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The housing market in Kenya continues to experience an excessive imbalance between supply and demand. This imbalance renders the housing market volatile, and stakeholders lose repeatedly. The purpose of the study was to forecast housing prices (HPs) in Kenya using simple and complex regression models to assess the best model for projecting the HPs in Kenya.

The study used time series data from 1975 to 2020 of the selected macroeconomic factors sourced from Kenya National Bureau of Statistics, Central Bank of Kenya and Hass Consult Limited. Linear regression, multiple regression, autoregressive integrated moving average (ARIMA) and autoregressive distributed lag (ARDL) models regression techniques were used to model HPs.

The study concludes that the performance of the housing market is very sensitive to changes in the economic indicators, and therefore, the key players in the housing market should consider the performance of the economy during the project feasibility studies and appraisals. From the results, it can be deduced that complex models outperform simple models in forecasting HPs in Kenya. The vector autoregressive (VAR) model performs the best in forecasting HPs considering its lowest root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and bias proportion coefficient. ARIMA models perform dismally in forecasting HPs, and therefore, we conclude that HP is not a self-projecting variable.

A model for projecting HPs could be a game changer if applied during the project appraisal stage by the developers and project managers. The study thoroughly compared the various regression models to ascertain the best model for forecasting the prices and revealed that complex models perform better than simple models in forecasting HPs. The study recommends a VAR model in forecasting HPs considering its lowest RMSE, MAE, MAPE and bias proportion coefficient compared to other models. The model, if used in collaboration with the already existing hedonic models, will ensure that the investments in the housing markets are well-informed, and hence, a reduction in economic losses arising from poor market forecasting techniques. However, these study findings are only applicable to the commercial housing market i.e. houses for sale and rent.

While more research has been done on HP projections, this study was based on a comparison of simple and complex regression models of projecting HPs. A total of five models were compared in the study: the simple regression model, multiple regression model, ARIMA model, ARDL model and VAR model. The findings reveal that complex models outperform simple models in projecting HPs. Nonetheless, the study also used nine macroeconomic indicators in the model-building process. Granger causality test reveals that only household income (HHI), gross domestic product, interest rate, exchange rates (EXCR) and private capital inflows have a significant effect on the changes in HPs. Nonetheless, the study adds two little-known indicators in the projection of HPs, which are the EXCR and HHI.

The COVID-19 pandemic and the onset of the conflict in Ukraine led to a sustained downturn in tourist arrivals (TA) in Russia. This paper aims to explore the influence of geopolitical risk (GPR) and other indices on TA over 1995–2023.

We employ a nonlinear autoregressive distributed lag (NARDL) model to analyze the effects, capturing both the positive and negative shocks of these variables on TA.

Our research demonstrates that the NARDL model is more effective in elucidating the complex dynamics between macroeconomic factors and TA. Both an increase and a decrease in GPR lead to an increase in TA. A 1% negative shock in GPR leads to an increase in TA by 1.68%, whereas a 1% positive shock in GPR also leads to an increase in TA by 0.5%. In other words, despite the increase in GPR, the number of tourists coming to Russia increases by 0.5% for every 1% increase in that risk. Several explanations could account for this phenomenon: (1) risk-tolerant tourists: some tourists might be less sensitive to GPR or they might find the associated risks acceptable; (2) economic incentives: increased risk might lead to a depreciation in the local currency and lower costs, making travel to Russia more affordable for international tourists; (3) niche tourism: some tourists might be attracted to destinations experiencing turmoil, either for the thrill or to gain firsthand experience of the situation; (4) lagged effects: there might be a time lag between the increase in risk and the actual impact on tourist behavior, meaning the effects might be observed differently over a longer period.

Our study, employing the NARDL model and utilizing a dataset spanning from 1995 to 2023, investigates the impact of GPR, gross domestic product (GDP), real effective exchange rate (REER) and economic policy uncertainty (EPU) on TA in Russia. This research is unique because the dataset was compiled by the authors. The results show a complex relationship between GPR and TA, indicating that factors influencing TA can be multifaceted and not always intuitive.

