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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Graph and download economic data for Delinquency Rate on Single-Family Residential Mortgages, Booked in Domestic Offices, Banks Ranked 1st to 100th Largest in Size by Assets (DRSFRMT100N) from Q1 1991 to Q1 2025 about domestic offices, delinquencies, 1-unit structures, mortgage, family, residential, domestic, assets, 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.
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.
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.
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United States - Delinquency Rate on Single-Family Residential Mortgages, Booked in Domestic Offices, All Commercial Banks was 1.78% 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, All Commercial Banks reached a record high of 11.49 in January of 2010 and a record low of 1.41 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, All Commercial Banks - last updated from the United States Federal Reserve on July of 2025.
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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.
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.
The data set is based upon https://www.kaggle.com/prateikmahendra/loan-data"> Lending Club Information .
- TheIrish Dummy Banks is a peer to peer lending bank based in the ireland, in which bank provide funds for potential borrowers and bank earn a profit depending on the risk they take (the borrowers credit score). Irish Fake bank provides loan to their loyal customers. The complete data set is borrowed from Lending Club For more basic information about the company please check out the wikipedia article about the company. This dataset is copied and clean from kaggle but it has been changed. The any kind of similarity is just for learning purposes. I dont have any intention for Plagiarism I just like to be clear myself.
<a src="https://en.wikipedia.org/wiki/Lending_Club"> Lending Club Information </a>
The central idea and coding is abstract from Kevin mark ham youtube video series, Introduction to machine learning with scikit-learn video series. You can find link under resources section.
LoanStatNew Description
addr_state The state provided by the borrower in the loan application
annual_inc The self-reported annual income provided by the borrower during registration.
annual_inc_joint The combined self-reported annual income provided by the co-borrowers during registration
application_type Indicates whether the loan is an individual application or a joint application with two co-borrowers
collection_recovery_fee post charge off collection fee
collections_12_mths_ex_med Number of collections in 12 months excluding medical collections
delinq_2yrs The number of 30+ days past-due incidences of delinquency in the borrower's credit file for the past 2 years
desc Loan description provided by the borrower
dti A ratio calculated using the borrower’s total monthly debt payments on the total debt obligations, - - - excluding mortgage and the requested LC loan, divided by the borrower’s self-reported monthly income.
dti_joint A ratio calculated using the co-borrowers' total monthly payments on the total debt obligations, - excluding mortgages and the requested LC loan, divided by the co-borrowers' combined self-reported monthly income
earliest_cr_line The month the borrower's earliest reported credit line was opened
emp_length Employment length in years. Possible values are between 0 and 10 where 0 means less than one year
and 10 means ten or more years.
emp_title The job title supplied by the Borrower when applying for the loan.*
fico_range_high The upper boundary range the borrower’s FICO at loan origination belongs to.
fico_range_low The lower boundary range the borrower’s FICO at loan origination belongs to.
funded_amnt The total amount committed to that loan at that point in time.
funded_amnt_inv The total amount committed by investors for that loan at that point in time.
grade LC assigned loan grade
home_ownership The home ownership status provided by the borrower during registration. Our values are: RENT, OWN, MORTGAGE, OTHER.
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.
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License information was derived automatically
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 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 FHA Office of Housing last conducted a series of mortgage loan sales under the Single Family Loan Sale (SFLS) Initiative in 2016. The current sales structure consisted of whole loan, competitive auctions, offering for purchase defaulted single family mortgages provided by FHA-approved loan servicers. The loans sold contained specified representations and warranties and may be sold with post-sale restrictions and/or reporting requirements. FHA sold loans in large national pools, as well as loan pools in designated geographical areas that are aimed at a neighborhood stabilization outcome (“NSO pools”).
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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License information was derived automatically
Overview This dataset contains 45,000 records of loan applicants, with various attributes related to personal demographics, financial status, and loan details. The dataset can be used for predictive modeling, particularly in credit risk assessment and loan default prediction.
Dataset Content The dataset includes 14 columns representing different factors influencing loan approvals and defaults:
Personal Information
person_age: Age of the applicant (in years). person_gender: Gender of the applicant (male, female). person_education: Educational background (High School, Bachelor, Master, etc.). person_income: Annual income of the applicant (in USD). person_emp_exp: Years of employment experience. person_home_ownership: Type of home ownership (RENT, OWN, MORTGAGE). Loan Details
loan_amnt: Loan amount requested (in USD). loan_intent: Purpose of the loan (PERSONAL, EDUCATION, MEDICAL, etc.). loan_int_rate: Interest rate on the loan (percentage). loan_percent_income: Ratio of loan amount to income. Credit & Loan History
cb_person_cred_hist_length: Length of the applicant's credit history (in years). credit_score: Credit score of the applicant. previous_loan_defaults_on_file: Whether the applicant has previous loan defaults (Yes or No). Target Variable
loan_status: 1 if the loan was repaid successfully, 0 if the applicant defaulted. Use Cases Loan Default Prediction: Build a classification model to predict loan repayment. Credit Risk Analysis: Analyze the relationship between income, credit score, and loan defaults. Feature Engineering: Extract new insights from employment history, home ownership, and loan amounts. Acknowledgments This dataset is synthetic and designed for machine learning and financial risk analysis.
