30 Year Mortgage Rate in the United States decreased to 6.56 percent in August 28 from 6.58 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 6.69 percent in the week ending August 22 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.

Graph and download economic data for 30-Year Fixed Rate Conforming Mortgage Index: Loan-to-Value Greater Than 80, FICO Score Between 720 and 739 (OBMMIC30YFLVGT80FB720A739) from 2017-01-03 to 2025-08-04 about score, 30-year, fixed, mortgage, rate, indexes, and USA.

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.

30-Year Fixed Mortgage Rate - High Loan-to-Value, High FICO - Historical chart and current data through 2025.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

What are 30 year mortgage rates?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

30-Year Fixed Rate Mortgage - High Credit, Low Loan-to-Value - Historical chart and current data through 2025.

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.

Graph and download economic data for 15-Year Fixed Rate Mortgage Average in the United States (MORTGAGE15US) from 1991-08-30 to 2025-08-28 about 15-year, mortgage, fixed, interest rate, interest, rate, and USA.

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.

Mortgage rates increased at a record pace in 2022, with the 10-year fixed mortgage rate doubling between March 2022 and December 2022. With inflation increasing, the Bank of England introduced several bank rate hikes, resulting in higher mortgage rates. In May 2025, the average 10-year fixed rate interest rate reached **** percent. As borrowing costs get higher, demand for housing is expected to decrease, leading to declining market sentiment and slower house price growth. How have the mortgage hikes affected the market? After surging in 2021, the number of residential properties sold declined in 2023, reaching just above *** million. Despite the number of transactions falling, this figure was higher than the period before the COVID-19 pandemic. The falling transaction volume also impacted mortgage borrowing. Between the first quarter of 2023 and the first quarter of 2024, the value of new mortgage loans fell year-on-year for five straight quarters in a row. How are higher mortgages affecting homebuyers? Homeowners with a mortgage loan usually lock in a fixed rate deal for two to ten years, meaning that after this period runs out, they need to renegotiate the terms of the loan. Many of the mortgages outstanding were taken out during the period of record-low mortgage rates and have since faced notable increases in their monthly repayment. About **** million homeowners are projected to see their deal expire by the end of 2026. About *** million of these loans are projected to experience a monthly payment increase of up to *** British pounds by 2026.

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.

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.

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 World Bank complies with all sanctions applicable to World Bank transactions.

United States Mortgage Fixed Rate: Wk Ending: 30 Year: Point data was reported at 0.500 % pa in 26 Jul 2018. This records an increase from the previous number of 0.400 % pa for 19 Jul 2018. United States Mortgage Fixed Rate: Wk Ending: 30 Year: Point data is updated weekly, averaging 0.600 % pa from Jan 2004 (Median) to 26 Jul 2018, with 760 observations. The data reached an all-time high of 0.900 % pa in 18 Nov 2010 and a record low of 0.300 % pa in 08 May 2008. United States Mortgage Fixed Rate: Wk Ending: 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 USA – Table US.M012: Mortgage Interest Rate.

An index that can be used to gauge broad financial conditions and assess how these conditions are related to future economic growth. The index is broadly consistent with how the FRB/US model generally relates key financial variables to economic activity. The index aggregates changes in seven financial variables: the federal funds rate, the 10-year Treasury yield, the 30-year fixed mortgage rate, the triple-B corporate bond yield, the Dow Jones total stock market index, the Zillow house price index, and the nominal broad dollar index using weights implied by the FRB/US model and other models in use at the Federal Reserve Board. These models relate households' spending and businesses' investment decisions to changes in short- and long-term interest rates, house and equity prices, and the exchange value of the dollar, among other factors. These financial variables are weighted using impulse response coefficients (dynamic multipliers) that quantify the cumulative effects of unanticipated permanent changes in each financial variable on real gross domestic product (GDP) growth over the subsequent year. The resulting index is named Financial Conditions Impulse on Growth (FCI-G). One appealing feature of the FCI-G is that its movements can be used to measure whether financial conditions have tightened or loosened, to summarize how changes in financial conditions are associated with real GDP growth over the following year, or both.

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 raw datasets provided here are intended for use in a Data in Brief article. These comprehensive files, sourced from the Freddie Mac website, offer quarterly snapshots of mortgage loans that have been originated in the USA since 1999, along with details of their subsequent repayment behaviours. This data remains current and is updated every three months. Specifically, the loan origination data present here encompasses amortized fixed-rate mortgage loans from 1999 up to June 2022. In contrast, the performance data is presented on a monthly basis, detailing loan repayment profiles from 1999 until September 30, 2022. Both the origination and performance datasets feature a unique loan ID, which can be utilized to integrate the data on loan originations with that of loan repayments.

Mortgage Rate in Sweden decreased to 2.84 percent in July from 3.01 percent in June of 2025. This dataset includes a chart with historical data for Sweden Average Interest Rate on New Agreements for Mortgages to Households.