30 Year Mortgage Rate in the United States decreased to 6.23 percent in November 26 from 6.26 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 6.40 percent in the week ending November 21 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.

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.

Content

The Federal Reserve Board has discontinued this series as of October 11, 2016. More information, including possible alternative series, can be found at http://www.federalreserve.gov/feeds/h15.html.

Contract interest rates on commitments for fixed-rate first mortgages. Source: Primary Mortgage Market Survey data provided by Freddie Mac.

Copyright, 2016, Freddie Mac. Reprinted with permission.

Context

This is a dataset from the Federal Reserve hosted by the Federal Reserve Economic Database (FRED). FRED has a data platform found here and they update their information according to the frequency that the data updates. Explore the Federal Reserve using Kaggle and all of the data sources available through the Federal Reserve organization page!

• Update Frequency: This dataset is updated daily.

• Observation Start: 1971-04-01

• Observation End : 2016-09-01

Acknowledgements

This dataset is maintained using FRED's API and Kaggle's API.

Cover photo by Ian Schneider on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

This dataset provides values for 30 YEAR MORTGAGE RATE reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Graph and download economic data for 15-Year Fixed Rate Mortgage Average in the United States (MORTGAGE15US) from 1991-08-30 to 2025-11-26 about 15-year, mortgage, fixed, interest rate, interest, rate, and USA.

United States Mortgage Fixed Rate: Mth Avg: 30 Year data was reported at 4.870 % pa in Nov 2018. This records an increase from the previous number of 4.830 % pa for Oct 2018. United States Mortgage Fixed Rate: Mth Avg: 30 Year data is updated monthly, averaging 7.635 % pa from Apr 1971 (Median) to Nov 2018, with 572 observations. The data reached an all-time high of 18.450 % pa in Oct 1981 and a record low of 3.350 % pa in Dec 2012. United States Mortgage Fixed Rate: Mth Avg: 30 Year data remains active status in CEIC and is reported by Federal Home Loan Mortgage Corporation, Freddie Mac. The data is categorized under Global Database’s United States – Table US.M012: Mortgage Interest Rate.

15 Year Mortgage Rate in the United States decreased to 5.51 percent in November 27 from 5.54 percent in the previous week. This dataset includes a chart with historical data for the United States 15 Year Mortgage Rate.

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 October 2025, the average 10-year fixed mortgage rate stood at **** 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.

United States Mortgage Fixed Rate: Mth Avg: 30 Year: Point data was reported at 0.500 % pa in Nov 2018. This stayed constant from the previous number of 0.500 % pa for Oct 2018. United States Mortgage Fixed Rate: Mth Avg: 30 Year: Point data is updated monthly, averaging 1.100 % pa from Jan 1972 (Median) to Nov 2018, with 563 observations. The data reached an all-time high of 2.600 % pa in Sep 1985 and a record low of 0.400 % pa in May 2018. United States Mortgage Fixed Rate: Mth Avg: 30 Year: Point data remains active status in CEIC and is reported by Federal Home Loan Mortgage Corporation, Freddie Mac. The data is categorized under Global Database’s United States – Table US.M012: Mortgage Interest Rate.

This dataset was a manual combination of 3 separate datasets

Inflation Rates (Data set was manually manipulated to match the format of the other datasets) https://data.bls.gov/timeseries/CUUR0000SA0?years_option=all_years

Feds Funds Rate https://fred.stlouisfed.org/series/FEDFUNDS#

Monthly Mortgage Rate https://fred.stlouisfed.org/series/MORTGAGE30US

Additional Data Manipulation: Both the monthly feds fund rate and the monthly mortgage rate is an annualized rate. I created annualized inflation rate based on a 12 month lag. To find the inflation rate for a give month, the formula used was (current month CPI - 12-months prior CPI / 12-months prior CPI)

Mortgage Rate in Sweden decreased to 2.80 percent in September from 2.84 percent in August of 2025. This dataset includes a chart with historical data for Sweden Average Interest Rate on New Agreements for Mortgages to Households.

United States Mortgage Fixed Rate: Wk Ending: 30 Year data was reported at 4.540 % pa in 26 Jul 2018. This records an increase from the previous number of 4.520 % pa for 19 Jul 2018. United States Mortgage Fixed Rate: Wk Ending: 30 Year data is updated weekly, averaging 4.550 % pa from Jan 2004 (Median) to 26 Jul 2018, with 760 observations. The data reached an all-time high of 6.800 % pa in 20 Jul 2006 and a record low of 3.310 % pa in 22 Nov 2012. United States Mortgage Fixed Rate: Wk Ending: 30 Year data remains active status in CEIC and is reported by Federal Home Loan Mortgage Corporation, Freddie Mac. The data is categorized under Global Database’s USA – Table US.M012: Mortgage Interest Rate.

