30 Year Mortgage Rate in the United States increased to 6.34 percent in October 2 from 6.30 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 6.46 percent in the week ending September 26 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.

15 Year Mortgage Rate in the United States increased to 5.55 percent in October 2 from 5.49 percent in the previous week. This dataset includes a chart with historical data for the United States 15 Year Mortgage Rate.

In May 29, 2019, FHFA published its final Monthly Interest Rate Survey (MIRS), due to dwindling participation by financial institutions. MIRS had provided information on a monthly basis on interest rates, loan terms, and house prices by property type (all, new, previously occupied); by loan type (fixed- or adjustable-rate), and by lender type (savings associations, mortgage companies, commercial banks and savings banks); as well as information on 15-year and 30-year, fixed-rate loans. Additionally, MIRS provided quarterly information on conventional loans by major metropolitan area and by Federal Home Loan Bank district, and was used to compile FHFA’s monthly adjustable-rate mortgage index entitled the “National Average Contract Mortgage Rate for the Purchase of Previously Occupied Homes by Combined Lenders,” also known as the ARM Index.

Mortgage Application in the United States decreased by 4.70 percent in the week ending October 3 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.

WAS: Effective Rate: 5-Year ARM: 1-Wk Change data was reported at -0.070 Point in 20 Jul 2018. This records a decrease from the previous number of 0.000 Point for 13 Jul 2018. WAS: Effective Rate: 5-Year ARM: 1-Wk Change data is updated weekly, averaging -0.010 Point from Jan 2011 (Median) to 20 Jul 2018, with 392 observations. The data reached an all-time high of 0.290 Point in 11 Dec 2015 and a record low of -0.270 Point in 09 Jan 2015. WAS: Effective Rate: 5-Year ARM: 1-Wk Change data remains active status in CEIC and is reported by Mortgage Bankers Association. The data is categorized under Global Database’s USA – Table US.M013: Weekly Applications Survey: Mortgage Interest Rate.

WAS: Contract Rate: 5-Year ARM: 1-Wk Change data was reported at 0.050 Point in 23 Nov 2018. This records an increase from the previous number of -0.210 Point for 16 Nov 2018. WAS: Contract Rate: 5-Year ARM: 1-Wk Change data is updated weekly, averaging -0.010 Point from Jan 2011 (Median) to 23 Nov 2018, with 410 observations. The data reached an all-time high of 0.270 Point in 11 Dec 2015 and a record low of -0.250 Point in 09 Jan 2015. WAS: Contract Rate: 5-Year ARM: 1-Wk Change data remains active status in CEIC and is reported by Mortgage Bankers Association. The data is categorized under Global Database’s United States – Table US.M013: Weekly Applications Survey: Mortgage Interest Rate.

These rates are the daily secondary market quotation on the most recently auctioned Treasury Bills for each maturity tranche (4-week, 13-week, 26-week, and 52-week) that Treasury currently issues new Bills. Market quotations are obtained at approximately 3:30 PM each business day by the Federal Reserve Bank of New York. The Bank Discount rate is the rate at which a Bill is quoted in the secondary market and is based on the par value, amount of the discount and a 360-day year. The Coupon Equivalent, also called the Bond Equivalent, or the Investment Yield, is the bill's yield based on the purchase price, discount, and a 365- or 366-day year. The Coupon Equivalent can be used to compare the yield on a discount bill to the yield on a nominal coupon bond that pays semiannual interest.

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

WAS: Effective Rate: 30-Year Jumbo data was reported at 4.810 % in 20 Jul 2018. This records an increase from the previous number of 4.750 % for 13 Jul 2018. WAS: Effective Rate: 30-Year Jumbo data is updated weekly, averaging 4.260 % from Jan 2011 (Median) to 20 Jul 2018, with 393 observations. The data reached an all-time high of 5.680 % in 11 Feb 2011 and a record low of 3.670 % in 30 Sep 2016. WAS: Effective Rate: 30-Year Jumbo data remains active status in CEIC and is reported by Mortgage Bankers Association. The data is categorized under Global Database’s USA – Table US.M013: Weekly Applications Survey: Mortgage Interest Rate.

