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This study aims to investigate the spatial distribution of housing prices and identify the affecting factors (independent variable) on the cost of residential units (dependent variable).

The method of the present study is descriptive-analytical and has an applied purpose. The used statistical population in this study is the residential units’ price in Tehran in 2021. For this purpose, the average per square meter of residential units in the city neighborhoods was entered in the geographical information system. Two techniques of ordinary least squares regression and geographically weighted regression have been used to analyze housing prices and modeling. Then, the results of the ordinary least squares regression and geographically weighted regression models were compared by using the housing price interpolation map predicted in each model and the accurate housing price interpolation map.

Based on the results, the ordinary least squares regression model has poorly modeled housing prices in the study area. The results of the geographically weighted regression model show that the variables (access rate to sports fields, distance from gas station and water station) have a direct and significant effect. Still, the variable (distance from fault) has a non-significant impact on increasing housing prices at a city level. In addition, to identify the affecting variables of housing prices, the results confirm the desirability of the geographically weighted regression technique in terms of accuracy compared to the ordinary least squares regression technique in explaining housing prices. The results of this study indicate that the housing prices in Tehran are affected by the access level to urban services and facilities.

Identifying factors affecting housing prices helps create sustainable housing in Tehran. Building sustainable housing represents spending less energy during the construction process together with the utilization phase, which ultimately provides housing at an acceptable price for all income deciles. In housing construction, the more you consider the sustainable housing principles, the more sustainable housing you provide and you take a step toward sustainable development. Therefore, sustainable housing is an important planning factor for local authorities and developers. As a result, it is necessary to institutionalize an integrated vision based on the concepts of sustainable development in the field of housing in the Tehran metropolis.

The purpose of the paper is to examine the impact of infrastructure investment and development on economic growth in Brazil, Russia, India, China and South Africa (BRICS) countries. The effect is examined for each country separately and also collectively by combining each country.

Ordinary least square regression method is applied to examine the effects of infrastructure investment and development on economic growth for each country. Panel data techniques such as panel least square method, panel least square fixed-effect model and panel least square random effect model are used to examine the collective impact by combining all countries in BRICS. The dynamic panel model is also incorporated for analysis in the study.

The results of the study are mixed. The association between infrastructure investment and development and economic growth for countries within BRICS is not robust. There is an insignificant relationship between infrastructure investment and development and economic growth in Brazil and South Africa. Energy and transportation infrastructure investment and development lead to economic growth in Russia. Telecommunication infrastructure investment and development and economic growth have a negative relationship in India, whereas there is a negative association between transport infrastructure investment and development and economic growth in China. Panel data results conclude that energy infrastructure investment and development lead to economic growth, whereas telecommunication infrastructure investment and development are significant and negatively linked with economic growth.

The study is novel as time series analysis and panel data analysis are used, taking the time span for 38 years (1980–2017) to investigate the influence of infrastructure investment and development on economic growth in BRICS Countries. Time-series regression analysis is used to test the impact for individual countries separately, whereas panel data regression analysis is used to examine the impact collectively for all countries in BRICS.

The purpose of this study is to compare the predictive performance of the hedonic multivariate regression model with the probabilistic neural network (PNN)-based general regression neural network (GRNN) model of housing prices in “Pune-India.”

Data on 211 properties across “Pune city-India” is collected. The price per square feet is considered as a dependent variable whereas distances from important landmarks such as railway station, fort, university, airport, hospital, temple, parks, solid waste site and stadium are considered as independent variables along with a dummy for amenities. The data is analyzed using a hedonic type multivariate regression model and GRNN. The GRNN divides the entire data set into two sets, namely, training set and testing set and establishes a functional relationship between the dependent and target variables based on the probability density function of the training data (Alomair and Garrouch, 2016).

While comparing the performance of the hedonic multivariate regression model and PNN-based GRNN, the study finds that the output variable (i.e. price) has been accurately predicted by the GRNN model. All the 42 observations of the testing set are correctly classified giving an accuracy rate of 100%. According to Cortez (2015), a value close to 100% indicates that the model can correctly classify the test data set. Further, the root mean square error (RMSE) value for the final testing for the GRNN model is 0.089 compared to 0.146 for the hedonic multivariate regression model. A lesser value of RMSE indicates that the model contains smaller errors and is a better fit. Therefore, it is concluded that GRNN is a better model to predict the housing price functions. The distance from the solid waste site has the highest degree of variable senstivity impact on the housing prices (22.59%) followed by distance from university (17.78%) and fort (17.73%).

The study being a “case” is restricted to a particular geographic location hence, the findings of the study cannot be generalized. Further, as the objective of the study is restricted to just to compare the predictive performance of two models, it is felt appropriate to restrict the scope of work by focusing only on “location specific hedonic factors,” as determinants of housing prices.

