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 Q2 2025 about domestic offices, delinquencies, 1-unit structures, mortgage, family, commercial, residential, domestic, banks, depository institutions, rate, and USA.

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.

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 second 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.

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 October of 2025.

The S&P/Experian first 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 first mortgages was **** percent. This was lower than the default rate index of second mortgages and home equity loans. Although the index rose in 2022, it remained below the levels observed in the first two months of 2020 when it amounted to approximately *** percent.

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.

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)

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.…

Graph and download economic data for Delinquency Rate on Commercial Real Estate Loans (Excluding Farmland), Booked in Domestic Offices, Banks Ranked 1st to 100th Largest in Size by Assets (DRCRELEXFT100S) from Q1 1991 to Q2 2025 about farmland, domestic offices, delinquencies, real estate, commercial, domestic, loans, assets, banks, depository institutions, rate, and USA.

Abstract (en): Texas is the only US state that limits home equity borrowing to 80 percent of home value. This paper exploits this policy discontinuity around Texas' interstate borders and uses a multidimensional regression discontinuity design framework to find that limits on home equity borrowing in Texas lowered the likelihood of mortgage default by about 1.5 percentage points for all mortgages and 4–5 percentage points for non-prime mortgages. Estimated non-prime mortgage default hazards within 25 to 100 miles on either side of the Texas border are about 20 percent smaller when crossing into Texas.

View monthly updates and historical trends for S&P/Experian First Mortgage Default Index (DISCONTINUED). from United States. Source: Standard and Poor's. …

Lending Club offers peer-to-peer (P2P) loans through a technological platform for various personal finance purposes and is today one of the companies that dominate the US P2P lending market. The original dataset is publicly available on Kaggle and corresponds to all the loans issued by Lending Club between 2007 and 2018. The present version of the dataset is for constructing a granting model, that is, a model designed to make decisions on whether to grant a loan based on information available at the time of the loan application. Consequently, our dataset only has a selection of variables from the original one, which are the variables known at the moment the loan request is made. Furthermore, the target variable of a granting model represents the final status of the loan, that are "default" or "fully paid". Thus, we filtered out from the original dataset all the loans in transitory states. Our dataset comprises 1,347,681 records or obligations (approximately 60% of the original) and it was also cleaned for completeness and consistency (less than 1% of our dataset was filtered out).

TARGET VARIABLE

The dataset includes a target variable based on the final resolution of the credit: the default category corresponds to the event charged off and the non-default category to the event fully paid. It does not consider other values in the loan status variable since this variable represents the state of the loan at the end of the considered time window. Thus, there are no loans in transitory states. The original dataset includes the target variable “loan status”, which contains several categories ('Fully Paid', 'Current', 'Charged Off', 'In Grace Period', 'Late (31-120 days)', 'Late (16-30 days)', 'Default'). However, in our dataset, we just consider loans that are either “Fully Paid” or “Default” and transform this variable into a binary variable called “Default”, with a 0 for fully paid loans and a 1 for defaulted loans.

EXPLANATORY VARIABLES

The explanatory variables that we use correspond only to the information available at the time of the application. Variables such as the interest rate, grade, or subgrade are generated by the company as a result of a credit risk assessment process, so they were filtered out from the dataset as they must not be considered in risk models to predict the default in granting of credit.

FULL LIST OF VARIABLES

Loan identification variables:

• id: Loan id (unique identifier).

• issue_d: Month and year in which the loan was approved.

Quantitative variables:

• revenue: Borrower's self-declared annual income during registration.

• dti_n: Indebtedness ratio for obligations excluding mortgage. Monthly information. This ratio has been calculated considering the indebtedness of the whole group of applicants. It is estimated as the ratio calculated using the co-borrowers’ total payments on the total debt obligations divided by the co-borrowers’ combined monthly income.

• loan_amnt: Amount of credit requested by the borrower.

• fico_n: Defined between 300 and 850, reported by Fair Isaac Corporation as a risk measure based on historical credit information reported at the time of application. This value has been calculated as the average of the variables “fico_range_low” and “fico_range_high” in the original dataset.

• experience_c: Binary variable that indicates whether the borrower is new to the entity. This variable is constructed from the credit date of the previous obligation in LC and the credit date of the current obligation; if the difference between dates is positive, it is not considered as a new experience with LC.

Categorical variables:

• emp_length: Categorical variable with the employment length of the borrower (includes the no information category)

• purpose: Credit purpose category for the loan request.

• home_ownership_n: Homeownership status provided by the borrower in the registration process. Categories defined by LC: “mortgage”, “rent”, “own”, “other”, “any”, “none”. We merged the categories “other”, “any” and “none” as “other”.

• addr_state: Borrower's residence state from the USA.

• zip_code: Zip code of the borrower's residence.

Textual variables

• title: Title of the credit request description provided by the borrower.

• desc: Description of the credit request provided by the borrower.

We cleaned the textual variables. First, we removed all those descriptions that contained the default description provided by Lending Club on its web form (“Tell your story. What is your loan for?”). Moreover, we removed the prefix “Borrower added on DD/MM/YYYY >” from the descriptions to avoid any temporal background on them. Finally, as these descriptions came from a web form, we substituted all the HTML elements by their character (e.g. “&” was substituted by “&”, “<” was substituted by “<”, etc.).

RELATED WORKS

This dataset has been used in the following academic articles:

• Sanz-Guerrero, M. Arroyo, J. (2024). Credit Risk Meets Large Language Models: Building a Risk Indicator from Loan Descriptions in P2P Lending. arXiv preprint arXiv:2401.16458. https://doi.org/10.48550/arXiv.2401.16458
• Ariza-Garzón, M.J., Arroyo, J., Caparrini, A., Segovia-Vargas, M.J. (2020). Explainability of a machine learning granting scoring model in peer-to-peer lending. IEEE Access 8, 64873 - 64890. https://doi.org/10.1109/ACCESS.2020.2984412

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.

This article examines the federal response to mortgage distress during the Great Depression. It documents features of the housing cycle of the 1920s and early 1930s, focusing on the growth of mortgage debt and the subsequent sharp increase in mortgage defaults and foreclosures during the Depression. It summarizes the major federal initiatives to reduce foreclosures and reform mortgage market practices, focusing especially on the activities of the Home Owners' Loan Corporation (HOLC), which acquired and refinanced one million delinquent mortgages between 1933 and 1936. Because the conditions under which the HOLC operated were unusual, the author cautions against drawing strong policy lessons from the HOLC's activities. Nonetheless, similarities between the Great Depression and the recent episode suggest that a review of the historical experience can provide insights about alternative policies to relieve mortgage distress.

Introduction

Financial loan services are leveraged by companies across many industries, from big banks to financial institutions to government loans. One of the primary objectives of companies with financial loan services is to decrease payment defaults and ensure that individuals are paying back their loans as expected. In order to do this efficiently and systematically, many companies employ machine learning to predict which individuals are at the highest risk of defaulting on their loans, so that proper interventions can be effectively deployed to the right audience.

Description

This dataset has been taken from Coursera's Loan Default Prediction Challenge and will provide you the opportunity to tackle one of the most industry-relevant machine learning problems with a unique dataset that will put your modeling skills to the test. The dataset contains 255,347 rows and 18 columns in total.

Features

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Image credits: Image by vectorjuice on Freepik

Please, provide an upvote👍if the dataset was useful for your task. It would be much appreciated😄

Provides the statistics about Student Loan Default Statistics

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.

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.