strong effect on real house prices and consumption.

PurposeThe paper considers if house price movements in the United Kingdom (UK) can be linked to the political cycle as governments realise homeowners represent a large portion of the voter base and their voting decisions could be influenced by the magnitude and direction of house price changes. Specifically, this paper aims to investigate whether house prices behave differently before and after elections and under different political regimes.Design/methodology/approachThe paper analyses quarterly house price data from 1960 to 2018 together with data on UK parliamentary elections for the same period. Descriptive statistics and significance tests are used to analyse the impact of the political cycle on house price movements in the UK.FindingsWhile there is no evidence that house prices in the UK performed significantly differently under different political parties, the authors observed that house prices performed much better in the last year before an election compared to the first year after an election. On average, house prices increased by 5.3% per annum in the last year before an election compared to 1.3% per annum in the first year following an election.Research limitations/implicationsThe study highlights significant variations in the performance of UK house prices around election times.Practical implicationsIt is imperative that the political cycle is given adequate consideration when making residential property investment decisions.Social implicationsHouse buyers and investors in the residential property market could include the election timings as part of their decision-making process.Originality/valueThis paper represents a unique systematic examination of the influence of the political cycle on residential houses prices in the UK.

Purpose – This paper aims to use Markov switching vector auto regression (MSVAR) methods to examine UK house price cycles in UK regions at NUTS1 level. There is extensive literature on UK regional house price dynamics, yet empirical work focusing on the duration and magnitude of regional housing cycles has received little attention. The research findings indicate that the regional structure of UK exhibits that UK house price changes are best described as two large groups of regions with marked differences in the amplitude and duration of the cyclical regimes between the two groups. Design/methodology/approach – MSVAR principal component analysis NUTS1 data are used. Findings – The housing cycles can be divided into two super regions based on magnitude, duration and the way they behave during recession, boom and sluggish periods. A north-south divide, a uniform housing policy and a monetary policy increase the diversion among the regions. Research limitations/implications – Markov switching needs high-frequency data and long time spans. Practical implications – Questions a uniform housing policy in a heterogeneous housing market. Questions the impact of monetary policy on a heterogeneous housing market. The way the recovery of the housing market varies among regions depends on regional economic performance, housing market structure and the labour market. House price convergence, beta-convergence. Originality/value – No such work has been done looking at duration and magnitude of regional housing cycles. A new econometric method was used.

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.