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 Q4 2025 about domestic offices, delinquencies, 1-unit structures, mortgage, residential, commercial, family, 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.

Graph and download economic data for Delinquency Rate on Single-Family Residential Mortgages, Booked in Domestic Offices, Banks Not Among the 100 Largest in Size by Assets (DRSFRMOBN) from Q1 1991 to Q4 2025 about domestic offices, delinquencies, 1-unit structures, mortgage, residential, family, domestic, assets, banks, depository institutions, rate, and USA.

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 fourth 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.

These tables provide additional detail on the loan assets of U.S. depository institutions by reporting mortgage and consumer loan portfolios broken down by the banks' estimates of the probability of default, as defined below. This information facilitates analysis of the potential concentration of risk in specific loan categories. The institutions reporting this information are generally those with $10 billion or more of assets.

Federal Housing Administration (FHA) loans had the highest delinquency rate in the United States in 2025. As of the second quarter of the year, ***** percent of the outstanding 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.

View quarterly updates and historical trends for US Mortgages Delinquent by 90 or More Days. from United States. Source: Federal Reserve Bank of New York.…

The delinquency rate on real estate loans at commercial banks in the United States rose slightly between the fourth quarter of 2022 and the fourth quarter of 2025. Nevertheless, with the exception of commercial real estate loans, delinquencies remained below the 2020 levels, when the share of loans past due 30 days rose due to the COVID-19 pandemic. Recently, the gap between residential and commercial real estate loans has narrowed, with the delinquency rate for commercial real estate rising faster than for residential.

United States - Delinquency Rate on Single-Family Residential Mortgages, Booked in Domestic Offices, All Commercial Banks was 1.82% in October of 2024, according to the United States Federal Reserve. Historically, United States - Delinquency Rate on Single-Family Residential Mortgages, Booked in Domestic Offices, All Commercial Banks reached a record high of 11.36 in January of 2010 and a record low of 1.40 in January of 2005. 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, All Commercial Banks - last updated from the United States Federal Reserve on March of 2026.

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 Q4 2025 about farmland, domestic offices, delinquencies, real estate, commercial, domestic, loans, banks, depository institutions, rate, and USA.

ATTOM’s Propensity to Default dataset is a predictive Mortgage Loans Data solution that identifies the likelihood a residential property will enter mortgage default (pre-foreclosure) within the next 12 months. The model evaluates only properties with active mortgages where a mortgage-related foreclosure could occur and explicitly excludes non-mortgage defaults such as HOA or tax liens.

The model is built using two core data sources: ATTOM’s historical mortgage and pre-foreclosure records and Powerlytics’ proprietary database of anonymized U.S. household tax return data. These datasets are combined and analyzed using machine learning techniques to generate more accurate default risk predictions than either source can provide independently.

Each property record includes both a Propensity Group ranking and a Foreclosure Probability Score ranging from 0–100. Propensity Groups provide relative risk positioning across the dataset, while the probability score enables finer differentiation between properties with similar risk profiles. Higher scores indicate a greater likelihood of mortgage default within the next 12 months.

The dataset is paired with ATTOM public record data to deliver a comprehensive view of property, ownership, transaction, valuation, and default risk attributes at scale. Model accuracy testing shows the top 30 Propensity Groups capture 95.5% of properties that enter foreclosure within 12 months and are 3.2x more likely to default than a randomly selected property.

Updated monthly and refreshed through quarterly model validation, Propensity to Default supports risk analytics, investment analysis, and mortgage servicing workflows with nationwide coverage across 2,848 counties and more than 55.6 million residential properties.

The S&P/Experian second mortgage default index stood at **** as of May 2022, meaning that based on data from the most recent three months, the annualized share of default second mortgages and home equity loans was **** percent. This was higher than the first mortgage default rate for the same period. Although the index rose in 2022, it remained below the levels observed in December 2017, when it spiked at **** percent.

Anonymised Loan Default data, including loan amount, term, Interest rate, instalment, employment length, home ownership, annual income, loan status and purpose, etc. as well as the binary repay_fail (1 or 0).

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 mortgage delinquency rate for Federal Housing Administration (FHA) loans in the United States has declined since 2020, when it peaked at ***** percent. In the second quarter of 2025, ***** percent of FHA loans were delinquent. Historically, FHA mortgages have the highest delinquency rate of all mortgage types.

FHA mortgages lead at 11.52%, auto loans hit a record 5.21%, and total delinquency reaches 4.81%. Every major loan category compared: current rate, year-over-year change, and historical peak.

Mortgage delinquency rates increased in most states in 2024. That year, the percentage of total mortgage debt that was more than ** days delinquent was the highest in Louisiana, at **** percent. Conversely, Wisconsin and Montana had the lowest delinquency rates, at under **** percent. The overall mortgage delinquency rate in the United States declined since spiking at the beginning of the pandemic, as the U.S. job market rebounded over the course of 2020 and 2021.

Banks run into losses when customers don't pay their loans on time. Because of this, every year, banks have losses in crores, and this also impacts the country's economic growth to a large extent. In this hackathon, we look at various attributes such as funded amount, location, loan, balance, etc., to predict whether a person will be a loan defaulter.

To solve this problem, MachineHack has created a training dataset of 67,463 rows and 35 columns and a testing dataset of 28,913 rows and 34 columns. The hackathon demands a few pre-requisite skills like big datasets, underfitting vs overfitting, and the ability to optimize “log_loss” to generalize well on unseen data.

We examine the effects of constituents, special interests, and ideology on congressional voting on two of the most significant pieces of legislation in US economic history. Representatives whose constituents experience a sharp increase in mortgage defaults are more likely to support the Foreclosure Prevention Act, especially in competitive districts. Interestingly, representatives are more sensitive to defaults of their own-party constituents. Special interests in the form ofhigher campaign contributions from the financial industry increase the likelihood of supporting the Emergency Economic Stabilization Act. However, ideologically conservative representatives are less responsive to both constituent and special interests. (JEL D72, G21, G28)

This dataset provides detailed mortgage loan records, including borrower profiles, property information, loan terms, payment history, and default/foreclosure status. It is designed to help lenders assess and predict mortgage default risk, optimize portfolio management, and support regulatory compliance and risk modeling initiatives.