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This study investigated the asymmetric effects of changes in policy uncertainty on real sector variables in Brazil, China, India and South Africa.

The study used the nonlinear autoregressive distributed lag (NARDL) modeling framework.

The results showed that both in the long run and short run, rising uncertainty not only increases consumer prices significantly in these economies, but also impedes aggregate and sectoral output growths, and deters investment, employment and private consumption. Contrary to economic expectation, the results also showed that in the long run, declining uncertainty impedes aggregate and sectoral output growths in these economies, and significantly hinders employment in South Africa and Brazil. This suggests that in the long run, economic agents in these economies somewhat behave as if uncertainty is rising. The authors also found significant asymmetric effects in the response of real sector variables to uncertainty both in the long run and short run, which justifies the choice of NARDL framework for this study.

The sample is limited to Brazil, India, China and South Africa. While Brazil, India and China are three of the most prominent large emerging market economies, South Africa is the largest emerging market economy in Africa.

To lessen the adverse effects of policy uncertainty observed in the results, there is need for sound institutions and policy regimes that can promote predictable policy responses in these economies so that policy neither serves as a source of uncertainty nor as a channel through which the effects of other shocks are transmitted.

Apart from using the NARDL framework to capture the asymmetric effects of policy uncertainty, this study also accounted for the sectoral effects of uncertainty in emerging markets.

This study aims to provide an empirical investigation into rising capital flight and the role of institutional quality to mitigate its effect on the real sector in sub-Saharan Africa (SSA).

The study uses the system generalized method of moments and uses data spanning from 1989 to 2020 from 26 SSA countries.

The findings show that capital flight has no direct impact on the real sector while institutional quality adversely impacted the agricultural and industrial sectors. The study also found that institutional quality is unable to mitigate the effect of capital flight on the industrial sector.

This study investigates if institutional quality mitigates the impact of capital flight on the real sector proxied by industrial value-added and agriculture value-added.

This study aims to examine the impact of intangible investment on non-financial performance. This study also examines the moderating effect of the COVID-19 pandemic on this relationship.

This study extracted data from annual reports for a sample of Egyptian firms from 2012 to 2020. This study used the generalized method of moment for testing research.

This study finds that intangible investment positively affects non-financial performance and the COVID-19 pandemic has weakened this positive effect.

A small sample size is one of the limitations of this study. Furthermore, because of the lack of data in Egypt, the analysis does not include other measures of intangible investment. Finally, the sectoral analysis does not include all sectors because of the lack of observations in some sectors.

This study offers practical and social implications. It would help policymakers, regulators and shareholders to realize the importance of the intangible investment and also shed light on the consequences of the COVID-19 pandemic. It also offers managerial implications. It motivates managers to invest more in intangible investment as an important resource to increase customer satisfaction and loyalty, enhance the internal operating performance and improve learning and growth, which result in creating sustainable competitive advantage.

This study provides new empirical evidence on the impact of intangible investment on different dimensions of non-financial performance. To the best of the authors’ knowledge, this paper offers the first empirical evidence on the moderating role of the COVID-19 pandemic in the relationship between intangible investment and non-financial performance.

Foreign direct investment in the real estate (FDIRE) sector is required to bridge the gap between investment needed and domestic funds. Further, foreign direct investment is gaining importance because other sources of raising finance such as External Commercial Borrowing and foreign currency convertible bonds have been banned in the Indian real estate sector. Therefore, the objective of the study is to explore the determinants attracting foreign direct investment in real estate and to assess the impact of those variables on foreign direct investments in real estate.

Johansen cointegration test, vector error correction model along with variance decomposition and impulse response function are employed to understand the nexus of the relationship between various macroeconomic variables and foreign direct investment in real estate.

The results indicate that infrastructure, GDP and tourism act as drivers of foreign direct investment in real estate. However, interest rates act as a barrier.

This article aimed at exploring factors attracting FDIRE along with estimating the impact of identified variables on FDI in real estate. Unlike other studies, this study considers FDI in real estate instead of foreign real estate investments.

Many studies have analysed the impact of various variables on the ability of companies to raise capital. While most of these studies are sector-agnostic, literature on the effects of macroeconomic variables on sectors that established over the last 20 years like property technology and financial technology, is scarce. This study aims to identify macroeconomic factors that influence the ability of both sectors and is extended by real estate variables.

The impact of macroeconomic and real estate related factors is analysed using multiple linear regression and quantile regression. The sample covers 338 observations for PropTech and 595 for FinTech across 18 European countries and 5 deal types between 2000–2001 with each observation representing the capital invested per year for each deal type and country.

Besides confirming a significant impact of macroeconomic variables on the amount of capital invested, this study finds that additionally the real estate transaction volume positively impacts PropTech while the real estate yield-bond-gap negatively impacts FinTech.

For PropTech and FinTech companies and their investors it is critical to understand the dynamic with mac-ro variables and also the real estate industry. The direct connection identified in this paper is critical for a holistic understanding of the effects of measurable real estate variables on capital investments into both sectors.

The analysis fills the gap in the literature between variables affecting investment into firms and effects of the real estate industry on the investment activity into PropTech and FinTech.

This study is based on the consumer purchase intention (CPI) in real estate. The purpose of this study is to investigate the link between CPI and preferred individual investment capacity in buying real estate properties. This study investigates if commonly known factors of CPI such as attitude, social power or subjective norms, perceived behavior power or control, location, surrounding environment and socialization can influence a consumer’s preferred investment amount when buying a house, either for own use or for rental purpose.

