Borrower Credit Research Articles

A Crisis of Missed Opportunities? Foreclosure Costs and Mortgage Modification During the Great Recession

We investigate the impact of Great Recession policies in California that substantially increased lender pecuniary and time costs of foreclosure. We estimate that the California Foreclosure Prevention Laws (CFPLs) prevented 250,000 California foreclosures (a 20% reduction) and created $300 billion in housing wealth. The CFPLs boosted mortgage modifications and reduced borrower transitions into default. They also mitigated foreclosure externalities via increased maintenance spending on homes that entered foreclosure. The CFPLs had minimal adverse side effects on the availability of mortgage credit for new borrowers. Altogether, findings suggest that policy interventions that keep borrowers in their homes may be broadly beneficial during times of widespread housing distress.

A Crisis of Missed Opportunities? Foreclosure Costs and Mortgage Modification During the Great Recession

AbstractWe investigate the impact of Great Recession policies in California that substantially increased lender pecuniary and time costs of foreclosure. We estimate that the California Foreclosure Prevention Laws (CFPLs) prevented 250,000 California foreclosures (a 20% reduction) and created $\$$300 billion in housing wealth. The CFPLs boosted mortgage modifications and reduced borrower transitions into default. They also mitigated foreclosure externalities via increased maintenance spending on homes that entered foreclosure. The CFPLs had minimal adverse side effects on the availability of mortgage credit for new borrowers. Altogether, findings suggest that policy interventions that keep borrowers in their homes may be broadly beneficial during times of widespread housing distress.

METODE MULTI ATTRIBUTE DECISION MAKING DENGAN MODEL SIMPLE ADDITIVE WEIGHTING SEBAGAI PENDUKUNG KEPUTUSAN PEMBERIAN PINJAMAN

Abstract Decision support systems are used to solve a problem that is commonly done by users and industry, in this case one of the application of a decision support system in providing loans to cooperative members. Judging from the form of business, cooperatives are one of the community financial institutions that are formed and are not only business-oriented, but social factors are very strongly applied. Because the cooperative was formed to help and prosper its members, such as saving and borrowing (credit) from its members. In this study, decision making is used to help support the selection of loan applications for members. There are several types of decision models that are integrated in a Decision Support System such as statistical models, optimization models, heuristic models and multi-criteria / attribute models. The Simple Additive Weighting (SAW) method is used with the basic concept of finding the weighted sum of the performance ratings on each criterion. According to the MADM model which is used as decision support who can select criteria in determining the eligibility of members in receiving loans (credit). The system provides a solution in determining loan recommendations to prospective borrowers in cooperatives. The system is only able to represent 10 sample members as a reference in calculating credit lending.

Credit Analysis Basics

Capital markets facilitate the appropriate exchange of money. For lenders and borrowers, the interest rate is the primary pricing mechanism that markets use in this exchange. For borrowers deemed to have higher credit risk, lenders add a risk premium to the interest rate to compensate for higher risk. Credit risk is the risk that the borrower will default on (not pay) the payments agreed upon in the loan. Such risk-based interest rates ensure that money flows appropriately between lenders and borrowers.Credit analysis is the process of determining a potential borrower's credit risk. This note explores credit analysis and its relation to the credit-risk premium in interest rates. Excerpt UVA-F-1898 Mar. 9, 2020 Credit Analysis Basics Capital markets facilitate the appropriate exchange of money. For lenders and borrowers, the interest rate is the primary pricing mechanism that markets use in this exchange. For borrowers deemed to have higher credit risk, lenders add a risk premium to the interest rate to compensate for higher risk. Credit risk is the risk that the borrower will default on (not pay) the payments agreed upon in the loan. Such risk-based interest rates ensure that money flows appropriately between lenders and borrowers. Credit analysis is the process of determining a potential borrower's credit risk. This note explores credit analysis and its relation to the credit-risk premium in interest rates. . . .

Оценка заёмщиков микрокредитов нечётким методом максиминной свёртки

The problem of timely repayment of loans at all times has been and continues to be actual for commercial banks. Overcoming this problem substantially depends on the quality of the solvency assessment of potential borrowers, which is carried out by experts on the basis of retrospective information. In the microcredit system, the assessment of the borrower's credit history is usually carried out by an expert who mainly relies on his heuristic knowledge and intuition, which usually extols subjective considerations that do not have sufficient grounds. In practice, the opinions of different analysts or those responsible for making credit decisions often differ, especially if controversial situations are considered that have many acceptable alternative solutions. As a result, in assessing the solvency of potential microloan borrowers, the subjective opinion of the expert and the incompetent or deliberate interpretation of the information resulting in the adoption of decisions that are detrimental to the microfinance organization are overweight. To increase the degree of objectivity, the paper discusses an approach to assessing the responsibility and solvency of microloan borrowers, based on the use of the fuzzy method of maximin convolution. This approach, given the poorly structured personal data of applicants, allows them to be flexibly and quickly assessed for the provision of microloans. The qualitative assessment criteria applied in this case are weighed based on expert opinions regarding the priority of each of them. An important advantage of the proposed model is that it is simple, convenient to use and able to adapt to the requirements of various micro-financial organizations.

