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.
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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.
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.
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Graph and download economic data for Delinquency Rate on Loans to Finance Agricultural Production, All Commercial Banks (DRFAPGACBN) from Q1 1987 to Q1 2025 about delinquencies, finance, agriculture, commercial, production, loans, banks, depository institutions, rate, and USA.
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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.
Delinquency rates for credit cards picked up in 2025 in the United States, leading to the highest rates observed since 2008. This is according to a collection of one of the United States' federal banks across all commercial banks. The high delinquency rates were joined by the highest U.S. credit card charge-off rates since the Financial Crisis of 2008. Delinquency rates, or the share of credit card loans overdue a payment for more than ** days, can sometimes lead into charge-off, or a writing off the loan, after about six to 12 months. These figures on the share of credit card balances that are overdue developed significantly between 2021 and 2025: Delinquencies were at their lowest point in 2021 but increased to one of their highest points by 2025. This is reflected in the growing credit card debt in the United States, which reached an all-time high in 2023.
Federal Housing Administration (FHA) loans had the highest delinquency rate in the United States in 2024. As of the second quarter of the year, **** percent of one-to-four family housing mortgage loans were ** days or more delinquent. This percentage was lower for conventional loans and Veterans Administration loans. Despite a slight increase, the delinquency rate for all mortgages was one of the lowest on record.
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The Latin American home mortgage finance market, valued at approximately $XX million in 2025, is projected to experience steady growth, exhibiting a Compound Annual Growth Rate (CAGR) of 3.00% from 2025 to 2033. This growth is fueled by several key drivers, including increasing urbanization, rising disposable incomes across various socioeconomic segments, and government initiatives aimed at boosting homeownership rates. Furthermore, the expansion of the formal financial sector and the availability of innovative mortgage products, such as adjustable-rate mortgages catering to diverse financial profiles, contribute to market expansion. However, economic volatility in certain Latin American nations and fluctuating interest rates pose significant challenges. The market is segmented by mortgage type (fixed-rate and adjustable-rate), loan tenure (ranging from under 5 years to over 25 years), and geography, with Brazil, Chile, Colombia, and Peru representing significant market shares. Competition is intense, with major players including Caixa Economica Federal, Banco do Brasil, Itaú, Bradesco, Santander, and others vying for market dominance. The market's future trajectory hinges on managing economic instability, maintaining affordable interest rates, and continuing to improve access to credit for a broader range of borrowers. The segment analysis reveals that fixed-rate mortgages currently dominate the market, though adjustable-rate mortgages are gaining traction due to their flexibility. Longer-tenure mortgages (11-24 years and 25-30 years) are increasingly popular as borrowers seek more manageable monthly payments. Geographically, Brazil holds the largest market share, reflecting its substantial population and relatively developed financial sector. However, Chile, Colombia, and Peru are showing promising growth potential, driven by improving economic conditions and increased government support for housing initiatives. The Rest of Latin America segment offers considerable untapped potential. Continued economic development and infrastructure improvements in these regions will be instrumental in further propelling market growth in the coming years. A focus on financial literacy and responsible lending practices will be essential for sustainable market development and to mitigate potential risks associated with rapid expansion. Recent developments include: In August 2022, Two new mortgage fintech start-ups emerged in Latin America: Toperty launched in Colombia and Saturn5 is about to launch in Mexico. Toperty offers to purchase a customer's new house outright and provides a payment schedule that allows the customer to purchase the house while renting it from the business. Saturn5 wants to give its clients the skills and resources they need to buy a house on their own., In August 2022, During a conference call on August 5, Brazilian lender Banco Bradesco SA startled analysts by reporting an increase in default rates in the second quarter of 2022. The average 90-day nonperforming loan ratio for Bradesco, the second-largest private bank in Latin America, increased by 30 basis points. Delinquency in the overall portfolio increased to 3.5% from 2.5% and 3.2%, respectively, in the first quarter.. Notable trends are: Increase in Economic Growth and GDP per capita.
