Search results

The purpose of this paper is to validate and quantify the effect of key macroeconomic drivers on London house prices using annual data over the period 1983–2016.

Within this context, the authors estimate alternative error-correction and partial-adjustment models (PAMs), which have been widely used in the empirical literature in modelling the slow adjustments of house prices to demand and supply shocks.

The results verify the existence of a strong long-term relationship between London house prices and key macroeconomic variables, such as UK GDP, London population and housing completions. A key finding of the study relevant to the debate on the causes of the housing affordability crisis is that the results provide little evidence in support of the argument that user demand, which is captured in the author’s model by Greater London population, may have had a diminished role in driving house price inflation in London.

The practical and policy implications of the results are that increased homebuilding activity in London will undoubtedly help limit house price increases. Also, any potential reduction of immigration and economic growth due to Brexit will also have a similar effect.

The originality of this research lies in the use of annual data that may better capture the long-term effect of macroeconomic drivers on house prices and the estimation of such effects through both error-correction and partial-adjustment models.

Examines regional variations in house prices and the rate of house price inflation over the last 20 years. Shows the existence of marked cyclical variations and argues that the frequent downturn of house prices in the South East of England was predictable and will lead to a reduction in the North/South house price divide.

The purpose of this paper is to introduce an empirical model for house price spillovers between real estate markets. The model is presented by using data from the US-UK and London-New York housing markets over a period of 1975Q1-2016Q1 by employing both static and dynamic methodologies.

The research analyzes long-run static and dynamic spillover elasticity coefficients by employing three methods, namely, autoregressive distributed lag, the fully modified ordinary least square and dynamic ordinary least squares estimator under a Kalman filter approach. The empirical method also investigates dynamic correlation between the house prices by employing the dynamic control correlation method.

The paper shows how a dynamic spillover pricing analysis can be applied between real estate markets. On the empirical side, the results show that country-level causality in housing prices is running from the USA to UK, whereas city-level causality is running from London to New York. The model outcomes suggest that real estate portfolios involving US and UK assets require a dynamic risk management approach.

One of the findings is that the dynamic conditional correlation between the US and the UK housing prices is broken during the crisis period. The paper does not discuss the reasons for that break, which requires further empirical tests by applying Markov switching regime shifts. The timing of the causality between the house prices is not empirically tested. It can be examined empirically by applying methods such as wavelets.

The authors observed a unidirectional causality from London to New York house prices, which is opposite to the aggregate country-level causality direction. This supports London’s specific power in the real estate markets. London has a leading role in the global urban economies residential housing markets and the behavior of its housing prices has a statistically significant causality impact on the house prices of New York City.

The house price co-integration observed in this research at both country and city levels should be interpreted as a continuity of real estate and financial integration in practice.

The paper is the first research which applies a dynamic spillover analysis to examine the causality between housing prices in real estate markets. It also provides a long-term empirical evidence for a dynamic causal relationship for the global housing markets.

This paper aims to extend existing research in relation to both the importance of volume effects within housing markets and the specific behaviour of the London housing market. A detailed borough-level examination is undertaken of the relationships between volume, house prices and house price volatility. Support for alternative housing market theories, the degree of heterogeneity in house price behaviour across boroughs and the extent to which housing displays differing properties to other financial assets are examined.

Correlation analyses, causality testing and volatility modelling are undertaken in extended forms which synthesise and extend approaches within the housing, economics and finance literatures. The various modelling and testing techniques are supplemented via the use of alternative variable transformations to evaluate housing market behaviour in detail.

Novel findings are provided concerning both volume effects within housing markets generally and the specific properties of London housing market. Evidence concerning bubbles, the volatility-reducing effects of volume, the importance of geographical and price-related factors underlying the relationship between volume and both house price growth and volatility and the presence of asymmetric adjustment in the London housing market are all provided. The extent and nature of the support available for alternative housing market theories are evaluated.

The volatility-reducing effects of volume within housing markets, along with volume effects and the presence of asymmetric adjustment within the London housing market are examined for the first time. New empirical evidence on the support for alternative housing market theories and the differing empirical characteristics of housing relative to other financial assets are presented.

In the current low-interest market environment, more and more asset managers have started to consider to invest in property markets. To implement adequate and forward-looking risk management procedures, this market should be analyzed in more detail. Therefore, this study aims to examine the housing market data from the UK. More specifically, sentiment data and house prices are examined, using techniques of time-series econometrics suggested by Toda and Yamamoto (1995). The monthly data used in this study is the RICS Housing Market Survey and the Nationwide House Price Index – covering the period from January 2000 to December 2018. Furthermore, the authors also analyze the stability of the implemented Granger causality tests. In sum, the authors found clear empirical evidence for unidirectional Granger causality from sentiment indicator to the house prices index. Consequently, the sentiment indicator can help to forecast property prices in the UK.

By investigating sentiment data for house prices using techniques of time-series econometrics (more specifically the procedure suggested by Toda and Yamamoto, 1995), the research question whether sentiment indicators can be helpful to predict property prices in the UK is analyzed empirically.

