Search results

1 – 10 of 649
Article
Publication date: 3 January 2024

Halim Yusuf Agava and Faoziah Afolashade Gamu

This study evaluated the effect of macroeconomic factors on residential real estate (RE) investment returns in the cities of Abuja and Lagos, Nigeria, with a view to guiding RE…

Abstract

Purpose

This study evaluated the effect of macroeconomic factors on residential real estate (RE) investment returns in the cities of Abuja and Lagos, Nigeria, with a view to guiding RE investors and researchers.

Design/methodology/approach

A survey research design was employed using a questionnaire to collect RE transaction data from 2008 to 2022 from estate surveying and valuation firms in the study areas. Rental and capital value data collected were used to construct rental and capital value indices and total returns on investment. The macroeconomic data used were retrieved from the archives of the Central Bank of Nigeria (CBN). Granger causality (GC) and multiple regression models were adopted to evaluate the effect of selected macroeconomic variables on residential RE investment returns in the study areas.

Findings

The study found a progressive upward movement in rental and capital values of residential RE investment in the study areas within the study period. Total and risk-adjusted returns on investment were equally positive within the study period. Only the inflation rate, unemployment rate and real gross domestic product (GDP) per capita were found to be the major determinants of residential RE investment returns in the study areas within the study period.

Research limitations/implications

The secrecy associated with property transaction information/data by RE practitioners in the study areas posed a challenge. Property transaction data were not adequately kept in a way for easier access and retrieval in many of the estate firms and agent offices. Consequently, there was a lack of data that spanned the study period in some of the sampled estate firms or agent offices. This data collection challenge was, however, overcome by the excess time spent retrieving the required data for this study to ensure that the findings appropriately answer the research questions.

Practical implications

Inflation and GDP per capita have been found to be significant factors that influence residential RE investment performance in the study areas. Therefore, investors should pay attention to these identified macroeconomic factors for residential RE investment in the study areas whilst making investment decisions in order to mitigate a possible loss of income or return. The government should formulate and implement economic policies that would address the current high unemployment and inflation rates in Nigeria at large.

Originality/value

This study has extended and further enriched the existing body of knowledge in the field of RE investment analysis in Nigeria. To the best of the authors' knowledge, this study is the first to adopt the Cornish Fisher value-at-risk and modified Sharpe ratio models to analyse risk and risk-adjusted returns on residential RE investment, respectively, in Nigeria. It has therefore redirected the focus of RE researchers and practitioners to a more objective approach to RE investment performance analysis in Nigeria.

Details

Journal of Property Investment & Finance, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1463-578X

Keywords

Open Access
Article
Publication date: 30 September 2021

Kesuh Jude Thaddeus, Chi Aloysius Ngong, Njimukala Moses Nebong, Akume Daniel Akume, Jumbo Urie Eleazar and Josaphat Uchechukwu Joe Onwumere

The purpose of this paper is to examine key macroeconomic determinants on Cameroon's economic growth from 1970 to 2018.

3592

Abstract

Purpose

The purpose of this paper is to examine key macroeconomic determinants on Cameroon's economic growth from 1970 to 2018.

Design/methodology/approach

Data were obtained from the World Development Indicators and applied on time series data econometric techniques. The auto-regressive distributed lag (ARDL) bounds model analyzed the data since the variables had different order of integration.

Findings

The results showed long and short runs’ positive and significant connection between economic growth in Cameroon and government expenditure; trade openness, gross capital formation and exchange rate. Human capital development, foreign aid, money supply, inflation and foreign direct investment negatively and significantly affected economic growth in the short and long-runs. Hence, the macroeconomic indicators are not death.

Research limitations/implications

The present research paper has tried to capture the impact of nine macroeconomic determinants on economic growth such as the government expenditure (LNGOVEXP), human capital development (LNHCD), foreign aids (AID), trade openness (LNTOP), foreign direct investment (LNFDI), gross capital formation (INVEST), broad money (LNM2), official exchange rate (LNEXHRATE) and Inflation (LNINFLA). However, these variables have the tendency to affect each other in a unidirectional or bidirectional manner. Further, the present research paper is unable to capture the impact of other macroeconomic variable due to the unavailability of data.

