The role of neighborhood characteristics in mortgage default risk: Evidence from New York City

Using a rich database of non-prime mortgages from New York City, we find that census tract level neighborhood characteristics are important predictors of default behavior, even after controlling for an extensive set of controls for loan and borrower characteristics. First, default rates increase with the rate of foreclosure notices and the number of lender-owned properties (REOs) in the tract. Second, default rates on home purchase mortgages are higher in census tracts with larger shares of black residents, regardless of the borrower’s own race. We explore possible explanations for this second finding and conclude that it likely reflects differential treatment of black neighborhoods by the mortgage industry in ways that are unobserved in our data.     

Spatial dependence in microfinance credit default

Credit scoring model development is very important for the lending decisions of financial institutions. The creditworthiness of borrowers is evaluated by assessing their hard and soft information. However, microfinance borrowers are very sensitive to a local economic downturn and extreme (weather or climate) events. Therefore, this paper is devoted to extending the standard credit scoring models by taking into account the spatial dependence in credit risk. We estimate a credit scoring model with spatial random effects using the distance matrix based on the borrowers’ locations. We find that including the spatial random effects improves the ability to predict defaults and non-defaults of both individual and group loans. Furthermore, we find that several loan characteristics and demographic information are important determinants of individual loan default but not group loans. Our study provides valuable insights for professionals and academics in credit scoring for microfinance and rural finance.     

Bank lending rules for managing sovereign risk

A simple domestic lending rule is one that ensures that the loan rate exceeds the bank's cost of capital and the borrower's expected cashflows exceed the terminal value of the loan. Because a sovereign loan is not collateralized and lacks recourse, the domestic lending rule is not adequate for making sovereign lending decisions. Three modifications are suggested. First, the sovereign borrower's time preference for consumption needs to be considered. Second, the domestic borrower's decision to default voluntarily is made after observing the value of the collateral whereas the sovereign borrower's decision is made after observing earnings. In this paper, the sovereign borrower upgrades expectations in a Bayesian manner. Although no lending rule will completely prevent a default, the probability of default can be managed leading to a third modification.     

Predicting default risk under asymmetric binary link functions

In this article we propose the use of an asymmetric binary link function to extend the proportional hazard model for predicting loan default. The rationale behind this approach is that the symmetry assumption that has been widely used in the literature could be considered as quite restrictive, especially during periods of financial distress. In our approach we allow for a flexible level of asymmetry in the probability of default by the use of the skewed logit distribution. This enable us to estimate the actual level of asymmetry that is associated with the data at hand. We implement our approach to both simulated data and a rich micro dataset of consumer loan accounts. Our results provide clear evidence that ignoring the actual level of asymmetry leads to seriously biased estimates of the slope coefficients, inaccurate marginal effects of the covariates of the model, and overestimation of the probability of default. Regarding the predictive power of the covariates of the model, we have found that loan-specific covariates contain considerably more information about the loan default than macroeconomic covariates, which are often used in practice to carry out macroprudential stress testing.     

Why Do African Banks Lend So Little?

We put forward a plausible explanation of African banking sector under‐development in the form of a bad credit market equilibrium. Using an appropriately modified Industrial Organization model of banking, we show that the root of the problem could be unchecked moral hazard (strategic loan defaults) or adverse selection (a lack of good projects). Applying a dynamic panel estimator to a large sample of African banks, we show that loan defaults are a major factor inhibiting bank lending when institutional quality is low. We also find that once a threshold level of institutional quality has been reached, improvements in the default rate or institutional quality do not matter. This provides support for our theoretical predictions.     

基于产业异质性的银行贷款政策效应分析——来自上市银行的经验证据

在银行信贷资金配置存在产业间差异的假定下,建立引入产业因素的银行贷款决定模型,考察产业因素影响银行贷款渠道有效性的内在机制,并运用2007年到2010年14家上市银行和5组产业数据展开动态面板模型估计,结果表明:从贷款需求角度看,各个产业的产出对产业贷款的影响效应存在产业间差异;从贷款供给角度看,人民银行运用货币政策工具调控产业贷款的影响效应也存在产业间差异。也就是说,产业因素确实影响到人民银行调控各个特定产业贷款的力度。     

Using your regular contacts as collateral: The information value of call logs

This paper aims to uncover the deterrent effect of external social ties on online peer-to-peer lending by extracting valuable information from the phone usage data. Contrary to the studies viewing online internal friendships as the signal of credit quality, we find that the total number of outgoing calls made in the month before loan requests is positively associated with the probability of default, which suggests that more social interactions offline mediated by mobile phones are more likely to signal poor trustworthiness. Only the features pertaining to the calls made during the morning for a given day have the potential as an effective predictor for the borrower’s credible social collateral. However, the online peer-to-peer lending platform studied by us fails to perfectly judge borrowers by their external social ties since the total number of outgoing calls generally has a negative relationship with the interest rates charged for funded loans. Overall, our work advances the understanding of the economic value carried by call logs in the context of disintermediated financial markets with information asymmetry.     