Banks Financing and Industrial Sector Performance in Nigeria

This paper examined the effect of banks financing on industrial sector growth in Nigeria. The objectives of the Study were to examine the effects of domestic money supply, banks credit and maximum bank lending rate on industrial sector performance in Nigeria. The Study is established on Bank-based monetary framework on the grounds that the hypothesis focuses on the positive functions of banks in industrial growth and development. Descriptive and Ex-post facto research designs were adopted to investigate the contribution of various bank financing variables to industrial sector growth measured by manufacturing sector output in Nigeria over a period of 15 years (2004-2018). Method of analysis was the linear regression model using fully modified ordinary least square model to estimate the individual effects of banks financing variables measured by banks credits, domestic money supply, and maximum bank lending rate on industrial sector growth measured by manufacturing sector output. The study revealed that industrial sector growth is strongly impacted upon by banks credits, domestic money supply, and maximum bank lending rate. The study concluded that, there is positive significant relationship between bank credits, domestic money supply and growth in the industrial sector. Therefore, the study recommended that, banks should continue to support the industrial sector through credit borrowing, this way, the dwindling nature of Nigeria industrial sector can be redressed through adequate credits provided by these banks. However, these credits should be given at lower interest rate.

FinTech Marketplace Lending, Default Risk, and the Business Cycle

This paper explores default in the FinTech marketplace lending market using a dataset of both extensive credit and soft information for borrowers from the largest marketplace lender in the United States. I find that both macro and regional economic conditions play a role in consumer default and should be taken into consideration when assessing credit risk. I show that lenders operating in this market increasingly focus on subprime borrowers, whose default rates are more sensitive to macro and regional economic conditions than those of prime borrowers. Based on estimates from a duration model, I provide counterfactual analyses of what default rates and the associated total losses would look like in different economic scenarios. In the case of a recession, the losses would be 37 percent higher than in the case of an expansion. For the same volume of loans in the recession, doubling the subprime share would lead to an additional 7.5 percent increase in losses.

Design of comprehensive evaluation index system for P2P credit risk of “three rural” borrowers

In the emerging peer-to-peer (P2P) lending industry, risks such as credit risk and default risk will bring huge losses to online lending platforms and investors. Therefore, it is necessary to design a reasonable evaluation index system of credit risk to scientifically evaluate the risk level of borrowers. This paper studies the design of comprehensive evaluation index system for P2P credit risk of “three rural” (i.e., agriculture, rural areas and farmers) borrowers. Concretely, we construct the feature set for P2P credit risk of “three rural” borrowers. Based on the traditional index system, we add the static indexes specific to the agriculture-related borrowers and the dynamic indexes reflect the Internet as the preliminary indexes of the feature set and select the borrowers data of the “Pterosaur loan” platform as the research sample. Then, 35 borrower credit features are extracted as a feature set of credit risk. Then, we present a two-stage feature selection method based on filter and wrapper to select the main features from 35 initial borrower credit features. In the stage of filter, three filter methods are used to calculate the importance of the unbalanced features. In the stage of wrapper, a Lasso-logistic method is proposed to filter the feature subset through heuristic search algorithm. In the end, 21 main independent features are selected according to the classification accuracy, which constitute the evaluation index system of credit risk of “three rural” borrowers.

