Fixed 30-year mortgage rates in the United States averaged 6.67 percent in the week ending August 8 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.

30 Year Mortgage Rate in the United States decreased to 6.58 percent in August 14 from 6.63 percent in the previous week. This dataset includes a chart with historical data for the United States 30 Year Mortgage Rate.

15 Year Mortgage Rate in the United States decreased to 5.71 percent in August 14 from 5.75 percent in the previous week. This dataset includes a chart with historical data for the United States 15 Year Mortgage 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.

Mortgage lending information comes from the Federal Financial Institutions Examination Council's (FFIEC) Home Mortgage Disclosure Act (HMDA) data. Loan originations are the creation of a loan after bank approval. Loan origination rates are calculated from the number of loan applications that were either approved or denied—what is termed as decisioned applications. For all charts, the loan’s purpose can be selected via a dropdown list. Trends are summarized by all loan purposes and by Loans for home purchase, home improvement, or refinancing.

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! 😀

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.

Interest rates to be paid on debentures issued with respect to a loan or mortgage insured by the Federal Housing Commissioner under the provisions of the National Housing Act (the Act). The Department will continue to publish semi-annually the debenture interest rate for the next six-month period in the Federal Register. Concurrently with the publication in the Federal Register, the Department will update the list of debenture interest rates.

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.

This dataset contains a wealth of information from 52,000 loan applications, offering detailed insights into the factors that influence loan approval decisions. Collected from financial institutions, this data is highly valuable for credit risk analysis, financial modeling, and predictive analytics. The dataset is particularly useful for anyone interested in applying machine learning techniques to real-world financial decision-making scenarios.

Overview: This dataset provides information about various applicants and the loans they applied for, including their demographic details, income, loan terms, and approval status. By analyzing this data, one can gain an understanding of which factors are most critical for determining the likelihood of loan approval. The dataset can also help in evaluating credit risk and building robust credit scoring systems.

Dataset Columns: Applicant_ID: Unique identifier for each loan application. Gender: Gender of the applicant (Male/Female). Age: Age of the applicant. Marital_Status: Marital status of the applicant (Single/Married). Dependents: Number of dependents the applicant has. Education: Education level of the applicant (Graduate/Not Graduate). Employment_Status: Employment status of the applicant (Employed, Self-Employed, Unemployed). Occupation_Type: Type of occupation, which provides insights into the nature of the applicant’s job (Salaried, Business, Others). Residential_Status: Type of residence (Owned, Rented, Mortgage). City/Town: The city or town where the applicant resides. Annual_Income: The total annual income of the applicant, a key factor in loan eligibility. Monthly_Expenses: The monthly expenses of the applicant, indicating their financial obligations. Credit_Score: The applicant's credit score, reflecting their creditworthiness. Existing_Loans: Number of existing loans the applicant is servicing. Total_Existing_Loan_Amount: The total amount of all existing loans the applicant has. Outstanding_Debt: The remaining amount of debt yet to be paid by the applicant. Loan_History: The applicant’s previous loan history (Good/Bad), indicating their repayment reliability. Loan_Amount_Requested: The loan amount the applicant has applied for. Loan_Term: The term of the loan in months. Loan_Purpose: The purpose of the loan (e.g., Home, Car, Education, Personal, Business). Interest_Rate: The interest rate applied to the loan. Loan_Type: The type of loan (Secured/Unsecured). Co-Applicant: Indicates if there is a co-applicant for the loan (Yes/No). Bank_Account_History: Applicant’s banking history, showing past transactions and reliability. Transaction_Frequency: The frequency of financial transactions in the applicant’s bank account (Low/Medium/High). Default_Risk: The risk level of the applicant defaulting on the loan (Low/Medium/High). Loan_Approval_Status: Final decision on the loan application (Approved/Rejected).

