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This paper aims to document the economic importance of the housing sector, as measured by its contribution to gross domestic product (GDP), which is not fully recognized. In response to the joint economic and health crises caused by the COVID-19 pandemic, there is an opportunity for emerging market countries to develop and implement inclusive housing strategies that stimulate the economy and improve community health outcomes. However, so far housing does not feature prominently in the recovery plans of many emerging market countries.

This paper uses national account data and informal housing estimates for 11 emerging market economies to estimate the contribution of housing investments and housing services to the GDP of these countries.

This paper finds that the combined contribution of housing investments and housing services represents between 6.9% and 18.5% of GDP, averaging 13.1% in the countries with information about both. This puts the housing sector roughly on par with other key sectors such as manufacturing. In addition, if the informal housing sector is undercounted in the official national account figures used in this analysis by 50% or 100%, for example, then the true averages of housing investments and housing services’ contribution to GDP would increase to 14.3% or 16.1% of GDP, respectively.

Further efforts to improve data collection about housing investments and consumption, particularly imputed rent for owner occupiers and informal activity require national government to conduct regular household and housing surveys. Researcher can help make these surveys more robust and leverage new data sources such as scraped housing price and rent data to complement traditional surveys. Better data are needed in order to capture housing contribution to the economy.

The size of the housing sector and its impact in terms of employment and community resilience indicate the potential of inclusive housing investments to both serve short-term economic stimulus and increase long-term community resilience.

The role of housing in the economy is often limited to housing investment, despite the importance of housing services and well-documented methodologies to include them. This analysis highlights the importance of housing to the economy of emerging market countries (in addition to all the non-GDP related impact of housing on welfare) and indicate data limitation that need to be addressed to further strengthen the case for focusing on housing as part of economic recovery plans.

This study aims to explore the effect of Saudi Vision 2030 and its government initiatives on macroeconomic variables related to housing.

This exploratory study used an empirical–analytical approach. Based on secondary data, a set of hypotheses was contrasted to verify whether there has been any change in the trends of macroeconomic variables related to housing after Saudi Vision 2030 entered into force.

The results show that the trend of percentage of housing ownership went from a continuous decrease to accelerated growth since the implementation of Saudi Vision 2030. However, the effect of these advances is not observed in non-oil gross domestic product (GDP) or in the economic activities of the construction, real estate and financial services sectors.

This study notes that despite successful housing outcomes, it appears that Saudi Vision 2030 does not have a positive impact on non-oil GDP. Consequently, government entities should review the degree to which other economic activities contribute to non-oil GDP. A limitation of the study was that the GDP of housing construction and marketing and that of granting mortgage loans were not specifically available, nor were data on public and private investment made for implementing government initiatives.

To the best of the authors’ knowledge, this is the first study to explore the effect of Saudi Vision 2030 on housing and its contribution to the economy.

This paper aims to empirically examine the relationship between macroeconomic factors and housing prices in Malaysia from 1991 to 2016.

This study was based on annual macroeconomic data from 1991 to 2016. For the model that was constructed, house prices were treated as the dependent variable and other variables, namely, the interest rate, the gross domestic product (GDP) and the consumer price index (CPI) as explanatory or regression variables. All these data come from the annual publication of the World Bank’s statistical report. In the meantime, data on house prices have been obtained through the publication of the National Property Information Centre in the form of an index. In the study, the bounds testing approach for cointegration was used because the Engel Granger cointegration tests can be used only if all the series are stationary at first difference. And it is against this background the author could not use it because our variables are a combination of series integrated of orders I (0) and I (1).

The results of this study revealed a positive equilibrium relationship in the long run between interest rate, CPI and housing prices and negative for the GDP. In addition, in the short run, housing prices were shown to have a positive relationship with GDP and CPI, but no relationship with the interest rate, based on the assumption that “all other things being equal”.

The data on economic factors (GDP, RI and CPI) used in this study are secondary and not from the Malaysian Department of Statistics and Economic Studies. Furthermore, the result of this research only reflects the Malaysian reality, and therefore, cannot be generalised to the entire housing market worldwide.

The result of this study can be used for housing valuation by Malaysian property market players, investors and especially policymakers.

This study’s findings can help Malaysian policymakers to limit the high price fluctuation in the housing industry and help Malaysian citizens to buy their own homes at a reasonable price.

