Housing Tenure and Mortgage Choice

This paper examines the relationship between housing tenure and mortgage contract. We present a model showing that, given expected mobility, borrowers will have incentive to self-select into the appropriate mortgage product such that their fixed-rate period is directly related to their probability of moving. We empirically test this hypothesis using housing tenure data derived from a large national database of repeat mortgage transactions. After controlling for borrower characteristics, the mobility hazards of 3/1, 5/1 and 7/1 ARMs, compared to a 30-year fixed rate, are estimated to be 28%, 14% and 11% higher, respectively     

Mortgage Choice as a Natural Field Experiment on Choice under Risk

Data on approximately 280,000 borrowers from the UK Survey of Mortgage Lenders are used to model choices between variable and fixed rate mortgages. The choice is assumed to depend on three factors: risk attitude, interest‐rate expectations, and individual discount rate. The ordered probit model is used for estimation, while taking account of a number of econometric issues including missing counterfactuals, selectivity, and endogeneity. A large number of strong effects are found, including: higher income borrowers are less risk averse and have a lower discount rate, and risk aversion rises with the amount borrowed, providing evidence of increasing relative risk aversion.     

Optimal Mortgage Contract Choice Decision in the Presence of Pay Option Adjustable Rate Mortgage and the Balloon Mortgage

The unprecedented run-up in global house prices of the 2000s was preceded by a revolution in U.S. mortgage markets in which borrowers faced a plethora of mortgages to choose from collectively known as nontraditional mortgages (NTMs), whose poor performance helped ignite the global financial crisis in 2007. This paper studies the choice of mortgage contracts in an expanded framework where the menu of contracts includes the pay option adjustable rate mortgage (PO-ARM), and the balloon mortgage (BM), alongside the traditional long horizon fixed rate mortgage (FRM) and the short horizon regular ARM. The inclusion of the PO-ARM is based on the fact it is the most controversial and perhaps the riskiest of the NTMs, whereas the BM has not been analyzed in the literature despite its different risk-sharing arrangement and long vintage. Our inclusive model relates the structural differences of these contracts to the horizon risk management problems and affordability constraints faced by the households that differ in terms of expected mobility. The numerical solutions of the model generates a number of interesting results suggesting that households select mortgage contracts to match their horizon, manage horizon risk and mitigate liquidity or affordability constraints they face. From a risk management and welfare perspectives, we find that the optimal contract for households with shorter horizons, specifically households who expect to move house once every 1 to 2 years, is the PO-ARM. Beyond 2 years the welfare advantage of the PO-ARM diminishes and BM becomes the more optimal contract up to 5-year horizon. Overall, the results suggest that households are neither as risk averse as the selection of the FRM would suggest, nor are they as risk-seeking as the selection of PO-ARM or regular ARM would suggest. The results also suggest that the exuberance demonstrated for NTMs by borrowers, especially PO-ARMs, may be both rational and irrational.     

The Effect of Down Payment Assistance on Mortgage Choice

Lack of wealth for a down payment is one of the most recognized barriers to home ownership. In response to this barrier, state and federal government have implemented many programs that provide down payment assistance to potential home buyers. Numerous studies have shown that this assistance can increase homeownership rates, but few have measured how receiving assistance may alter borrowing behavior. Using data from a down payment assistance grant in the Midwest, this study compares the loan type and size of grant recipients to other borrowers that report similar income and buy homes in the same census tract. Results indicate grant recipients are more likely to use conventional loans, which are less expensive than other loan types that require a smaller down payment. Estimates also suggest that the grant may reduce loan size for borrowers who are on the margin of using a conventional loan.     

Reverse Mortgage Loans: A Quantitative Analysis

Reverse mortgage loans (RMLs) allow older homeowners to borrow against housing wealth without moving. Despite rapid growth in this market, only 1.9% of eligible homeowners had RMLs in 2013. In this paper, we analyze reverse mortgages in a calibrated life‐cycle model of retirement. The average welfare gain from RMLs is $252 per homeowner, and $1,770 per RML borrower. Bequest motives, uncertainty about health and expenses, and loan costs account for low demand. According to the model, the Great Recession's impact differs across age, income, and wealth distributions, with a threefold increase in RML demand for lowest income and oldest households.     

商业银行不动产抵押价值类型选择研究

不动产抵押是商业银行一种重要的信用风险缓释工具,加强抵押品的价值认定管理对商业银行风险控制具有重要作用,而价值类型是抵押资产价值评估的基础.目前抵押价值类型主要有三种不同观点:市场价值类型、抵押贷款价值类型和清算价值类型.本文重点对上述三种观点进行界定和区分,明确了商业银行不动产抵押价值类型选择原则,指出当前适合商业银行风险管理要求的抵押价值类型应为市场价值类型.     

