Graph and download economic data for Delinquency Rate on Single-Family Residential Mortgages, Booked in Domestic Offices, All Commercial Banks (DRSFRMACBS) from Q1 1991 to Q1 2025 about domestic offices, delinquencies, 1-unit structures, mortgage, family, residential, commercial, domestic, banks, depository institutions, rate, and USA.

The Global Financial Crisis of 2008-09 was a period of severe macroeconomic instability for the United States and the global economy more generally. The crisis was precipitated by the collapse of a number of financial institutions who were deeply involved in the U.S. mortgage market and associated credit markets. Beginning in the Summer of 2007, a number of banks began to report issues with increasing mortgage delinquencies and the problem of not being able to accurately price derivatives contracts which were based on bundles of these U.S. residential mortgages. By the end of 2008, U.S. financial institutions had begun to fail due to their exposure to the housing market, leading to one of the deepest recessions in the history of the United States and to extensive government bailouts of the financial sector.

Subprime and the collapse of the U.S. mortgage market

The early 2000s had seen explosive growth in the U.S. mortgage market, as credit became cheaper due to the Federal Reserve's decision to lower interest rates in the aftermath of the 2001 'Dot Com' Crash, as well as because of the increasing globalization of financial flows which directed funds into U.S. financial markets. Lower mortgage rates gave incentive to financial institutions to begin lending to riskier borrowers, using so-called 'subprime' loans. These were loans to borrowers with poor credit scores, who would not have met the requirements for a conventional mortgage loan. In order to hedge against the risk of these riskier loans, financial institutions began to use complex financial instruments known as derivatives, which bundled mortgage loans together and allowed the risk of default to be sold on to willing investors. This practice was supposed to remove the risk from these loans, by effectively allowing credit institutions to buy insurance against delinquencies. Due to the fraudulent practices of credit ratings agencies, however, the price of these contacts did not reflect the real risk of the loans involved. As the reality of the inability of the borrowers to repay began to kick in during 2007, the financial markets which traded these derivatives came under increasing stress and eventually led to a 'sudden stop' in trading and credit intermediation during 2008.

Market Panic and The Great Recession

As borrowers failed to make repayments, this had a knock-on effect among financial institutions who were highly leveraged with financial instruments based on the mortgage market. Lehman Brothers, one of the world's largest investment banks, failed on September 15th 2008, causing widespread panic in financial markets. Due to the fear of an unprecedented collapse in the financial sector which would have untold consequences for the wider economy, the U.S. government and central bank, The Fed, intervened the following day to bailout the United States' largest insurance company, AIG, and to backstop financial markets. The crisis prompted a deep recession, known colloquially as The Great Recession, drawing parallels between this period and The Great Depression. The collapse of credit intermediation in the economy lead to further issues in the real economy, as business were increasingly unable to pay back loans and were forced to lay off staff, driving unemployment to a high of almost 10 percent in 2010. While there has been criticism of the U.S. government's actions to bailout the financial institutions involved, the actions of the government and the Fed are seen by many as having prevented the crisis from spiraling into a depression of the magnitude of The Great Depression.

Following the drastic increase directly after the COVID-19 pandemic, the delinquency rate started to gradually decline, falling below *** percent in the second quarter of 2023. In the second half of 2023, the delinquency rate picked up, but remained stable throughout 2024. In the first quarter of 2025, **** percent of mortgage loans were delinquent. That was significantly lower than the **** percent during the onset of the COVID-19 pandemic in 2020 or the peak of *** percent during the subprime mortgage crisis of 2007-2010. What does the mortgage delinquency rate tell us? The mortgage delinquency rate is the share of the total number of mortgaged home loans in the U.S. where payment is overdue by 30 days or more. Many borrowers eventually manage to service their loan, though, as indicated by the markedly lower foreclosure rates. Total home mortgage debt in the U.S. stood at almost ** trillion U.S. dollars in 2024. Not all mortgage loans are made equal ‘Subprime’ loans, being targeted at high-risk borrowers and generally coupled with higher interest rates to compensate for the risk. These loans have far higher delinquency rates than conventional loans. Defaulting on such loans was one of the triggers for the 2007-2010 financial crisis, with subprime delinquency rates reaching almost ** percent around this time. These higher delinquency rates translate into higher foreclosure rates, which peaked at just under ** percent of all subprime mortgages in 2011.

BackgroundWhere the data come fromThe Mortgage Performance Trends data come from the NMDB, a joint project we’ve undertaken with the Federal Housing Finance Agency (FHFA). For more information, visit the NMDB program page .The core data in the NMDB come from data maintained by one of the top three nationwide credit repositories. The NMDB has a nationally representative, 5 percent sample of all outstanding, closed-end, first-lien, 1–4 family residential mortgages.The data and analyses presented herein are the sole product of the CFPB. Use of information downloaded from our website, and any alteration or representation regarding such information by a party, is the responsibility of such party.Why the data matterMortgage delinquency rates reflect the health of the mortgage market, and the health of the overall economy.The 30–89 mortgage delinquency rate is a measure of early stage delinquencies. It generally captures borrowers that have missed one or two payments. This rate can be an early indicator of mortgage market health. However, this rate is seasonally volatile and sensitive to temporary economic shocks.The 90–day delinquency rate is a measure of serious delinquencies. It generally captures borrowers that have missed three or more payments. This rate measures more severe economic distress.PrivacyThe Mortgage Performance Trends data have many protections in place to protect personal identity. Before the CFPB or the FHFA receive any data for the NMDB, all records are stripped of information that might reveal a consumer’s identity, such as names, addresses, and Social Security numbers. All data shown are aggregated by state, metropolitan statistical area, or county.

