Auto Loans Research Articles

Geographic disparities in bank lending: Evidence from an auto loan market

We investigate geographic biases against nonlocal borrowers in the Chinese auto loan market. Using proprietary loan-level data from a major commercial bank in a regional market, we discover that nonlocal borrowers encounter geographic discrimination, manifested in higher loan rates and shorter loan durations compared to similar local counterparts, even after adjusting for borrowers' risk profiles. This bias is exacerbated by information asymmetries as well as economic and institutional stereotypes. The disparity is particularly pronounced in cases involving elevated loan-to-value (LTV) ratios, higher-value or luxury vehicles, and among male or older borrowers. Our findings shed light on the human biases in lending decisions in the credit market.

A GLOBAL COMPARISON OF CREDIT BUREAUS BASED ON DATA UTILIZATION IN CREDIT SCORING

The basic concept of credit scoring is to assess an individual`s payment ability as well as the specific individual`s credit default risk, hence determining an individual`s creditworthiness. Based on the credit score, financial institutions, insurance companies, telecommunication companies and other businesses decide whether consumers are eligible for a mortgage, credit card, auto loan, and other credit products. However, in many countries, potential tenants and insurance applicants also use credit scores extensively for screening. Accordingly, Credit Bureaus (CB) or Consumer Reporting Agencies (CRA) exert an essential gatekeeper function for important economic areas of consumers’ everyday life. However, when examining CBs globally, there are considerable differences in the use of data to calculate credit scores. Interestingly, the influence of CBs on credit rating receives little to no attention in academic research. This is particularly evident in the absence of a framework for classifying Credit Bureaus. Therefore, 24 traditional and non-traditional Credit Bureaus operating in 17 different countries are analyzed. First, the study identifies the different data types underlying credit reports and credit scores. Second, CBs are classified and clustered according to the type of information used for credit scoring. Furthermore, promising areas of research, in particular the ethical conflict between data protection and economic participation are highlighted.

Who Provides Credit in Times of Crisis? Evidence from the Auto Loan Market

Who Provides Credit in Times of Crisis? Evidence from the Auto Loan Market

Rising Auto Loan Delinquencies and High Monthly Payments

Delinquency rates on auto loans rose substantially to above pre-pandemic levels by the end of 2023, after falling to historical lows during the COVID-19 pandemic. Because auto loans are an important sector in consumer credit, accounting for about 25 percent of nonmortgage consumer credit, a deeper analysis of the increase in delinquencies can give insights into the financial health of borrowers in consumer credit markets and overall household financial well-being.

Living Free from Personal Debt: A Possibility or A Mere Wish?

The steady growth in personal debt over the years in the United States is beginning to raise concerns because people are slowly drowning in debt, and it is taking a huge toll on their everyday lives, their relationships, and their mental health. People are increasingly getting stressed, anxious, and depressed because the debts are overwhelming, and the payments are becoming difficult because of growing inflation and high interest rates. 8 out of every 10 adult Americans are reportedly drowning in debt, and it cuts across young adults to older adults who are retiring with debts. Debt is generally categorized as good debt and bad debt – good debt (student loans, business loans, and mortgages) is owed on things that will build wealth in the future, while bad debt (auto loans, credit cards, personal loans, payday loans) hardly improves one’s financial stability. This paper examines how possible it is to become debt-free and establishes that it is a journey that requires some level of discipline and positivity and will take a while to accomplish for anyone who is determined to get out of debt.

HOW EQUATED MONTHLY INSTALMENT (EMI) SCHEMES AND BUY NOW, PAY LATER (BNPL) SERVICES FUEL IMPULSIVE BUYING: A CLOSER LOOK AT CONSUMER BEHAVIOUR

An Equated Monthly Instalment (EMI) is a fixed payment amount made by a borrower to a lender at a specified date each calendar month. The EMI consists of both the principal amount and the interest on the loan, which is spread over a predetermined period. EMIs are commonly used in home loans, personal loans, and auto loans, where the borrower repays the loan in regular instalments over time. Credit card companies typically offer this facility, but nowadays, it is also available with debit cards. Many banks are now providing a pre-approved credit limit for their debit card holders as well. BNPL stands for "Buy Now, Pay Later". It is a payment option that allows consumers to purchase goods and services and pay for them later, typically with no interest or fees. BNPL schemes offer customers the convenience of paying for purchases over time, without needing a credit card or traditional loan. This payment option has become increasingly popular among consumers, particularly among younger generations, to manage their finances and make larger purchases more affordable. However, it also has potential drawbacks, such as higher spending and risks of missed payments, that should be considered before utilizing this payment option. The objective of this study is to examine the factors that motivate consumers to choose EMI schemes and BNPL services as payment options, and to evaluate their perceived advantages and the risks associated with them. The study primarily relied on primary data collected from 100 samples using convenient sampling, along with secondary sources such as published research reports on BNPL.

