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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.

The purpose of this study is to explore the intricate relationship between oil prices, house prices in the UK and Norway, and the mediating role of gold and stock prices in both the short- and long-term, unraveling these complex linkages by employing an empirical approach.

This study benefits from a comprehensive set of econometric tools, including a multiequation vector autoregressive (VAR) system, Granger causality test, impulse response function, variance decomposition and a single-equation autoregressive distributed lag (ARDL) system. This rigorous approach enables to identify both short- and long-run dynamics to unravel the intricate linkages between Brent oil prices, housing prices, gold prices and stock prices in the UK and Norway over the period from 2005:Q1 to 2022:Q2.

The findings indicate that rising oil prices negatively impact house prices, whereas the positive influence of stock market performance on housing is more pronounced. A two-way causal relationship exists between stock market indices and house prices, whereas a one-way causal relationship exists from crude oil prices to house prices in both countries. The VAR model reveals that past housing prices, stock market indices in each country and Brent oil prices are the primary determinants of current housing prices. The single-equation ARDL results for housing prices demonstrate the existence of a long-run cointegrating relationship between real estate and stock prices. The variance decomposition analysis indicates that oil prices have a more pronounced impact on housing prices compared with stock prices. The findings reveal that shocks in stock markets have a greater influence on housing market prices than those in oil or gold prices. Consequently, house prices exhibit a stronger reaction to general financial market indicators than to commodity prices.

This study may have several limitations. First, the model does not include all relevant macroeconomic variables, such as interest rates, unemployment rates and gross domestic product growth. This omission may affect the accuracy of the model’s predictions and lead to inefficiencies in the real estate market. Second, this study does not consider alternative explanations for market inefficiencies, such as behavioral finance factors, information asymmetry or market microstructure effects. Third, the models have limitations in revealing how predictors react to positive and negative shocks. Therefore, the results of this study should be interpreted with caution.

These findings hold significant implications for formulating dynamic policies aimed at stabilizing the housing markets of these two oil-producing nations. The practical implications of this study extend to academics, investors and policymakers, particularly in light of the volatility characterizing both housing and commodity markets. The findings reveal that shocks in stock markets have a more profound impact on housing market prices compared with those in oil or gold prices. Consequently, house prices exhibit a stronger reaction to general financial market indicators than to commodity prices.

These findings could also serve as valuable insights for future research endeavors aimed at constructing models that link real estate market dynamics to macroeconomic indicators.

Using a variety of econometric approaches, this paper presents an innovative empirical analysis of the intricate relationship between euro property prices, stock prices, gold prices and oil prices in the UK and Norway from 2005:Q1 to 2022:Q2. Expanding upon the existing literature on housing market price determinants, this study delves into the role of gold and oil prices, considering their impact on industrial production and overall economic growth. This paper provides valuable policy insights for effectively managing the impact of oil price shocks on the housing market.

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.

The purpose of this paper is to compare responses of house prices in three important markets when faced with permanent and temporary shocks to income. It additionally decomposes each historical house price series into its permanent, temporary and deterministic components.

Using quarterly data over 1973‐2008, two‐variable systems of house prices and income are specified for three major house‐owning economies: New Zealand (NZ), the United Kingdom (UK) and the United States of America (USA).

NZ and UK housing markets are sensitive to both permanent and temporary shocks to income, while the US market reacts to temporary shocks with the permanent component having a largely insignificant role to play in house price composition. In NZ, the temporary component of house prices has tended to be positive over time, pushing prices higher than they would have been otherwise; while in the UK, both permanent and temporary components have tended to reinforce each other.

The paper uses state‐of‐the‐art methods to analyse the relationships between income and house prices in three economies.

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.

The study looks at the characteristics of upswings and downswings for UK housing cycles. Specifically, the purpose of this paper is to empirically analyse cycles in house prices and housing affordability on the characteristics of persistence, magnitude and severity.

The paper draws upon the triangular methodology of cycles and utilises housing data from the last three decades.

From an empirical perspective, the study obtained four main results. First, the graphical trajectory of cycles in house price and housing affordability is highly synchronized. Second, upturns in both cycles tend to be longer than downturns on average. Third, the recent upturn in house prices and housing affordability is characterised by larger duration, magnitude and severity than the earlier case. Fourth, the latest downturn in both cycles is highly synchronised in terms of time occurrence, persistence, magnitude and severity; in addition, in both cases, the latest downturn is considerably smaller than the previous one. The study additionally indicates that on average the length of a complete house price and housing affordability cycle is 19 years on a peak-to-peak basis.

This paper is essentially exploratory and raises a number of questions for further investigation. Future research should, first, arrive at a more nuanced definition of affordability and, second, examine causality. The fact that two phenomena appear to have some significant synchronicity is not an indication that they are interdependent, although logic would suggest they might be.

This is among the few papers that analyses cycles in UK house prices. It is the first study that draws attention to the housing affordability cycle and the first to compare cycles in house prices with cycles in housing affordability.

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.

The purpose of this paper is to examine the role of monetary liquidity in house price evolution through examining the Asset (housing) Inflation channel. It identifies the main channels of transmission affecting house prices from monetary supply channels to house price change, examining how the Asset Price channel transmits changes in M1 to housing prices in Spain and the UK.

The paper uses Vector Auto Regression (VAR) and Error Correction models to test the Asset Inflation channel in the UK and Spain from 1991 to 2013 in two steps. In the first step, the supply elasticity is estimated through the long-term relationship between house prices and stock supply. The second step estimates a Vector Error Correction (VEC) to explain house price dynamics conditioned on supply reactions. The latter is defined as a long-term inverse demand model where housing prices are controlled by fundamentals in each market. Models allow forecast testing using Choleski impulse responses methodology.

Several results are found. In the supply model, both countries show rapid convergence to equilibrium with a larger elasticity of supply in Spain than in the UK but with a short run effect of new supply on prices in the UK. Regarding the Asset Inflation Channel model, the paper finds evidence of the existence of a housing accelerator effect in Spain, but not in the UK where changes in liquidity fully impact house prices in one direction.

Implications of findings are mainly to forecast the effects of Monetary Policy measures in different economies.

The model supports the evaluation of different impacts of monetary policy in territories. It shows that the same policy will have different impacts in different housing markets and therefore highlights the importance of examining each market separately to identify the appropriate policy interventions.

This is the first paper that estimates the impact of the Asset Inflation Channel on house prices that endogenises housing market conditions and compares effects and interrelationships in two different economies.

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.

The 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.

The 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.

While 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.

The study highlights significant variations in the performance of UK house prices around election times.

It is imperative that the political cycle is given adequate consideration when making residential property investment decisions.

House buyers and investors in the residential property market could include the election timings as part of their decision-making process.

This paper represents a unique systematic examination of the influence of the political cycle on residential houses prices in the UK.