The U.S. Department of Housing and Urban Development (HUD) periodically produce its HUD Subprime and Manufactured home Lender List for the lenders who specialize in either subprime or manufactured home lending, even though beginning with 2004 HMDA reporting, lenders are required to identify loans for manufactured housing and loans in which the APR on the loan exceeds a comparable Treasury APR. The HUD Subprime and Manufactured home Lender List contains the names of those lenders, their agency codes, and lender identification numbers. The agency code and lender identification numbers should be used to link to the individual HMDA loan application records (LARS). The file also contains a concatenated field, IDD, which is a combination of the agency code and lender identification number. The HUD Subprime and Manufactured home Lender List has annually been updated and revised in response to feedback from lenders, policy analysts, housing advocacy groups, and other users of the list. HUD deletes lenders and adds others based on that feedback.

During the month of March 2025, the company with the largest share of the reverse mortgage market in the United States was Mutual Of Omaha Mortgage Inc. Its share of 22.4 percent was around three percent greater than the market share of Finance Of America Reverse LLC. Reverse mortgage volume increases Mutual Of Omaha Mortgage Inc. was the top lender of Home Equity Conversion Mortgages (HECMs) in 2023, with the highest number of loan originations. In 2023, the company, which specializes in home equity retirement solutions, closed a total of over 5,000 HECMs and ended the year as the leading reverse mortgage company in the United States. Despite the overall number of HECMs in the United States dropping dramatically between 2009 and 2019, this trend reversed in the following years, with 2022 recording the highest 10-year figure. Banks withdraw from reverse mortgage market In the past, some of the largest banks in the United States featured in the list of leading reverse mortgage lenders; as of 2024, financial services firm Wells Fargo remained the all-time leading reverse mortgage company in the country. However, banks have exited the reverse mortgage business, and the rankings now feature companies that focus primarily on HECMs. In 2011, Wells Fargo and Bank of America – the two largest providers of HECMs at the time – stopped offering the service because of an unpredictable housing market and the creditworthiness of borrowers.

Mortgage lending information comes from the Federal Financial Institutions Examination Council's (FFIEC) Home Mortgage Disclosure Act (HMDA) data. Loan originations are the creation of a loan after bank approval. Loan origination rates are calculated from the number of loan applications that were either approved or denied—what is termed as decisioned applications. For all charts, the loan’s purpose can be selected via a dropdown list. Trends are summarized by all loan purposes and by Loans for home purchase, home improvement, or refinancing.

The 10 largest mortgage lenders in the United Kingdom accounted for approximately 81 percent of the total market, with the top three alone accounting for 41 percent in 2023. Lloyds Banking Group had the largest market share of gross mortgage lending, with nearly 36.8 billion British pounds in lending in 2023. HSBC, which is the largest UK bank by total assets, ranked fourth. Development of the mortgage market In 2023, the value of outstanding in mortgage lending to individuals amounted to 1.6 trillion British pounds. Although this figure has continuously increased in the past, the UK mortgage market declined dramatically in 2023, registering the lowest value of mortgage lending since 2015. In 2020, the COVID-19 pandemic caused the market to contract for the first time since 2012. The next two years saw mortgage lending soar due to pent-up demand, but as interest rates soared, the housing market cooled, leading to a decrease in new loans of about 100 billion British pounds. The end of low interest rates In 2021, mortgage rates saw some of their lowest levels since recording began by the Bank of England. For a long time, this was particularly good news for first-time homebuyers and those remortgaging their property. Nevertheless, due to the rising inflation, mortgage rates started to rise in the second half of the year, resulting in the 10-year rate doubling in 2022.

This dataset presents a static list of the UK's top mortgage lenders based on gross lending and mortgage balances outstanding for 2021, including rank, value, and market share.

In 2023, mortgage lending by the 15 largest lenders in the United Kingdom (UK) amounted to over 196 billion British pounds. Lloyds banking group topped the list for mortgage lending, with approximately 36.8 billion British pounds in gross lending. Nationwide BS and NatWest Group completed the top three mortgage lenders with roughly 29.9 billion and 24.6 billion British pounds in gross lending, respectively.

Advance America is a prominent payday loan company offering short-term, small-dollar loans to consumers. The Advance America business model revolves around providing quick cash advances to individuals who need funds urgently, typically to cover unexpected expenses or bridge gaps between paychecks. Advance America generates revenue primarily through the interest and fees charged on these loans. Advance America operates both online and through a vast network of physical storefronts, making their services readily accessible. Advance America emphasizes responsible lending practices and compliance with state regulations, providing resources on financial literacy and budgeting. Advance America also offers other financial services like installment loans, title loans, and lines of credit, aiming to diversify their offerings and provide more flexible options for customers. You can download the complete list of key information about Advance America car dealership locations, contact details, services offered, and geographical coordinates, beneficial for various applications like store locators, business analysis, and targeted marketing. The Advance America car dealership data you can download includes:

Identification & Location: 


  store_number, store_type, store_location, address, city, state, zip_code, latitude, longitude, country, country_code, county, geo_accuracy


Contact Information: 


  phone_number, website_address


Operational Details & Services: 


   store_hours, loans_and_services,

In 2023, the buy-to-let gross lending amounted to approximately 30 billion British pounds, with the 15 largest lenders accounting for about 23.7 billion British pounds. Nationwide BS topped the list for mortgage lending in the UK with approximately 3.13 billion British pounds. Lloyds Banking Group and NatWest Group finished the top three mortgage lenders with three billion and 2.6 billion British pounds in gross lending respectively.

