• The Journal of Asian Finance, Economics and Business
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• v.1 no.4
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• pp.5-13
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• 2014

This study contributes to addressing the problem of an aging population by providing important information that determines feasible monthly payments for the clients of Chinese reverse mortgage products and by promoting the implementation of reverse mortgages in China. The variables used in this study include mean values obtained from time series data, of the rate of increase of housing prices, and the probability value, interest rate, and mortality rate obtained through the geometric Brownian motion (GBM). For mortality rates, China Life Insurance female mortality rates (2000-2003) were used. This study aims to apply the main variables that affect reverse mortgage products in a monthly payment model based on Chinese financial market conditions, and determine loan values. In this study, Shanghai's reverse mortgage monthly payments, by age levels, were calculated through the loan-to-value (LTV) and payment (PMT) methods to evaluate the value of the reverse mortgages. Based on the optimal combination of the three factors of payment amount, loan interest rates, and the level of acceptance of prices, efforts must be made to extract the best value for the elderly. Only in this way can the interests of both lenders and borrowers be protected, by increasing the market share and economies of scale of the reverse mortgage industry and effectively improving the living standards of the elderly.

• The Journal of Society for e-Business Studies
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• v.23 no.3
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• pp.207-224
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• 2018

In this article, an empirical study was conducted by using public dataset from Lending Club Corporation, the largest online peer-to-peer (P2P) lending in the world. We explore significant predictor variables related to P2P lending default that housing situation, length of employment, average current balance, debt-to-income ratio, loan amount, loan purpose, interest rate, public records, number of finance trades, total credit/credit limit, number of delinquent accounts, number of mortgage accounts, and number of bank card accounts are significant factors to loan funded successful on Lending Club platform. We developed online P2P lending default prediction models using discriminant analysis, logistic regression, neural networks, and decision trees (i.e., CART and C5.0) in order to predict P2P loan default. To verify the feasibility and effectiveness of P2P lending default prediction models, borrower loan data and credit data used in this study. Empirical results indicated that neural networks outperforms other classifiers such as discriminant analysis, logistic regression, CART, and C5.0. Neural networks always outperforms other classifiers in P2P loan default prediction.

• Journal of Energy Engineering
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• v.23 no.1
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• pp.67-74
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• 2014

The measure to encourage participation of ESCO business for companies with core technology may vary on the conditions of preference policy considerations. Accordingly, the three measures are can be suggested, evaluation points for incentives, loan allocated to a separate, and participation with consortium. This study recommends that the evaluation points for incentives is preferred in the ways. If the assumption that the allocation of large amounts of money, the amount of the loan allocated to a separate may be considered as suboptimal.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.7
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• pp.81-91
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• 2022

The aim of this paper was to analyze the determinants of the extent of individual credit rationing at Can Tho Military Commercial Joint Stock Bank (MB). The data was collected from 150 customers according to the systematic random sampling method listed in the bank. This study employed quantitative analysis methods, and Tobit regression model, to test the proposed hypotheses. The results showed that the average loan amount of an individual customer was 1,181.3 million VND, the average credit limit was 48.6%, and the average interest rate was 10.9% per year. Most of the individual customers borrowed money to buy properties. In addition, the analysis results also indicated that individual borrowers still faced some difficulties in accessing bank credit, such as cumbersome procedures, long waiting times, insufficient collateral assets, and loan documents. The results of the Tobit model pointed out that there were five factors affecting the degree of credit rationing to individual customers at the bank, including (1) Collateral, (2) Income, (3) Credit history, (4) Loan purpose, (5) Relationship between borrower and bank. Based on the empirical findings, the possible solutions for the bank and individual borrowers to improve credit efficiency for individual customers at commercial banks are obtained.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.5
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• pp.229-238
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• 2019

Before the scholarship loan system was implemented at the Korea Scholarship Foundation, the government's role was strengthened by the direct lending of student funds to banks and other financial institutions. However, the low repayment performance of student loans has raised concerns over the future of student loans and the government's financial burden. Moreover, since student loans are repaid even after graduating from college to support low-income families, it is highly unlikely that the repayment rate of student loans will improve unless the employment rate and income level of the borrower improve. In this paper, the final visualization graph is presented of the repayment amount of the student loan through the collection, storage, processing and analysis phase in the Big Data-based system. This could be the basis for visually checking the amount of student loans to come up with various ways to reduce the burden on the current student loan system.

