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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.

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).

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.

Eastern European countries have an important history of quality and tradition in winemaking. The radical economic modification that the wine sector had to face during communism exposed these countries to various problems during transition, when their market share in Western European markets shrunk. This paper aims to argue that an important cause of the decline is the inability of price to convey information about the quality level of the wine, as from the theory of asymmetric information.

This paper uses the econometric theory of hedonic pricing to explore the research question, relating price to the characteristics of the wine, including a quality rating from a respected British wine expert for the year 2006. Results are presented for a sample of 134 wines coming from different Eastern European countries (EEC) sold in the UK. The analysis also tackles the issue of understanding which parameters contributed to the quality level of Eastern European wines, modelling the qualitative description in order to identify the keywords that explains the change in quality level.

The results show evidence of no correlation between ratings and price in the considered segment of the UK wine market, suggesting an imperfect communication of quality via the price information. Moreover, Romania, which is a very important producing country, has a weak brand value, suggesting the need for restructuring the industry and improving quality management schemes to compete in their newly enlarged destination market. The quality model shows that fruity flavours are those playing a most important role in defining the quality level; hence a more targeted product supply should be encouraged.

No previous research has used hedonic modelling to the case of Eastern European wines. Furthermore, the context used here is to understand the weaknesses of the specific segment, and the tool is used to investigate the loss of competitive advantage of Eastern European products in the UK markets, focusing on the quality/price relation.

This chapter develops an asymptotic theory for a general transformation model with a time trend, stationary regressors, and unit root nonstationary regressors. This model extends that of Han (1987) to incorporate time trend and nonstationary regressors. When the transformation is specified as an identity function, the model reduces to the conventional cointegrating regression, possibly with a time trend and other stationary regressors, which has been studied in Phillips and Durlauf (1986) and Park and Phillips (1988, 1989). The limiting distributions of the extremum estimator of the transformation parameter and the plug-in estimators of other model parameters are found to critically depend upon the transformation function and the order of the time trend. Simulations demonstrate that the estimators perform well in finite samples.

The purpose of this paper is to propose an approach to evaluate “on time” performance for a class of Indian railways (IR). This approach is build-up on the theme of six sigma level computation for continues data. The arrival data obtained from a class of IR called “Rajdhani Express” exhibited a unique characteristic: neither did the data followed a distribution nor could it be transformed to fit into a distribution. In this work, the authors present an approach to evaluate on time performance of IR, given such “unruly” data.

An attempt is made to develop a lucid approach, given an unruly data. Initially, the authors plot a histogram using Scott’s method. Later, the authors use Taguchi’s quality loss function to assign weights to each of the bins in histogram. Weights to each of the bins are assigned based on the predefined rules. Finally, sigma level is computed by using weighted defect per million opportunities (DPMO) approach. In this paper, the authors discuss the proposed algorithm, an illustrative example with an emphasis on a class of IR.

Given the unique characteristic (unruly data) of arrival data of IR, the proposed methodology helps in quantifying it is on time performance. This method extends the conventional DPMO approach of computing the sigma level. The proposed method is validated using various data sets of different distributions. Further, this approach can be generalized and applied to data set of any distribution. Since distribution of the data is not the pre-requisite, the proposed approach can be applied to compare (and benchmark) data sets of different distributions. This methodology, thus, paves path to develop newer approaches in quantifying and benchmarking the sigma levels.

In this manuscript, the authors present a case of Rajdhani Express trains, a class of IR. A practicing manager can use this approach to compare the performance of various classes of railways and benchmark their performance. Such an approach, with suitable modifications (if any) can be applied to evaluate performance in various service industries.

Usually, if the data are unruly, the sigma level is computed by using ad-hoc methods that may provide compromising solutions and/or inaccurate results. The developed methodology proposes a unique approach to quantify sigma level, given such an unruly nature of the data. This approach thus fills the long needed gap in addressing such situations. This approach can be applied in various similar situations. A case of IR is presented.