In the first quarter of 2025, roughly **** percent of all consumer loans at commercial banks in the United States were delinquent. The delinquency rate on this type of credit has been rising again since 2021. Loans are delinquent when the borrower does not pay their obligations on time. One of the reasons for the delinquency rate decreasing during the first years of the COVID-19 pandemic was that the personal saving rate in the U.S. soared during that period. What is the trend in consumer credit levels in the United States? Consumer credit refers to the various types of loans and credit extended to individuals for personal use, often to fund everyday purchases or larger expenses. When credit levels rise, it often signals that consumers are more confident in their ability to manage debt and make future payments. After a period of strong growth between 2021 and early 2023, consumer credit in the United States has been growing at a slower pace. By early 2024, consumer credit levels reached over **** trillion U.S. dollars. What is the main channel for acquiring consumer credit? In 2024, the leading type of consumer credit among consumers in the U.S. was credit card bills. Credit card usage in the North American country was substantial and credit card penetration was expected to reach over **** percent by 2029. Car loans ranked next as a common source of consumer credit, while other types of debt, such as medical bills, home equity lines of credit, and personal educational loans, had lower percentages.
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The global loan servicing software market was valued at USD 563 million in 2019 and is estimated to reach USD 1216 million by 2026, expanding at a CAGR of 11% during the forecast period, 2020 – 2026. The growth of the market is attributed to rising banking and financial institutes and increasing need of streamlining loan process.
Several financial institutions need higher risk controls during the loan process to ensure enhance capital, fewer losses and lending ability according to the regulatory requirements. Loan servicing software is useful for banks, credit unions, and mortgage lenders for real time data deliver precise analysis related to price setting and credit profiles of potential clients. Loan servicing software also helps commercial finance companies, specialty lenders, wholesale lenders, and banks to manage all mortgages, notes, contracts, and installment loans. Loan servicing software works as a broad credit reporting software, transaction processing and banking, collection management system, investor accounting, and loan servicing system. Financial institutions and banks are more focused on decreasing the delinquency rates of the loans with the help of available technological solutions, such as internet of things, analytics solutions and big data, for the same.
Attributes | Details |
Base Year | 2019 |
Historic Data | 2018–2019 |
Forecast Period | 2020–2026 |
Regional Scope | Asia Pacific, North America, Latin America, Europe, and Middle East & Africa |
Report Coverage | Company Share, Market Analysis and Size, Competitive Landscape, Growth Factors, and Trends, and Revenue Forecast |
Since the start of the coronavirus (COVID-19) crisis, many businesses have had to close their doors or have struggled to pay rent. As a result, commercial property landlords suffered loss of income, leading to failure to repay mortgage loans. In 2020, the default rate of commercial real estate mortgages rose to 4.6 percent, which is the highest value observed since the global financial crisis.
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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.
This table contains 102 series, with data starting from 2013, and some select series starting from 2016. This table contains data described by the following dimensions (Not all combinations are available): Geography (1 item: Canada), Components (51 items: Total, funds advanced, residential mortgages, insured; Variable rate, insured; Fixed rate, insured, less than 1 year; Fixed rate, insured, from 1 to less than 3 years; ...), and Unit of measure (2 items: Dollars; Interest rate). For additional clarification on the component dimension, please visit the OSFI website for the Report on New and Existing Lending.
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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.
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.
