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The purpose of this paper is to illustrate the limitations and potential bias in securitized residential mortgage data and examine the importance of such data issues for typical studies of residential mortgage-backed security (RMBS) market and the financial crisis.

We use trustee data on mortgage characteristics provided by BlackBox Logic – the BBx data – to study the extent to which undisclosed mortgage characteristics distort the available data and impact risk analysis of RMBS collateral pools.

We illustrate that substantial amounts of loan characteristic data in crucial fields like occupancy, property type, loan purpose and FICO are missing from the trustee data. The frequency of missing values is staggering, ranging from just under 9 per cent for property type to 29 per cent for FICO, up to almost 85 per cent for originator name, all variables used in recent studies. The omissions are correlated to some degree with the securitization sponsor and even more dramatically with the identity of the deal trustee.

Analysis of RMBS collateral should be built not on the entirety of mortgage databases, but on stratified samples and should otherwise control for important sponsor and trustee fixed effects.

The revisions for Regulation AB which require loan-level disclosure should be adopted to standardize mortgage disclosure.

This is the first paper that examines selection bias in loan characteristics relied upon for a wide variety of mortgage market research that has substantially affected policy decisions in the post-crisis era.

This article estimates the loan spread equation taking into account the endogenous matching between banks and firms in the loan market. To overcome the endogeneity problem, I supplement the loan spread equation with a two-sided matching model and estimate them jointly. Bayesian inference is feasible using a Gibbs sampling algorithm that performs Markov chain Monte Carlo (MCMC) simulations. I find that medium-sized banks and firms tend to be the most attractive partners, and that liquidity is also a consideration in choosing partners. Furthermore, banks with higher monitoring ability charge higher spreads, and firms that are more leveraged or less liquid are charged higher spreads.

The authors construct two time-varying political connection (PC) indexes to measure a firm's political tendencies toward ruling and opposing parties and analyze whether a firm with ruling party tendencies obtains better bank loan contracts compared to the contracts obtained by a firm with opposing party tendencies and a firm with fixed PC tendencies.

Linguistic text mining is used to construct the two time-varying PC indexes from news sources that reflect the tone and frequencies of characteristic texts to determine a firm's tendencies to favor the ruling or opposing parties.

The results show that varying PC firms connected to the ruling party receive preferential loan contracts when their political tendencies increase but varying PC firms connected to the opposition party do not. In contrast, fixed PC firms gain similar benefits only when the connection is determined in the presidential election year but not in other years. Firms supporting two parties receive minimal financial rewards in terms of loan terms.

In past studies, once a firm is identified as having a connection with a political party, it is assumed to have PC throughout the sample period (i.e. fixed PC firms). The authors lift this assumption and examine how varying PC affect bank loan contracts. The two time-varying PC indexes can identify a firm's more immediate party tendencies and more precise effects of a firm's party tendencies on bank loan contracts.

The authors analyse the determinants of loan defaults in micro, small and medium enterprises (MSME) loans in India from the survival duration perspective to draw inferences that have implications for lenders and policymakers.

The authors use the Kaplan–Meier survivor function and the Cox Proportional Hazard model to analyse 4.29 lakhs MSME loan account data originated by a large bank having a national presence from 1st January 2016 to 31st December 2020.

The estimated Kaplan–Meier survival function by various categories of loan and socio-demographic characteristics reflects heterogeneity and identifies the trigger points for actions. The authors identify the key identified default drivers. The authors find that the subsidy amount is more effective at the lower level and its effectiveness diminishes significantly beyond an optimum level. The simulated values show that the effects of rising interest rates on survival rates vary across industries and types of loans.

The identified points of inflection in the default dynamics would help banks to initiate actions to prevent loan defaults. The default drivers identified would foster more nuanced lending decisions. The study estimation of the survival rate based on the simulated values of interest rate and subsidy provides insight for policymakers.

This study is the first to investigate default drivers in MSME loans in India using micro-data. The study findings will act as signposts for the planners to guide the direction of the interest rate to be charged by banks in MSME loans, interest subvention and tailoring subsidy levels to foster sustainable growth.

The purpose of this paper is to develop a better understanding of the pricing decisions of banks for project finance (PF) loans and the main drivers affecting the cost of debt in infrastructure deals. As infrastructure projects are typically highly leveraged, the cost of bank lending is an important driver of the overall funding costs for the project.

