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 (DRSFRMOBS) from Q1 1991 to Q1 2025 about domestic offices, delinquencies, 1-unit structures, mortgage, family, residential, domestic, assets, 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.

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.

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 July of 2025.

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.

In Hong Kong's banking sector, the default rate on mortgages is very low. In 2024, the delinquency ratio of residential mortgage lending by authorized banking institutions stood at ****. The value of issued mortgages exceeded **** trillion Hong Kong dollars.

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.

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.

The FHFA House Price Index (FHFA HPI®) is the nation’s only collection of public, freely available house price indexes that measure changes in single-family home values based on data from all 50 states and over 400 American cities that extend back to the mid-1970s. The FHFA HPI incorporates tens of millions of home sales and offers insights about house price fluctuations at the national, census division, state, metro area, county, ZIP code, and census tract levels. FHFA uses a fully transparent methodology based upon a weighted, repeat-sales statistical technique to analyze house price transaction data. ​ What does the FHFA HPI represent? The FHFA HPI is a broad measure of the movement of single-family house prices. The FHFA HPI is a weighted, repeat-sales index, meaning that it measures average price changes in repeat sales or refinancings on the same properties. This information is obtained by reviewing repeat mortgage transactions on single-family properties whose mortgages have been purchased or securitized by Fannie Mae or Freddie Mac since January 1975. The FHFA HPI serves as a timely, accurate indicator of house price trends at various geographic levels. Because of the breadth of the sample, it provides more information than is available in other house price indexes. It also provides housing economists with an improved analytical tool that is useful for estimating changes in the rates of mortgage defaults, prepayments and housing affordability in specific geographic areas. U.S. Federal Housing Finance Agency, All-Transactions House Price Index for Connecticut [CTSTHPI], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/CTSTHPI, August 2, 2023.

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)

The mortgage delinquency rate for Veterans Administration (VA) loans in the United States has decreased since 2020. Under the effects of the coronavirus pandemic, the mortgage delinquency rate for VA loans spiked from **** percent in the first quarter of 2020 to **** percent in the second quarter of the year. In the second quarter of 2024, the delinquency rate amounted to **** percent. Historically, VA mortgages have significantly lower delinquency rate than conventional mortgages.

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)

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.

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.

As of March 2025, the 30-day delinquency rate for commercial mortgage-backed securities (CMBS) varied per property type. The share of late payments for office CMBS was the highest at over **** percent, about ***** percentage points higher than the average for all asset classes. A 30-day delinquency refers to payments that are one month late, regardless of how many days the month has. Commercial mortgage-backed securities are fixed-income investment products which are backed by mortgages on commercial property.

This paper studies the impact of unemployment insurance (UI) on the housing market. Exploiting heterogeneity in UI generosity across US states and over time, we find that UI helps the unemployed avoid mortgage default. We estimate that UI expansions during the Great Recession prevented more than 1.3 million foreclosures and insulated home values from labor market shocks. The results suggest that policies that make mortgages more affordable can reduce foreclosures even when borrowers are severely underwater. An optimal UI policy during housing downturns would weigh, among other benefits and costs, the deadweight losses avoided from preventing mortgage defaults.

Residential Mortgage Service Market Outlook

The global residential mortgage service market size was valued at approximately USD 2.5 trillion in 2023 and is projected to reach USD 4.3 trillion by 2032, growing at a robust CAGR of 5.8% during the forecast period. This anticipated growth is driven by several factors including increasing urbanization, rising disposable incomes, and ongoing technological advancements in financial services. The market is witnessing substantial growth due to the surge in demand for housing loans, as more individuals aspire to own homes, encouraged by favorable government policies and low-interest rates.

One of the primary factors propelling the growth of the residential mortgage service market is the increasing rate of urbanization. As more people move from rural to urban areas in search of better employment opportunities and living standards, the demand for residential properties skyrockets. This urban influx creates a significant need for mortgage services to facilitate the purchase of homes. Furthermore, the development of infrastructure in urban areas makes them more appealing to potential homeowners, thereby driving the market for residential mortgage services.

Another crucial growth driver is the rise in disposable incomes and the overall improvement in economic conditions across various regions. As individuals' financial situations become more stable, they are more likely to invest in long-term assets such as real estate. Moreover, the availability of diverse mortgage products tailored to meet the specific needs of different consumer segments further stimulates market growth. Financial institutions are constantly innovating to offer more flexible and appealing mortgage solutions, catering to both high-income and middle-income groups.

Technological advancements in the financial sector are also playing a pivotal role in the expansion of the residential mortgage service market. The incorporation of artificial intelligence (AI) and machine learning (ML) in mortgage services has streamlined the loan approval and underwriting processes, making them faster and more efficient. Digital platforms and mobile applications have made it easier for consumers to apply for mortgages and manage their loans, enhancing customer experience and satisfaction. This technological integration not only improves operational efficiency for lenders but also attracts a tech-savvy consumer base.

In terms of regional outlook, North America holds a significant share of the global residential mortgage service market, thanks to its well-developed financial sector and high demand for housing. Europe follows closely, with countries like Germany and the UK showing strong growth due to favorable economic conditions and government policies supporting home ownership. The Asia Pacific region is expected to witness the highest growth rate, driven by rapid urbanization and rising disposable incomes in countries like China and India. Latin America and the Middle East & Africa are also poised for growth, albeit at a slower pace, as they continue to develop their financial infrastructures.