A Comparison of Neural Network, Statistical Methods, and Variable Choice for Life Insurers' Financial Distress Prediction

This study examines the effect of the statistical/mathematical model selected and the variable set considered on the ability to identify financially troubled life insurers. Models considered are two artificial neural network methods (back‐propagation and learning vector quantization (LVQ)) and two more standard statistical methods (multiple discriminant analysis and logistic regression analysis). The variable sets considered are the insurance regulatory information system (IRIS) variables, the financial analysis solvency tracking (FAST) variables, and Texas early warning information system (EWIS) variables, and a data set consisting of twenty‐two variables selected by us in conjunction with the research staff at TDI and a review of the insolvency prediction literature. The results show that the back‐propagation (BP) and LVQ outperform the traditional statistical approaches for all four variable sets with a consistent superiority across the two different evaluation criteria (total misclassification cost and resubstitution risk criteria), and that the twenty‐two variables and the Texas EWIS variable sets are more efficient than the IRIS and the FAST variable sets for identification of financially troubled life insurers in most comparisons.     

A Dynamic Analysis of Fixed- and Adjustable-Rate Mortgage Terminations

This paper provides a side-by-side comparison of loan-level statistical models for fixed- and adjustable-rate mortgages. Multinomial logit models for quarterly conditional probabilities of default and prepayment are estimated. We find that the estimated impacts of embedded option values for prepayment and default are generally quite similar across both FRM and ARM loans, providing additional empirical support for the basic predictions of the options theory. We also find that differences in estimates of conditional probabilities of prepayment and default associated with mortgage age, origination period, original LTV, and relative loan size, indicate the continued significance of these other economic and demographic factors for empirical models of mortgage terminations.     

农村信用治理机制优化的路径选择——基于农地抵押融资的分析框架

从农地抵押融资的视角阐释和分析了农村信用治理的需求与基础,科学定位农村信用体系的建设目标框架,构建以农村信用法律法规、产权制度、诚信教育、信用服务、信用监管等为核心内容的农村信用治理体系,完善以信用为基础的新型农村信用治理模式和信用监管机制.通过建立完善农村信用治理机制、信用监管机制和信用治理模式,优化农村信用治理机制,激活农户和农业经营主体的信用意识和守信观念,提升农户和农业经营主体的信用水平,建立农地抵押融资的长效机制,改善农村金融生态环境.     

Grey Relational Analysis and Neural Network Forecasting of REIT returns

This study employs the Grey Relational Analysis (GRA) and Artificial Neural Network (ANN) to measure the impact of key elements on the forecasting performance of real estate investment trust (REIT) returns. To manage risks from a real estate price bubble, the findings of GRA suggest that the REIT is best influenced by industrial production index, lending rate, dividend yield, stock index and its own lagged performance. Consequently, this paper adjusts the parameters from GRA and inserts the key elements into the fitted ANN model by comparing the learning effect of the Back-propagation Neural Network (BPN). This study found that the ranking provided by the GRA is significant in correcting prediction errors using the learning outcome of the BPN. The neural network model proved to minimize error function and was able to adjust weighted values in order to enhance prediction accuracy.     

A Model of Mortgage Default

In this paper, we solve a dynamic model of households' mortgage decisions incorporating labor income, house price, inflation, and interest rate risk. Using a zero‐profit condition for mortgage lenders, we solve for equilibrium mortgage rates given borrower characteristics and optimal decisions. The model quantifies the effects of adjustable versus fixed mortgage rates, loan‐to‐value ratios, and mortgage affordability measures on mortgage premia and default. Mortgage selection by heterogeneous borrowers helps explain the higher default rates on adjustable‐rate mortgages during the recent U.S. housing downturn, and the variation in mortgage premia with the level of interest rates.     

Detection of Management Fraud: A Neural Network Approach

The detection of management fraud is an important issue facing the auditing profession. A major contributor to this issue is the Loebbecke and Willingham (1988) conceptual model for the detection of management fraud. A cascaded Logit approach using the Loebbecke and Willingham model was developed in Bell et al. (1993). The present study offers an alternative approach using Artificial Neural Networks (ANNs). This paper develops a successful discriminator of management fraud using both the generalized adaptive neural network architectures (GANNA) and the Adaptive Logic Network (ALN) approaches to designing neural networks. The discriminant functions can distinguish between fraudulent and non-fraudulent companies with superior accuracy to the cascaded Logit results of Bell et al. (1993). Finally, the discriminant function provides a parsimonious set of questions useful for detecting management fraud.     