Source: From lending institutions and local authorities The loan payments dataset stops in 2007. The figures on fixed interest rate mortgages relate to mortgages which provide that the rate of interest may not be changed, or may only be changed at intervals of not less than one year. The most current data is published on these sheets. Previously published data may be subject to revision. Any change from the originally published data will be highlighted by a comment on the cell in question. These comments will be maintained for at least a year after the date of the value change.

Formaat: PDFOmvang: 60 KbOnline beschikbaar: [01-12-2014]This article was published on the Guardian website at 20.25 BST on Thursday 11 June 2009. A version appeared on p1 of the Main section section of the Guardian on Friday 12 June 2009. It was last modified at 12.21 BST on Monday 19 May 2014.© 2014 Guardian News and Media Limited or its affiliated companies. All rights reserved.

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.

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

1. Data Source

This dataset is a synthetic version inspired by the original Credit Risk dataset on Kaggle and enriched with additional variables based on Financial Risk for Loan Approval data. SMOTENC was used to simulate new data points to enlarge the instances. The dataset is structured for both categorical and continuous features.

2. Metadata

The dataset contains 45,000 records and 14 variables, each described below:

3. Data Usage

The dataset can be used for multiple purposes:

• Exploratory Data Analysis (EDA): Analyze key features, distribution patterns, and relationships to understand credit risk factors.
• Classification: Build predictive models to classify the loan_status variable (approved/not approved) for potential applicants.
• Regression: Develop regression models to predict the credit_score variable based on individual and loan-related attributes.

Mind the data issue from the original data, such as the instance > 100-year-old as age.

This dataset provides a rich basis for understanding financial risk factors and simulating predictive modeling processes for loan approval and credit scoring.

Feel free to leave comments on the discussion. I'd appreciate your upvote if you find my dataset useful! 😀

United States WAS: Effective Rate: FRM 30-Year: 1-Wk Change data was reported at 0.000 Point in 20 Jul 2018. This records a decrease from the previous number of 0.020 Point for 13 Jul 2018. United States WAS: Effective Rate: FRM 30-Year: 1-Wk Change data is updated weekly, averaging -0.010 Point from Jan 1990 (Median) to 20 Jul 2018, with 1489 observations. The data reached an all-time high of 0.610 Point in 09 Oct 1998 and a record low of -0.530 Point in 28 Nov 2008. United States WAS: Effective Rate: FRM 30-Year: 1-Wk Change data remains active status in CEIC and is reported by Mortgage Bankers Association. The data is categorized under Global Database’s USA – Table US.M013: Weekly Applications Survey: Mortgage Interest Rate.

This dataset shows the average interest rates for U.S. Treasury securities for the most recent month compared with the same month of the previous year. The data is broken down by the various marketable and non-marketable securities. The summary page for the data provides links for monthly reports from 2001 through the current year. Average Interest Rates are calculated on the total unmatured interest-bearing debt. The average interest rates for total marketable, total non-marketable and total interest-bearing debt do not include the U.S. Treasury Inflation-Protected Securities.

United States WAS: Total Points: 30-Year Jumbo: 1-Wk Change data was reported at 0.010 Point in 20 Jul 2018. This records a decrease from the previous number of 0.070 Point for 13 Jul 2018. United States WAS: Total Points: 30-Year Jumbo: 1-Wk Change data is updated weekly, averaging 0.000 Point from Jan 2011 (Median) to 20 Jul 2018, with 392 observations. The data reached an all-time high of 0.230 Point in 30 Dec 2016 and a record low of -0.200 Point in 06 Jul 2018. United States WAS: Total Points: 30-Year Jumbo: 1-Wk Change data remains active status in CEIC and is reported by Mortgage Bankers Association. The data is categorized under Global Database’s USA – Table US.M013: Weekly Applications Survey: Mortgage Interest Rate.

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

Historical dataset of the 12 month LIBOR rate back to 1986. The London Interbank Offered Rate is the average interest rate at which leading banks borrow funds from other banks in the London market. LIBOR is the most widely used global "benchmark" or reference rate for short term interest rates.