30 Year Mortgage Rate in the United States decreased to 6.26 percent in September 18 from 6.35 percent in the previous week. This dataset includes a chart with historical data for the United States 30 Year Mortgage Rate.

The benchmark interest rate in China was last recorded at 3 percent. This dataset provides the latest reported value for - China Interest Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

This table contains data described by the following dimensions (Not all combinations are available): Geography (1 items: Canada ...).

Mortgage rates surged at an unprecedented pace in 2022, with the average 10-year fixed rate doubling between March and December of that year. In response to mounting inflation, the Bank of England implemented a series of rate hikes, pushing borrowing costs steadily higher. By August 2025, the average 10-year fixed mortgage rate had climbed to 4.49 percent. As financing becomes more expensive, housing demand has cooled, weighing on market sentiment and slowing house price growth. How have the mortgage hikes affected the market? After surging in 2021, the number of residential properties sold fell significantly in 2023, dipping to just above *** million transactions. This contraction in activity also dampened mortgage lending. Between the first quarter of 2023 and the first quarter of 2024, the value of new mortgage loans declined year-on-year for five consecutive quarters. Even as rates eased modestly in 2024 and housing activity picked up slightly, volumes remained well below the highs recorded in 2021. How are higher mortgages impacting homebuyers? For homeowners, the impact is being felt most acutely as fixed-rate deals expire. Mortgage terms in the UK typically range from two to ten years, and many borrowers who locked in historically low rates are now facing significantly higher repayments when refinancing. By the end of 2026, an estimated five million homeowners will see their mortgage deals expire. Roughly two million of these loans are projected to experience a monthly payment increase of up to *** British pounds by 2026, putting additional pressure on household budgets and constraining affordability across the market.

Fixed 30-year mortgage rates in the United States averaged 6.39 percent in the week ending September 12 of 2025. This dataset provides the latest reported value for - United States MBA 30-Yr Mortgage Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The benchmark interest rate in the United States was last recorded at 4.25 percent. This dataset provides the latest reported value for - United States Fed Funds Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The Federal Housing Finance Agency (FHFA) today issued its annual report on single-family guarantee fees charged by Fannie Mae and Freddie Mac (the Enterprises). Guarantee fees are intended to cover the credit risk and other costs that the Enterprises incur when they acquire single-family loans from lenders. These costs include projected credit losses from borrower defaults over the life of the loans, administrative costs, and a return on capital. The report compares year-over-year 2020 to 2019 and provides statistics back to 2018. Significant findings of the report include: For all loan products combined, the average single-family guarantee fee in 2020 decreased 2 basis points to 54 basis points. The upfront portion of the guarantee fee, which is based on the credit risk attributes (e.g., loan purpose, loan-to-value (LTV) ratio, and credit score), decreased 2 basis points to 11 basis points on average. The ongoing portion of the guarantee fee, which is based on the product type (fixed-rate or adjustable-rate, and loan term), remained unchanged at 43 basis points on average. The average guarantee fee in 2020 on 30-year and 15-year fixed rate loans remained unchanged at 58 basis points and 36 basis points, respectively. The fee on adjustable-rate mortgage (ARM) loans increased 1 basis point to 57 basis points. The Housing and Economic Recovery Act of 2008 requires FHFA to conduct ongoing studies of the guarantee fees charged by the Enterprises and to submit a report to Congress each year.

Interest-Expense Time Series for Open Lending Corp. Open Lending Corporation provides lending enablement and risk analytics solutions to credit unions, regional banks, finance companies, and captive finance companies of automakers in the United States. The company offers lenders protection platform (LPP), which is a cloud-based automotive lending enablement platform that provides loan analytics solutions and automated issuance of credit default insurance with third-party insurance providers. Its LPP products include loan analytics, risk-based loan pricing, risk modeling, and automated decision technology for automotive lenders. The company was founded in 2000 and is headquartered in Austin, Texas.

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

United States Mortgage Interest Paid: Owner & Tenant Occupied Residential Housing data was reported at 454,932.000 USD in Mar 2020. This records a decrease from the previous number of 459,833.000 USD for Dec 2019. United States Mortgage Interest Paid: Owner & Tenant Occupied Residential Housing data is updated quarterly, averaging 318,834.000 USD from Mar 1977 (Median) to Mar 2020, with 173 observations. The data reached an all-time high of 594,791.000 USD in Dec 2007 and a record low of 53,754.000 USD in Mar 1977. United States Mortgage Interest Paid: Owner & Tenant Occupied Residential Housing data remains active status in CEIC and is reported by Bureau of Economic Analysis. The data is categorized under Global Database’s United States – Table US.KB025: Mortgage Interest Paid. [COVID-19-IMPACT]

This table contains 102 series, with data starting from 2013, and some select series starting from 2016. This table contains data described by the following dimensions (Not all combinations are available): Geography (1 item: Canada), Components (51 items: Total, funds advanced, residential mortgages, insured; Variable rate, insured; Fixed rate, insured, less than 1 year; Fixed rate, insured, from 1 to less than 3 years; ...), and Unit of measure (2 items: Dollars; Interest rate). For additional clarification on the component dimension, please visit the OSFI website for the Report on New and Existing Lending.