我国民间借贷及其监管对策研究——以鄂尔多斯地区为例

鄂尔多斯地区民间借贷在近年来发展速度与规模非常快,对地区经济成长与发展起到积极推动作用的同时,其带来的一系列问题也成为人们关注的热点.游离于监管体制外的大量民间资金的存在也严重影响了国家宏观调控政策实施的效果,对地区社会经济稳定和金融安全带来巨大隐患,在分析鄂尔多斯民间借贷特点和形成原因的基础上,针对性提出了推动鄂尔多斯金融稳定健康发展的对策与措施.     

Credit rationing, race, and the mortgage market

This study applies microdata from the 1983 Survey of Consumer Finances to evaluate the effects of borrower race and default risk in mortgage lending. The empirical analysis is based on a probit model of whether borrowers obtain FHA or conventional mortgages; the former are fully insured and are characterized by easier downpayment constraints, but are typically more expensive. Hence, households borrowing through the FHA will tend to be credit constrained in the conventional market. Results of the analysis indicate that variables which proxy lender concerns about default risk and cost have an important effect on the type of loan borrowers obtain. Empirical estimates also suggest that minority households are significantly less likely to obtain conventional financing than whites, even after controlling for various proxies of default risk. These results suggest that race effects in mortgage lending may persist for reasons unrelated to borrower default risk.     

Assessing and predicting small industrial enterprises’ credit ratings: A fuzzy decision-making approach

Corporate credit-rating assessment plays a crucial role in helping financial institutions make their lending decisions and in reducing the financial constraints of small enterprises. This paper presents a new approach for small industrial enterprises’ credit-rating assessment using fuzzy decision-making methods and then tests this novel approach using real bank loan data from 1820 small industrial enterprises in China. The procedure of the proposed rating approach includes (1) using triangular fuzzy numbers to quantify the qualitative evaluation indicators; (2) adopting a correlation analysis, univariate analysis, and stepping backward feature selection method to select the input features; (3) employing the best-worst method (BWM) combined with the entropy weight method (EWM), the fuzzy c-means algorithm and the technique for order of preference by similarity to ideal solution (TOPSIS) to classify small enterprises into different rating classes; and (4) applying the lattice degree of nearness to predict a new loan applicant’s rating. We also conduct 10-fold cross-validation to evaluate the predictive performance of our proposed approach. The predictive results demonstrate that our proposed data-processing and feature selection approaches have better accuracy than the alternative approaches in predicting default, offering bankers a new valuable rating system to assist their decision making.     

基于朴素贝叶斯的供应链金融信用风险预测分析

“供应链金融”是近年来金融机构针对供应链上下游企业提供的一种全新的金融服务．这项业务在金融机构方面也存在一定的风险。针对由贷款企业造成的信用风险,文章利用朴素贝叶斯技术预测申请贷款企业的还款风险．金融机构能据此识别不同企业,并针对不同还款能力的企业制定不同的金融政策以控制和规避金融风险。     

Do foreign bank operations provide a stabilizing influence in Korea?

We examine data (1994–2001) to determine if foreign banks’ behavior differed from domestic banks and if foreign banks helped to stabilize Korean markets. Foreign banks’ financial ratios differed from Korean banks with two notable exceptions: provisions for loan losses and loan growth. Before the Asian financial crisis, all banks’ loans generally did not respond to Korean market conditions. Post crisis, foreign banks reduced total lending. Foreign banks increased and Korean banks decreased won-denominated loans when Korean economic conditions improved after the crisis. Finally, foreign banks’ lending reacted to changes in home-country GDP growth and real interest rates.     