The contributions of this study are structured around two points. The first is the contribution to the body of knowledge, where the results of the study will contribute to the growing number of literature in the housing sector. Moreover, the second contribution of this document is the strength of its recommendation to policymakers. This is because it analyses only the main macroeconomic determinants (IR, GDP and CPI) that are essential to better influence the housing sector as quickly as possible, as opposed to those that use many variables that could lead to possible specification errors.

The purpose of this paper is to examine how global liquidity affects international housing prices. The data sample covers 35 economies from 2000Q1 to 2017Q4.

The existing papers seldom investigated whether the impacts of global liquidity on housing prices display differences between advanced and developing economies. Cesa-Bianchi et al. (2015) is an exceptional study in that they focused on the impulse response of house price volatility to global liquidity shocks but did not examine the long-run equilibrium relationship. To fill the gap in the existing research, this paper used panel cointegration of Pedroni (2000, 2004) to estimate the long-run linkage between global liquidity and housing prices in both advanced and developing economies, and generalized impulse response function (GIRF) and generalized variance decomposition (GVDC) were also applied to capture the relative strengths and contribution of global liquidity shock on house price volatility.

First, the global liquidity elasticity of housing prices is 0.0679 in developing economies, and 0.0454 in advanced economies, implying that the positive effect of global liquidity on housing prices is higher in developing economies. Next, the results of generalized impulse response indicate that the innovation of global liquidity can significantly and positively impact housing prices only in developing economies and the duration is two quarters. Third, in light of the long-run portions of the global liquidity shock on house price volatility in individual economies, the two highest portions are 28.51% in the USA and 20.04% in the UK, while there are low portions, less than 10%, in most of the European economies. Moreover, comparing the long-run contributions of global liquidity and other variables shock on house price volatility, the contribution of the global liquidity shock ranks the highest or second highest in 21 out of 35 economies, confirming that it played a more important role than other economic variables in explaining house price volatility for most economies.

Compared with the related literature, the contributions of this paper are as follows. First, except for Cesa-Bianchi et al. (2015), the existing papers seldom investigated whether the impacts of global liquidity on housing prices display differences between advanced and developing economies. Hence, the study adopted a wider data sample, including 7 developing economies and 28 advanced economies, to examine the differences in the impact of global liquidity on housing prices between advanced and developing economies. Second, most of the relative literature calculated global liquidity by applying a monetary aggregate, such as M2 or M3, while Cesa-Bianchi et al. (2015) argued that global liquidity being measured by the international supply of credit is intuitively connected to housing prices. This paper follows the argument of Cesa-Bianchi et al. (2015) to use the international supply of credit as the measure of global liquidity, and both the long-run effects and the short-run relative strengths of global liquidity on housing prices are analyzed. Hence, this paper uses not only the GIRF to discuss the short-run relative strengths, as with Cesa-Bianchi et al. (2015) but also the panel cointegration of Pedroni (2000, 2004) to identify the long-run linkage between global liquidity and housing prices. Moreover, GVDC was used to estimate the contribution of a global liquidity shock on house price volatility in individual economies, which can confirm that global liquidity innovations are a very important factor in explaining house price volatility in most countries.

Most households in Sub-Saharan African cannot afford adequate housing. Most often, their pension benefits are also meagre, usually resulting from low contribution levels and mismanagement. Coupled with low life expectancies, most would not live to enjoy the benefits of pensions, thus validating the need to utilize their hitherto deferred pension benefits for immediate housing investment and consumption.

Quantitative research methodology via the present value technique was used in valuing pension benefits to demonstrate the potential of pension schemes as savings mobilization mechanisms for long-term pension-backed housing financing in Ghana.

Policy wise, the paper provides some evidence to support proposals for the development of pension-backed housing finance systems in Ghana with lessons for Sub-Saharan Africa. The authors demonstrate that the Tier 2 defined contribution mandatory occupational pension scheme could serve the purpose of a savings mobilization mechanism for long-term housing financing. The authors observe that by increasing the Tier 2 contribution rate to 30 per cent, the majority of the sample, mainly of the middle-income class, could accumulate between US$11,000 and US$17,000 over their working life. At the same rate, between US$5,783 and US$9,550 could have been raised as savings between 2010 (when implementation began) and 2014. This could form a substantial equity contribution in a mortgage investment and or borrowed on a housing microfinance basis.

