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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.

MSVAR principal component analysis NUTS1 data are used.

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.

Markov switching needs high-frequency data and long time spans.

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.

No such work has been done looking at duration and magnitude of regional housing cycles. A new econometric method was used.

Studies into ripple effects have previously focused on the interconnections between house price movements across cities over space and time. These interconnections were widely investigated in previous research using vector autoregression models. However, the effects generated from spatial information could not be captured by conventional vector autoregression models. This research aimed to incorporate spatial lags into a vector autoregression model to illustrate spatial‐temporal interconnections between house price movements across the Australian capital cities.

Geographic and demographic correlations were captured by assessing geographic distances and demographic structures between each pair of cities, respectively. Development scales of the housing market were also used to adjust spatial weights. Impulse response functions based on the estimated SpVAR model were further carried out to illustrate the ripple effects.

The results confirmed spatial correlations exist in housing price dynamics in the Australian capital cities. The spatial correlations are dependent more on the geographic rather than the demographic information.

This research investigated the spatial heterogeneity and autocorrelations of regional house prices within the context of demographic and geographic information. A spatial vector autoregression model was developed based on the demographic and geographic distance. The temporal and spatial effects on house prices in Australian capital cities were then depicted.

The purpose of this paper is to empirically analyze volatility properties of the house price returns of Turkey and Istanbul, Ankara and Izmir provinces over the period of July 2007-June 2014.

The paper uses conditional variance models, namely, ARCH, GARCH and E-GARCH. As the supportive approach for the discussions, we also use correlation analysis and qualitative inputs.

Empirical findings suggest several points. First, city/country-level house price return volatility series display volatility clustering pattern and therefore volatilities in house price returns are time varying. Second, it seems that there were high (excess) and stable volatility periods during observation term. Third, a significant economic event may change country/city-level volatilities. In this context, the biggest and relatively persistent shock was the lagged negative shocks of global financial crisis. More importantly, short-lived political/economic shocks have not significant impacts on house price return volatilities in Turkey, Istanbul, Ankara and Izmir. Fourth, however, house price return volatilities differ across geographic areas, volatility series may show some co-movement pattern. Fifth, volatility comparison across cities reveal that Izmir shows more excess volatility cases, Ankara recorded the highest volatility point and Istanbul and national series show lower and insignificant volatilities.

The study uses maximum available data and focuses on some house price return volatility patterns. The first implication of the findings is that micro/macro dimensions of house price return volatilities should be carefully analyzed to forecast upside/downside risks of house price returns. Second, defined volatility clustering pattern implies that rate of return of housing investment may show specific patterns in some periods and volatile periods may result in some large losses in the returns. Third, model results generally suggest that however data constraint is a major problem, market participants should analyze regional idiosyncrasies during their decision-making in housing portfolio management. Fourth, because house prices are not sensitive to relatively less structural shocks, housing may represent long-term investment instrument if it provides satisfactory hedging from inflation.

The evidences and implications would be useful for housing market participants aiming to manage/use externalities of housing price movements. From a practical contribution perspective, the study provides a tool that will allow measuring first time of the return volatility patterns of house prices in Turkey and her three biggest provinces. Local level analysis for Istanbul, Ankara and Izmir provinces, as the globally fastest growing cities, would be found specifically interesting by international researchers and practitioner.

In this paper, we identify the relationship between (local) housing supply and (local) house price developments, especially in The Netherlands.

We measure the influence of new building on house prices by comparing areas designated for concentrated new building (main Dutch cities) with areas where no large housing projects are developed. On the basis of classical economic theory, if the housing market is functioning as it should, then supply will soon respond to a shock in demand and restore stability in house prices.

For the main Dutch cities, we found that an increase in supply triggers a fall in prices. In other areas the correlation coefficients are more or less zero, which can lead us to conclude that the expansion of the housing stock is market‐compliant.

The housing market is not functioning, as it should: new supplies depend on the complex decisions of the suppliers, thus making it difficult to express statistically the causality between the house price developments and the new supplies.

Most studies suggest that macro data are unable to measure the true dependency between the house prices and the new building and claim at the same time that micro data sets are incomplete. Also our research was hampered by a shortage of usable data.

New building can push up the value of the surrounding housing because it is associated with a qualitatively better housing stock. We conclude that in regions where new building has been concentrated in designated areas, the relationship between housing production and price development is inverse.

The purpose of this paper is to examine the sensitivity of the Kuwait housing market to major local and regional geo-political and economic events.

This paper examines the market dynamics of the housing market in Kuwait. Kuwait provides an interesting market to consider owing to its position as a major oil producer, its sensitivity to geo-political events and its unusual demographic characteristics.

The error-correction model highlights that market is relatively volatile, with evidence of mean-reverting behaviour. Only when the data is smoothed are their more consistent findings with respect to underlying fundamentals. This paper also examines the response of the market to seven regional and local events. Of particular interest is that the one event that results in a consistent significant response is domestic legislation directly concerned with housing. This has a far greater impact than local or regional geo-political events.

Very few papers have considered how economic and political shocks directly impact housing markets using an event study approach. Given its geographic location and also its economic dependence on oil, Kuwait is an interesting market to consider.