Service Type Analysis

The residential mortgage service market can be segmented by service type into loan origination, underwriting, loan servicing, loan closing, and others. Loan origination covers the initial stage of the mortgage process, where potential borrowers apply for a mortgage. This segment is crucial as it sets the stage for the entire mortgage process, involving tasks such as pre-approval, credit checks, and property appraisals. The efficiency and effectiveness of the loan origination process can significantly impact customer satisfaction and the overall success of the mortgage provider. Technological advancements in this segment, such as automated underwriting systems, have enhanced the speed and accuracy of loan originations.

Underwriting, another critical segment, involves assessing the risk of lending to a borrower based on credit history, employment status, and financial health. The underwriting process determines whether the lender will approve the mortgage application and under what terms. This segment has seen significant innovation with the use of AI and big data analytics, which help in making more accurate risk assessments and reducing the time required for underwriting. For mortgage lenders, efficient underwriting processes are essential to minimize defaults and enhance profitability.

Loan servicing includes managing the ongoing loan payments, ensuring timely repayments, and handling customer service issues. This is a

FHFA House Price IndexThe FHFA House Price Index (FHFA HPI®) is a comprehensive​ collection of publicly available house price indexes that measure changes in single-family home values based on data that extend back to the mid-1970s from all 50 states and over 400 American cities. The FHFA HPI incorporates tens of millions of home sales and offers insights about house price fluctuations at the national, census division, state, metro area, county, ZIP code, and census tract levels. FHFA uses a fully transparent methodology based upon a weighted, repeat-sales statistical technique to analyze house price transaction data.What does the FHFA HPI represent?The FHFA HPI is a broad measure of the movement of single-family house prices. The FHFA HPI is a weighted, repeat-sales index, meaning that it measures average price changes in repeat sales or refinancings on the same properties. This information is obtained by reviewing repeat mortgage transactions on single-family properties whose mortgages have been purchased or securitized by Fannie Mae or Freddie Mac since January 1975.The FHFA HPI serves as a timely, accurate indicator of house price trends at various geographic levels. Because of the breadth of the sample, it provides more information than is available in other house price indexes. It also provides housing economists with an improved analytical tool that is useful for estimating changes in the rates of mortgage defaults, prepayments and housing affordability in specific geographic areas.

The 2010 Census Production Settings Demographic and Housing Characteristics Demonstration Noisy Measurement File (2023-04-03) is an intermediate output of the 2020 Census Disclosure Avoidance System (DAS) TopDown Algorithm (TDA) (as described in Abowd, J. et al [2022], and implemented in DAS 2020 Redistricting Production Code). The NMF was produced using the official "production settings," the final set of algorithmic parameters and privacy-loss budget allocations, that were used to produce the 2020 Census Redistricting Data (P.L. 94-171) Summary File and the 2020 Census Demographic and Housing Characteristics File. The NMF consists of the full set of privacy-protected statistical queries (counts of individuals or housing units with particular combinations of characteristics) of confidential 2010 Census data relating to the 2010 Demonstration Data Products Suite - Redistricting and Demographic and Housing Characteristics File - Production Settings (2023-04-03). These statistical queries, called "noisy measurements" were produced under the zero-Concentrated Differential Privacy framework (Bun, M. and Steinke, T [2016]; see also Dwork C. and Roth, A. [2014]) implemented via the discrete Gaussian mechanism (Cannone C., et al., [2023]), which added positive or negative integer-valued noise to each of the resulting counts. The noisy measurements are an intermediate stage of the TDA prior to the post-processing the TDA then performs to ensure internal and hierarchical consistency within the resulting tables. The Census Bureau has released these 2010 Census demonstration data to enable data users to evaluate the expected impact of disclosure avoidance variability on 2020 Census data. The 2010 Census Production Settings Demographic and Housing Characteristics (DHC) Demonstration Noisy Measurement File (2023-04-03) has been cleared for public dissemination by the Census Bureau Disclosure Review Board (CBDRB-FY22-DSEP-004). The 2010 Census Production Settings Demographic and Housing Characteristics Demonstration Noisy Measurement File (2023-04-03) includes zero-Concentrated Differentially Private (zCDP) (Bun, M. and Steinke, T [2016]) noisy measurements, implemented via the discrete Gaussian mechanism. These are estimated counts of individuals and housing units included in the 2010 Census Edited File (CEF), which includes confidential data initially collected in the 2010 Census of Population and Housing. The noisy measurements included in this file were subsequently post-processed by the TopDown Algorithm (TDA) to produce the 2010 Census Production Settings Privacy-Protected Microdata File - Redistricting (P.L. 94-171) and Demographic and Housing Characteristics File (2023-04-03) (Demonstration Data Products Suite/2023-04-03/). As these 2010 Census demonstration data are intended to support study of the design and expected impacts of the 2020 Disclosure Avoidance System, the 2010 CEF records were pre-processed before application of the zCDP framework. This pre-processing converted the 2010 CEF records into the input-file format, response codes, and tabulation categories used for the 2020 Census, which differ in substantive ways from the format, response codes, and tabulation categories originally used for the 2010 Census. The NMF provides estimates of counts of persons in the CEF by various characteristics and combinations of characteristics including their reported race and ethnicity, whether they were of voting age, whether they resided in a housing unit or one of 7 group quarters types, and their census block of residence after the addition of discrete Gaussian noise (with the scale parameter determined by the privacy-loss budget allocation for that particular query under zCDP). Noisy measurements of the counts of occupied and vacant housing units by census block are also included. Lastly, data on constraints--information into which no noise was infused by the Disclosure Avoidance System (DAS) and used by the TDA to post-process the noisy measurements into the 2010 Census Production Settings Privacy-Protected Microdata File - Redistricting (P.L. 94-171) and Demographic and Housing Characteristics File (2023-04-03) --are provided. These data are available for download (i.e. not restricted access). Due to their size, they must be downloaded through the link on this metadata page and not through the standard ICPSR downloa