First, the paper provides a general review of the drivers of the cost of funds in PF. Second, the paper develops a regression analysis of the loan’s spread on four categories: project, loan, bank characteristics and the economic environment. By using a new data set of InfraDeals containing data on bank spreads of more than 700 infrastructure projects worldwide from 2006 to 2016.

The results show that the cost of debt is predominantly affected by the market and the business cycle, rather than the structuring of the project. This implicates that the timing when the deal is closed weighs more heavily than the specificities of the project itself.

The results have important policy implications. As PF deals are often paid for by taxpayers, this paper could help policymakers to use public funds for infrastructure in the most efficient way.

One weakness of existing studies in PF loan pricing is that they undervalue the role of the economic environment in the cost of debt. Few studies in the literature include macroeconomic control variables in their model and the others do not seem to find significant results. This paper reveals new insights on the pricing decisions of banks for PF loans.

This study aims to investigate the determinants of credit spreads in hotel loans securitized into commercial mortgage-backed securities (CMBS) between 2010 and 2015.

The sample represents 1,579 US hotel fixed interest rate whole loans with an aggregate mortgage value of $26.6bn at loan origination. The relationship between credit spreads and property, loan and market characteristic is examined via multiple regression analysis. Additionally, the method of 2-stage least squares is used to control for endogeneity bias and identify the effect of the loan-to-value (LTV) ratio on credit spreads.

The multiple regression models explain 80 per cent of the variation in credit spreads and show a significant association of credit spreads with hotel and loan characteristics and market conditions. The findings indicate the debt coverage ratio to be the most important predictor of credit spreads followed by the loan maturity term, implied capitalization rate, LTV and yield curve. The results show the debt yield premium to be a stronger predictor of credit spreads than the debt yield ratio. The spread between the debt yield ratio and mortgage interest rate could be used in future research as an instrumental variable to identify the effect of the LTV on credit spreads.

This study is limited to the CMBS market and the period after the financial crisis. Additional limitations include sample selection bias, exclusion of multi-property loans and variable interest rate loans.

Interest rate increases in an expanding economy would likely increase the cost of borrowing for hotel owners leading to higher debt service payments and lower profitability. If an increase in interest rates is offset by a decline in credit spreads, hotel owners will still benefit from the ensuing stability in borrowing interest rates. The evidence also suggests that CMBS lenders favor select service and extended stay hotels. Owners and operators of these efficient and profitable hotels will likely obtain loans with lower credit spreads given their lower risk of default.

The current study provides evidence on the effects of loan and property characteristics in the pricing of loan risk and serves to inform CMBS market participants about the factors that drive credit spreads in hotel mortgage loans.

The purpose of this paper is to examine the drivers of default in the Malaysian housing market in the light of various policy interventions by the country’s central bank, and the government’s expressed concern to ensure balanced growth in the market. This paper assesses the importance of considering the endogeneity of loan-to-value (LTV) in predicting housing loan default and its implications.

In this paper, the author addresses the endogeneity problem in the LTV variable using two instrumental variables (IV) in this probit regression: national residential property gains tax and the statutory reserve ratio of Bank Negara Malaysia. This study uses the instrumental variable probit model to consider endogeneity bias. This study assumes a latent (unobservable) variable (Y*), representing a borrower’s tendency to default, which is associated linearly with the borrower’s and loan characteristics and other variables (Xi). This study uses individual borrower-level information of 43,156 housing loan borrowers from the files of a well-established housing bank in Malaysia.

This study’s results confirm that endogeneity causes a substantial difference in the magnitude of the estimated effects of LTV on the default tendency. At the lower values of LTV, the probability of default is over-estimated, and at the higher values, the default probability is substantially underestimated. Endogeneity bias also affects the estimated coefficients of loan and borrower characteristics. The authors find that the interest rate is less relevant in predicting loan default. Other loan characteristics, such as loan age, tenure, payment amount and the built-up area, are relevant. This study’s result confirms that the borrower’s location matters, and an increase in state gross domestic product per capita and an increase in the supply of residential units reduce default probability.

The present study did not explore the applicability of the “equity theory of default” in the Malaysian housing market. This study did not assess “strategic default” issues and the effect of borrowers’ characteristics, personality traits and self-control of Malaysian housing loan borrowers in the mortgage decision-making process. The evolving dynamics of the Malaysian housing market microstructure in property valuation remained unexplored in the present study.

