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.

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.

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.

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.

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.

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.

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

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.

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.

Housing is a major component of wealth. Since house prices fluctuate considerably over time, it is important to understand how these fluctuations affect households' consumption decisions. Rising house prices may stimulate consumption by increasing households' perceived wealth, or by relaxing borrowing constraints. This paper investigates the response of household consumption to house prices using UK micro data. We estimate the largest effect of house prices on consumption for older homeowners, and the smallest effect, insignificantly different from zero, for younger renters. This finding is consistent with heterogeneity in the wealth effect across these groups. It suggests that as the population ages and becomes more concentrated in the old homeowners group, aggregate consumption may become more responsive to house prices. In addition, we find that regional house prices affect regional consumption growth. Predictable changes in house prices are correlated with predictable changes in consumption, particularly for households that are more likely to be borrowing constrained, but this effect is driven by national rather than regional house prices and is important for renters as well as homeowners, suggesting that UK house prices are correlated with aggregate financial market conditions.

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

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

Housing is a major component of wealth. Since house prices fluctuate considerably over time, it is important to understand how these fluctuations affect households' consumption decisions. Rising house prices may stimulate consumption by increasing households' perceived wealth, or by relaxing borrowing constraints. This paper investigates the response of household consumption to house prices using UK micro data. We estimate the largest effect of house prices on consumption for older homeowners, and the smallest effect, insignificantly different from zero, for younger renters. This finding is consistent with heterogeneity in the wealth effect across these groups. In addition, we find that regional house prices affect regional consumption growth. Predictable changes in house prices are correlated with predictable changes in consumption, particularly for households that are more likely to be borrowing constrained, but this effect is driven by national rather than regional house prices and is important for renters as well as homeowners, suggesting that UK house prices are correlated with aggregate financial market conditions.

Housing is a major component of wealth. Since house prices fluctuate considerably over time, it is important to understand how these fluctuations affect households’ consumption decisions. Rising house prices may stimulate consumption by increasing households’ perceived wealth, or by relaxing borrowing constraints. This paper investigates the response of household consumption to house prices using UK micro data. We estimate the largest effect of house prices on consumption for older homeowners, and the smallest effect, insignificantly different from zero, for younger renters. This finding is consistent with heterogeneity in the wealth effect across these groups. In addition, we find that regional house prices affect regional consumption growth. Predictable changes in house prices are correlated with predictable changes in consumption, particularly for households that are more likely to be borrowing constrained, but this effect is driven by national rather than regional house prices and is important for renters as well as homeowners, suggesting that UK house prices are correlated with aggregate financial market conditions.

Inter-regional rail travel and housing markets connectedness between London and other regions