Lenders and borrowers’ collaboration-based risk mitigation credit market

Purpose The purpose of this paper is to study an innovative rice value chain financing system (VCFS) established in Benin, to identify the determinants of producers and processors access to formal credit, both at intensive and extensive margins. It focuses on multi-stakeholder platforms (MSP) which connect producers and processors in need of credit to potential financial lenders. Design/methodology/approach The empirical analysis uses rich cross-sectional survey data collected in Northern Benin in 2018. The sample consists of 215 rice producers and 217 rice processors randomly selected through a multi-stage sampling and interviewed with structured questionnaires. The empirical models analyze the determinants of the likelihood to receive a credit and the amount of credit received. To account for the sample selection and censored nature of the main outcome variable, the study considers a Heckman two-stage model coupled with a Tobit model for robustness checks. Findings The study finds that the MSP are effective in increasing access to formal credit and the amount borrowed. Producers and processors who are members of the MSP are more likely to receive credit and, conditional on being approved for credit borrower, a larger amount. Other key factors that significantly explain access to credit include the use of soft guarantee for securing a loan, the degree of participation in the platform and demographic characteristics. These findings are consistent across the Heckman and Tobit models. Research limitations/implications The study attempts to rigorously analyze the factors explaining producers and processors access to credit using cross-sectional survey data. But it has some limitations. The main limitation is the type of data used. Ideally, one would like to run a randomized control trial (RCT) to randomly assign participation in the MSP to causally estimate its impact of access to credit. The second-best option would be to have a panel data covering the period before and after the establishment of the platform. However, in the absence of an RCT or panel data, the study resorts to cross-sectional data and empirical models that account for sample selection bias and the censored nature of the credit received. Practical implications One of the key findings of the study is that participation in the MSP (through different value chain stages associations) increases access to formal credit. This highlights an important and effective mechanism, a well-coordinated value chains that integrated lenders, that policymakers can leverage to facilitate access to credit in the agricultural sector. Social implications Access to credit is important to boost agricultural productivity and income. Hence, the findings of the study have social implications in terms of poverty reduction in rural areas. Originality/value The study contributes to earlier theories and empirical studies on the demand for credit. It focuses on an innovative VCFS, increasingly adopted in many developing countries, adds originality and value to the understanding of mechanisms to unlock agricultural actors’ access to credit in low-income countries.

Borrowing costs and the role of multilateral development banks: Evidence from cross-border syndicated bank lending

Cross-border bank lending is a growing source of external finance in emerging and developing countries and could play a key role in infrastructure and development financing. This paper looks at the role of multilateral development banks (MDBs) on the terms of syndicated loans, focusing on loan pricing. The results show that MDBs’ participation is associated with higher borrowing costs and longer maturities—signaling a greater willingness by MDBs to finance risky projects which may not be financed by the private sector. We also show that MDBs are more likely to lend to borrowers located in countries with high credit and financial risk. Overall, our findings suggest that MDBs could play a role in easing access to credit for high risk borrowers.

Seeing is important: the usefulness of video information in P2P

AbstractP2P lending is an important research subject of rising internet finance research. This paper uses unique data from Renrendai, a leading platform in China, to test the influence of video information on P2P lending behavior. Results suggest that, first, the lower the borrower's credit rating is, the more likely they are to provide video information. Second, compared to the video‐information‐absent borrowers, the otherwise borrowers can get easier access to a loan and offer a lower interest rate. These results indicate that compared to text messages, video information can increase the borrower's creditworthiness and reduce the transaction risk. Thereupon seeing is important in online P2P lending. Third, when the borrower's credit rating is lower, the video effect is significant. The study makes sense in terms of the enrichment of P2P lending literature and the enlightenment on decision‐making of both lenders and borrowers.

Appraisal Accuracy and Automated Valuation Models in Rural Areas

Accurate and unbiased property value estimates are essential to credit risk management. Along with loan amount, they determine a mortgage’s loan-to-value ratio, which captures the degree of homeowner equity and is a key determinant of borrower credit risk. For home purchases, lenders generally require an independent appraisal, which, in addition to a home’s sales price, is used to calculate a value for the underlying collateral. A number of empirical studies have shown that property appraisals tend to be biased upwards, and over 90 percent of the time, either confirm or exceed the associated contract price. Our data suggest that appraisal bias is particularly pervasive in rural areas where over 25 percent of rural properties are appraised at more than five percent above contract price. Given this significant upward bias, we examine a host of alternate valuation techniques to more accurately estimate rural property values.

Factors Affecting Micro Finance Credit Use of Women Entrepreneurs: The Case of Sodo Zuria Woreda, Wolaita Zone, Southern, Regional State, Ethiopia

Microfinance and credit for small and medium-sized enterprises, particularly for women are important for enhancing the social and economic impact on the woman. MFI can compensate for some of the weaknesses in capital markets and can help low-income woman entrepreneurs to improve their enterprises. The general objective of the study was to analyze factors affecting the amount of micro credit borrowing by women entrepreneurs in the study area of omo micro finance institution. A multistage sampling technique was employed to select 90 women entrepreneur borrowers from the omo micro finance institution. Both quantitative and qualitative data were collected from sampled rural women through interview. Both descriptive and econometric data analyses techniques were applied. The findings of this study showed that average amount of loan obtained by women borrowers was 4201.67 ETB in the area. The inferential statistics revealed that personal variables like age, family size and economic variables: monthly saving, annual income and collateral type were found to be significantly influencing the amount of women institutional borrowing in the study area .The study recommend that micro finance institutions to improve their service in terms of speedy release of loan, reducing interest rate from the existing 15 percent, accepting oily group collateral and not insisting immovable collateral and encourage saving. Keywords : Women entrepreneur, microcredit, Micro finance institution. DOI : 10.7176/RJFA/10-15-04 Publication date : August 31 st 2019

