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The purpose of this paper is to consider borrower-specific characteristics to understand the factors affecting both the probability and quantum of loan default by individual borrowers under Islamic and conventional banking.

Borrower-specific characteristics that explain the probability of default may not necessarily be similar factors that determine the quantum of default. The authors therefore apply a Box-Cox double hurdle model to treat both the probability and quantum of default in a two-step approach. The authors also explain the differences in default risk and quantum of default between Islamic and conventional banking borrowers from their behavioral perspectives following the Sharia principles in financial transactions between lenders and borrowers. The authors use borrower-specific information of two separate bank branches of the United Arab Emirates that solely deal with either Islamic or conventional banking products.

The paper demonstrates that the probability of default and the quantum of default appear to be influenced by different set of client-specific factors. The results suggest that the probability of default does not vary significantly between Islamic and conventional banking borrowers. The evidence also shows that Islamic banking defaulters, compared to those in conventional banking, repay a large quantum of overdue when their financial leverage improves. However, they do not tend to reduce their outstanding quantum of overdue faster than conventional banking defaulters.

Availability of data from only two bank branches may limit the explanatory power of empirical findings.

The study findings will enable the Islamic and conventional banks to appropriately address Basel Capital requirements based on the borrowers’ behavior.

The study findings have the potential for Islamic and conventional financing institutions to be more flexible with equity in their lending practices.

Religious beliefs are crucial in borrower’s default behavior in Islamic banking.

This paper aims to understand housing demand of urban Indian households in terms of housing and household-level characteristics. Because a house is a bundle of certain characteristics which vary across houses, each characteristic has an implicit price. Finding this implicit price for certain important characteristics is the first objective of this study. The second objective of the paper is to compute the income elasticity and price elasticity of housing demand for these cities.

To achieve comparable estimates, household-level data from India’s National Sample Survey Organisation housing surveys for the years 2002 and 2008-2009 have been used. A hedonic price function is estimated using ordinary least squares (OLS) and Box-Cox functional forms to estimate the implicit prices of housing characteristics. This exercise is attempted for owned and rented houses separately. Demand function required for computing the elasticities, uses the hedonic price index derived from the implicit prices and household characteristics.

The study finds housing demand to be income elastic and price inelastic for the six cities across both the time periods.

Firstly, this study includes housing characteristics such as individual access to drinking water, modern sanitation facility, separate kitchen, condition of the structure, existence of a road with street light and whether the house is in a slum or non-slum area in the hedonic price function. These variables were not used in any of the earlier studies pertaining to India. Secondly, it uses the Box-Cox non-linear form to derive the hedonic price function, a specification not used earlier. Thirdly, this is the first study analysing housing demand across the six largest Indian cities.

The purpose of this paper is to reduce the artifacts in fast Bayesian reconstruction images in electrical tomography. This is in particular important with respect to object detection in electrical tomography applications.

The authors suggest to apply the Box-Cox transformation in Bayesian linear minimum mean square error (BMMSE) reconstruction to better accommodate the non-linear relation between the capacitance matrix and the permittivity distribution. The authors compare the results of the original algorithm with the modified algorithm and with the ground truth in both, simulation and experiments.

The results show a reduction of 50 percent of the mean square error caused by artifacts in low permittivity regions. Furthermore, the algorithm does not increase the computational complexity significantly such that the hard real time constraints can still be met. The authors demonstrate that the algorithm also works with limited observations angles. This allows for object detection in real time, e.g., in robot collision avoidance.

This paper shows that the extension of BMMSE by applying the Box-Cox transformation leads to a significant improvement of the quality of the reconstruction image while hard real time constraints are still met.

Discusses the application of multiple regression analysis (MRA) in the process of maintaining equity and consistency with the market place in valuation. Illustrates the case by giving examples using data maintained by a local valuer′s office. Outlines the uses of regression analysis in order to specify whether or not a given value is within a specified probability limit. Shows how the procedure is straightforward to use with microcomputer technology, and compares the system against the standard linear model form. Suggests that the extra time and effort required to identify non‐linearities, may not provide a sufficient justification for its use.