United States - Delinquency Rate on Single-Family Residential Mortgages, Booked in Domestic Offices, Banks Ranked 1st to 100th Largest in Size by Assets was 1.89% in January of 2025, according to the United States Federal Reserve. Historically, United States - Delinquency Rate on Single-Family Residential Mortgages, Booked in Domestic Offices, Banks Ranked 1st to 100th Largest in Size by Assets reached a record high of 12.81 in January of 2010 and a record low of 1.38 in October of 2004. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Delinquency Rate on Single-Family Residential Mortgages, Booked in Domestic Offices, Banks Ranked 1st to 100th Largest in Size by Assets - last updated from the United States Federal Reserve on July of 2025.

Graph and download economic data for Delinquency Rate on Commercial Real Estate Loans (Excluding Farmland), Booked in Domestic Offices, All Commercial Banks (DRCRELEXFACBS) from Q1 1991 to Q1 2025 about farmland, domestic offices, delinquencies, real estate, commercial, domestic, loans, banks, depository institutions, rate, and USA.

In 2022, the student loan default rate in the United States was highest for Black borrowers, at **** percent. In comparison, Asian borrowers were least likely to default on their student loans.

In 2023, the delinquency rates of all types of mortgage lenders in Canada increased. As of the fourth quarter of the year, approximately 1.05 percent of loans in the loan portfolios of mortgage investment entities (MIEs) were classified as delinquent, which was a decrease from the 0.78 percent delinquency rate a year ago. A loan is reported by lenders as being delinquent after 270 days of late payments.

Graph and download economic data for Delinquency Rate on Consumer Loans, All Commercial Banks (DRCLACBS) from Q1 1987 to Q1 2025 about delinquencies, commercial, loans, consumer, banks, depository institutions, rate, and USA.

The mortgage delinquency rate for Federal Housing Administration (FHA) loans in the United States declined since 2020, when it peaked at 15.65 percent. In the second quarter of 2024, 10.6 percent of FHA loans were delinquent. Historically, FHA mortgages have the highest delinquency rate of all mortgage types.

Graph and download economic data for Delinquency Rate on All Loans, All Commercial Banks (DRALACBS) from Q1 1985 to Q1 2025 about delinquencies, commercial, loans, banks, depository institutions, rate, and USA.

United States - Delinquency Rate on Credit Card Loans, All Commercial Banks was 3.05% in January of 2025, according to the United States Federal Reserve. Historically, United States - Delinquency Rate on Credit Card Loans, All Commercial Banks reached a record high of 6.77 in April of 2009 and a record low of 1.53 in July of 2021. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Delinquency Rate on Credit Card Loans, All Commercial Banks - last updated from the United States Federal Reserve on July of 2025.

Graph and download economic data for Delinquency Rate on Credit Card Loans, All Commercial Banks (DRCCLACBS) from Q1 1991 to Q1 2025 about credit cards, delinquencies, commercial, loans, banks, depository institutions, rate, and USA.

In 2022, the student loan default rate in the United States was highest for borrowers who did not complete a degree, at 45 percent. In comparison, borrowers with a bachelor's degree or higher were least likely to default on their student loans.

Context

Financial institutions incur significant losses due to the default of vehicle loans. This has led to the tightening up of vehicle loan underwriting and increased vehicle loan rejection rates. The need for a better credit risk scoring model is also raised by these institutions. This warrants a study to estimate the determinants of vehicle loan default. A financial institution has hired you to accurately predict the probability of loanee/borrower defaulting on a vehicle loan in the first EMI (Equated Monthly Instalments) on the due date. Following Information regarding the loan and loanee are provided in the datasets: Loanee Information (Demographic data like age, Identity proof etc.) Loan Information (Disbursal details, loan to value ratio etc.) Bureau data & history (Bureau score, number of active accounts, the status of other loans, credit history etc.) Doing so will ensure that clients capable of repayment are not rejected and important determinants can be identified which can be further used for minimising the default rates.

From May to June 2020, the commercial mortgage delinquency rate in the hotel sector in the United States went up from two percent to 11.49 percent. The leisure and hospitality sector was one of the most affected sectors by the COVID-19 pandemic. As for the multifamily sector, the delinquency rate increased only by 0.18 percent in that time period.

Description: Welcome to the "Loan Applicant Data for Credit Risk Analysis" dataset on Kaggle! This dataset provides essential information about loan applicants and their characteristics. Your task is to develop predictive models to determine the likelihood of loan default based on these simplified features.

