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.

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.

15 Year Mortgage Rate in the United States increased to 5.86 percent in July 10 from 5.80 percent in the previous week. This dataset includes a chart with historical data for the United States 15 Year Mortgage Rate.

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

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

Bank Lending Rate in the United States remained unchanged at 7.50 percent in June. This dataset provides - United States Average Monthly Prime Lending Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Analysis of ‘ Zillow Housing Aspirations Report’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/zillow-housing-aspirations-reporte on 13 February 2022.

--- Dataset description provided by original source is as follows ---

About this dataset

Additional Data Products

Product: Zillow Housing Aspirations Report

Date: April 2017

Definitions

Home Types and Housing Stock

• All Homes: Zillow defines all homes as single-family, condominium and co-operative homes with a county record. Unless specified, all series cover this segment of the housing stock.
• Condo/Co-op: Condominium and co-operative homes.
• Multifamily 5+ units: Units in buildings with 5 or more housing units, that are not a condominiums or co-ops.
• Duplex/Triplex: Housing units in buildings with 2 or 3 housing units.

Additional Data Products

• Zillow Home Value Forecast (ZHVF): The ZHVF is the one-year forecast of the ZHVI. Our forecast methodology is methodology post.
• Zillow creates our negative equity data using our own data in conjunction with data received through our partnership with TransUnion, a leading credit bureau. We match estimated home values against actual outstanding home-related debt amounts provided by TransUnion. To read more about how we calculate our negative equity metrics, please see our here.
• Cash Buyers: The share of homes in a given area purchased without financing/in cash. To read about how we calculate our cash buyer data, please see our research brief.
• Mortgage Affordability, Rental Affordability, Price-to-Income Ratio, Historical ZHVI, Historical ZHVI and Houshold Income are calculated as a part of Zillow’s quarterly Affordability Indices. To calculate mortgage affordability, we first calculate the mortgage payment for the median-valued home in a metropolitan area by using the metro-level Zillow Home Value Index for a given quarter and the 30-year fixed mortgage interest rate during that time period, provided by the Freddie Mac Primary Mortgage Market Survey (based on a 20 percent down payment). Then, we consider what portion of the monthly median household income (U.S. Census) goes toward this monthly mortgage payment. Median household income is available with a lag. For quarters where median income is not available from the U.S. Census Bureau, we calculate future quarters of median household income by estimating it using the Bureau of Labor Statistics’ Employment Cost Index. The affordability forecast is calculated similarly to the current affordability index but uses the one year Zillow Home Value Forecast instead of the current Zillow Home Value Index and a specified interest rate in lieu of PMMS. It also assumes a 20 percent down payment. We calculate rent affordability similarly to mortgage affordability; however we use the Zillow Rent Index, which tracks the monthly median rent in particular geographical regions, to capture rental prices. Rents are chained back in time by using U.S. Census Bureau American Community Survey data from 2006 to the start of the Zillow Rent Index, and Decennial Census for all other years.
• The mortgage rate series is the average mortgage rate quoted on Zillow Mortgages for a 30-year, fixed-rate mortgage in 15-minute increments during business hours, 6:00 AM to 5:00 PM Pacific. It does not include quotes for jumbo loans, FHA loans, VA loans, loans with mortgage insurance or quotes to consumers with credit scores below 720. Federal holidays are excluded. The jumbo mortgage rate series is the average jumbo mortgage rate quoted on Zillow Mortgages for a 30-year, fixed-rate, jumbo mortgage in one-hour increments during business hours, 6:00 AM to 5:00 PM Pacific Time. It does not include quotes to consumers with credit scores below 720. Traditional federal holidays and hours with insufficient sample sizes are excluded.

About Zillow Data (and Terms of Use Information)

• Zillow is in the process of transitioning some data sources with the goal of producing published data that is more comprehensive, reliable, accurate and timely. As this new data is incorporated, the publication of select metrics may be delayed or temporarily suspended. We look forward to resuming our usual publication schedule for all of our established datasets as soon as possible, and we apologize for any inconvenience. Thank you for your patience and understanding.
• All data accessed and downloaded from this page is free for public use by consumers, media, analysts, academics etc., consistent with our published Terms of Use. Proper and clear attribution of all data to Zillow is required.
• For other data requests or inquiries for Zillow Real Estate Research, contact us here.
• All files are time series unless noted otherwise.
• To download all Zillow metrics for specific levels of geography, click here.
• To download a crosswalk between Zillow regions and federally defined regions for counties and metro areas, click here.
• Unless otherwise noted, all series cover single-family residences, condominiums and co-op homes only.

Source: https://www.zillow.com/research/data/

This dataset was created by Zillow Data and contains around 200 samples along with Unnamed: 1, Unnamed: 0, technical information and other features such as: - Unnamed: 1 - Unnamed: 0 - and more.

How to use this dataset

• Analyze Unnamed: 1 in relation to Unnamed: 0
• Study the influence of Unnamed: 1 on Unnamed: 0
• More datasets

Acknowledgements

If you use this dataset in your research, please credit Zillow Data

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--- Original source retains full ownership of the source dataset ---

Mortgage Rate in Sweden decreased to 3.03 percent in May from 3.13 percent in April of 2025. This dataset includes a chart with historical data for Sweden Average Interest Rate on New Agreements for Mortgages to Households.

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.

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.

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.

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

China Lending Rate: Weighted Average: Individual Housing Loan data was reported at 3.090 % pa in Dec 2024. This records a decrease from the previous number of 3.310 % pa for Sep 2024. China Lending Rate: Weighted Average: Individual Housing Loan data is updated quarterly, averaging 5.395 % pa from Mar 2009 (Median) to Dec 2024, with 64 observations. The data reached an all-time high of 7.620 % pa in Dec 2011 and a record low of 3.090 % pa in Dec 2024. China Lending Rate: Weighted Average: Individual Housing Loan data remains active status in CEIC and is reported by The People's Bank of China. The data is categorized under China Premium Database’s Money Market, Interest Rate, Yield and Exchange Rate – Table CN.MA: Rediscount and Lending Rate.