The benchmark interest rate in Sweden was last recorded at 2 percent. This dataset provides the latest reported value for - Sweden Interest Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Finland BOF Forecast: Interest Rate: Average: New Loan Drawdowns data was reported at 2.200 % pa in 2020. This records an increase from the previous number of 2.000 % pa for 2019. Finland BOF Forecast: Interest Rate: Average: New Loan Drawdowns data is updated yearly, averaging 1.950 % pa from Dec 2015 (Median) to 2020, with 6 observations. The data reached an all-time high of 2.200 % pa in 2020 and a record low of 1.800 % pa in 2017. Finland BOF Forecast: Interest Rate: Average: New Loan Drawdowns data remains active status in CEIC and is reported by Bank of Finland. The data is categorized under Global Database’s Finland – Table FI.M007: Lending Rates: Forecast: Bank of Finland.

The benchmark interest rate in Japan was last recorded at 0.50 percent. This dataset provides - Japan Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

China Private Lending Rate: Wenzhou: Monthly Average: Real Estate Mortgage data was reported at 13.000 ‰ in 03 Jan 2020. This stayed constant from the previous number of 13.000 ‰ for 13 Dec 2019. China Private Lending Rate: Wenzhou: Monthly Average: Real Estate Mortgage data is updated daily, averaging 13.000 ‰ from Jul 2012 (Median) to 03 Jan 2020, with 251 observations. The data reached an all-time high of 20.000 ‰ in 18 Jul 2014 and a record low of 7.400 ‰ in 25 Jul 2014. China Private Lending Rate: Wenzhou: Monthly Average: Real Estate Mortgage data remains active status in CEIC and is reported by Whenzhou Private Lending Registration Service Center. The data is categorized under China Premium Database’s Money Market, Interest Rate, Yield and Exchange Rate – Table CN.MA: Private Lending Rate: Wenzhou.

Dataset in excel of main macroeconomic indicators growth from 2017 to 2021 for near 200 countries and according to IMF data. It allows us to quickly assess the impact of the COVID19 in the global economic

It includes: real GDP growth, GDP per capita, inflation, unemployment rate, general government net lending /borrowing.