Does Microcredit Bring the Women into Economic World: Evidence from Bangladesh

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Home equity conversion mortgages: The secondary market investor experience

Reverse mortgages are accrual notes of indeterminate maturity secured by home equity. In this paper, we analyze Fannie Mae's experience with the Home Equity Conversion Mortgage, the FHA-insured, non-recourse version of this product. Using loan and borrower characteristics, we model a number of relationships, including loss severity. We also show how the addition of credit information affects our models. Finally, we use credit data to conduct a policy experiment which illustrates how better screening might have affected portfolio performance. We find that imposing a minimum borrower credit score of 620 would have reduced originated volume by 23% and economic losses by 32%.

Evaluation of borrower's credit of P2P loan based on adaptive particle swarm optimisation BP neural network

Personal credit assessment is the main method to reduce the credit risk of P2P online loans. In this paper, after the adaptive mutation operator is used to reinitialise the particles with a certain probability and the global search capability of the particle swarm optimisation algorithm is used to optimise the weights and thresholds of the BP neural network, a method which adopts adaptive mutation particle swarm combined with BP neural network model is proposed to evaluate borrower's credit of P2P network loan. Result of simulation experiment shows that AMPSO-BP neural network model has higher prediction accuracy, smaller error variation range, better fitting ability and robustness than the BP neural network model in P2P network loan borrower credit evaluation applications.

Research on borrower's credit classification of P2P network loan based on LightGBM algorithm

The credit classification of a borrower is the main method to effectively reduce the credit risk of P2P online loans. In this paper, LightGBM algorithm has the advantage in the high accuracy of data classification. Feature extraction, selection and reconstruction of the original data are performed by feature engineering. The One hot Encoding technology is used to re-encode the discretised feature indicators. Z-score data normalisation normalises the characteristics of continuous variables. Re-sort all feature indicators by contribution and perform PCA dimensionality reduction, and filter out effective feature indicators for training and testing. Finally, the problem of imbalance of samples and optimisation of model parameters is solved by ten-fold cross-validation. Result of simulation experiment shows that the LightGBM model has good stability, good fitting ability and high classification prediction accuracy.

ANALYSIS OF EXISTING APPROACHES ABOUT MANAGEMENT AND MINIMIZATION OF CREDIT RISK

The efficiency of banking performance, related to ensuring reliable protection for banks from credit risk by borrowers, requires resolving multiple issues related to the analysis of their creditworthiness and reliability, as well as development of methods and models to predict the consequences of non-repayment or overdue loans from the borrowers‘ side for the further effective functioning of a bank. Taking into account a considerable amount of scientific works devoted to the research of influence of credit risk on banking there is still a necessity in improvement of existing methods of credit risk management. The article is devoted to the research of the influence of credit risk on banking in Ukraine, in regard to the analysis of the domestic and foreign approaches and methods of determining banking credit risk; quality of its management and minimization, analysis of the legislation on the definition of exposure to borrower's credit risk; analytical reviews of indicators of the banking system, in particular, research of the banking credit portfolio structure for revealing the reasons of change of particular indicators. And, consequently, their influence on the financial sustainability and bank solvency. The research pays attention to intermediary organizations as participants of contractual relations between banks and borrowers. The authors give their own vision of the efficiency of measures taken by the government to stabilize banking and assess the introduced models of banking credit risk management from a scientific perspective . The analysis of the indicators showed a growing share of non-performing loans, in particular, the number of overdue assets in the credit portfolio of banks. Which would be caused by the declining production and a decrease in the level of solvency of the population against the background of general political and economic instability in the country. The study identified the lack of effectiveness of the existing legislation related to credit policy and the work with non-performing loans, which would have contributed to the protection of the banking system from the existing credit risk and corresponded to realities of the modern state of the economy. Key words: banking, overdue assets, overdue loans, credit, credit risk, credit portfolio.

Research on borrower's credit classification of P2P network loan based on LightGBM algorithm

Research on borrower's credit classification of P2P network loan based on LightGBM algorithm

Evaluation of borrower's credit of P2P loan based on adaptive particle swarm optimisation BP neural network

Evaluation of borrower's credit of P2P loan based on adaptive particle swarm optimisation BP neural network