• Journal of the Korean housing association
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• v.23 no.5
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• pp.31-39
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• 2012

This study has analyzed the effect of the housing affordability improvement of the residents in the metropolitan areas, applying Transit Supportive Home Loan (TSHL) which extends the loan amount according to the transportation cost saving based on Location Efficiency Mortgage (LEM) System in the USA. The previous studies focused on introducing LEM system to Korea, whereas this study has analyzed TSHL effect for the first time, taking into account the situation in Korea. For analysis, The Transportation Savings (TS) is calculated and Housing Affordability Index (HAI) is drawn by applying increased TS and DTI (Debt to Income), so the improvements of housing purchasing ability is examined by HAI's improvement. As a result, transit commute 1 (having own car) and transit commute 2 (without having own cars) respectively produce the TS of monthly average 36 thousand won and 110 thousand won in comparison with car using commute. Each additional loan (TSHL) amounts to 16.57 million won and 54.07 million won. As the distance is farther and the house price cheaper, the improvement of HAI is more outstanding. City hall area showed the highest improvements of HAI by 9.3% (transit commute 1) and 21.9% (transit commute 2) increase, in comparison with car using commute.

• Korea Real Estate Review
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• v.28 no.4
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• pp.7-22
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• 2018

This study aimed to empirically analyze the factors that influence newlyweds' housing tenure. For this, a questionnaire survey was conducted among newlyweds living nationwide who had been married for 5 years or less and were under the age of 40. The results of the survey were empirically analyzed by applying various variables. With regard to parental financial support among the factors influencing newlyweds' housing tenure, the analysis results showed that the probability of owner occupancy was high among the households with parental financial support than among those with none, and the larger the size of the parental financial support was, the higher the probability that the newlyweds owned the house they were occupying. As for the housing loan variable, it was found that the probability of owner occupancy was high among the newlyweds with a housing loan than among those with none, and the larger the amount of the newlyweds' housing loan was, the higher the probability that they owned the house they were occupying. As opposed to what is generally recognized, however, the survey showed that the higher the ratio of dual earners is, the lower the owner occupancy probability due to the probability of job transition, etc. In addition, it was found that the probability of owner occupancy for the newlyweds living in Seoul was lower than that for the newlyweds living in other regions in South Korea. Accordingly, this study showed that appropriate levels of parental economic indicators should also be reflected in the stable housing support and loan policies for newlyweds in the context that the succession to their parents' fortunes is generalized.

• Journal of Family Resource Management and Policy Review
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• v.6 no.1
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• pp.73-85
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• 2002

This study examined which factors influence the parental borrowing for children's college education in the U.S. A double-hurdle model was applied to analyze both the parents’decision to borrow and the amount of borrowing. Parents’income and total costs of college attendance had significant positive effects on both the decision to borrow and the amount borrowed. Students’income, parents’cash and savings had significant negative impacts on the amount borrowed, while home equity and the students’expected future income were significant positive factors. Greater amounts of grants significantly increased the probability of borrowing, but reduced the loan amount. Parents with smaller household size, and those who were college graduates were more likely to borrow. White parents borrowed greater amounts than did their non-White counterparts. The age of the student was a significant negative factor in the probability, as well as the level, of borrowing.

• Journal of Information Management
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• v.39 no.2
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• pp.235-249
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• 2008

The combination of information and technology makes dramatically increase both information quality and quantity. Almost of company utilize customer information for the purpose of increasing sales amount and profitability. The purpose of this paper is to discover customer information's utilization practices in the Korean financial industry. The case of K Bank's information analysis in the inbound and outbound marketing is provided, The customer segmentation is used for the inbound marketing by using RFM analysis. And the loan card model is used for the outbound marketing by using logit analysis.

• The Journal of the Korea Contents Association
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• v.5 no.1
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• pp.109-116
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• 2005

This paper is to develop the prototype of a decision making for loan granting system at banks and to evaluate the effectiveness of it. The prototype is called at FENET-LG in this paper. The decision to grant a loan is an unstructured and vagueness task because it is required a tremendous amount of data and many complex relationships among them. Evaluating these many data and relationships is a difficult task even for most experienced decision maker of bank. Therefore, where complex judgement is required, the decision maker of bank may benefit from the use of fuzzy expert network to support the evaluation of ability to pay back. Given the characteristics of decision maker of banking facilities judgement task about ability to pay back, the prototype system named FENET-LG is constructed by integration of fuzzy expert system and neural network. The FENET-LG takes advantage of both the deductive approach of fuzzy expert system and the inductive approach of a neural network to provide a decision aid designed to support and facilitate the process of conducting a judgement of ability to pay back.