A total of 334 respondents participated in this study. The survey data was analyzed using factor analysis technique, ordinary least square technique and Poisson pseudo maximum likelihood technique.

This study finds that location, surrounding environment, property/construction papers, roads, mosque/temple and fire services significantly influence the preferred investment amount of a real estate investor.

This study suggests that a link exists between CPI and real estate investment decision through factors such as location, surrounding environment, legal documentation and communication facility. These identified CPI factors require serious consideration by the real estate developers and their financing partners.

This paper examines the main drives of encouraging Iranian investors in the Turkish real estate market, focusing on the interface between push factors and pull factors that drive them abroad.

This paper examines the main drives of encouraging Iranian investors in the Turkish real estate market, focusing on the interface between push factors and pull factors that drive them abroad. For this purpose, the trend of housing price growth in Iran and Turkey was compared. The review of the 11-year trend of rates shows that housing prices in both countries have been continuously rising, and these prices have undoubtedly experienced increasing shocks in Iran. For further analysis, 13 main variables leading to the repulsion of investment in Iran's housing market and 15 variables shaping the attractiveness of investment in Turkey were identified in this sector. Thirty experts subsequently ranked the significant variables based on a closed-end questionnaire using quantitative strategic planning matrix. Examining housing investment elasticity in Turkey also shows that “Turkey's economic stability compared to neighboring countries” and “acquiring Turkish citizenship through real estate investment” are among the most important variables. On the other hand, the pressure variables of housing investment in Iran were “decrease in the value of the Iranian currency in recent years,” “currency price fluctuations” and “severe fluctuations and instability in the Iranian housing market.”

Examining housing investment elasticity in Turkey also shows that “Turkey's economic stability compared to neighboring countries” and “acquiring Turkish citizenship through real estate investment” are among the most important variables. On the other hand, the pressure variables of housing investment in Iran were “decrease in the value of the Iranian currency in recent years,” “currency price fluctuations” and “severe fluctuations and instability in the Iranian housing market.”

From a theoretical standpoint, foreign investment is in support of Turkey and harmful to Iran because the Turkish government is bolstering investment attractiveness to bring increased capital inflows into this country. Practically speaking, Turkey has aimed to create a rational framework for investors by strengthening and changing its economic system, as well as amending existing constitutions in this domain. Nevertheless, Iran resists any changes in its economic system and legislation. Therefore, a wide range of attractiveness and repulsion variables has led to the migration of Iranian investors to Turkey. In the present study, such variables are illuminated.

The purpose of this paper is to assess the long-run and short-run drivers of real house prices in Nigeria from 1991Q1 to 2020Q4.

Vector autoregression and cointegration tests were used to assess the key drivers of Nigeria’s real house prices in the long run and short run.

The empirical findings revealed that household disposable income is the most important determinant of house prices in Nigeria. House prices increased by 1.6% and 60.8% in response to a 1% increase in disposable income in the long run and short run, respectively, while real mortgage credits pushed up house prices by 5% and have no long-run effects, suggesting that most Nigerians depend on their money income rather than credits in securing a home. In addition, prices of oil sector products and real interest rates had negative and significant relationship with house prices, while positive correlations were found for real effective exchange rate and real housing investments regardless of the time horizon. The impact of construction costs and cement prices was also documented.

This is likely a pioneering study of its kind to focus on the determinants of real house prices in Nigeria. It is probably the first study, the best of the author’s knowledge, to empirically examine the impact of the oil sector on house prices in the country.

The present study aimed to examine the effect of received remittance inflows on the price level ratio of the purchasing power parity conversion factor to the market exchange rate in 36 developed and developing countries from 2004 to 2020.

The panel data conducted a comparative analysis and used panel least squares, regression with Driscoll-Kraay standard errors of fixed effect, random effect, feasible generalised least squares and maximum likelihood robust least squares to overcome the heterogeneity issue. Furthermore, the two-step difference generalised method of moments to overcome the endogeneity issue. Diagnostic tests were used to increase robustness.

In the studied countries, there was a statistically significant negative relationship between received remittance inflows and the price-level ratio of the purchasing power parity conversion factor to the market exchange rate. This relationship explains why remittance flows depreciate the real exchange rate. The study’s results also indicated that attracting investments can improve the quality of institutions despite high tax rates, leading to low tax revenue.

The current study findings enrich the understanding of policies of how governments should minimise tariff rates on capital imports and introduce export-oriented incentive programmes. The study also revealed that Dutch disease can occur due to differences in the demand structure and manufacturing development policy.

Mauritius’s residential real estate sector has undergone an increase in foreign investment over the past decades. This study aims to establish if the increasing level of foreign real estate investments (FREI) has increased land demand and land prices. The study also aims to depict whether the relation between FREI and land prices prevails at an aggregate and/ or a regional level.

Data from 26 regions, classified as urban, rural and coastal is collected on an annual basis over the period 2000 to 2019, and a dynamic panel regression framework, namely, an autoregressive distributed lag model, is used to take into account the dynamic nature of land price modeling.

The findings show that, at the aggregate level, in the long-term, FREI does not have a significant influence on land prices, while in the short term, a positive significant relationship is noted between the two variables. A regional breakdown of the data into urban, rural and coastal was done. In the long term, only in coastal regions, a positive significant link was observed, whereas in urban and rural regions FREI did not influence land prices. In the short term, the positive link subsists in the coastal regions, and in rural regions also land prices are positively affected by FREI.

Unlike other studies which have used quite general measures of FREI, the present research has focused on FREI mainly undertaken in the residential real estate market and how these have affected residential land prices. This study also contributes to research on the determinants of land prices which is relatively scarce compared to research on housing prices.