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.

Singapore Housing Loan Rate for 15 Years: 10 Finance Co Average data was reported at 3.160 % pa in Sep 2018. This stayed constant from the previous number of 3.160 % pa for Aug 2018. Singapore Housing Loan Rate for 15 Years: 10 Finance Co Average data is updated monthly, averaging 6.160 % pa from Jan 1983 (Median) to Sep 2018, with 429 observations. The data reached an all-time high of 12.420 % pa in Jan 1983 and a record low of 2.870 % pa in Jul 2013. Singapore Housing Loan Rate for 15 Years: 10 Finance Co Average data remains active status in CEIC and is reported by Monetary Authority of Singapore. The data is categorized under Global Database’s Singapore – Table SG.M001: Lending 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.

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.

This dataset includes one dataset which was custom ordered from Statistics Canada.The table includes information on housing suitability and shelter-cost-to-income ratio by number of bedrooms, housing tenure, age of primary household maintainer, household type, and income quartile ranges for census subdivisions in British Columbia. The dataset is in Beyond 20/20 (.ivt) format. The Beyond 20/20 browser is required in order to open it. This software can be freely downloaded from the Statistics Canada website: https://www.statcan.gc.ca/eng/public/beyond20-20 (Windows only). For information on how to use Beyond 20/20, please see: http://odesi2.scholarsportal.info/documentation/Beyond2020/beyond20-quickstart.pdf https://wiki.ubc.ca/Library:Beyond_20/20_Guide Custom order from Statistics Canada includes the following dimensions and variables: Geography: Non-reserve CSDs in British Columbia - 299 geographies The global non-response rate (GNR) is an important measure of census data quality. It combines total non-response (households) and partial non-response (questions). A lower GNR indicates a lower risk of non-response bias and, as a result, a lower risk of inaccuracy. The counts and estimates for geographic areas with a GNR equal to or greater than 50% are not published in the standard products. The counts and estimates for these areas have a high risk of non-response bias, and in most cases, should not be released. Housing Tenure Including Presence of Mortgage (5) 1. Total – Private non-band non-farm off-reserve households with an income greater than zero by housing tenure 2. Households who own 3. With a mortgage1 4. Without a mortgage 5. Households who rent Notes: 1) Presence of mortgage - Refers to whether the owner households reported mortgage or loan payments for their dwelling. 2015 Before-tax Household Income Quartile Ranges (5) 1. Total – Private households by quartile ranges1, 2, 3 2. Count of households under or at quartile 1 3. Count of households between quartile 1 and quartile 2 (median) (including at quartile 2) 4. Count of households between quartile 2 (median) and quartile 3 (including at quartile 3) 5. Count of households over quartile 3 Notes: 1) A private household will be assigned to a quartile range depending on its CSD-level location and depending on its tenure (owned and rented). Quartile ranges for owned households in a specific CSD are delimited by the 2015 before-tax income quartiles of owned households with an income greater than zero and residing in non-farm off-reserve dwellings in that CSD. Quartile ranges for rented households in a specific CSD are delimited by the 2015 before-tax income quartiles of rented households with an income greater than zero and residing in non-farm off-reserve dwellings in that CSD. 2) For the income quartiles dollar values (the delimiters) please refer to Table 1. 3) Quartiles 1 to 3 are suppressed if the number of actual records used in the calculation (not rounded or weighted) is less than 16. For cases in which the renters’ quartiles or the owners’ quartiles (figures from Table 1) of a CSD are suppressed the CSD is assigned to a quartile range depending on the provincial renters’ or owners’ quartile figures. Number of Bedrooms (Unit Size) (6) 1. Total – Private households by number of bedrooms1 2. 0 bedrooms (Bachelor/Studio) 3. 1 bedroom 4. 2 bedrooms 5. 3 bedrooms 6. 4 bedrooms Note: 1) Dwellings with 5 bedrooms or more included in the total count only. Housing Suitability (6) 1. Total - Housing suitability 2. Suitable 3. Not suitable 4. One bedroom shortfall 5. Two bedroom shortfall 6. Three or more bedroom shortfall Note: 1) 'Housing suitability' refers to whether a private household is living in suitable accommodations according to the National Occupancy Standard (NOS); that is, whether the dwelling has enough bedrooms for the size and composition of the household. A household is deemed to be living in suitable accommodations if its dwelling has enough bedrooms, as calculated using the NOS. 'Housing suitability' assesses the required number of bedrooms for a household based on the age, sex, and relationships among household members. An alternative variable, 'persons per room,' considers all rooms in a private dwelling and the number of household members. Housing suitability and the National Occupancy Standard (NOS) on which it is based were developed by Canada Mortgage and Housing Corporation (CMHC) through consultations with provincial housing agencies. Shelter-cost-to-income-ratio (4) 1. Total – Private non-band non-farm off-reserve households with an income greater than zero 2. Spending less than 30% of households total income on shelter costs 3. Spending 30% or more of households total income on shelter costs 4. Spending 50% or more of households total income on shelter costs Note: 'Shelter-cost-to-income ratio' refers to the proportion of average total income of household which is spent on shelter costs. Household Statistics (8) 1....

Mortgage Rate in the United Kingdom decreased to 6.98 percent in June from 7.09 percent in May of 2025. This dataset provides - United Kingdom BBA Mortgage Rate- actual values, historical data, forecast, chart, statistics, economic calendar and news.