This paper aims to examine house price drivers in Dubai, addressing nonlinearity and heterogeneity.

The study applies a combination of linear and nonlinear, as well as quantile regression, specifications to address these concerns and better explain the real-world phenomenon.

The study shows the double-log quantile regression approach is an overarching description of house price drivers, confirming that not only the price of housing and its determinants are non-linearly related but also that their relationship is heterogeneous across house price quantiles. The findings reveal the prevalence of sub-market differentials in house price sensitivity to house attributes such as size (in square meters), location and type of house, as well as government laws. The study also identifies the peaks and deflation, as well as the rebounding nature of the house price bubble in Dubai.

The data used are limited, in that information on only a few house attributes was available. Future research should include data on other house attributes such as house quality, zip codes and composition.

The findings of this study are expected to suggest results with significant ramifications for researchers, practitioners and policy makers. From a policy perspective, there is an obvious interest in understanding whether the price of housing is affected by different attributes differently along its distribution.

This study allows policy makers, developers and buyers of higher-priced houses to behave differently from buyers of lower-priced or medium-priced houses.

Methodologically, it demonstrates alternative linear and nonlinear, as well as quantile regression, specifications to address two increasing concerns in the house price literature: nonlinearity and heterogeneity. Unlike most other studies, this study used a rich data (140,039 day-to-day transactions of 10 years’ pooled data). The Dubai housing market presents an interesting case. UAE (Dubai, in particular) is named as the second-hottest marketplace for global residential property investors, ahead of Singapore, the UK and Hong Kong (Savills plc, 2015).

In Hong Kong, a balcony is often perceived as a “green” provision in modern residential buildings. However, how the market values the benefits of balconies is seldom studied due to the difficulty in separating such benefits from other associated effects such as view enjoyment and security concerns. This paper attempts to study the implicit value of a balcony, the green effects of balconies on the prices of residential properties, and the effect of security concerns on balconies situated on lower floors. A sample of transactions in a private housing estate in Hong Kong has been studied. The sample contains apartments with and without balconies. A balcony is found to have a positive effect on the value of a property irrespective of the quality of the view. The negative effects of air and noise pollution on property prices are also found to be highly significant. Although security concerns are found on the low stories of a building, the provision of a balcony does not aggravate the hazard. Finally, the log‐linearity assumption in the empirical price model is relaxed by applying the Box‐Cox transformation to the continuous variables.

The bank is termed as an imperative part of the marketing economy. The failure or success of an institution relies on the ability of industries to compute the credit risk. The loan eligibility prediction model utilizes analysis method that adapts past and current information of credit user to make prediction. However, precise loan prediction with risk and assessment analysis is a major challenge in loan eligibility prediction.

This aim of the research technique is to present a new method, namely Social Border Collie Optimization (SBCO)-based deep neuro fuzzy network for loan eligibility prediction. In this method, box cox transformation is employed on input loan data to create the data apt for further processing. The transformed data utilize the wrapper-based feature selection to choose suitable features to boost the performance of loan eligibility calculation. Once the features are chosen, the naive Bayes (NB) is adapted for feature fusion. In NB training, the classifier builds probability index table with the help of input data features and groups values. Here, the testing of NB classifier is done using posterior probability ratio considering conditional probability of normalization constant with class evidence. Finally, the loan eligibility prediction is achieved by deep neuro fuzzy network, which is trained with designed SBCO. Here, the SBCO is devised by combining the social ski driver (SSD) algorithm and Border Collie Optimization (BCO) to produce the most precise result.

The analysis is achieved by accuracy, sensitivity and specificity parameter by. The designed method performs with the highest accuracy of 95%, sensitivity and specificity of 95.4 and 97.3%, when compared to the existing methods, such as fuzzy neural network (Fuzzy NN), multiple partial least squares regression model (Multi_PLS), instance-based entropy fuzzy support vector machine (IEFSVM), deep recurrent neural network (Deep RNN), whale social optimization algorithm-based deep RNN (WSOA-based Deep RNN).