The study opens up a new dimension for scholars working in the field of housing prices/valuation. Authors do not rule out the use of traditional statistical techniques such as ordinary least square regression but strongly recommend that it is high time scholars use advanced statistical methods to develop the domain. The application of GRNN, artificial intelligence or other techniques such as auto regressive integrated moving average and vector auto regression modeling helps analyze the data in a much more sophisticated manner and help come up with more robust and conclusive evidence.

To the best of the author’s knowledge, it is the first case study that compares the predictive performance of the hedonic multivariate regression model with the PNN-based GRNN model for housing prices in India.

The purpose of this paper is to devise a robust statistical process control methodology that will enable service managers to better monitor the performance of correlated service measures.

A residuals control chart methodology based on least absolute value regression (LAV) is developed and its performance is compared to a traditional control chart methodology that is based on ordinary least squares (OLS) regression. Sensitivity analysis from the goal programming formulation of the LAV model is also performed. The methodology is applied in an actual service setting.

The LAV based residuals control chart outperformed the OLS based residuals control chart in identifying out of control observations. The LAV methodology was also less sensitive to outliers than the OLS approach.

The findings from this study suggest that the proposed LAV based approach is a more robust statistical process control method than the OLS approach. In addition, the goal program formulation of the LAV regression model permits sensitivity analysis whereas the OLS approach does not.

This paper shows that compared to the traditional OLS based control chart, the LAV based residuals chart may be better suited to actual service settings in which normality requirements are not met and the amount of data is limited.

This paper is the first study to use a least absolute value regression model to develop a residuals control chart for monitoring service data. The proposed LAV methodology can help service managers to do a better job monitoring related performance metrics as part of a quality improvement program such as six sigma.

Estimates of systematic risk or beta are an important determinant of the cost of capital. The standard technique used to compile beta estimates is an ordinary least squares regression of stock returns on market returns using four to five years of monthly data. This convention assumes that a longer time series of data will not adequately capture risks associated with existing assets. This paper seeks to address this issue.

Each year from 1980 to 2004, equity betas are estimated for 1,717 Australian firms over periods of four to 45 years, and form equal value portfolios of high, medium and low beta stocks. The paper compares expected returns – derived from the capital asset pricing model (CAPM) and subsequent realised market returns – and actual returns over subsequent annual and four‐year periods.

The paper shows that the ability of beta estimates to predict future stock returns systematically increases with the length of the estimation window and when the Vasicek bias correction is applied. However, estimation error is insignificantly different from that associated with a naïve assumption that beta equals one for all stocks.

The implication is that using all available returns data in beta estimation, along with the Vasicek bias correction, reduces the imprecision of expected returns estimates derived from the CAPM. A limitation of the method is the use of conditional realised returns as a proxy for expected returns, given that it is not possible directly to observe expected returns incorporated into share prices.

The paper contributes to the understanding of corporate finance practitioners and academics, who routinely use beta estimates derived from ordinary least squares regression.

The world over is becoming urbanized, and people are migrating to cities in large numbers in search of opportunities. The increased urbanization has posed challenges such as congestion, rising crime, and growing urban poverty. The governments respond by providing amenities such as schools, hospitals, and housing to meet to increase in demand for these facilities. However, there is a need for the provision of facilities that meets the expectations of the people, particularly on the proximity of amenities and bundles of utility-bearing housing characteristics. In an attempt to address the challenge mentioned, the study estimated the hedonic characteristics influencing the willingness to accept and willingness to pay for housing facilities in the Eastern Cape Province, South Africa. Using a multiple linear regression model and artificial neural network, the study found out that properties with a bathroom, garage and large floor size have a higher value compared to properties without these facilities.When making decisions to acquire a property, buyers consider the availability of discounts and the prevailing property price. Overall, willingness to pay and accept decisions are mainly determined by location and the price at which homogeneous neighborhood properties were sold. Therefore, the study recommends that urban town planners and other housing authorities prioritize the construction of properties with larger floor areas, parking bays, and bathrooms using a cost-effective mechanism that makes the properties affordable to residents.

This paper aims to examine whether capital market rewards firms with good corporate sustainability practices in an international setting by using the Dow Jones Sustainability Index (DJSI hereafter) as an integrated measure of firm sustainability performance.

There are two alternative theories regarding the impact of sustainability on firm value. The value-creating theory predicts that integration of environmental and social responsibility into corporate strategies and practices reduces firm risk and promotes long-term value creation. The value-destroying theory on sustainability suggests that managers may engage in socially responsible activities at the expense of shareholders. To perform empirical tests, we use a large international sample for a period of 13 years between 1999 (the first year when DJSI became available) and 2011. To control for self-selection bias and simultaneity, the authors use lagged values of sustainability performance in a robustness check.

