Nahid Baharlou; Ali Akbar Aminbeydokhti; Mohammad Javad Mohagheghnia
Abstract
One of the main tasks of the financial institutions is to give loan to the customers. Prediction and evaluation of the credit risks due to loan and consequently managing this risk is one of the greatest ongoing challenges for the financial institutions. The main aim of this work is to provide an optimized ...
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One of the main tasks of the financial institutions is to give loan to the customers. Prediction and evaluation of the credit risks due to loan and consequently managing this risk is one of the greatest ongoing challenges for the financial institutions. The main aim of this work is to provide an optimized logistic regression model for credit scoring of real customers. Here the effects of increasing the customer’s credit classification from two (binary) to four (multinomial) distinct groups on the results of the logistic regression has been investigated. Identification of the most important parameters in prediction of the real customers’ credit scoring is the other important outcome of this work. The results of both binary and multinomial logistic regression show the relative importance of the education level and the age of the customer compared with other independent variables. The results of this work show that either increasing the number of classification types of the dependent variable, real customer’s credit, to four distinct groups has no sharp effect on the results of the optimized models or this conclusion can be due to improper distribution of the number of customers in different groups.
Abd-ol-Rasul Ghasemi; Elham Shadabfar
Abstract
With respect to the fact that housing satisfies a social need, in addition to the impact of economic factors, the social factors are alsoimportant in housing market. The broad economic and social developments in previous years require a study on their impacts on home ownership, so recognizing important ...
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With respect to the fact that housing satisfies a social need, in addition to the impact of economic factors, the social factors are alsoimportant in housing market. The broad economic and social developments in previous years require a study on their impacts on home ownership, so recognizing important factors is a good guide for planners and policy makers. Since people with higher education need to spend some money on their education and this education status increases the expectation of people with higher degrees with respect to their salaries and employment opportunities, therefore studying the impact of education level on the probability of home ownership and evaluating its trend in different time horizons has a critical importance. Since about twenty percent of higher education graduates of Iran do reside in Tehran province, this effect for Tehran province is stronger and of higher importance. In this study with emphasis on recognizing the effect of factors identified in theoretical basis, we have used logit and probit models to investigate the importance of some factors affecting the status of home ownership with emphasis on education level of head of households during the years 1997, 2002 and 2007. The results show that the socio-economic characteristics of households have a significant effect on housing ownership and the relationship between education level of head of household and home ownership is negative. Also the probability of home ownership in families with higher education decrease in the years under study.
Adel Berjesyan; Saeed Abedin Dour Koush
Volume 12, Issue 45 , July 2012, , Pages 55-74
Abstract
In this research, we try to specify the establishment location of private banks’ branches at the whole 22 areas of Tehran. We rank areas by using effective financial variables on bank services demand. For reaching this goal, we used Logit regression and numerical Taxonomi in a linear manner, and ...
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In this research, we try to specify the establishment location of private banks’ branches at the whole 22 areas of Tehran. We rank areas by using effective financial variables on bank services demand. For reaching this goal, we used Logit regression and numerical Taxonomi in a linear manner, and then by evaluating these two methods, we selected the best areas to establishing branches.