While Blockchain can serve us, Bitcoin threatens our survival. If Bitcoin is assumed to be a country, it will rank 38th globally for energy consumption. With 90.2 metric million tonnes of carbon dioxide, Bitcoin mining and trading has emerged as an environmental threat. The current study investigates how the trading-specific variables, the prices of Crypto Index and Ethereum, affect bitcoin-based energy consumption. Also, the role of mining-specific variables is analyzed.

The study uses monthly data from various sources collected from December 2018 to January 2023. The authors used the Autoregressive Distributed Lag (ARDL) Model to determine the short- and long-term relationships between variables. This study uses the Theory of Green Marketing and the Theory of Cross Elasticity of Demand as a theoretical lens.

The findings show that escalating crypto market index and Ethereum prices with a one-month lag increases bitcoin-specific electricity consumption and carbon emissions. Green investors may shift to cryptocurrencies based on consensus other than of Proof-of-Work. Ethereum behaves like a substitute for Bitcoin, reflected by the long-term positive relationship between Bitcoin's energy consumption and Ethereum prices.

The study analyses how the crypto market index and Ethereum price affect bitcoin-based energy use. The relationships identified are substantiated by the literature to provide suggestions to green investors and policymakers to mitigate the harmful impact of Bitcoin's colossal energy consumption on the natural environment.

In a first of its kind, this paper tries to explore the long-run relationship between investors' sentiment and selected industries' returns over the period January 2010 to December 2021.

The paper uses 23 market and macroeconomic proxies to measure investor sentiment. Principal component analysis has been used to create sentiment sub-indices that represent investor sentiment. The autoregressive distributed lag (ARDL) model and other sophisticated econometric techniques such as the unit root test, the cumulative sum (CUSUM) stability test, regression, etc. have been used to achieve the objectives of the study.

The authors find that there is a significant relationship between sentiment sub-indices and industries' returns over the period of study. Market and economic variables, market ratios, advance-decline ratio, high-low index, price-to-book value ratio and liquidity in the economy are some of the significant sub-indices explaining industries' returns.

The study has relevant implications for retail investors, policy-makers and other decision-makers in the Indian stock market. Results are helpful for the investor in improving their decision-making and identifying those sentiment sub-indices and the variables therein that are relevant in explaining the return of a particular industry.

The study contributes to the existing literature by exploring the relationship between sentiment and industries' returns in the Indian stock market and by identifying relevant sentiment sub-indices. Also, the study supports the investors' irrationality, which arises due to a plethora of behavioral biases as enshrined in classical finance.

This study aims to assess how real income, oil prices and gold prices affect housing prices in China from 2010 to 2021.

This study uses a novel bootstrap autoregressive distributed lag (ARDL) testing to empirically analyze the short and long links among the tested variables.

The ARDL estimations demonstrate a positive impact of oil price shocks and real income on housing market prices in both the phrases of the short and long run. Furthermore, the results reveal that gold price shocks negatively affect housing prices both in the short and long run. The result can be attributed to China’s housing market and advanced infrastructure, resulting in a drop in housing prices as gold prices increase. Additionally, the prediction of housing market prices will provide a base and direction for housing market investors to forecast housing prices and avoid losses.

To the best of the authors’ knowledge, this is the first attempt to analyze the effect of gold price shocks on housing market prices in China.

Digitalisation has become closely related to various economic sectors in terms of economic impact and discovery of new technologies. In this regard, this study aims to examine the relationship between the digital economy, as measured by four proxies (infrastructure, empowerment of society, technological economic growth and digitalisation development), and the productivity and monetary system of Oman from 1985 to 2019.

The autoregressive distributed lag methodology and diagnostic tests were used to increase the robustness of findings.

The analysis showed significant positive long-run relationships between infrastructure (measured as the number of fixed telephone subscriptions), technological economic growth (measured as medium- and high-tech exports as a percentage of manufactured exports) and the monetary system. There was also a significant negative short-run relationship between digitalisation development, measured as the number of individuals (percentage of the population) using the internet, and the monetary system. Furthermore, there were significant positive short- and long-run relationships between digitalisation development and productivity. Only short-run relationships were identified between empowerment of society, measured as the number of mobile cellular subscriptions, and productivity.