In today's financial landscape, assessing credit risk is crucial for lenders and financial institutions. This dataset offers a simplified view of the factors that contribute to credit risk, making it an excellent opportunity for data scientists to apply their skills in machine learning and predictive modeling.

Column Descriptions:

• ID: Unique identifier for each loan applicant.
• Age: Age of the loan applicant.
• Income: Income of the loan applicant.
• Home: Home ownership status (Own, Mortgage, Rent).
• Emp_Length: Employment length in years.
• Intent: Purpose of the loan (e.g., education, home improvement).
• Amount: Loan amount applied for.
• Rate: Interest rate on the loan.
• Status: Loan approval status (Fully Paid, Charged Off, Current).
• Percent_Income: Loan amount as a percentage of income.
• Default: Whether the applicant has defaulted on a loan previously (Yes, No).
• Cred_Length: Length of the applicant's credit history.

Explore this dataset, preprocess the data as needed, and develop machine learning models, especially using Random Forest, to predict loan default. Your insights and solutions could contribute to better credit risk assessment methods and potentially help lenders make more informed decisions.

Remember to respect data privacy and ethics guidelines while working with this data. Good luck, and happy analyzing!

South Africa: Percent of mortgage debt balance 90+ days delinquent: The latest value from is percent, unavailable from percent in . In comparison, the world average is 0.00 percent, based on data from countries. Historically, the average for South Africa from to is percent. The minimum value, percent, was reached in while the maximum of percent was recorded in .

NPL Management Market Outlook

The global NPL (Non-Performing Loans) Management market size was valued at approximately USD 3.2 billion in 2023 and is projected to grow at a compound annual growth rate (CAGR) of 6.8% from 2024 to 2032, reaching an estimated USD 5.9 billion by 2032. The market is experiencing significant growth due to increasing financial pressures on institutions to manage non-performing assets effectively and efficiently.

One of the primary growth factors driving the NPL Management market is the increasing volume of non-performing loans globally. Economic downturns, geopolitical tensions, and economic challenges such as inflation and unemployment have led to higher default rates, necessitating advanced systems and solutions for managing these problematic assets. Financial institutions require robust tools and services to mitigate risks and recover debts, driving demand for NPL management solutions.

Additionally, regulatory pressures are compelling banks and financial institutions to adopt more stringent frameworks for NPL management. Governments and regulatory bodies worldwide are mandating stricter controls and reporting mechanisms to ensure financial stability and transparency. This regulatory environment acts as a catalyst for the adoption of sophisticated NPL management software and services, contributing to market growth.

Technological advancements in data analytics, artificial intelligence, and machine learning are revolutionizing the NPL management market. These technologies enable more accurate risk assessments, predictive analytics, and automated processes, significantly enhancing the efficiency and effectiveness of NPL management. By leveraging advanced technologies, financial institutions can better forecast defaults, optimize recovery strategies, and improve overall decision-making processes.

Regionally, the market dynamics vary significantly. North America and Europe are the dominant regions, with a high adoption rate of advanced financial technologies and stringent regulatory frameworks. Meanwhile, the Asia Pacific region is witnessing rapid growth due to the expanding banking sector and increasing NPL volumes in emerging economies such as India and China. These regional differences highlight the need for tailored solutions to meet specific market demands and regulatory requirements.

Solution Type Analysis

The NPL Management market is segmented by solution types, including Debt Collection Software, Risk Management Software, Analytics and Reporting Tools, and Others. Debt Collection Software is a critical component, enabling institutions to streamline and automate the collections process. This software helps in tracking overdue accounts, managing communications with debtors, and ensuring compliance with regulatory requirements. The importance of efficient debt collection cannot be overstated, as it directly impacts the financial health of institutions by improving recovery rates and reducing the burden of bad debts.

Risk Management Software plays a pivotal role in identifying, assessing, and mitigating the risks associated with non-performing loans. This software uses advanced algorithms and data analytics to predict default probabilities, evaluate borrower creditworthiness, and devise proactive strategies to minimize losses. By providing a comprehensive view of risk exposure, this software helps financial institutions make informed decisions and implement effective risk mitigation measures.

Analytics and Reporting Tools are essential for providing insights into NPL portfolios. These tools enable the aggregation and analysis of vast amounts of data to generate detailed reports on loan performance, recovery rates, and other key metrics. By offering granular insights, these tools help institutions identify trends, track progress, and make data-driven decisions to enhance NPL management strategies. The ability to generate customized reports also ensures that institutions meet regulatory reporting requirements efficiently.

Other solutions in the NPL management market include specialized software for loan restructuring, asset valuation, and legal case management. These solutions cater to specific aspects of NPL management, providing targeted functionalities to address unique challenges. For example, loan restructuring software helps in renegotiating loan terms to make them more manageable for borrowers, while asset valuation tools assist in determining the fair market value of collateral assets.

Repor

In 2022, the student loan default rate in the United States was highest for borrowers in the bottom ** percent of the family income bracket, at ** percent. In comparison, borrowers in the top 25 percent were least likely to default on their student loans.