Mortgage Default: Classification Trees Analysis

We apply the powerful, flexible, and computationally efficient nonparametric Classification and Regression Trees (CART) algorithm to analyze real estate mortgage data. CART is particularly appropriate for our data set because of its strengths in dealing with large data sets, high dimensionality, mixed data types, missing data, different relationships between variables in different parts of the measurement space, and outliers. Moreover, CART is intuitive and easy to interpret and implement. We discuss the pros and cons of CART in relation to traditional methods such as linear logistic regression, nonparametric additive logistic regression, discriminant analysis, partial least squares classification, and neural networks, with particular emphasis on real estate. We use CART to produce the first academic study of Israeli mortgage default data. We find that borrowers features, rather than mortgage contract features, are the strongest predictors of default if accepting icbadli borrowers is more costly than rejecting good ones. If the costs are equal, mortgage features are used as well. The higher (lower) the ratio of misclassification costs of bad risks versus good ones, the lower (higher) are the resulting misclassification rates of bad risks and the higher (lower) are the misclassification rates of good ones. This is consistent with real-world rejection of good risks in an attempt to avoid bad ones.     

A Theoretic Analysis of Extent of Lender Participation in a Participating Mortgage

A participating mortgage is a loan in which a lender accepts a below-market coupon rate in return for a share (participation) in the cash flows generated by income-producing real property. The cash flows provided by participation are classified as contingent interest and are intended to compensate the lender for additional risk exposure as well as the reduction in coupon rate. In this paper, we present a partial equilibrium wealth-maximizing model to estimate the extent of lender participation and an analysis of the factors affecting it. The results of formal comparative statics analysis show that the lender's percentage participation is, in general, positively related to changes in the loan-to-value ratio and threshold cash flows above which participation is payable. Among yet other results, a change in the contracted loan life has an ambiguous effect on the lender's percentage participation. Then, in an effort to resolve ambiguities in the comparative statics results, we employ a numerical procedure in conducting sensitivity analyses. This allows us to estimate percentage participation levels, and their elasticities, under various assumptions regarding the underlying factors. JEL Classification: G21, C65     

基于神经网络的我国货运市场分析

采用BP神经网络方法建立了我国货运市场的预测模型,并以1994-1996年的货运数据进行检验,误差均值仅为0.02%-0.04%,可见预测模型具有良好的可行性和实用性.另外,还从宏观经济发展角度出发,系统地探讨了我国货运市场发展变化的内外在因素,并提出了相关政策建议.     

A Neural Network Approach for Analyzing Small Business Lending Decisions

In this paper, we apply the neural network method to small business lending decisions. We use the neural network to classify the loan applications into the groups of acceptance or rejection, and compare the model results with the actual decisions made by loan officers. Data were collected from a leading bank in Central New York. The sample contains important financial statement and business information of borrowers and the loan officers' decisions. We conduct the network training on the data sample and find that the neural network has a stronger discriminating power for classifying the acceptance and rejection groups than traditional parametric and nonparametric classifiers. The results show that the neural network model has a high predictive ability. Our findings suggest that neural networks can be a very useful tool for enhancing small-business lending decisions and reducing loan processing time and costs.     

基于小波神经网络的Shibor预测研究

上海银行间同业拆放利率(Shibor)的推出是中国利率市场化重要的一步。在阐述了Shibor的背景、功能以及对经济发展的重大意义之后,分别建立了小波神经网络和回归时间序列组合模型对2周品种Shibor进行预测对比分析,研究结果表明,小波神经网络的拟合和预测精度较高,具有一定的科学性和实用性。     

按揭与让与担保之比较

按揭是英美法系国家一种以转移所有权为特征的提保制度，让与担保与大际同法系国家一种非典型担保制度，两者不乏相似之处，即也存在诸多差异。我国引入的按揭制度溶入了中国特色，从而与香港按揭制度有差异，也与让与担保明显不同，我国不能以让与担保制度否定按揭制度，应采取二者并存的立法方略。     

国外住房抵押贷款担保制度的分析和借鉴

建立完善的住房抵押贷款担保体系,可以有效防范住房贷款中存在的金融风险,同时其信用提高功能可以有效促进我国中低收入家庭购房,有利于改善我国中低收入家庭的住房条件。目前我国的房贷担保机构的体制尚不完善,本文在介绍和分析国外发达国家的担保体系运作模式的基础上,对我国住房抵押贷款担保体系发展模式提出若干建议。