The findings have crucial relevance in the decision-making process of commercial banks, the central bank and the government to frame policies to foster balanced growth and development in the housing market. The authors argue that striking a subtle balance between the concerns of financial stability and productive risk-taking by commercial banks in Malaysia remains a continuing challenge for the country’s central bank. The authors also argue that designing suitable taxation policies by the government can deliver its cherished goal of balanced development in the housing market.

Empirical research on the Malaysian housing market based on micro-level data is scarce due to a paucity of relevant data. This study is based on the individual borrower-level information of 43,156 housing loan borrowers from the files of a well-established housing bank in Malaysia. In this analysis, the authors find clear evidence of endogeneity in LTV and argue that any attempts to decipher the default drivers of housing loans without addressing the issue of endogeneity may lead to faulty interpretation. Therefore, this study is unique in recognizing endogeneity and has gone deeper in identifying the default drivers in the Malaysian housing market not addressed by earlier papers.

The purpose of this paper is to identify determinants of feasible outcome events (expired with no loss, settled for loss, still performing) and time to event of Farm Service Agency (FSA) operating and farm ownership (FO) loan guarantees.

Data on 19,126 FSA guaranteed loans, which were made by various lenders to farmers who have limited ability to obtain loans from normal sources without the Federal guarantee, were collected. Cox proportional hazards models for operating loans (OLs) and FO loans are estimated to identify borrower characteristics, loan characteristics, lender types, and farm and macroeconomic environment factors that influence guarantee outcomes.

Loans with different characteristics (loan amount, loan term, lender type, region originated) and assistance programs (Beginning Farmer, Interest Assistance) have differing guarantee outcomes. Contemporaneous variables, in particular delinquency status, have a significant impact on guarantee outcomes.

All loans were originated in calendar years 2004 and 2005. Since FO loans may have as long as 40 year terms, results are not as robust for FO loans as for OLs.

Different loan characteristics and macroeconomic conditions significantly influence the occurrence of possible guarantee outcomes and time to the outcomes.

Guaranteed loans are the primary method of government credit assistance to US farm operators. Data on individual borrowers have been difficult to obtain for much of the life of the guaranteed program because loan applications are held privately. This study provides insight on how various factors drive guarantee performance which is useful to policy makers trying to increase guaranteed loan program efficiency.

The paper investigates whether stock liquidity of firms is valued by lending banks revealing that firms with higher liquidity in the capital market pay lower spreads for the loans they obtain. This relationship is causal as evidenced by using the decimalization of tick size as an exogenous shock-to-stock liquidity in a difference-in-differences setting. Reduction in financial constraint and improvement in corporate governance induced by higher stock liquidity are potential mechanisms through which liquidity impacts loan spreads. These higher liquidity firms also receive less stringent nonprice loan terms, for example, longer loan maturity and less required collateral.

The purpose of this paper is to apply duration methods to a sample of Farm Service Agency (FSA) direct, seven‐year operating loans to identify those variables that influence the time to loan termination and type of termination. Variables include both those known at time of loan origination and those that characterize the changing economic environment over the life of the loan. Also, to examine the impact of various FSA programs promoting policy objectives.

A systematic sample of 877 seven‐year, FSA direct loans originated between October 1, 1993 and September 30, 1996 was collected. Cox regression, competing risks models are estimated as a function of borrower and loan characteristics observable at loan origination. Economic indicator variables emphasizing the farm economy and observed quarterly over the life of the loan are also included as explanatory variables.

Loan characteristics, borrower financial characteristics and degree of borrower interaction with FSA observable at origin are significant variables in determining type of loan outcome (default or paid‐in‐full) and time to outcome. Changes in the economic environment and farm economy during the life of the loan are significant.

The sample consists only of FSA direct loans which implies borrowers are at financial margin. Application of method to agricultural loans from conventional commercial lenders could identify different significant factors.

Using length of time to loan termination instead of just type of outcome provides for a richer analysis of loan performance. Loan performance over time is influenced by the larger economy and should be incorporated into loan performance modeling.

The study described in the paper demonstrates use of competing risks models on intermediate agricultural loans and develops how this technique can be used to learn about dynamic aspects of loan performance. Sample consists of observations on individual FSA direct loan borrowers. The FSA direct loan program is the major source of credit for agricultural borrowers at the financial margin.