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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.
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BASE YEAR | 2024 |
HISTORICAL DATA | 2019 - 2024 |
REPORT COVERAGE | Revenue Forecast, Competitive Landscape, Growth Factors, and Trends |
MARKET SIZE 2023 | 2.07(USD Billion) |
MARKET SIZE 2024 | 2.17(USD Billion) |
MARKET SIZE 2032 | 3.2(USD Billion) |
SEGMENTS COVERED | Deployment Type ,Application Type ,Loan Type ,Functionality ,Technology Integration ,Regional |
COUNTRIES COVERED | North America, Europe, APAC, South America, MEA |
KEY MARKET DYNAMICS | Growing demand for digital mortgage servicing Increasing adoption of cloudbased solutions Rising focus on customer experience Regulatory compliance and risk management Technological advancements |
MARKET FORECAST UNITS | USD Billion |
KEY COMPANIES PROFILED | TCS ,DXC Technology ,NTT Data ,FIS ,HCL Technologies ,Verisk Analytics (now Moody's) ,L&T Technology Services ,ICE Mortgage Technology ,Ellie Mae (now ICE Mortgage Technology) ,Wipro (now Capgemini) ,Infosys ,Black Knight |
MARKET FORECAST PERIOD | 2025 - 2032 |
KEY MARKET OPPORTUNITIES | Digital transformation Cloudbased solutions Data analytics Automation Regulatory compliance |
COMPOUND ANNUAL GROWTH RATE (CAGR) | 4.99% (2025 - 2032) |
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.
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According to Cognitive Market Research, the global Mortgage Loans Software market size will be USD XX million in 2025. It will expand at a compound annual growth rate (CAGR) of XX% from 2025 to 2031.
North America held the major market share for more than XX% of the global revenue with a market size of USD XX million in 2025 and will grow at a CAGR of XX% from 2025 to 2031.
Europe accounted for a market share of over XX% of the global revenue with a market size of USD XX million in 2025 and will grow at a CAGR of XX% from 2025 to 2031.
Asia Pacific held a market share of around XX% of the global revenue with a market size of USD XX million in 2025 and will grow at a CAGR of XX% from 2025 to 2031.
Latin America had a market share of more than XX% of the global revenue with a market size of USD XX million in 2025 and will grow at a CAGR of XX% from 2025 to 2031.
Middle East and Africa had a market share of around XX% of the global revenue and was estimated at a market size of USD XX million in 2025 and will grow at a CAGR of XX% from 2025 to 2031.
The cloud-based deployment category is the fastest-growing segment of the Mortgage Loans Software industry. Drivers
The rise of fintech lenders drives market growth
The mortgage industry has undergone a significant transformation with the emergence of FinTech lenders, who have introduced fully digital platforms to streamline the loan application and approval processes. These platforms allow borrowers to complete applications online, upload necessary documents, and receive decisions swiftly, often within minutes. This efficiency is primarily achieved through centralized underwriting systems and automation, reducing reliance on traditional, branch-based operations.
Studies indicate that FinTech lenders process mortgage applications approximately 20% faster than traditional lenders without compromising loan quality or increasing default risks [source]. This acceleration enhances borrower satisfaction and allows for more elastic responses to market demand fluctuations, thereby alleviating capacity constraints inherent in conventional mortgage lending. Furthermore, in areas with higher FinTech lending activity, borrowers are more likely to refinance, especially when it is in their financial interest to do so. This suggests that technological innovation has improved the efficiency of financial intermediation in the U.S. mortgage market.
The integration of technology in mortgage lending has also facilitated better access to credit, as FinTech platforms can efficiently handle increased application volumes and adjust supply more elastically in response to exogenous mortgage demand shocks. This adaptability is particularly beneficial during periods of fluctuating market conditions, ensuring that borrower needs are met promptly.
In conclusion, the rise of FinTech lenders has revolutionized the mortgage landscape by leveraging technology to enhance efficiency, responsiveness, and borrower satisfaction, marking a pivotal shift in how mortgage lending operates in the digital age.
https://www.fdic.gov/media/168606
Increasing demand for automation and advanced analytics is driving market growth of Mortgage Loans Software
The mortgage industry is rapidly adopting automation and advanced analytics to optimize lending operations and enhance decision-making. AI-driven automation enables lenders to streamline critical functions such as credit evaluation, fraud detection, and compliance monitoring, significantly reducing manual workload and processing times. By automating document verification, loan underwriting, and risk assessment, financial institutions can process mortgage applications with greater speed and accuracy, improving overall efficiency
Machine learning algorithms further enhance accuracy in risk assessments and borrower profiling, leading to more precise loan approvals. By analyzing vast datasets, AI models can predict creditworthiness more effectively than traditional scoring methods, allowing lenders to offer better terms to low-risk borrowers while mitigating potential defaults. Additionally, automated fraud detection tools can flag suspicious activities in real time, preventing identity theft an...
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.