30 Year Mortgage Rate in the United States increased to 6.72 percent in July 10 from 6.67 percent in the previous week. This dataset includes a chart with historical data for the United States 30 Year Mortgage Rate.

Fixed 30-year mortgage rates in the United States averaged 6.77 percent in the week ending July 4 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.

United States Mortgage Debt Outstanding: Effective Interest Rate data was reported at 3.799 % in Mar 2020. This records a decrease from the previous number of 3.872 % for Dec 2019. United States Mortgage Debt Outstanding: Effective Interest Rate data is updated quarterly, averaging 7.677 % from Mar 1977 (Median) to Mar 2020, with 173 observations. The data reached an all-time high of 11.449 % in Mar 1985 and a record low of 3.750 % in Dec 2017. United States Mortgage Debt Outstanding: Effective Interest Rate 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 data described by the following dimensions (Not all combinations are available): Geography (1 items: Canada ...).

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.

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

Average Mortgage Size in the United States increased to 379.21 Thousand USD in May 31 from 376.99 Thousand USD in the previous week. This dataset includes a chart with historical data for the United States Average Mortgage Size.

The benchmark interest rate in Norway was last recorded at 4.25 percent. This dataset provides the latest reported value for - Norway 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 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 Mexico was last recorded at 8 percent. This dataset provides - Mexico Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar 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.

Mortgage Application in the United States increased by 9.40 percent in the week ending July 4 of 2025 over the previous week. This dataset provides - United States MBA Mortgage Applications - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The benchmark interest rate in Ireland was last recorded at 4.50 percent. This dataset provides - Ireland Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Debt financing (mortgage, line of credit, term loan, credit card) terms and conditions, average interest rates and average length of term for small and medium enterprises in 2020 by region, CMA level, North American Industry Classification System (NAICS), demographics, age of business, employment size, rate of growth, etc.

Listing of SONYMA target areas by US Census Bureau Census Tract or Block Numbering Area (BNA). The State of New York Mortgage Agency (SONYMA) targets specific areas designated as ‘areas of chronic economic distress’ for its homeownership lending programs. Each state designates ‘areas of chronic economic distress’ with the approval of the US Secretary of Housing and Urban Development (HUD). SONYMA identifies its target areas using US Census Bureau census tracts and block numbering areas. Both census tracts and block numbering areas subdivide individual counties. SONYMA also relates each of its single-family mortgages to a specific census tract or block numbering area. New York State identifies ‘areas of chronic economic distress’ using census tract numbers. 26 US Code § 143 (current through Pub. L. 114-38) defines the criteria that the Secretary of Housing and Urban Development uses in approving designations of ‘areas of chronic economic distress’ as: i) the condition of the housing stock, including the age of the housing and the number of abandoned and substandard residential units, (ii) the need of area residents for owner-financing under this section, as indicated by low per capita income, a high percentage of families in poverty, a high number of welfare recipients, and high unemployment rates, (iii) the potential for use of owner-financing under this section to improve housing conditions in the area, and (iv) the existence of a housing assistance plan which provides a displacement program and a public improvements and services program. The US Census Bureau’s decennial census last took place in 2010 and will take place again in 2020. While the state designates ‘areas of chronic economic distress,’ the US Department of Housing and Urban Development must approve the designation. The designation takes place after the decennial census.

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.

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.

Data on the number of OSAP loan recipients who received repayment assistance: * 2010-11 OSAP loan recipients who received repayment assistance before July 2013. * 2011-12 OSAP loan recipients who received repayment assistance before July 2014. * 2012-13 OSAP loan recipients who received repayment assistance before July 2015. * 2013-14 OSAP loan recipients who received repayment assistance before July 2016. * 2014-15 OSAP loan recipients who received repayment assistance before July 2017. * 2015-16 OSAP loan recipients who received repayment assistance before July 2018. * 2016-17 OSAP loan recipients who received repayment assistance before July 2019. * 2017-18 OSAP loan recipients who received repayment assistance before July 2020. Data is presented at the following levels: * all of Ontario * postsecondary sector * individual postsecondary institution * individual program of individual postsecondary institution Data fields are: * postsecondary sector (university, college of applied arts and technology, private career college, other private or publicly funded postsecondary institutions) * institution name * program name (starting with the 2014 rates) * number of OSAP loan recipients who last received an OSAP loan in 2010-11, 2011-12, 2012-13, 2013-14, 2014-15, 2015-16, 2016-17, and 2017-18 * number of Repayment Assistance Plan participants as of July 2013, July 2014, July 2015, July 2016, July 2017, July 2018, July 2019, and July 2020 * repayment assistance participation rate for 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020 Get more information about OSAP loan default rates. *[OSAP]: Ontario Student Assistance Program

The benchmark interest rate in Netherlands was last recorded at 4.50 percent. This dataset provides - Netherlands Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.