The authors find a positive relation between sustainability performance and firm value, after controlling for variables that have been found to affect firm value in the existing literature. The test results are consistent with the value enhancing theory (as opposed to the shareholder expense theory) regarding the role of sustainability engagement in firm valuation. Furthermore, the positive impact of sustainability engagement on firm value is primarily driven by countries with strong investor protection and with high disclosure levels.

A positive impact of sustainability performance on firm value supports the value-creating theory and rejects the value-destroying theory. Test results also suggest a more pronounced market response to corporate sustainability in countries with stronger shareholders protection and higher requirement for financial transparency.

Given the growing international capital market and intensifying global competition, the valuation implications of sustainability in an international context is of practical interest to management, investors and regulators worldwide.

First, it is an initial attempt to test an integrated measure of the “triple-bottom-line” definition of sustainability in an international setting. Second, our paper studies the international variation in market valuation of firm sustainability performance in terms of the value enhancing versus shareholder expense theories on sustainability. The authors explore the relevance of sustainability performance in relation to the investor protection and the reporting environment across countries.

The purpose of this paper is to investigate the performance of the revolving fund (RF) regarding the ability of smallholder debtors to retrieve land title deeds, and also to examine the factors influencing the outstanding debts and percentage of outstanding interest of the smallholders in the Central and Northeastern regions of Thailand.

Primary data were collected from 430 debtors in the Central and Northeastern regions of Thailand in order to compare the differences in livelihood assets as well as their opinions on benefits derived from the operation of the RF. Secondary data were also collected from the RF administration, in order to evaluate the effectiveness and efficiency of the fund. Heteroskedasticity-corrected ordinary least squares and Tobit regression models were employed to examine the factors influencing the outstanding debts and percentage of outstanding interest of the smallholders, respectively. Furthermore, the student’s t-test was used to examine the differences in the livelihood assets among debtors in the two regions; and one-way analysis of variance (ANOVA) was used to examine differences in livelihood indicator scores among the three types of debtors.

The empirical results revealed that the RF is effective as the fund could provide loan to smallholders to enable them redeem their land title deeds from their previous creditors. The t-test results reveal significant differences in the livelihood assets among debtors in the two regions. One-way ANOVA indicates differences in livelihood indicator scores among the three types of debtors. The results of the heteroskedasticity-corrected ordinary least squares regression revealed that being married, low frequency of floods and less influence of third parties significantly reduced the outstanding debts. The results of the censored Tobit regression revealed that increased frequency of meeting with the RF administration, less influence of third parties, high land potential and interaction of age and experience significantly decreased the percentage of outstanding interest.

It is imperative to intensify information and education regarding the regulations, payment terms and modalities to clients in order to facilitate repayments of the loans disbursed. The organization of the RF should pay particular attention to the role of the committees involved, information administration and loan repayment monitoring. The RF should increase the frequency of meetings with smallholders, minimize the influence of third parties and give priority to old and experienced smallholders who possess land with high potential for earning incomes to enable them repay the loans.

To the best of the authors’ knowledge, this is the first study that examined the effectiveness of the RF to enable smallholders retrieve their land title deeds.

We study the statistical properties of Pearson correlation coefficients of Gaussian ranks, and Gaussian rank regressions – ordinary least-squares (OLS) models applied to those ranks. We show that these procedures are fully efficient when the true copula is Gaussian and the margins are non-parametrically estimated, and remain consistent for their population analogs otherwise. We compare them to Spearman and Pearson correlations and their regression counterparts theoretically and in extensive Monte Carlo simulations. Empirical applications to migration and growth across US states, the augmented Solow growth model and momentum and reversal effects in individual stock returns confirm that Gaussian rank procedures are insensitive to outliers.

The theoretical and empirical relationships between corporate social responsibility (CSR) and corporate financial performance are not without controversy. Yet, CSR activities are increasingly undertaken by a large number of firms, not only in developed countries but also in emerging countries. This paper aims to investigate the moderating effect of different aspects of corporate governance, which are foreign and state ownership, board size and board independence, on the relationship between CSR and financial performance.

A sample of Vietnamese listed firms is analyzed. Robust regression analysis is performed using ordinary least squares as well as fixed-effects and two-stage least squares model.

Ordinary least squares regression results show that CSR activities affect the financial performance of firms positively. Furthermore, corporate governance features like foreign ownership, board size and board independence strengthen the positive relationship between CSR and financial performance, but there is no such impact of state ownership.

Previous studies mostly investigate the direct effect of CSR on financial performance. A few studies examine the moderating effect of corporate governance, which is ownership concentration and board gender diversity. As an emerging country, Vietnam has some specific characteristics on corporate governance. This paper contributes by investigating the moderating effect of few major aspects of corporate governance, which are foreign and state ownership, board size and board independence.