The dataset shows structure of interest rates

Note: 1. For the year 1995-96, interest rate on deposits of maturity above 3 years, and from 1996-97 onwards, interest rates on deposit for all the maturities refer to the deposit rates of 5 major public sector banks as at end-March. 2. From 1994-95 onwards, data on minimum general key lending rates prescribed by RBI refers to the prime lending rates of 5 major public sector banks. 3. For 2011-12, data on deposit rates and Base rates of 5 major public sector banks refer to the period up to July 31, 2010. From July 1, 2010 BPLR System is replaced by Base Rate System. Accordingly the data reflects the Base Rate of five major public sector banks. Data for 2010-11 for Call/Notice Money rates are average of April-July 2010. 4. Data for dividend rate and yield rate for units of UTI are based on data received from Unit Trust of India. 5. Data on annual(gross) redemption yield of Government of India securities are based on redemption yield which is computed from 2000-01 as the mean of the daily weighted average yield of the transactions in each traded security. The weight is calculated as the share of the transaction in a given security in the aggregated value. 6. Data on prime lending rates for IDBI, IFCI and ICICI for the year 1999-00 relates to long-term prime lending rates in January 2000. 7. Data on prime lending rates for State Financial Corporation for all the years and for other term lending institutions from 2002-03 onwards relate to long-term (over 36-month) PLR. 8. Data on prime lending rate of IIBI/ IRBI from 2003-04 onwards relate to single PLR effective July 31, 2003. 9. IDBI ceased to be term lending institution on its conversion into a banking entity effective October 11, 2004. 10. ICICI ceased to be a term-lending institution after its merger with ICICI Bank. 11. Figures in brackets indicate lending rate charged to small-scale industries. 12. IFCI has become a non-bank financial company. 13. IIBI is in the process of voluntary winding up. 14. Figures for 2015-16 are as on July 14, 2015. 15. 2024-25 data : As on September 1, 2024; except for WALRs, WADTDR and 1-year median MCLR (July 2023). 16. * : Data on deposit and lending rates relate to five major Public Sector Banks up to 2003-04. While for the subsequent years, they relate to five major banks. 17. # : Savings deposit rate from 2011-12 onwards relates to balance up to 1 lakh. Savings deposit rate was deregulated with effect from October 25, 2011. 18. $ : Data on Weighted Average Lending Rates (WALRs), weighted Average Domestic Term Deposit Rate (WADTDR) and 1-year median marginal cost of funds-based lending rate (MCLR) pertain to all scheduled commercial banks (excluding RRBs and SFBs). 19. Data on lending rates in column (7) relate to Benchmark Prime Lending Rate (BPLR) for the period 2004-05 to 2009-10; Base Rate for 2010-11 to 2015-16 and Marginal Cost of Funds Based Lending Rate (MCLR) (overnight) for 2016-17 onwards. BPLR system was replaced by the Base Rate System from July 1, 2010, which, in turn, was replaced by the MCLR System effective April 1, 2016.

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.

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 Rate in the United Kingdom increased to 6.99 percent in July from 6.98 percent in June of 2025. This dataset provides - United Kingdom BBA Mortgage Rate- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Mortgage Application in the United States increased by 10.90 percent in the week ending August 8 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 Federal Perkins Loan Cohort Default Rates is a data collection that is part of the Federal Perkins Loan program; the most recent Federal Perkins Loan Cohort Default Rates are available . Historical program data is available electronically since 2006 at . The data collection is conducted using a web-based entry system wherein postsecondary institutions must submit information electronically if they participate in the Federal Perkins Loan program. Key statistics produced from this data collection are the Federal Perkins Loan cohort default rates (previously known as the Orange Book).

Overview This dataset contains 45,000 records of loan applicants, with various attributes related to personal demographics, financial status, and loan details. The dataset can be used for predictive modeling, particularly in credit risk assessment and loan default prediction.