Abstract copyright UK Data Service and data collection copyright owner.The Opinions and Lifestyle Survey (formerly known as the ONS Opinions Survey or Omnibus) is an omnibus survey that began in 1990, collecting data on a range of subjects commissioned by both the ONS internally and external clients (limited to other government departments, charities, non-profit organisations and academia).Data are collected from one individual aged 16 or over, selected from each sampled private household. Personal data include data on the individual, their family, address, household, income and education, plus responses and opinions on a variety of subjects within commissioned modules. The questionnaire collects timely data for research and policy analysis evaluation on the social impacts of recent topics of national importance, such as the coronavirus (COVID-19) pandemic and the cost of living, on individuals and households in Great Britain. From April 2018 to November 2019, the design of the OPN changed from face-to-face to a mixed-mode design (online first with telephone interviewing where necessary). Mixed-mode collection allows respondents to complete the survey more flexibly and provides a more cost-effective service for customers. In March 2020, the OPN was adapted to become a weekly survey used to collect data on the social impacts of the coronavirus (COVID-19) pandemic on the lives of people of Great Britain. These data are held in the Secure Access study, SN 8635, ONS Opinions and Lifestyle Survey, Covid-19 Module, 2020-2022: Secure Access. From August 2021, as coronavirus (COVID-19) restrictions were lifting across Great Britain, the OPN moved to fortnightly data collection, sampling around 5,000 households in each survey wave to ensure the survey remains sustainable. The OPN has since expanded to include questions on other topics of national importance, such as health and the cost of living. For more information about the survey and its methodology, see the ONS OPN Quality and Methodology Information webpage.Secure Access Opinions and Lifestyle Survey dataOther Secure Access OPN data cover modules run at various points from 1997-2019, on Census religion (SN 8078), cervical cancer screening (SN 8080), contact after separation (SN 8089), contraception (SN 8095), disability (SNs 8680 and 8096), general lifestyle (SN 8092), illness and activity (SN 8094), and non-resident parental contact (SN 8093). See Opinions and Lifestyle Survey: Secure Access for details. Main Topics:Each month's questionnaire consists of two elements: core questions, covering demographic information, are asked each month together with non-core questions that vary from month to month. The non-core questions for this month were: Company Cars (Module 1a): questions about the number of company cars in the household; total mileage and total business mileage; age of car and value of car when new; engine size. Mortgage Arrears (Module 2): source of mortgage, if any; whether behind in payments, and if so reasons for falling behind. Also question on whether bought from a Right to Buy scheme. Investment (Module 7a): ownership of shares and income from shares, bank accounts and building society accounts. Overseas Transactions (Module 58): financial transactions (receipts or payments) made as a private individual in the past 12 months; value in pound sterling; currency of transaction; reasons for transaction. Youth Services (Module 76): young people aged 11-25 were asked about leisure time activities; whether belongs or goes to a youth club, youth centre, youth group or youth organisation, or takes part in any other youth service activity; whether has ever belonged to a youth organisation; types of groups belongs to and who runs them; how often attends; any voluntary organisations belongs to; type of youth project takes part in and who runs it; whether has taken part in running a youth organisation; attitudes toward the Youth Service; reasons for attending/not attending. GP Accidents (Module 78): accidents in previous three months that resulted in seeing a doctor or going to hospital; where accident happened; whether saw a GP or went straight to hospital. Arrears and Repossessions (Module 79): questions about mortgage arrears and repossessions or voluntary surrenders of accommodation as a result of falling behind with mortgage payments. Marital Status and Cohabitation (Module 90): marital status and marital history; reasons for getting married if living together before marrying; history of previous cohabitation relationships that did not lead to marriage. Buying With a Mortgage (Module 91): reasons for becoming an owner occupier; year present home was bought; purchase price and original amount borrowed; whether previously owned home; whether bought under right to buy scheme; whether re-mortgaged or extended amount borrowed; value of house now; mortgage repayments; assistance with mortgage interest from the Department of Social Security; mortgage arrears in past three years; whether has mortgage protection policy and if so whether has tried to draw on it in past three years; debts on loans, hire purchase or services; net income and sources of income of respondent and spouse; increase or decrease of income over last three years and reasons; whether has any difficulties in paying for housing at present. The data for module 90 are under embargo and are therefore not currently available.

Price-To-Book-Ratio Time Series for Lument Finance Trust Inc. Lument Finance Trust, Inc., a real estate investment trust, focuses on investing in, financing, and managing a portfolio of commercial real estate (CRE) debt investments in the United States. It invests in transitional floating rate CRE mortgage loans on middle market multi-family assets; and other CRE -related investments, including mezzanine loans, preferred equity, commercial mortgage-backed securities, fixed rate loans, construction loans, and other CRE debt instruments. The company is qualified as a real estate investment trust under the Internal Revenue Code of 1986. As a REIT, it would not be subject to federal income taxes if it distributes at least 90% of its taxable income to its stockholders. The company was formerly known as Hunt Companies Finance Trust, Inc. and changed its name to Lument Finance Trust, Inc. in December 2020. Lument Finance Trust, Inc. was incorporated in 2012 and is headquartered in New York, New York.

Bulgaria Lending Rate: NB: HN: RF: Mortgage: USD data was reported at 8.623 % pa in Feb 2020. This records a decrease from the previous number of 18.437 % pa for Oct 2019. Bulgaria Lending Rate: NB: HN: RF: Mortgage: USD data is updated monthly, averaging 9.702 % pa from Jan 2007 (Median) to Feb 2020, with 57 observations. The data reached an all-time high of 18.437 % pa in Oct 2019 and a record low of 3.042 % pa in Mar 2017. Bulgaria Lending Rate: NB: HN: RF: Mortgage: USD data remains active status in CEIC and is reported by Bulgarian National Bank. The data is categorized under Global Database’s Bulgaria – Table BG.M004: Banks Lending Rate: New Business.

Slovenia Lending Rate: New Loans: Household: for Other Purposes: Over 5 Years Rate Fixation data was reported at 6.870 % pa in Mar 2025. This records an increase from the previous number of 6.750 % pa for Feb 2025. Slovenia Lending Rate: New Loans: Household: for Other Purposes: Over 5 Years Rate Fixation data is updated monthly, averaging 6.440 % pa from Feb 2003 (Median) to Mar 2025, with 253 observations. The data reached an all-time high of 16.220 % pa in Feb 2003 and a record low of 2.940 % pa in Dec 2020. Slovenia Lending Rate: New Loans: Household: for Other Purposes: Over 5 Years Rate Fixation data remains active status in CEIC and is reported by Bank of Slovenia. The data is categorized under Global Database’s Slovenia – Table SI.M005: Lending Rates.