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The studies on international housing markets have not modeled frequency domain and focused only on the time domain. The purpose of the present research is to fill this gap by using the state-of-the-art econometric technique of wavelets to understand how differences in the horizon of analysis across time impact international housing markets’ relationship with some of the key macroeconomic variables. The purpose is to also analyze the direction of causation in the relationships.

The author uses the novel time–frequency analysis of international housing markets’ linkages to the macroeconomic drivers. Unlike conventional approaches that do not distinguish between time and frequency domain, the author uses wavelets to study house prices’ relationship with its drivers in the time–frequency space. The novelty of the approach also allows gaining insights into the debates that deal with the direction of causation between house price changes and macroeconomic variables.

Results show that the relationship between house prices and key macroeconomic indicators varies significantly across countries, time, frequencies and the direction of causation. House prices are most related to interest rates at the higher frequencies (short-run) and per capita income growth at the lower frequencies (long-run). The role of industrial production and income growth has switched over time at lower frequencies, particularly, in Finland, France, Sweden and Japan. The stock market’s nexus with the housing market is significant mainly at high to medium frequencies around the recent financial crisis.

The present research implies that in contrast to the existing approaches that are limited to the only time domain, the frequency considerations are equally, if not more, important.

Results show that interested researchers and analysts of international housing markets need to account for the both horizon and time under consideration. Because the factors that drive high-frequency movements in housing market are very different from low-frequency movements. Furthermore, these roles vary over time.

The insights from the present study suggest policymakers interested in bringing social change in the housing markets need to account for the time–frequency dynamics found in this study.

The paper is novel on at least two dimensions. First, to the best of the author’s knowledge, this study is the first to propose the use of a time–frequency approach in modeling international housing market dynamics. Second, unlike present studies, it is the first to uncover the direction of causation between house prices and economic variables for each frequency at every point of time.

While mortgage markets have gradually emerged in many African countries, substantial barriers still hinder their growth and expansion. Affordability has been widely cited as a prominent issue that doggedly remains at the core of urban housing problems. Hence, this paper aims to investigate the determinants of mortgage price affordability.

Data were gathered using semi-structured questionnaires obtained from a sample drawn from three major West African mortgage financing institutions. Respondents rated the variables using a five-point Likert item rating. The survey results were analysed using exploratory factor analysis.

In total, 11 variables that influence mortgage affordability were categorised within five principal components, namely, economic factors, financial factors, property characteristics, developmental factors and geographical factors.

The results provide insightful guidance to policymakers and practitioners on how to mitigate affordability issues within Ghana’s fledgling mortgage market. Failure to address the mortgage price affordability conundrum will place enormous pressure upon social housing and rental accommodation.

The research findings expand existing frontiers of knowledge by investigating and reporting upon the determinants of mortgage price affordability. The work also engenders wider debate on the need to establish mortgage packages targeted at low-to-middle-income earners. The culmination of analysis and debate will provide a robust basis for developing a future housing policy framework.

This paper aims to examine how the exogenous shock of the global financial crisis has had a differential impact on the housing markets of the USA and UK.

The paper begins by examining the nature and dynamics of the global financial crisis. It presents a detailed comparison of institutional and housing market characteristics in each country. A particular focus is the differences in mortgage funding and subprime lending trends over the decade leading up to the financial crisis.

The analysis demonstrates the distinctiveness of the recent housing cycles and the geography of the downward price adjustments. Relative unemployment rates play a key role in these outcomes. Despite the different dynamics of the boom and bust, there is a common legacy in terms of the collapse of house building, repossessions/foreclosures and falling home ownership rates. The short-term policy responses by both governments addressed the same target issues in alternative ways but with different outcomes. Longer-term solutions are still being debated in both countries.

Innovatory insights are provided by the comparison of the sub-national spatial pattern of the recent house price cycle in two countries.

Housing activity is an important indicator of general economic activity, and house price movements are an important variable in international financial markets. In this chapter we utilise vector autoregressive models to examine how the interrelationship between housing activity and general economic activity has evolved in four OECD countries. Our results provide support for the hypothesis that the relationship between housing activity and general economic activity has changed in many OECD countries. For Australia, however, no such evidence was found. These results suggest that caution needs to be exercised when using previous experience to forecast both housing cycles and general economic activity.

