Determinants of Home Loan Default Risk among Salaried and Self- Employed Borrowers in India: A Comparative Empirical Study Using PLS-SEM and Logistic Regression
This study proposes and validates a comparative empirical framework using PLS-SEM and logistic regression to identify how financial, behavioural, and macroeconomic determinants of home loan default risk differ between salaried and self-employed borrowers in India, demonstrating that credit-history discipline significantly impacts risk mitigation more for salaried individuals while providing a ready-to-deploy analytical pipeline for differentiated underwriting and risk pricing.
Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer
In the bustling landscape of India's financial markets, the home loan is more than just a transaction; it is a promise between a borrower and a bank, a promise that stretches over decades. For lenders, the central challenge is predicting who will keep that promise and who might stumble. While traditional banking has long relied on cold, hard numbers—such as how much of a person's monthly income goes toward the loan payment or the value of the house compared to the debt—researchers are increasingly realizing that human behavior plays an equally critical role. People are not just spreadsheets; their financial choices are shaped by how well they understand money, their psychological traits like impulsivity, and how they perceive the stability of the wider economy. A key question remains: do these factors affect a salaried employee, who receives a steady paycheck, in the same way they affect a self-employed business owner, whose income can fluctuate wildly with the seasons?
This study sets out to answer that question by building a comprehensive model of home loan risk that blends financial data with human behavior. The researchers focused on two distinct groups in India: salaried workers and self-employed individuals. They examined seven specific drivers of risk: the stability of a person's income, the burden of their monthly payments, the adequacy of the house's value as security, their history of repaying debts, their financial knowledge, their psychological tendencies, and their perception of economic stress. The goal was to see if these factors predict a borrower's own feeling of risk—how likely they think they are to miss a payment—and to test whether employment type changes the strength of these relationships. To ensure their methods were sound before collecting real-world data, the team first tested their entire approach on a simulated dataset of 400 borrowers, creating a digital mirror of the real world to see if their tools worked.
The results from this simulated trial revealed a clear picture of what drives the fear of default. All seven factors the researchers investigated were found to be significant predictors. When income is unstable, when monthly payments feel heavy, when the loan amount is too high compared to the house value, or when a borrower has a history of missed payments, the perceived risk of default rises. Conversely, having a strong credit history, high financial literacy, and stable psychological traits acts as a shield, lowering that perceived risk. The model was robust enough to explain nearly 28 percent of the variation in how borrowers viewed their own risk, and it successfully predicted outcomes better than a simple guess would have. This confirmed that a model combining financial ratios with behavioral insights is a viable way to understand loan risk.
However, the most revealing finding emerged when the researchers compared the two groups. They discovered that the importance of these factors is not the same for everyone. Specifically, the link between a borrower's credit history and their risk of default was significantly stronger for salaried employees than for the self-employed. For a salaried worker, a clean credit record is a powerful signal of reliability, carrying substantial weight in predicting their behavior. For the self-employed, whose income streams are often irregular and harder to document, a credit history is a noisier signal; it matters, but it does not carry the same decisive power. This suggests that the rules banks use to judge risk might need to be different for these two groups, as a single standard may not accurately reflect the reality of how each group manages debt.
To bridge the gap between these behavioral feelings and actual financial outcomes, the study also ran a supplementary analysis that looked at hard data like the ratio of payments to income and credit bureau scores. This part of the study confirmed that higher payment burdens and lower credit scores directly increase the odds of a loan going bad. It also showed that even after accounting for these financial numbers, financial literacy still acts as a protective factor, reducing the likelihood of default. The analysis demonstrated the methodological viability of connecting the behavioral model to observable loan-level outcomes, showing good discrimination between defaulting and non-defaulting accounts on the simulated data, though these specific performance metrics must be re-verified on real loan data.
The authors are careful to note that these findings come from a simulated pilot study, a necessary first step to validate their methods before they deploy the survey to real people. The numbers and patterns described here are illustrative of a working framework, not yet a final verdict on the Indian housing market. Yet, the study offers a clear path forward for housing finance institutions. It suggests that lenders should consider tailoring their risk assessments, perhaps placing different weights on credit history depending on whether the applicant is salaried or self-employed. Furthermore, it highlights the value of financial literacy programs, particularly for self-employed borrowers who face income volatility, as a way to build resilience against default. By understanding that the human element of risk behaves differently across employment types, the financial sector can move toward more precise, fair, and effective lending practices.
Drowning in papers in your field?
Get daily digests of the most novel papers matching your research keywords — with technical summaries, in your language.