Practical implications

The study recommends that Cameroon should use proper planning and strategic policy interventions to achieve higher sustainable economic growth with human capital development, foreign aid, money supply, foreign direct investment and moderate inflation.

Social implications

Macroeconomic indicators, if managed well, increase economic growth.

Originality/value

This paper to the best of the researcher's knowledge presents new background information to both policymakers and researchers on the main macroeconomic determinants using econometric analysis.

Details

Journal of Business and Socio-economic Development, vol. 4 no. 1
Type: Research Article
ISSN: 2635-1374

Keywords

Open Access
Article
Publication date: 14 March 2024

Andreas Joel Kassner

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…

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Practical implications

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.

Originality/value

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.

Details

Journal of European Real Estate Research, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1753-9269

Keywords

Article
Publication date: 27 February 2024

Valery Yakubovsky and Kateryna Zhuk

This study aims to provide a comprehensive analysis of various approaches to the residential property market evolution modelling and to examine the macroeconomic fundamentals that…

Abstract

Purpose

This study aims to provide a comprehensive analysis of various approaches to the residential property market evolution modelling and to examine the macroeconomic fundamentals that have shaped this market development in Ukraine in recent years.

Design/methodology/approach

The study uses a comprehensive data set encompassing relevant macroeconomic indicators and historical apartment prices. Multifactor linear regression (MLR) and ridge regression (RR) models are constructed to identify the impact of multiple predictors on apartment prices. Additionally, the ARIMAX model integrates time series analysis and external factors to enhance modelling and forecasting accuracy.

Findings

The investigation reveals that MLR and RR yield accurate predictions by considering a range of influential variables. The hybrid ARIMAX model further enhances predictive performance by fusing external indicators with time series analysis. These findings underscore the effectiveness of a multidimensional approach in capturing the complexity of housing price dynamics.

Originality/value

This research contributes to the real estate modelling and forecasting literature by providing an analysis of multiple linear regression, RR and ARIMAX models within the specific context of property price prediction in the turbulent Ukrainian real estate market. This comprehensive analysis not only offers insights into the performance of these methodologies but also explores their adaptability and robustness in a market characterized by evolving dynamics, including the significant influence of external geopolitical factors.

Details

International Journal of Housing Markets and Analysis, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1753-8270

Keywords

Article
Publication date: 18 August 2023

Mahmoud Arayssi and Noura Yassine

This paper aims to estimate a statistical model of the country risk determination as represented by the country price earnings ratio (PE) to identify potentially mispriced…

Abstract

Purpose

This paper aims to estimate a statistical model of the country risk determination as represented by the country price earnings ratio (PE) to identify potentially mispriced countries. It uses the gross domestic product (GDP) growth rate and a dummy indicator for market-related events (i.e. financial crises), both approximating the business cycle. The model is used to compare a major Asian country’s (i.e. Japan) risk with Western countries’ risk.

Design/methodology/approach

The model used finance variables such as the systemic, non-diversifiable, risk and foreign direct investments to characterize any country risk. A random effects model with panel data estimated the effects of macroeconomic and financial variables on PE. The simultaneity problem was checked using two stage least squares and some lagged independent variables.

Findings

The results explained to investors the country risk contributing factors: PE was positively correlated with variables that may increase dividends and market risk premia similar to GDP growth rates and total risk and negatively correlated with variables that increase market risk, namely, nominal risk-free interest rates and financial crises. Japan’s PE seemed to exceed most of the Western countries considered here, implying lower risks, lower interest rates and higher growth in the major Asian country Japan.

Originality/value

This paper focuses on the effectiveness of country risk measures in predicting periods of intense instability, similar to financial crises. This study contributes a model to measure market risk premium, using PE (or inversely, the earnings yield) as a proxy variable. Investors can use this risk measure in picking less risky stocks to include in their portfolio, calling for liberalizing Asian countries’ financial markets to improve their stock market capitalization.