The paper contributes to the ongoing debate on the need to develop alternate savings mechanisms and collateral assets using pension assets, other than property, for mortgage financing. The proposals made are aimed at influencing policy by way of advocating for the use of latent pension equity to improve the housing conditions of members while they are alive, and also to suggest pension-backed housing financing as an alternative investment option. A comprehensive study would be required to settle issues of scalability, pricing and model design.

The purpose of this paper is to analyse how London Interbank Offered Rate Index (LIBOR) and the spread between LIBOR and the base rate of interest as set by the Bank of England (BoE) influences the variation in house prices in the UK.

This paper uses monthly data over a long time series, since 1986, to investigate the relationships between house price and LIBOR. Data are drawn from several different sources to include housing, financial and macro-economic variables. The time series is sub-divided into a series of splines based on stages in the economic and property market cycle. Both value-based and percentage change models are developed.

The results show that BoE base/LIBOR margin variable has a strong positive and significant effect on house price; however, the percentage change model infers a weaker and inverse relationship. The spline analysis re-emphasised the significance of the BoE base/LIBOR margin variable. Where variation between base rates and LIBOR is reduced, a significant positive effect can be observed in the average house price; however, where significant variation exists, the BoE base/LIBOR margin has little effect and LIBOR itself becomes a significant driver.

The results highlight that the predictive qualities of the BoE base/LIBOR margin, as the contribution of this margin to the explanation of house price, exceeds both the base rate and LIBOR variables individually. Also highlighted is the contribution of unemployment to the explanation of house price. In both the value and percentage change models, unemployment is shown as a negative and highly significant contributor.

Previous papers have demonstrated the important linkage between house price and interest rates, the originality in this paper lies in examining the impact of LIBOR and the spreads between LIBOR and base rate as key variables influencing variation in UK house prices.

Several studies have examined the impact of market fundamentals on house prices. However, the effect of economic sectors on housing prices is limited despite the existence of two-speed economies in some countries, such as South Africa. Therefore, this study aims to examine the impact of mining activities on house prices. This intends to understand the direction of house price spreads and their duration so policymakers can provide remediation to the housing market disturbance swiftly.

This study investigated the effect of mining activities on house prices in South Africa, using quarterly data from 2000Q1 to 2019Q1 and deploying an auto-regressive distributed lag model.

In the short run, we found that changes in mining activities, as measured by the contribution of this sector to gross domestic product, impact the housing price of mining towns directly after the first quarter and after the second quarter in the non-mining cities. Second, we found that inflationary pressure is instantaneous and impacts house prices in mining towns only in the short run but not in the long run, while increasing housing supply will help cushion house prices in both submarkets. This study extended the analysis by examining a possible spillover in house prices between mining and non-mining towns. This study found evidence of spillover in housing prices from mining towns to non-mining towns without any reciprocity. In the long run, a mortgage lending rate and housing supply are significant, while all the explanatory variables in the non-mining towns are insignificant.

These results reveal that enhanced mining activities will increase housing prices in mining towns after the first quarter, which is expected to spill over to non-mining towns in the next quarter. These findings will inform housing policymakers about stabilising the housing market in mining and non-mining towns. To the best of the authors’ knowledge, this study is the first to measure the contribution of mining to house price spillover.

The purpose of this paper is to evaluate the impact of macroeconomic condition and real estate price trend on the amount of residential loan.

The paper using a sample of 16 European Countries for the time period 2007–2015 evaluates the impact of change in the gross domestic product (GDP) growth and the inflation rate on the amount of residential loans. The analysis is performed by using a vector autoregressive (VAR) and generalized VAR approach for the full sample and for each country considered.

For a short-term horizon, shocks to mortgages, the house price index (HPI) and the GDP have a positive effect on the GDP, a shock to the amount of mortgages has a positive effect on the mortgage supply and a shock to the GDP has a negative effect on HPI. The main results for the long-term horizon are that a GDP shock has a positive and persistent effect on the amount of mortgages, a shock to HPI has a negative and persistent effect on mortgages and a shock to the amount of mortgages seems to have no persistent effect on the GDP or the HPI. Moreover, the analysis shows that a spillover risk among countries exists and a GDP shock in a European area has an effect on the GDP, real estate prices and residential mortgages in almost all European countries.

Results obtained show that both macroeconomic and housing prices shocks matter for the real estate lending and the effect are different in the short- and in the medium–long-term horizon. Results are also different country by country and they are affected by the level of financial development of the country.

The paper studies a lending crisis period and evaluates for the European market the impact of shock on macro-variables for mortgages focusing the attention for the first time only on residential mortgages.