The empirical results show that the RICS Housing Market Survey can help to predict the house prices in the UK.

Given these findings, the information provided by property market sentiment indicators certainly should be used in a forward-looking early warning system for house prices in the UK.

To authors’ knowledge, this is the first paper that uses the procedure suggested by Toda and Yamaoto to search for suitable early warning indicators for investors in UK real estate assets.

This 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.

The 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.

Rather 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.

This work only covers two countries and nineteen regions. Gibrat’s law in regional house prices may be better examined using a multi-country analysis.

As 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.

To 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.

This study aims to measure the association between local retail grocery provision and private residential rental prices in England. Renting is an important sector of the housing market in England and local grocery provision is an important aspect of service provision and consumers are known to be highly sensitive to the branding of this type of retailing.

This research uses a novel data source from a property rental Web platform to estimate a hedonic model for the rental market. These models incorporate information on the nature of the properties and their neighbourhoods, with an emphasis on how different retail brands are associated with rental prices. This retail brand is captured on two scales: the provision of local branded convenience stores and the provision of larger stores.

The study finds clear differentials in how the local grocery brand is associated with rental prices. When controlling for commonly explored confounding factors, “Luxury” retailers such as Waitrose and Marks and Spencer are associated with higher rental prices, while “Discounter” retailers are associated with lower rental prices. This finding has many implications, particularly in relation to potential price changes in an already challenging housing market for many people.

This is an observational study and as such only associations (not causation) can be implied by these findings.

The focus of this research is on the private residential property market, an important market in England but one that has enjoyed less scrutiny than the sales or socially rented markets. Rather than using general accessibility to retail, this research has differentiated the association by the retail brand and store size, two very important aspects of consumer choice.

The UK experienced a substantial rise in owner occupation over the twentieth century, and many tenants still aspire to homeownership. These strong aspirations to own are attributed to a set of financial and non-financial benefits. This paper aims to calculate, for the first time, the financial returns from buying versus renting in Britain for first-time buyers in 11 regions.

It applies a DCF approach based on historical housing and mortgage market data from 1975 to 2012.

The paper finds strong evidence that, in purely financial terms, buying has been always superior to renting in all regions of the UK over the period.

It gives a clear message of the financial benefits of homeownership over renting in Britain, even over very short time periods.

The paper is the first to apply a comprehensive DCF model to the choice between renting and owning.

The purpose of this paper is to identify the drivers of residential price as well as the degree co-movement of housing among different states in Malaysia.

This study adopted an advanced econometrics technique: the dynamic autoregressive-distributed lag (DARDL) and – the time-frequency domain approach known as the wavelet coherence test. The DARDL model was applied to identify the cointegrating relationships and the CWT was used to analyze the co-movement and lead–lag relationships among four states’ regional housing prices. The extracted data were mainly on annual basis and comprised macroeconomics and financial factors. Information with regard to residential prices and other variables was extracted from the National Property Information Centre (NAPIC) website, the Central Bank of Malaysia Statistics Report, the Department of Statistics, Malaysia, I-Property.com and the World Bank (WB). The data covered in this study were the pool data from four main states in Malaysia and different categories of residential properties.

The empirical results indicate that there were long-run cointegration relationships between the housing price and capital gain and loss, rental per square feet, disposable income, inflation, number of marriages, deposit rate, risk premium and loan-to-value (LTV) ratio. While the wavelet analysis shows that (1) in the long run, Kuala Lumpur housing price having strong co-movement with Selangor, Penang and Melaka housing prices except for Johor and (2) the lead–lag relationship also postulates Kuala Lumpur housing price having in-phase category with Selangor, Penang and Melaka housing prices except for Johor.

This study offers relevant practical implications. First, the study proposes an active collaboration between the private sector and government support which may help to smooth the pricing issue of residential properties. More low-cost residential projects are needed for focus groups including middle- and low-income earners. Furthermore, the results are expected to provide real estate investor in Malaysia, an improved understanding of the regional housing market price dynamics.

The findings of this study were obtained from various reliable sources; therefore, the results reflected the analysis of price drivers and co-movements. Furthermore, findings from this study lend some support to the argument on the rise of residential prices and offer several policy implications from a practical point of view with regard to the residential market.

Housing prices in the UK offer an inspiring, yet a complex and under-explored research area. The purpose of this paper is to investigate the critical factors that affect UK’s housing prices.

The authors utilize the recently developed nonlinear ARDL approach of Shin et al. (2014) over the period 1969–2016.

The authors find that both the long-run and short-run impact of the price-to-rent (PTR) ratio and credit-to-GDP ratio on house prices (HP) is asymmetric whilst ambiguous results are established for mortgage rates, industrial production and equities. Apart from the novel framework of analysis, this study also establishes a positive association between HP and the PTR ratio which suggests a speculative behaviour and could imply the formation of a housing bubble.

It is the first study for the UK housing market that explores the underlying fundamental relationships by looking at nonlinearities hence, allowing HP to be tied by asymmetric relationships in the long as well as in the short run. Modelling the inherent nonlinearities enhances significantly the understanding of UK housing market which can prove useful for policymaking and forecasting purposes.