Dataset Content The dataset includes 14 columns representing different factors influencing loan approvals and defaults:

Personal Information

person_age: Age of the applicant (in years). person_gender: Gender of the applicant (male, female). person_education: Educational background (High School, Bachelor, Master, etc.). person_income: Annual income of the applicant (in USD). person_emp_exp: Years of employment experience. person_home_ownership: Type of home ownership (RENT, OWN, MORTGAGE). Loan Details

loan_amnt: Loan amount requested (in USD). loan_intent: Purpose of the loan (PERSONAL, EDUCATION, MEDICAL, etc.). loan_int_rate: Interest rate on the loan (percentage). loan_percent_income: Ratio of loan amount to income. Credit & Loan History

cb_person_cred_hist_length: Length of the applicant's credit history (in years). credit_score: Credit score of the applicant. previous_loan_defaults_on_file: Whether the applicant has previous loan defaults (Yes or No). Target Variable

loan_status: 1 if the loan was repaid successfully, 0 if the applicant defaulted. Use Cases Loan Default Prediction: Build a classification model to predict loan repayment. Credit Risk Analysis: Analyze the relationship between income, credit score, and loan defaults. Feature Engineering: Extract new insights from employment history, home ownership, and loan amounts. Acknowledgments This dataset is synthetic and designed for machine learning and financial risk analysis.

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 ---

Abstract

The purpose of the SEPHER data set is to allow for testing, assessing and generating new analysis and metrics that can address inequalities and climate injustice. The data set was created by Tedesco, M., C. Hultquist, S. E. Char, C. Constantinides, T. Galjanic, and A. D. Sinha.

Methodology

SEPHER draws upon four major source datasets: CDC Social Vulnerability Index, FEMA National Risk Index, Home Mortgage Disclosure Act, and Evictions datasets. The data from these source datasets have been merged, cleaned, and standardized and all of the variables documented in the data dictionary.

CDC Social Vulnerability Index

CDC Social Vulnerability Index (SVI) dataset is a dataset prepared for the Centers for Disease Control and Prevention for the purpose of assessing the degree of social vulnerability of American communities to natural hazards and anthropogenic events. It contains data on 15 social factors taken or derived from Census reports as well as rankings of each tract based on these individual factors, groups of factors corresponding to four related themes (Socioeconomic, Household Composition & Disability, Minority Status & Language, and Housing Type & Transportation) and overall. The data is available for the years 2000, 2010, 2014, 2016, and 2018.

FEMA National Risk Index

The National Risk Index (NRI) dataset compiled by the Federal Emergency Management Agency (FEMA) consists of historic natural disaster data from across the United States at a tract-level. The dataset includes information about 18 natural disasters including earthquakes, tsunamis, wildfires, volcanic activity and many others. Each disaster is detailed out in terms of its frequency, historic impact, potential exposure, expected annual loss and associated risk. The dataset also includes some summary variables for each tract including the total expected loss in terms of building loss, human loss and agricultural loss, the population of the tract, and the area covered by the tract. It finally includes a few more features to characterize the population such as social vulnerability rating and community resilience.

Home Mortgage Disclosure Act

The Home Mortgage Disclosure Act (HMDA) dataset contains loan-level data for home mortgages including information on applications, denials, approvals, and institution purchases. It is managed and expanded annually by the Consumer Financial Protection Bureau based on the data collected from financial institutions. The dataset is used by public officials to make decisions and policies, uncover lending patterns and discrimination among mortgage applicants, and investigate if lenders are serving the housing needs of the communities. It covers the period from 2007 to 2017.

Evictions

The Evictions dataset is compiled and managed by the Eviction Lab at Princeton University and consists of court records related to eviction cases in the United States between 2000 and 2016. Its purpose is to estimate the prevalence of court-ordered evictions and compare eviction rates among states, counties, cities, and neighborhoods. Besides information on eviction filings and judgments, the dataset includes socioeconomic and real estate data for each tract including race/ethnic origin, household income, poverty rate, property value, median gross rent, rent burden, and others.

The benchmark interest rate in the United States was last recorded at 4.50 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.