The conclusions support the paradigm of diffusion of innovation theory, which aims to understand the use of modern technologies to obtain the maximum economic benefit, and show both the dark and bright sides of technology. Furthermore, the effect of the digitalisation economy paradigm on productivity should be determined by increasing logistical services. This will support the growth of foreign and domestic investments and promote cooperation between the public and private sectors, thereby achieving digitalisation in Oman and enabling reflection on the country’s monetary policy development and economic growth.

This study aims to investigate the dynamic interaction between interest rates and commercial property financing offered by Islamic banks in Malaysia.

The authors use the autoregressive distributed lag (ARDL) cointegration methodology to analyse the short- and long-run effect of the interest rates and rental rates on commercial property financing of Islamic banks in Malaysia between 2010: Q1 and 2018: Q2.

The findings reveal that changes in interest rates affect Islamic commercial property financing. This indicates that Islamic banks still rely on interest rates as a benchmark without fully implementing Islamic rental rates. This corroborates the subsequent finding, where overnight policy rates influence commercial property financing.

Despite the authors’ attempt to provide insights into Islamic commercial property financing, the study is limited to secondary data; further research can use survey information to obtain other details that are not included in this study. Similarly, this study does not cover the operation and financial lease debate in Musharakah Mutanaqisah. Future studies can examine the challenges faced by the financial institution towards implementing rental rates in other emerging and developing countries using a different methodology.

This study is the first to investigate the dynamic changes in overnight policy rates, average lending rates and rental rates on Islamic commercial property financing in Malaysia using ARDL techniques. The authors uncover the research and institutional implications of Islamic commercial property financing rates and provide policy and future research directions coupled with the proposed modified rental rate to be developed.

Renewable energy is at the forefront of countries’ concerns due to its global economic and environmental impacts. Previous studies have thoroughly examined the impact of renewable energy on overall national income, and this paper aims to shed light on an indicator that has received insufficient attention in research regarding its impact on economic growth, using data from 2000 to 2018.

This study examines the causal relationship between trade balance, renewable energy consumption and CO₂ emissions per capita in Organization for Economic Cooperation and Development (OECD) countries using an auto regression distributed lag model (ARDL) and Johansen Cointegration Test.

The findings reveal that there is evidence of a long-run and short-run cointegrating relationship and that renewable energy consumption in the long run impacts the trade balance positively and in the short run negatively.

Therefore, bioenergy trade between countries and local investment should be prioritized to increase the trade balance surplus, since many of OECD countries suffer from deficit problems.

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 objective of this study is to explore the dynamic inter-linkage between foreign aid and economic growth in Sri Lanka over the period of 1960–2018.

Both exploratory and inferential data analysis tools have been employed to examine the objective of this study. The exploratory data analysis covered the scatter plots, confidence ellipse with kernel fit. The inferential data analysis included the augmented Dickey–Fuller (ADF) and Phillips–Perron (PP) unit root tests, the autoregressive distributed lag (ARDL) Bounds co-integration technique and the Granger causality test.

The test result of exploratory data analysis indicates that there is a positive relationship between foreign aid and economic growth. The ADF and PP unit root tests results indicate that the variables used in this study are stationary at their 1st difference. The co-integration test result confirms the presence of long-run relationship between foreign aid and economic growth in Sri Lanka. The estimated coefficient of foreign aid in the long-run and the short-run shows that foreign aid has a positive relationship with economic growth in Sri Lanka. The estimated coefficient of error correction term indicates that approximately 26.6% of errors are adjusted each year and further shows that the response variable of economic growth moves towards the long-run equilibrium path. The Granger causality test result shows that foreign aid in short-run Granger causes economic growth in Sri Lanka which means that one-way causality from foreign aid to economic growth is confirmed. Further, the estimated coefficient of error correction term confirms that there is the long-run Granger causal relationship between foreign aid and economic growth in Sri Lanka.

The findings of this study have some important policy implications for the design of efficient policy related to foreign aid and economic growth, the knowledge of which will help follow sustainable foreign aid and growth nexus.

This study contributes to the existing literature by using the newly introduced ARDL Bounds cointegration technique to investigate the dynamic inter-linkage between foreign aid and economic growth in Sri Lanka.