The average mortgage interest rate decreased in nearly every country in Europe between 2012 and 2021, followed by an increase in response to inflation. In the first quarter of 2025, Poland, Hungary, and Romania topped the ranking as the countries with the highest mortgage interest rates in Europe. Conversely, Finland, Belgium, and Spain displayed the lowest interest rates. The UK, which is the country with the largest value of mortgages outstanding, had an interest rate of **** percent.

The benchmark interest rate in Sweden was last recorded at 1.75 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.

United States WAS: Effective Rate: FRM 30-Year data was reported at 5.290 % in 16 Nov 2018. This records a decrease from the previous number of 5.330 % for 09 Nov 2018. United States WAS: Effective Rate: FRM 30-Year data is updated weekly, averaging 6.460 % from Jan 1990 (Median) to 16 Nov 2018, with 1507 observations. The data reached an all-time high of 11.070 % in 27 Apr 1990 and a record low of 3.570 % in 07 Dec 2012. United States WAS: Effective Rate: FRM 30-Year 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.

Description

This data set represents thousands of loans made through the Lending Club platform, which is a platform that allows individuals to lend to other individuals. Of course, not all loans are created equal. Someone who is a essentially a sure bet to pay back a loan will have an easier time getting a loan with a low interest rate than someone who appears to be riskier. And for people who are very risky? They may not even get a loan offer, or they may not have accepted the loan offer due to a high interest rate. It is important to keep that last part in mind, since this data set only represents loans actually made, i.e. do not mistake this data for loan applications!

Format

A data frame with 10,000 observations on the following 55 variables.

emp_title

Job title.

emp_length

Number of years in the job, rounded down. If longer than 10 years, then this is represented by the value 10.

state

Two-letter state code.

homeownership

The ownership status of the applicant's residence.

annual_income

Annual income.

verified_income

Type of verification of the applicant's income.

debt_to_income

Debt-to-income ratio.

annual_income_joint

If this is a joint application, then the annual income of the two parties applying.

verification_income_joint

Type of verification of the joint income.

debt_to_income_joint

Debt-to-income ratio for the two parties.

delinq_2y

Delinquencies on lines of credit in the last 2 years.

months_since_last_delinq

Months since the last delinquency.

earliest_credit_line

Year of the applicant's earliest line of credit

inquiries_last_12m

Inquiries into the applicant's credit during the last 12 months.

total_credit_lines

Total number of credit lines in this applicant's credit history.

open_credit_lines

Number of currently open lines of credit.

total_credit_limit

Total available credit, e.g. if only credit cards, then the total of all the credit limits. This excludes a mortgage.

total_credit_utilized

Total credit balance, excluding a mortgage.

num_collections_last_12m

Number of collections in the last 12 months. This excludes medical collections.

num_historical_failed_to_pay

The number of derogatory public records, which roughly means the number of times the applicant failed to pay.

months_since_90d_late

Months since the last time the applicant was 90 days late on a payment.

current_accounts_delinq

Number of accounts where the applicant is currently delinquent.

total_collection_amount_ever

The total amount that the applicant has had against them in collections.

current_installment_accounts

Number of installment accounts, which are (roughly) accounts with a fixed payment amount and period. A typical example might be a 36-month car loan.

accounts_opened_24m

Number of new lines of credit opened in the last 24 months.

months_since_last_credit_inquiry

Number of months since the last credit inquiry on this applicant.

num_satisfactory_accounts

Number of satisfactory accounts.

num_accounts_120d_past_due

Number of current accounts that are 120 days past due.

num_accounts_30d_past_due

Number of current accounts that are 30 days past due.

num_active_debit_accounts

Number of currently active bank cards.

total_debit_limit

Total of all bank card limits.

num_total_cc_accounts

Total number of credit card accounts in the applicant's history.

num_open_cc_accounts

Total number of currently open credit card accounts.

num_cc_carrying_balance

Number of credit cards that are carrying a balance.

num_mort_accounts

Number of mortgage accounts.

account_never_delinq_percent

Percent of all lines of credit where the applicant was never delinquent.

tax_liens

a numeric vector

public_record_bankrupt

Number of bankruptcies listed in the public record for this applicant.

loan_purpose

The category for the purpose of the loan.

application_type

The type of application: either individual or joint.

loan_amount

The amount of the loan the applicant received.

term

The number of months of the loan the applicant received.

interest_rate

Interest rate of the loan the applicant received.

installment

Monthly payment for the loan the applicant received.

grade

Grade associated with the loan.

sub_grade

Detailed grade associated with the loan.

issue_month

Month the loan was issued.

loan_status

Status of the loan.

initial_listing_status

Initial listing status of the loan. (I think this has to do with whether the lender provided the entire loan or if the loan is across multiple lenders.)

disbursement_method

Dispersement method of the loan.

balance

Current...

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

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.

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.