This dataset contains the Information for the customers and vendors of failed banks including detailed transaction information. Additional resources for borrowers, depositors, creditors, court clerks and law enforcement agencies.

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.

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

This dataset provides values for LENDING RATE reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Chase Bank, a subsidiary of JPMorgan Chase & Co., is one of the largest banks in the United States. Chase Bank operates as a retail bank, offering a wide range of financial products and services to individual consumers including checking and savings accounts, credit cards, mortgages, auto loans, and investment products. Additionally, Chase Bank provides digital banking services, allowing customers to manage their finances online and through mobile banking apps. Chase Bank's business model is centered around generating revenue through various means. Interest income from loans and investments is a significant source of revenue. Chase Bank also earns fees from services like account maintenance, overdraft fees, and credit card usage. Lastly, Chase benefits from revenue generated through investment banking, asset management, and wealth management services offered by its parent company, JPMorgan Chase & Co. You can download the complete list of key information about Chase Bank locations, contact details, services offered, and geographical coordinates, beneficial for various applications like store locators, business analysis, and targeted marketing. The Chase Bank data you can download includes:

Identification & Location:


  Store_ name, store_number, store_type, address, address_line_2, city, state, zip_code, latitude, longitude, geo_accuracy, country_code, county, 


Contact Information:


   Phone_number, website_address


Operational Details & Services:


  Store_hours, lobby_hours, drive_up_hours, atm_count store_services, 

Huntington National Bank is a regional bank primarily serving the Midwest and Southeast United States. Huntington National Bank strives to be a one-stop shop for all things finance, offering a comprehensive range of services to individuals, businesses, and organizations. This includes everything from everyday banking needs like checking and savings accounts to more complex financial products such as loans, mortgages, credit cards, and investment services. The Huntington National Bank business model generates income through traditional banking activities like interest on loans and fees for services. Additionally, they offer wealth management and investment services, providing another avenue for revenue generation. Huntington National Bank emphasizes customer retention, recognizing the value of long-term relationships and building trust with their clients. Beyond their core financial services, Huntington National Bank actively invests in local initiatives, supports community development programs, and participates in philanthropic activities. This community focus not only strengthens their brand image but also fosters goodwill and contributes to the economic well-being of the regions they serve. You can download the complete list of key information about Huntington National Bank locations, contact details, services offered, and geographical coordinates, beneficial for various applications like store locators, business analysis, and targeted marketing. The Huntington National Bank data you can download includes:

Identification & Location:


  Store_ name, store_type, store_number, address, city, state, zip_code, latitude, longitude, geo_accuracy, country_code, county, 


Contact Information:


   Phone_number, 


Operational Details & Services:


  All_services, lobby_hours, drive_through

An aggregated dataset of PPP (Paycheck Protection Program) SBA (Small Business Administration) loans involving 3 million businesses would be a comprehensive collection of financial information, aimed at analyzing the distribution and impact of these loans. This dataset would include key details such as the names of the businesses, loan amounts, loan disbursement dates, and the terms of the loans. Additionally, the dataset would contain information on board members of these businesses, providing insights into the governance structures and potential networks influencing the flow of SBA funds. This aspect of the dataset can be crucial for understanding the distribution patterns of PPP loans, identifying trends in funding allocation among different types of businesses, and examining any correlations between board composition and loan receipt. Such a dataset would be valuable for various analyses, including: Financial Analysis: Assessing the financial health and stability of businesses that received PPP loans, and understanding how these loans have impacted their operations during challenging economic times. Governance Analysis: Evaluating the role of board members in acquiring PPP loans, and whether certain types of governance structures were more successful in securing funds. Economic Impact Assessment: Measuring the broader economic impact of the PPP loans, such as job retention, business survival rates, and sector-wise distribution of funds. Network Analysis: Mapping the connections between different businesses and their board members to identify any potential networks or clusters that may have influenced the flow of funds. Policy Evaluation: Providing data-driven insights to policymakers for assessing the effectiveness of the PPP program and for planning future economic relief measures.

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 Q1 2025 about domestic offices, delinquencies, 1-unit structures, mortgage, family, residential, commercial, domestic, banks, depository institutions, rate, and USA.

This data set contains candidate and political committee loan information for the last 17 years. Data includes loans received, loan repayments, interest payments, and loans forgiven. Descriptions attached to this dataset do not constitute legal definitions; please consult RCW 42.17A and WAC Title 390 for legal definitions and additional information regarding political finance disclosure requirements. CONDITION OF RELEASE: This publication and or referenced documents constitutes a list of individuals prepared by the Washington State Public Disclosure Commission and may not be used for commercial purposes. This list is provided on the condition and with the understanding that the persons receiving it agree to this statutorily imposed limitation on its use. See RCW 42.56.070(9) and AGO 1975 No. 15.

In 2024, the ten largest mortgage lenders in Australia had a market share of roughly ** percent of the mortgage market. The Commonwealth Bank of Australia and Westpac Banking Corporation were the largest mortgage lenders, with approximately *** and *** billion Australian dollars in gross mortgage lending, respectively.

The International Bank for Reconstruction and Development (IBRD) loans are public and publicly guaranteed debt extended by the World Bank Group. IBRD loans are made to, or guaranteed by, countries that are members of IBRD. IBRD may also make loans to IFC. IBRD lends at market rates. Data are in U.S. dollars calculated using historical rates. This dataset contains the latest available snapshot of the Statement of Loans.

The list of delegated institutions and bank branch and hotline numbers for outsourced marketing of housing and vehicle loans will be continued to be entrusted through evaluation.