Auto Finance in the Electric Vehicle Transition

Financing cost differentials tilt the calculus for households toward electric vehicles (EVs). Using 85 million observations on U.S. auto loans, we study households’ credit risk by engine type, seek to uncover the sources and ask if credit risk differentials are being priced. We find that EV borrowers default 29% less relative to internal combustion engine vehicle (ICEV) borrowers with a back-of-the-envelope value of $1,457 in lender savings. To disentangle selection from expost exposure to differential costs of running an EV, we implement a differential shock exposure by treatment model of Borusyak and Hull (2023). Do lenders passalong these savings to borrowers? EV borrowers pay 2.2 percentage point lower interest rate, the equivalent of $2,711 in foregone payments. This lower rate is only for captive (manufacturer-based) lenders, not for bank and nonbank lenders, suggestive of policy and strategic motives by manufacturers, not a passing along of credit risk value. Another $1,457 is probably not being priced to households. Finally, we find that the ABS market knows, at least partially, allowing for less in loan loss reserves buffering the ABS, reflecting $233 in savings for the ABS issuer.

One Month Longer, One Month Later? Prepayments in the Auto Loan Market

We document a secular trend of increasing auto loan maturity from 30 months to over 70 months during the past 50 years, partly reflecting improved vehicle durability. Analyzing over half of the auto loans originated during the past 16 years, we find that longer-maturity new car loans have significantly higher interest rates with a yield curve much steeper than comparable-maturity Treasury securities. In addition, we show that the majority of auto loans were prepaid, including loans of zero-interest, and that many prepaying borrowers could have paid less interest by choosing loans of a shorter maturity. We argue that factors such as liquidity constraints, uncertainty about future income, and monthly payment targeting likely account for only a portion of borrowers' choice of long-maturity loans.

Disentangling policy and structural effects on car-ownership for car-owning and carless US households during and after the COVID-19 pandemic

This research analyzed car-ownership in the US during and after the COVID-19 pandemic, by utilizing a nationally representative household survey spanning from January 2020 to March 2022. Using a multilevel Hurdle model with month and state random effects, vehicle ownership trends are separately modeled for car-owning and carless households within the same modeling framework, while accounting for endogeneity and unobserved heterogeneity. An increase in the total amount of economic stimulus funding received increased the odds of vehicle ownership, while high car prices, high levels of housing expenditures, living in multi-family dwelling, and being from minority and low-skilled worker families negatively affected car-ownership. Greater household labor force participation, increases in household size and young persons in the household, living in states with high COVID caseloads and with moderately stringent Stay-at-Home social distancing mandates affected car-ownership propensities differently for car-owning and carless households. The significance of the research is that it disentangled pandemic-related and transportation policy variables from changes in household structure, living arrangements and employment-related characteristics. The analysis jointly considered how short-term pandemic-related influences as well as long-term demographic and occupational factors differently affect car-ownership for car-owning households and those without cars. Policy implications are drawn for consumer protection in the car ownership process, auto loan forbearance in future economic disruptions, strategies for public transportation which has continued to suffer from lower levels of use, and sustainability programs due to higher volumes of older used cars.

Credit Cars: Or How I Learned to Stop Worrying and Love Auto Loans

Drawing on trade publications, contemporaneous newspaper stories, and other historical sources from the early twentieth-century United States, this article explains how installment plans overcame moral and business concerns to become the standard way people bought cars. Prominent figures in the automobile industry and financial institutions initially denounced the idea of selling cars on credit, and many banks declined to extend credit to would-be auto buyers. However, the relevant legal infrastructure heavily favored creditors, allowing them to circumvent usury laws and guaranteeing their right to repossess assets if borrowers missed payments. When the profit-making that these aspects of the law enabled became clear, moral objections to the idea of selling cars on credit yielded to a new moral consensus among powerful actors that valorized buying cars on credit and concentrated disapprobation on just those borrowers who defaulted on their payments. Thus, the characteristics of the legal infrastructure functionally presupposed the resolution of the erstwhile debate about the fundamental morality of selling cars on credit. Ultimately, lending practices building on the legal and moral foundation established in the early twentieth century led to the establishment of subprime auto lenders whose business model revolves around exorbitant interest rates, high fees, and aggressive repossession.