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 monetary policy is an important driver of the real estate sector’s performance. The recent wave of monetary tightening in 2022 in response to the cost-of-living crisis has been associated with the decline in housing prices across the globe. There are two main channels through which the US monetary policy may affect the real estate market in the dollar-pegged countries: the cost of serving mortgages (financing cost) and the exchange rate channel (for example, the appreciation of the US dollar and consequently the local currency). The exchange rate channel, which involves the appreciation of the US dollar and the subsequent effect on the local currency, is particularly significant in the case of Dubai, given how international the housing market in Dubai and might be viewed as a tradable good. Using recent data, the purpose of this study to evaluate the spillover impact of the US monetary policy on the housing market performance in the dollar-pegged countries using Dubai as a case study.

For this purpose, this study collected unique longitudinal data on the volume of the monthly transactions of residential properties and performs a panel-data analysis using within-variation models. The changes in the interest rate policy in the USA are determined by the domestic inflation in the USA, thereby, representing an exogenous shock in the UAE.

The results are robust to different specifications and suggest that a strong negative correlation between the interest rate in the USA and the housing sector demand in Dubai. Fiscal policy measures can be taken to mitigate tighter financial conditions in case of policy misalignment.

Few studies have looked at the spillover impact of the global monetary conditions on the real estate market in the GCC region. This study fills this gap by exploring the impact of the US financial conditions on Dubai’s real estate, using panel data analysis.

House price-to-rent (P-t-R) ratios are among the most widely used measures of housing market conditions. Given the theoretical and apparent bidirectional, causal relationships and imbalances between the housing market, broader economy and financial market determinants, it is important to understand the relationship between macro- and micro-economic characteristics in relation to the P-t-R ratio to enhance the understanding of housing market dynamics. This paper studies the joint dynamics and persistence of house prices and rents and examines the temporal interactions of the P-t-R ratio and economic and financial determinants.

The authors examine the lead–lag relationships between the P-t-R ratios and a spectrum of macroeconomic variables using cointegration and causality methods, initially at the aggregate position and also across housing types within the Northern Ireland housing market to establish whether there are subtle differences in how various housing segments react to changes in economic activity and market fundamentals.

The findings reveal price switching dynamics and some very distinct long- and short-run relationships between macroeconomic and financial indicators and the P-t-R ratios across the various housing segments. The results exhibit monetary supply, foreign exchange markets and the stock market to be important drivers of the P-t-R ratio, with P-t-R movements seemingly tied, or are in tandem, with the overall economy, particularly with the construction sector.

The study shows that the P-t-R ratio can be used as an early measure for establishing the effects of macroprudential policy changes and how these may manifest across housing tiers and quality, which can further act as a signal for preventing or at least mitigating future irrational price cyclicity. These insights serve to inform housing and economic policy and macroprudential policy design, principally within lending policy and the effect of regulatory interventions and further enhance the understanding of the P-t-R ratio on housing market structure and dynamics.

This study is the first in the housing literature that examines the causal relationships between the P-t-R ratio and macroeconomic activity at the sub-market level. It investigates whether and how money supply, inflation, foreign exchange markets, general economic productivity and other important macroeconomic factors interact with the pricing of different property types over time.

This paper aims to improve the housing affordability by measuring the housing affordability in a resource-rich economy and studying the impact of implementing new policies.

This paper seeks to test the impact of new policies introduced to the Kuwaiti housing market to improve affordability. In 2008, the Kuwaiti parliament introduced two policies: a tax on empty lands and, forbidding companies to own or develop residential lands or houses.

By constructing the housing affordability index and the price-to-income multiplier using observations from 2004 until 2017, it has been found that affordability has worsened over time regardless of the new policies introduced in 2008. Housing in Kuwait became “severely unaffordable” (equivalent to London in the UK, San Diego in USA and Toronto in Canada).

Even with its unique condition, as a rich country, small population and availability of white land and other resources, the affordability worsened over time. Introducing new policies without solving the central issue of housing supply challenges seems not worth it. This paper is the first of its kind on the Kuwait housing market, and it provides a valuable foundation for future research on this market and similar markets in the region.

The 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.

Structural 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.

The 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.

This 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.

This 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.

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.