Details

Journal of Asia Business Studies, vol. 18 no. 1
Type: Research Article
ISSN: 1558-7894

Keywords

Article
Publication date: 16 November 2023

Santi Gopal Maji and Rupjyoti Saha

This study investigates the effect of intellectual capital (IC) and its components on the technical efficiency of Indian commercial banks after controlling the influence of…

Abstract

Purpose

This study investigates the effect of intellectual capital (IC) and its components on the technical efficiency of Indian commercial banks after controlling the influence of bank-specific and macroeconomic variables.

Design/methodology/approach

The study selects a sample of 37 listed Indian commercial banks from 2005 to 2019 and uses the two-step data envelopment analysis (DEA) approach. Banks' technical efficiency scores are first estimated, while the relationship between IC and technical efficiency is examined in the second stage using the panel data Tobit model.

Findings

This study's findings suggest a fluctuating trend in the technical efficiency of Indian banks. Notably, from 2015 onwards, a declining technical efficiency trend is observed for all banks. However, private-sector banks outperform public-sector banks in terms of technical efficiency. This study's regression analysis indicates a positive relationship between IC and banks' technical efficiency scores. Further, by decomposing IC into its components like human capital, structural capital and capital employed, the study's findings show that human capital and structural capital enhance banks' technical efficiency. Notably, capital employed reduces technical efficiency. Moreover, bank size, diversification, capitalization, net interest margin and the country's growth rate significantly drive Indian banks' efficiency. In contrast, their operating cost ratio and the country's inflation negatively influence the same.

Originality/value

This study makes a novel endeavor to examine the IC and bank's technical efficiency nexus in the Indian context, encompassing a period of landmark banking reforms.

Details

Managerial Finance, vol. 50 no. 4
Type: Research Article
ISSN: 0307-4358

Keywords

Article
Publication date: 11 January 2024

Siti Hafsah Zulkarnain, Abdol Samad Nawi, Miguel Angel Esquivias and Anuar Husin

The purpose of this study is designed to achieve the learning process in producing studies involving economic issues and scenarios in business management in Malaysia. In addition…

Abstract

Purpose

The purpose of this study is designed to achieve the learning process in producing studies involving economic issues and scenarios in business management in Malaysia. In addition, this study will provide exposure to the integration of managerial skills by using both microeconomics and macroeconomics concepts and theories to aid decision-making in a business environment.

Design/methodology/approach

The research method comprised qualitative methodology of literature review, case study and quantitative methodology of multiple linear regression (MLR). In this case, seven microeconomics and macroeconomics factors which are believed to significantly affect house price index (HPI) are taken into consideration which includes gross domestic product, consumer price index (CPI), government tax and subsidy on housing, overnight policy rate, unemployment rate (UNEMP), the median income (INC) and cost of production index.

Findings

This research has resulted in three significant factors affecting HPI from MLR, which include CPI, UNEMP and INC where the increase of these factors will cause a high increment of HPI. The other four factors are not significant.

Originality/value

Malaysia has been facing the stagnancy in house market these recent years due to issues such as massive oversupply, impacting Malaysia’s economy specifically focusing on domestic direct investment. To avoid oversupply issues, the vitality of future house demand and pricing forecast should be comprehended by involved bodies for more effective planning for the house development industry. To make a better and bigger impact, this research is intended to analyse the microeconomic and macroeconomic factors affecting the HPI to better understand the significance of each of these factors to the changes of HPI to resolve these economic issues.

Details

International Journal of Housing Markets and Analysis, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1753-8270

Keywords

Article
Publication date: 16 September 2022

Xin Janet Ge, Vince Mangioni, Song Shi and Shanaka Herath

This paper aims to develop a house price forecasting model to investigate the impact of neighbourhood effect on property value.

Abstract

Purpose

This paper aims to develop a house price forecasting model to investigate the impact of neighbourhood effect on property value.

Design/methodology/approach

Multi-level modelling (MLM) method is used to develop the house price forecasting models. The neighbourhood effects, that is, socio-economic conditions that exist in various locations, are included in this study. Data from the local government area in Greater Sydney, Australia, has been collected to test the developed model.

Findings

Results show that the multi-level models can account for the neighbourhood effects and provide accurate forecasting results.

Research limitations/implications

It is believed that the impacts on specific households may be different because of the price differences in various geographic areas. The “neighbourhood” is an important consideration in housing purchase decisions.