The Effects of Credit Score Migration on Subprime Auto Loan and Credit Card Delinquencies

In the early stages of the pandemic, income support and forbearance programs led consumer loan delinquency rates to fall to near-record lows for borrowers across the credit score distribution. Since the second half of 2021, however, delinquency rates have risen, and by 2023:Q3, overall rates for auto and credit card loans had risen above their pre-pandemic levels.

Understanding the Recent Evolution of Auto Loans by Income Level

Understanding the Recent Evolution of Auto Loans by Income Level

Product Sales Incentive Spillovers to the Lending Market: Evidence from Subprime Auto Loan Defaults

This paper shows how convex incentives in vertical contracts between manufacturers and retailers can induce sales behavior with costs to consumers. We examine this problem in the automotive sector, where manufacturers commonly motivate new vehicle sales through dealer incentive programs with large discrete bonuses determined by monthly sales targets. Using subprime car loans from over 3,500 dealerships, we document high default rates on new car loans originated at the end of the month—the period when dealerships attempt to secure target-based bonuses by intensifying efforts to sell new cars. We provide evidence consistent with the observed higher default rates resulting from customers purchasing new vehicles at month end. New car purchases stretch borrower budgets and expose borrowers to rapid depreciation, which consigns the borrower with negative equity through much of the loan term. Our results imply that the quartile of customers with the highest payment-to-income ratio see default rates increase from 13.6% to 19.7% on the last day of the month. Although consumers bear high costs from increased defaults, we find no evidence that lenders that purchase the loans are hurt by the default increase. Our results demonstrate how the behaviors induced by convex incentive schemes for sales are borne by customers. This paper was accepted by David Simchi-Levi, business strategy. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2023.4935 .

Competition and selection in credit markets

Screening in consumer credit markets is often associated with large fixed costs. We present both theory and evidence that, when lenders use fixed-cost technologies to screen borrowers, increased competition may increase rather than decrease interest rates in subprime consumer credit markets. In more competitive markets, lenders have lower market shares, and thus lower incentives to invest in screening. Thus, when markets are competitive, all lenders face a riskier pool of borrowers, which can lead interest rates to be higher. We provide evidence for the model’s predictions in the auto loan market using administrative credit panel data.

Joint loan risk prediction based on deep learning‐optimized stacking model

AbstractIn recent years, China's automobile industry has undergone rapid development, creating new opportunities for the auto loan industry. Currently, auto financing companies are actively seeking to expand their cooperation with banks. Therefore, improving the approval rate and scale of joint loan business is of significant practical importance. In this paper, we propose a Stacking‐based financial institution risk approval model and select the optimal stacking model by comparing its performance with other models. Additionally, we construct a bank approval model using deep learning techniques on a biased data set, with feature extraction performed using convolution neural networks (CNN) and feature‐based counterfactual augmentation used for balanced sampling. Finally, we optimize the model of the prediction of auto finance companies by selecting the optimal coefficients of loss function based on the features and results of the bank approval model. The proposed approach leads to an approximately 6% increase in the joint loan approval rate on the actual data set, as demonstrated by experimental results.

The study "Unravelling the Impact: Assessing the Impacts of Rising Rates of Interest and Inflation on Individual Finances"

The impact of rising interest rates and hyperinflation on personal finances is examined in this abstract. It draws attention to how these two economic aspects are interconnected and what it means for people's financial security. This abstract offers insights into how changes in mortgage rates and inflation affect personal finances by looking at the impact on costs of borrowing, deposits, savings, and purchasing power. The link between rate of interest and borrowing costs is the first topic covered in the abstract. When interest rates increase, borrowing money becomes increasingly costly for people, which has an impact on many facets of personal finances like credit card debt, home loans, and auto loans. According to the abstract, increasing borrowing costs can result in bigger debt loads, decreased affordability, and possible difficulties with debt payback. The abstract also looks at how inflation affects people's personal budgets. Over time, inflation reduces the monetary value of money, raising the cost of goods and services. The abstract focuses on how inflationary affects the costs of living, covering necessities like housing, food, and medical care. It emphasizes how crucial it is to factor inflation into long-term financial planning and budgeting in order to preserve financial stability and lessen the loss of wealth. The abstract also discusses how the rate of interest and inflation interact. It illustrates how interest rates are frequently employed by central banks as a tool to manage inflation. By decreasing consumption and slowing economic expansion, an increase in financing costs can help control inflation. However, this additionally has a negative impact on a person's personal finances, especially if they have investments or loans with variable rates or loans that are susceptible to changes in interest rates. The abstract also looks at ways that people might manage the effects of inflation and higher interest rates on their personal budgets. It talks on the value of financial literacy, investment diversification, responsible management of debt, and proactive steps like refinancing or modifying spending patterns to accommodate shifting economic conditions.