Default risk and private student loans: Implications for higher education policies

In recent years, the proportion of students facing a binding constraint on government student loans has grown. This has led to substantially increased use of private loans as a supplementary source of finance for households׳ higher education investment. A critical aspect of the private market for student loans is that loan terms must reflect students׳ risk of default. College investment will therefore differ from a world in which government student loans, whose terms are not sensitive to credit risk, are expanded to no longer bind. Moreover, beyond simply crowding out private lending, expansions of the government student loan program will feed back into default risk on private loans. The goal of this paper is to provide a quantitative assessment of the likely effects of the private market for student loans on college enrollment. We build a model of college investment that reflects uninsured idiosyncratic risk and a well-defined life-cycle that is consistent with observed borrowing and default behavior across family income and college preparedness. We find that higher government borrowing limits increase college investment but lead to more default in the private market for student loans, while tuition subsides increase college investment and reduce default rates in the private market. Consequently, higher limits on government student loans have small negative welfare effects, while tuition subsidies increase aggregate welfare.     

Probability of default in collateralized credit operations for small business

The paper examines how the collateral affects the probability of default for small firms. We present a stylized theoretical model to derive the relationship between the level of collateral and subsequent loan default. We find that the probability of default is negatively correlated with the level of collateral, which is intuitive. Subsequently, we test this relationship by using a proprietary database of collateralized loans of small Brazilian enterprises.     

Neighborhood effects of concentrated mortgage foreclosures

As the national mortgage crisis has worsened, an increasing number of communities are facing declining housing prices and high rates of foreclosure. Central to the call for government intervention in this crisis is the claim that foreclosures not only hurt those who are losing their homes to foreclosure, but also harm neighbors by reducing the value of nearby properties and in turn, reducing local governments’ tax bases. The extent to which foreclosures do in fact drive down neighboring property values has become a crucial question for policy-makers. In this paper, we use a unique dataset on property sales and foreclosure filings in New York City from 2000 to 2005 to identify the effects of foreclosure starts on housing prices in the surrounding neighborhood. Regression results suggest that above some threshold, proximity to properties in foreclosure is associated with lower sales prices. The magnitude of the price discount increases with the number of properties in foreclosure, but not in a linear relationship.     

Financial crises and economic growth

This paper constructs a simple yet robust model of financial crises and economic growth where financial markets affect real economic activity. Financial markets increase real output by facilitating investment through the borrowing/lending of capital. However, the borrowing of capital is risky due to randomness in the firms’ production. Financial crises occur when output and liquid capital are insufficient to meet required loan payments and systemic defaults occur. In this model, a financial crisis caused by systemic defaults can shift the economy from an equilibrium with positive borrowing/lending to an equilibrium with no borrowing/lending. In this no-lending equilibrium, neither traditional fiscal or monetary policy tools are effective in increasing output. Fiscal and monetary policy can only increase the likelihood of the equilibrium evolving to a borrowing/lending equilibrium.     

需求不确定的电商供应链融资模式选择

探讨在市场需求不确定情况下,对电商和中小型供应商均有利的融资模式即银行借贷模式和电商借贷模式。研究发现,影响电商供应链各节点企业利润的重要指标是银行贷款利率,有利于电商供应链各节点企业利润的银行贷款利率存在一个确定的区间。若银行贷款利率属于该区间,则电商借贷模式更有利,否则银行借贷模式更有利。     

浅析银行贷款风险及应对策略

金融是现代经济的核心,任何一场金融危机必然会导致经济危机。而金融危机爆发的根源则是银行在放贷的过程中对相应的风险评估不足,缺乏必要和理性的控制。因此,对贷款的风险如何充分评估,又如何控制;不只是银行的经营问题,而是涉及国计民生的焦点问题。本文鉴于目前国内外各大银行在日常经营过程中所遇到的种种情况,结合一些实际案例,分析归纳出几种银行贷款风险及其相应的应对策略,为银行制定稳妥的经营策略、为国家制定合理的货币政策提供参考。     

The Role of Financial,Macroeconomic, and Non-financial Information in Bank Loan Default Timing Prediction

We assess the use of bank loan information in predicting the timing to default. We use unique data on defaults in small and medium enterprises maintained by the Central Bank of Portugal which includes financial accounting and macroeconomic indicators, as well as non-financial information. The findings are indicative of the incremental predictive ability of non-financial information over and above macroeconomic and financial accounting information in the baseline, industry, and in- and out-of-sample models. Specifically, total credit secured by firms is, as expected, negatively and significantly related to default. Gross domestic product is negatively and significantly related to default, and benchmark market rate is positively and significantly associated with default. The findings also reveal that firms which are operated by partners, which have stronger financial support from partners, and which possess operational assets exhibit lower hazards of default. The study indicates that non-financial information and macroeconomic indicators assessed alongside financial accounting data can significantly improve the forecasting performance of default models.