This paper devises SBCO-based deep neuro fuzzy network for predicting loan eligibility. Here, the deep neuro fuzzy network is trained with proposed SBCO, which is devised by combining the SSD and BCO to produce most precise result for loan eligibility prediction.

The purpose of this study is to highlight the determinants of market rents and to build a hedonic market rent index for each urban area and rental sector in France for the period 1970–2013. The authors also analyse the market rent dynamics over this period, with a special attention to the turning points in the French housing policy.

For this purpose, the authors implement a hedonic model, called stratified time dummy variable, using the Box–Cox transformation as a functional form.

The contribution of this study to the housing research is threefold: First, the study improves our understanding of the French’s rental submarket specificities and their valuation. It sheds new light on the determinants of rents. Second, this study builds a hedonic market rent index over the period 1970–2013 for each geographical and sectoral segment (Paris urban area, urban areas of more and less than 100,000 inhabitants and private and public rental sectors). Third, this study explains rent dynamics focusing on the turning points in the French housing policy.

Finally, the authors provide the first long-term market rent index in France by submarket (geographical and sectoral). In the case of the French market, no long-term market rent exists. The only long series available is an indexed rent.

Fraud transaction detection has become a significant factor in the communication technologies and electronic commerce systems, as it affects the usage of electronic payment. Even though, various fraud detection methods are developed, enhancing the performance of electronic payment by detecting the fraudsters results in a great challenge in the bank transaction.

This paper aims to design the fraud detection mechanism using the proposed Harris water optimization-based deep recurrent neural network (HWO-based deep RNN). The proposed fraud detection strategy includes three different phases, namely, pre-processing, feature selection and fraud detection. Initially, the input transactional data is subjected to the pre-processing phase, where the data is pre-processed using the Box-Cox transformation to remove the redundant and noise values from data. The pre-processed data is passed to the feature selection phase, where the essential and the suitable features are selected using the wrapper model. The selected feature makes the classifier to perform better detection performance. Finally, the selected features are fed to the detection phase, where the deep recurrent neural network classifier is used to achieve the fraud detection process such that the training process of the classifier is done by the proposed Harris water optimization algorithm, which is the integration of water wave optimization and Harris hawks optimization.

Moreover, the proposed HWO-based deep RNN obtained better performance in terms of the metrics, such as accuracy, sensitivity and specificity with the values of 0.9192, 0.7642 and 0.9943.

An effective fraud detection method named HWO-based deep RNN is designed to detect the frauds in the bank transaction. The optimal features selected using the wrapper model enable the classifier to find fraudulent activities more efficiently. However, the accurate detection result is evaluated through the optimization model based on the fitness measure such that the function with the minimal error value is declared as the best solution, as it yields better detection results.

There has been a growing public concern over the importance of building management in apartment buildings. However, people's views toward the effects of building management on building performance have long been divergent due to a lack of empirical study. This study aims to empirically test the relationship between building management regimes and the conditions of private apartment buildings in Hong Kong.

An assessment scheme was developed to assess the health and safety conditions of 134 apartment buildings. Multiple regression models were then applied to analyze the effect of building management regimes on building conditions. The optimal functional form of the regression models was selected using Box‐Cox transformation.

The empirical results suggested that the presence of incorporated owners and property management agents (PMA) are significant factors in enhancing building conditions.

The sample was confined to single block buildings located in one particular district in Hong Kong. Further research is needed to validate the findings in estate‐type developments as well as those in other districts.

The empirical results assisted building owners in determining which management regimes to adopt should they want better building conditions. The government may also consider giving more support to owners by incorporating them and employing PMAs to create a pleasant living environment for society.

Our study is the first in the literature to provide an empirical test reconciling the divergent views toward the effects of building management with the conditions of buildings.