Risk and Revenue Management in the Chinese Auto Loan Industry

The automobile consumption credit business promotes the development of the automobile industry. However, the current credit system in China requires further refinement. Thus, the credit loan business is associated with certain risks, and company profits are often negatively impacted by clients who default on loans. Based on the data, this article leverages the economic and financial theories of consumer credit risk control to construct a logistic model to predict customers' default probability. Then, a quadratic regression model is established to determine the optimal commission structure to balance profitability with incentives from retail stores. Results show that the macro-level variables are negatively associated with the probability of good behavior. The personal level variables exhibit a positive association. In addition, a negative coefficient in the quadratic profit equation indicates the presence of an inverted “U” relationship between profit and commission. Corresponding suggestions are put forward.

Evolution in the Consumer ABS Market (Past, Present, and Future)

The consumer ABS market provides a large and growing source of funding to the US economy. Historically the market consisted primarily of auto loans, credit card receivables, and student loans. Over the past decade, esoteric ABS issuance has proliferated, as more markets are able to securitize cash flows to unlock a lower cost of funding relative to comparable corporate alternatives. Sectors that did not exist 10 years ago have grown to be large and liquid market sectors, including marketplace lending, solar, PACE, and consumer handset receivables. This trend has been aided by a pullback in bank lending, driven by post-Great Financial Crisis (“GFC”) regulation and capital regime changes. The Fed’s imposition of higher interest rates throughout 2022 and into 2023 is designed to slow the economy and reduce inflation but is also likely to put pressure on consumer performance in the coming months. While consumer balance sheets remain relatively healthy, excess savings accumulated during the pandemic have been eroding, and delinquency rates are expected to rise. While performance is expected to deteriorate, consumer ABS structures have gotten safer since the GFC—including via more credit enhancement and better-quality collateral—and some of the longer-standing structures have shown their ability to withstand a variety of macroeconomic environments without taking a loss. Despite the more robust structures, performance of newer esoteric asset classes will be watched closely as we enter a more volatile macroeconomic environment. The ABS market will continue to evolve in the coming years, as large structural change has tended to correspond to material changes in macroeconomic conditions. Regardless of the changes, we expect the market to remain an integral source of funding for the US consumer.

Distribution-Free Contextual Dynamic Pricing

Contextual dynamic pricing aims to set personalized prices based on sequential interactions with customers. At each time period, a customer who is interested in purchasing a product comes to the platform. The customer’s valuation for the product is a linear function of contexts, including product and customer features, plus some random market noise. The seller does not observe the customer’s true valuation, but instead needs to learn the valuation by leveraging contextual information and historic binary purchase feedback. Existing models typically assume full or partial knowledge of the random noise distribution. In this paper, we consider contextual dynamic pricing with unknown random noise in the linear valuation model. Our distribution-free pricing policy learns both the contextual function and the market noise simultaneously. A key ingredient of our method is a novel perturbed linear bandit framework, in which a modified linear upper confidence bound algorithm is proposed to balance the exploration of market noise and the exploitation of the current knowledge for better pricing. We establish the regret upper bound and a matching lower bound of our policy in the perturbed linear bandit framework and prove a sublinear regret bound in the considered pricing problem. Finally, we demonstrate the superior performance of our policy on simulations and a real-life auto loan data set. Funding: Y. Liu and W.W. Sun acknowledge support from the National Science Foundation Division of Social and Economic Sciences [Grant NSF-SES 2217440]. Supplemental Material: The supplementary material is available at https://doi.org/10.1287/moor.2023.1369 .

A Practical Guide to the Valuation of Coupon-Bearing Fixed Income Securities

The purpose of this article is to illustrate the use of reduced-form credit risk models for valuing defaultable coupon-bearing securities, such as risky sovereign bonds, corporate bonds, and retail loans like auto loans and mortgage loans. We focus on issues in the application of these models and not the mathematical derivations of the formulas employed.