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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.

The purpose of this study was to investigate the dynamic interactive link between housing prices, stock market price and effective exchange rate in the Iranian economy for a monthly period from April, 2004, to March, 2019. In addition, for a more accurate analysis, three control and determinates variables including real interest rate, real GDP and FDI have been added to the base model.

For this purpose, we will consider this issue by developing the study of Lean & Smyth (2014), Ali & Zaman (2017) and Coskun et al (2017) in the framework of ADRL and NARDL models. Also, this study analyzed the asymmetric/non-linear impact of stock market indexes and effective exchange rate on Iran’s housing inflation. Asymmetries imply to both positive and negative changes in the variables.

The results obtained from the ADRL and NARDL models suggest that the existence of cointegration relationship between housing market price and its determinants. From linear model, we found that the exchange rate and stock market price have a positive effect on the real estate inflation in the short run; this relationship is also confirmed in the long run. Other empirical results indicate that the GDP stimulates housing price in both long and short run cases, while FDI and real interest rate have an opposite effect. In addition, the results provided by the asymmetric model lead to the rejection of the null hypothesis of no co-integration between the variables. In addition, we found that the effect of stock price in the short and long term are asymmetric and there also is an asymmetric long-run effect of real exchange rate on the real estate price.

Finally, to analyze the sensitivity, we entered two explanatory variables of inflation and money supply to the baseline equation. The finding represented that in both linear and nonlinear framework, a positive correlation between these two variables with housing prices have been proved.

The purpose of this paper is to create an endurance index of housing investor sentiment and use it to forecast housing stock returns. This study performs not only in-sample and out-of-sample forecasting, like many previous studies did, but also a true forecasting by using all lag terms of independent variables. In addition, an evaluation procedure is applied to quantify the quality of forecasts.

Using a binomial probability distribution model, this paper creates an endurance index of housing investor sentiment. The index reflects the probability of the high or low stock price being the close price for the Philadelphia Stock Exchange Housing Sector Index. This housing investor sentiment endurance index directly uses housing stock price differentials to measure housing investor reactions to all relevant news. Empirical results in this study suggest that the index can not only play a significant role in explaining variations in housing stock returns but also have decent forecasting ability.

Results of this study reveal the considerable forecasting ability of the index. Monthly forecasts of housing stock returns have an overall accuracy of 51 per cent, while the overall accuracy of 8-quarter rolling forecasts even reaches 84 per cent. In addition, the index has decent forecasting ability on changes in housing prices as suggested by the strong evidence of one-direction causal relations running from the endurance index to housing prices. However, extreme volatility of housing stock returns may impair the forecasting quality.

The endurance index of housing investor sentiment is easy to construct and use for forecasting housing stock returns. The demonstrated predictability of the index on housing stock returns in this study can have broad implications on housing-related business practices through providing an effective forecasting tool to investors and analysts of housing stocks, as well as housing policy-makers.

Despite different investor sentiment proxies suggested in the previous studies, few of them can effectively predict stock returns, due to some embedded limitations. Many increases and decreases inn prices cancel out each other during the trading day, as many unreliable sentiments cancel out each other. This dynamic process reveals not only investor sentiment but also resilience or endurance of sentiment. It is only long-lasting resilient sentiment that can be built in the closing price. It means that the only feasible way to use investor sentiment contained in stock prices to forecast future stock prices is to detach resilient investor sentiment from stock prices and construct an index of endurance of investor sentiment.

The purpose of this study, the nonlinear relationship between the real estate market and the stock market was investigated in Iran. For this intent, the monthly data from 2012:4 to 2022:5 is used.

In this study, the quantile-on-quantile estimation method is used, which is a combination of the nonparametric estimation methods and the quantile regression.

The research results show that, in the low quantiles, the effect of stock market return on the housing market return is negative or zero. In fact, in this situation, the increasing returns in the stock market will shift part of the financial resources of the economy to the market and create stagnation or even negative returns in the housing market. This situation is seen more strongly in some other quantiles, including the 0.25 and 0.75 quantiles; in contrast, the effect of high quantiles of stock market returns is positive on the housing market.

It seems that the demand in the housing market increase in a situation where the returns of the stock market are growing, and the market is in a bullish condition, and this causes an increase in the price and returns in this market. In addition, the results show that the effect of stock market returns on capital market returns is asymmetric and nonlinear.

The purpose of this paper is to examine the causal relationship between Chinese housing and stock markets. The authors discuss the three transmission mechanisms between the two markets: wealth effect, modern portfolio theory and credit-price effect. Moreover, the authors focus on the effects of inflation on the relationship between the two markets.

This paper uses wavelet analysis to test the housing and stock markets relationship both in the frequency domain and time domain.

The empirical results indicate that housing prices have a positive effect on stock prices, and these have the same effect on housing prices. Moreover, this positive effect means that stock prices have a wealth effect on housing prices and these have a credit-price effect on stock prices.

These results provide information to financial institutions and individual investors in China to assist them in constructing investment portfolios within these two asset markets.

The authors first use wavelet analysis to analyze Chinese housing and stock markets and to provide information both on the frequency domain and time domain. Moreover, the authors take the inflation factor as a control variable in the causal relationship between the housing and stock markets.