The Federal National Mortgage Association, commonly known as Fannie Mae, was created by the U.S. congress in 1938, in order to maintain liquidity and stability in the domestic mortgage market. The company is a government-sponsored enterprise (GSE), meaning that while it was a publicly traded company for most of its history, it was still supported by the federal government. While there is no legally binding guarantee of shares in GSEs or their securities, it is generally acknowledged that the U.S. government is highly unlikely to let these enterprises fail. Due to these implicit guarantees, GSEs are able to access financing at a reduced cost of interest. Fannie Mae's main activity is the purchasing of mortgage loans from their originators (banks, mortgage brokers etc.) and packaging them into mortgage-backed securities (MBS) in order to ease the access of U.S. homebuyers to housing credit. The early 2000s U.S. mortgage finance boom During the early 2000s, Fannie Mae was swept up in the U.S. housing boom which eventually led to the financial crisis of 2007-2008. The association's stated goal of increasing access of lower income families to housing finance coalesced with the interests of private mortgage lenders and Wall Street investment banks, who had become heavily reliant on the housing market to drive profits. Private lenders had begun to offer riskier mortgage loans in the early 2000s due to low interest rates in the wake of the "Dot Com" crash and their need to maintain profits through increasing the volume of loans on their books. The securitized products created by these private lenders did not maintain the standards which had traditionally been upheld by GSEs. Due to their market share being eaten into by private firms, however, the GSEs involved in the mortgage markets began to also lower their standards, resulting in a 'race to the bottom'. The fall of Fannie Mae The lowering of lending standards was a key factor in creating the housing bubble, as mortgages were now being offered to borrowers with little or no ability to repay the loans. Combined with fraudulent practices from credit ratings agencies, who rated the junk securities created from these mortgage loans as being of the highest standard, this led directly to the financial panic that erupted on Wall Street beginning in 2007. As the U.S. economy slowed down in 2006, mortgage delinquency rates began to spike. Fannie Mae's losses in the mortgage security market in 2006 and 2007, along with the losses of the related GSE 'Freddie Mac', had caused its share value to plummet, stoking fears that it may collapse. On September 7th 2008, Fannie Mae was taken into government conservatorship along with Freddie Mac, with their stocks being delisted from stock exchanges in 2010. This act was seen as an unprecedented direct intervention into the economy by the U.S. government, and a symbol of how far the U.S. housing market had fallen.
Homeowners in financial distress can use bankruptcy to avoid defaulting on their mortgages, since filing loosens their budget constraints. But the 2005 bankruptcy reform made bankruptcy less favorable to homeowners and therefore caused mortgage defaults to rise. We test this relationship and find that the reform caused prime and subprime mortgage default rates to rise by 23% and 14%, respectively. Default rates rose even more for homeowners who were particularly negatively affected by the reform. We calculate that bankruptcy reform caused mortgage default rates to rise by one percentage point even before the start of the financial crisis. (JEL D14, G01, G21, K35)
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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:
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!
In 2022, about ** trillion South Korean won in bogeumjari loans (mortgages) with a term of 30 years were issued in South Korea. The largest lender in terms of loan amount that year was Hana Bank, followed by Kookmin Bank.
Mortgage loans in South Korea Many prospective first-time homeowners took out bogeumjari loans due to their fixed and favorable rates. Other large banks in the country providing such loans include Shinhan Bank, Woori Bank, and NH Nonghyup Bank. Throughout the second quarter of 2023, the combined value of mortgage loans offered by the largest banks in South Korea continued to increase.
Rising household debt However, there are concerns about worsening household debt as loans are issued with higher interest rates. Interest rates for mortgage loans rose between 2021 and 2023 following the central bank base rate increases. Unsurprisingly, the national household loan delinquency rate has increased within the past year. As rising inflation and interest rates typically negatively impact household spending habits, the amount of loans provided is expected to slow.
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.
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This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.
Historical daily stock prices (open, high, low, close, volume)
Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)
Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)
Feature engineering based on financial data and technical indicators
Sentiment analysis data from social media and news articles
Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)
Stock price prediction
Portfolio optimization
Algorithmic trading
Market sentiment analysis
Risk management
Researchers investigating the effectiveness of machine learning in stock market prediction
Analysts developing quantitative trading Buy/Sell strategies
Individuals interested in building their own stock market prediction models
Students learning about machine learning and financial applications
The dataset may include different levels of granularity (e.g., daily, hourly)
Data cleaning and preprocessing are essential before model training
Regular updates are recommended to maintain the accuracy and relevance of the data
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.