Increasing regional wealth disparities have been explained by the role of agglomeration economies and the concentration of skilled mobile human capital. This paper aims to draw out the role of the housing market by considering the differential experience of Germany and the UK.

The empirical analysis is based on the comparison of regional house price trends in Germany and UK-based annual data from 1991 to 2015.

Regional house price inequality is found to have increased in both countries with the spatial concentration of skilled human capital. However, the main conclusion is that there are differential paths to regional house price inequality explained by the parameters of each country’s housing market.

The research is the first to compare and explain differential regional house price trends across countries.

This paper aims to examine the linkage of regional housing markets between Taiwan and China as increasing economic integration.

Two 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.

First, 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.

Although 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.

There has been declining home ownership and increased acceptance of long-term renting in many western countries including Australia; this has created a problem when examining housing markets as there are dual demand and include both owner-occupiers and investors. The purpose of this paper is to examine the long-run relationship between house prices, housing supply and demand, and to estimate the effects of the two types of demand (i.e. owner-occupier and investor) on house prices.

The econometric techniques for cointegration with vector error correction models are used to specify the proposed models, where the housing markets in the Australian states and territories illustrate the models.

The results highlight the regional long-run equilibrium and associated patterns in house prices, the level of new housing supply, owner-occupier demand for housing and investor demand for housing. Different types of markets were identified.

The findings suggest that policies that depress the investment demand can effectively prevent the housing bubble from further building up in the Australian states. The empirical findings shed light in the strategy of maintaining levels of housing affordability in regions where owner-occupiers have been priced out of the housing market.

There has been declining home ownership and increased acceptance of long-term renting in many western countries including Australia; this has created a problem when examining housing markets as there are dual demand and include both owner-occupiers and investors. This research has given to the relationship between supply and dual demand, which includes owner-occupation and investment, for housing and the influence on house prices.

This paper aims to examine the integration of housing markets in Canada by examining housing price data (1999–2016) of six metropolitan areas in different provinces, namely, Calgary, Vancouver, Winnipeg, Toronto, Montreal and Halifax. The authors test for cointegration, driver cities of long-run relationships, long-run Granger causality and instantaneous causality in light of the global financial crisis (GFC) (2007–2008).

The authors use Johansen’s system cointegration approach with structural breaks. Moving average representation is used for common stochastic trend(s) analysis. Finally, the authors apply vector error correction model-based Granger causality and instantaneous causality.

Cities’ housing prices are in long-run equilibrium. Post-crisis Canadian housing markets became more integrated. The Calgary, Vancouver, Toronto and Montreal markets drive the Canadian housing market, leading all cities toward long-run equilibrium. Strong long-run Granger causality exists, but the authors observe no instantaneous causality. Price information takes time to disseminate, and long-run price adjustments play a significant role in causation.

The findings of cointegration increasing after the GFC and strong lead–lag can be used by investors to arbitrage and optimize portfolios. This can also help national and local policymakers in mitigating risk. Incorporating these findings can lead to better price forecasting.

This study presents many novelties for the Canadian housing market: it is the first to use repeat-sales regional pricing indices to test long-run behaviors, conduct common stochastic trend analyzes and present causality relations.

This paper aims to examine the dynamics of house prices in metropolitan cities in an emerging economy. The purpose of this study is to characterise the house price dynamics and the spatial heterogeneity in the dynamics.

The author explores spatial heterogeneity in house price dynamics, using data for 35 Indian cities with a million-plus population. The research methodology uses panel econometrics allowing for spatial heterogeneity, cross-sectional dependence and non-stationary data. The author tests for spatial differences and analyses the income elasticity of prices, the role of construction costs and lending to the real estate industry by commercial banks.

Long-term fundamentals drive the Indian housing markets, where wealth parameters are stronger than supply-side parameters such as construction costs or availability of financing for housing projects. The long-term elasticity of house prices to aggregate household deposits (wealth proxy) varies considerably across cities. However, the elasticity estimated at 0.39 is low. The highest coefficient is for Ludhiana (1.14), followed by Bhubaneswar (0.78). The short-term dynamics are robust and show spatial heterogeneity. Short-term momentum (lagged housing price changes) has a parameter value of 0.307. The momentum factor is the crucial dynamic in the short term. The second driver, the reversion rate to long-term equilibrium (estimated at −0.18), is higher than rates reported from developed markets.

This research applies to markets that require some home equity contributions from buyers of housing services.

Stakeholders can characterise stable housing markets based on long-term fundamental value and short-run house price dynamics. Because stable housing markets benefit all stakeholders, weak or non-existent mean reversion dynamics may prompt the intervention of policymakers. The role of urban planners, and local and regional governance, is essential to remove the bottlenecks from the demand side or supply side factors that can lead to runaway prices.

Existing literature is concerned about the risk of a housing bubble due to relaxed credit norms. To prevent housing market bubbles, some regulators require higher contributions from home buyers in the form of equity. The dynamics of house prices in markets with higher owner equity requirements vary from high-leverage markets. The influence of wealth effects is examined using novel data sets. This research, documents in an emerging market context, the observations cited in low-leverage developed markets such as Germany and Japan.