The purpose of this study is to examine the linkages between Australian house prices and stock prices under the Toda and Yamamoto test framework. Specifically, it investigated whether there is a capital switching effect between house prices and stock prices.

This study examined the linkages between house prices and stock prices under the Toda and Yamamoto test framework. To accommodate the impact of the global financial crisis (GFC), a sub-period analysis was undertaken. To assess the impact of investor structure, the tests were also performed for small cap stocks and large cap stocks individually.

The empirical results reveal a negative lead–lag relationship between house prices and stock prices in Australia, suggesting the existence of capital switching activities between housing and stocks. The impact of the GFC on the lead–lag relationship between house prices and stock prices is also documented. Before the crisis, a causality transmission was running from house prices to stock prices, whilst stock prices appeared to lead house prices after the crisis. The capital switching activities between housing and stocks are more evident for small cap stocks.

This study is the first to examine the linkages between house prices and stock prices under the Toda and Yamamoto test framework. This is the first study to explore the impacts of the GFC on the lead–lag relationship between the two asset prices under the capital switching framework. This study is also the first to provide empirical evidence regarding the existence of capital switching activities between housing and stocks. In addition, the impact of investor structure on the interrelationship between the two asset prices is examined for the first time under the capital switching framework.

The purpose of this study is to investigate the Granger causal link between the stock market index and housing prices in terms of apartment and villa prices.

Monthly data from September 2005 to October 2013 on apartment prices, villa prices, the stock market index, mortgage rates and the consumer price index were used. Statistical methods were applied to explore the long-run co-integration and Granger causal link between the stock market index and apartment and villa prices in Sweden.

The results indicate that the stock market index and housing prices are co-integrated and that a long-run equilibrium relationship exists between them. According to the Granger causality tests, bidirectional relationships exist between the stock market index and apartment and villa prices, respectively, supporting the wealth and credit-price effects. Moreover, variations in apartment and villa prices are primarily caused by endogenous shocks.

To the authors’ best knowledge, this study represents a first analysis of the causal nexus between the stock market and the housing market in terms of apartment and villa prices in the Swedish context using a vector error-correction model to analyze monthly data.

Assessing the strength and time variation of spillovers between returns on residential real estate, real estate investment trusts (REITs), stocks and bonds in the United States. Spillovers reduce the benefits of portfolio diversification, especially in crisis times, when asset returns tend to be more correlated.

The Diebold–Yilmaz approach in the time domain and the Baruník–Krehlík methodology in the frequency domain are used. The latter allows distinguishing spillovers generating only short-lived volatility from those with a more persistent effect.

On average, spillovers between housing, stock and bond returns are relatively modest and shocks to stock and bond markets affect housing returns more than the other way round, even though with variations over time. Spillovers in both directions are much stronger between REITs and stocks than between REITs and housing. Shocks originating in the housing market are most persistent, particularly in the aftermath of the subprime crisis.

Housing provides a hedge against volatility in financial (including REITs) markets. However, hedging strategies involving housing need to take into account potential tail events such as the GFC and the investment horizon.

To the best of the knowledge of the authors, this paper is the first to apply the Baruník–Krehlík methodology to real estate price spillovers. Although the Diebold–Yilmaz methodology has been used in several studies on spillovers between residential real estate and financial asset returns, this paper covers a new set of variables and time span.

The purpose of this paper is to present information on the construction technology used to build Dublin City Council’s (DCC’s) housing stock, with an emphasis on wall construction.

The methodology applied was a mix of literature review and archival research. The research was undertaken as part of PhD research exploring the energy upgrade of a housing stock.

The research uncovered details of the construction technology used in the construction of DCC’s housing stock, especially wall construction. These details disprove perceptions and assumptions made on the evolution of construction technology in Dublin and Ireland.

The research is limited in that it primarily focused on the period between 1887 to the introduction of the 1991 Building Regulations. Further research is required on both DCC’s housing stock and the Irish housing stock to identify the specific changes in construction technology.

It is hoped this research will be a foundation for further research on the evolution of house construction technology, and housing stock asset intelligence in Ireland.

This research provides information for researchers and professionals with an interest in the evolution of Irish house construction technology. This is an area which has not received significant attention in Irish built-environment research.

The purpose of this paper is to explore the long‐run relation and short‐run dynamic correlations between consumption expenditure and household wealth, namely housing wealth and stock wealth.

This paper adopts aggregate time‐series data over the period of 1981Q1‐2010Q4 in Hong Kong. It employs the ARDL to cointegration procedure and the multivariate stochastic volatility (MSV) model to investigate the long‐run elasticity and dynamic correlations between aggregate consumption expenditure and household wealth indicators.

The results suggest that both housing wealth and stock wealth have significant effects on consumption expenditure after controlling for the aggregate income level. The long‐run elasticity of consumption expenditure with respect to housing wealth and stock wealth are 0.3877 and 0.1424 respectively, while the marginal propensity to consume for housing wealth and for stock wealth are 0.2159 and 0.0266 respectively. The dynamic correlation analysis implies that the decrease in housing and stock wealth may further depress consumer behavior and economic condition during the post‐financial crisis period.

This paper provides useful information with regard to the long‐run and dynamic relations between consumption and different types of wealth components.