Practical implications

While increasing housing supply provisions to match the housing demand, governments may consider improving the quality of neighbourhood conditions such as transportation, surrounding environment and public space security.

Originality/value

The demand and supply of housing in different locations have not behaved uniformly over time, that is, they demonstrate spatial heterogeneity. The use of MLM extends the standard hedonic model to incorporate physical characteristics and socio-economic variables to estimate dwelling prices.

Details

International Journal of Housing Markets and Analysis, vol. 17 no. 2
Type: Research Article
ISSN: 1753-8270

Keywords

Article
Publication date: 4 September 2023

Oussama Gafrej

This paper aims to evaluate the performance of the multiple linear regression (MLR) using a fixed-effects model (FE) and artificial neural network (ANN) models to predict the…

Abstract

Purpose

This paper aims to evaluate the performance of the multiple linear regression (MLR) using a fixed-effects model (FE) and artificial neural network (ANN) models to predict the level of customer deposits on a sample of Tunisian commercial banks.

Design/methodology/approach

Training and testing datasets are developed to evaluate the level of customer deposits of 15 Tunisian commercial banks over the 2002–2021 period. This study uses two predictive modeling techniques: the MLR using a FE model and ANN. In addition, it uses the mean absolute error (MAE), R-squared and mean square error (MSE) as performance metrics.

Findings

The results prove that both methods have a high ability in predicting customer deposits of 15 Tunisian banks. However, the ANN method has a slightly higher performance compared to the MLR method by considering the MAE, R-squared and MSE.

Practical implications

The findings of this paper will be very significant for banks to use additional management support to forecast the level of their customers' deposits. It will be also beneficial for investors to have knowledge about the capacity of banks to attract deposits.

Originality/value

This paper contributes to the existing literature on the application of machine learning in the banking industry. To the author's knowledge, this is the first study that predicts the level of customer deposits using banking specific and macroeconomic variables.

Details

Managerial Finance, vol. 50 no. 3
Type: Research Article
ISSN: 0307-4358

Keywords

Article
Publication date: 26 March 2024

Donia Aloui and Abderrazek Ben Maatoug

Over the last few years, the European Central Bank (ECB) has adopted unconventional monetary policies. These measures aim to boost economic growth and increase inflation through…

Abstract

Purpose

Over the last few years, the European Central Bank (ECB) has adopted unconventional monetary policies. These measures aim to boost economic growth and increase inflation through the bond market. The purpose of this paper is to study the impact of the ECB’s quantitative easing (QE) on the investor’s behavior in the stock market.

Design/methodology/approach

First, the authors theoretically identify the transmission channels of the QE shocks to the stock market. Then, the authors empirically assess the financial market’s responses to QE shocks in a data-rich environment using a factor augmented VAR (FAVAR).

Findings

The results show that the ECB’s unconventional monetary policy positively affects the stock market. A QE shock leads to an increase in stock prices and a drop in the realized volatility and the implied risk premium. The authors also suggest that the ECB’s QE is transmitted to the stock market through five main channels: the liquidity, the expectation, the portfolio reallocation, the interest rates and the risk premium channels.

Practical implications

The findings help to better understand the behavior of stock market assets in a data-rich economic context and guide investors and policymakers in the presence of unconventional monetary tools. For instance, decision-makers and investors should consider the short-term effect of the QE interventions and the changing behavior of the financial actors over time. In addition, high stock market returns can increase risk appetite. This can lead investors to underestimate the market risk. Decision-makers and market participants should take into consideration the impact of the large injection of money through the QE, which may raise the risk of a speculative bubble in the financial market.

Originality/value

To the best of the authors’ knowledge, this is the first study that incorporates a theoretical and empirical analysis to explore QE transmission to the stock market in the European context. Unlike previous studies, the authors use the shadow rate proposed by Wu and Xia (2017) to quantify the effect of the ECB’s QE in a data-rich environment. The authors also include two key risk indicators – the stock market risk premium and the realized volatility – to capture investors’ behavior in the stock market following QE shocks.

Details

Studies in Economics and Finance, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1086-7376

Keywords

1 – 10 of 649