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.

The 10-year treasury constant maturity rate in the U.S. is forecast to increase by *** percentage points by 2027, while the 30-year fixed mortgage rate is expected to fall by *** percentage points. From *** percent in 2024, the average 30-year mortgage rate is projected to reach *** percent in 2027.

Due to interest rates decreasing in recent years, mortgages in the United Kingdom have become overall more affordable: In 2007, when mortgages were the least affordable, a home buyer spent on average **** percent of their income on mortgage interest and *** percent on capital repayment. In 2019, the year with the most affordable mortgages, mortgage interest accounted for *** percent and capital repayment was **** percent of their income. As interest rates increase in response to the rising inflation, mortgage affordability is expected to worsen. Though below the levels observed before 2007, the total mortgage repayment between 2022 and 2026 is expected to exceed ** percent of income.

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.

More than ************* mortgage loans are projected to be affected by the increasing mortgage interest rates in Canada by 2025. About *********** of these mortgages are projected to be up for renewal in 2024. These loans were taken out at a time when interest rates were much lower, meaning that homeowners will be affected by a notable increase in their monthly payments.

1. Borrower’s Demographics

age (int64) – Borrower's age (in years).

gender (category) – Borrower's gender (Male/Female).

marital_status (category) – Marital status (Single, Married, Divorced).

education_level (category) – Education level (High School, Bachelor, Master, PhD).

2. Financial Information

annual_income (float64) – Borrower's yearly income.

monthly_income (float64) – Borrower's monthly income.

employment_status (category) – Current employment type (Employed, Self-Employed, Unemployed).

debt_to_income_ratio (float64) – Ratio of borrower’s debt to their income. Lower = better.

credit_score (int64) – Credit bureau score (e.g., FICO). Higher = less risky.

3. Loan Information

loan_amount (float64) – Amount of loan taken.

loan_purpose (category) – Loan purpose (Car, Education, Home, Medical, etc.).

interest_rate (float64) – Loan par annual interest rate (%).

loan_term (int64) – Loan repayment duration (months, e.g., 36 or 60).

installment (float64) – Monthly installment .

grade_subgrade (category) – Risk category assigned to loan (A1, B2, etc.).

4. Borrower’s Credit History

num_of_open_accounts (int64) – Total active credit accounts.

total_credit_limit (float64) – Borrower's total available credit limit.

current_balance (float64) – Borrower's outstanding balance (loan + credit card).

delinquency_history (int64) – Count of late payments in borrower’s history.

public_records (int64) – Negative public records (e.g., bankruptcies, legal actions).

num_of_delinquencies (int64) – Total delinquencies (missed payments).

5. Target Variable

loan_paid_back (int64) – Target variable:

1 → Borrower paid loan in full.

0 → Borrower defaulted (did not repay fully).

This dataset provides values for 15 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.

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.

Forecast: Household Expenditure on Mortgage Interest and Charges in the US 2022 - 2026 Discover more data with ReportLinker!

Loan Prediction Dataset

This dataset contains information about individuals applying for loans, including their financial and personal details. The dataset is designed to help predict whether an applicant is likely to default on a loan (indicated by the "Risk_Flag" column). The dataset includes 1,404 entries with 13 features, providing a comprehensive view of each applicant's profile.

Features:

• Id: Unique identifier for each applicant.
• Income: Annual income of the applicant.
• Age: Age of the applicant.
• Experience: Number of years of professional experience.
• Married/Single: Marital status of the applicant (married or single).
• House_Ownership: Housing status of the applicant (e.g., rented, owned, or no rent/no own).
• Car_Ownership: Whether the applicant owns a car (yes or no).
• Profession: The profession of the applicant (e.g., Software Developer, Mechanical Engineer, etc.).
• CITY: The city where the applicant resides.
• STATE: The state where the applicant resides.
• CURRENT_JOB_YRS: Number of years in the current job.
• CURRENT_HOUSE_YRS: Number of years in the current residence.
• Risk_Flag: Binary flag indicating whether the applicant is at risk of defaulting on the loan (1 = high risk, 0 = low risk).

Key Insights:

• The dataset includes a diverse range of professions, income levels, and geographic locations, making it suitable for building predictive models for loan default risk.
• The "Risk_Flag" column is the target variable, which can be used to train machine learning models for classification tasks.
• Features like income, age, experience, and housing status may play a significant role in determining the likelihood of loan default.

Potential Use Cases:

• Loan Default Prediction: Build machine learning models to predict whether an applicant is likely to default on a loan.
• Risk Assessment: Analyze the factors that contribute to loan default risk.
• Customer Segmentation: Segment applicants based on their financial and personal profiles for targeted marketing or risk management.

Dataset Source:

The dataset is synthetic and designed for educational purposes, making it ideal for practicing data analysis, feature engineering, and machine learning model building.

License:

This dataset is publicly available for educational and non-commercial use.

About 1.4 million households with mortgages up for renewal in the United Kingdom (UK) will face increasing monthly costs by the end of 2024 because of the aggressive mortgage interest hikes since the beginning of 2022. For about one million of these households, the increase will be between one British pound and 300 British pounds, while for 388,000 households, the increase will be higher. By December 2026, the number of households with rising mortgage payments is projected at 3.9 million. Meanwhile, about two million mortgage borrowers are expected to benefit from reduced mortgage payments by the end of 2026.

The size of the North America Mortgage/Loan Brokers Market market was valued at USD XX Million in 2023 and is projected to reach USD XXX Million by 2032, with an expected CAGR of 5.00% during the forecast period. Recent developments include: In November 2022, To expand the use of eNotes across 250 locations in 49 states, Primary Residential Mortgage Inc. (PRMI) employed the eVault and digital closing platform from Snapdocs., In August 2022, Due to the slowdown in home sales caused by rising interest rates, the two biggest mortgage lenders in the US are increasing pressure on their smaller rivals by providing discounts and other incentives. The two biggest mortgage originators in the US, Rocket Mortgage and United Wholesale Mortgage, respectively, are pursuing aggressive strategies at a time when many lenders are leaving the market or going out of business.. Notable trends are: Increase in Digitization in Lending and Blockchain Technology is driving the market.

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.

The size of the Mortgage Lending Market was valued at USD 1.58 Billion in 2023 and is projected to reach USD 2.89 Billion by 2032, with an expected CAGR of 9.00% during the forecast period. Key drivers for this market are: Digital platforms and AI-driven credit assessments have simplified the application process, improving accessibility and borrower experience. Potential restraints include: Fluctuations in interest rates significantly impact borrowing costs, affecting loan demand and affordability. Notable trends are: The adoption of online portals and mobile apps is transforming the mortgage process with faster approvals and greater transparency.

The Dutch Central Bank (DNB) estimated that the growth of mortgage lending will slow down in 2023 and 2024, after originations rose by *** percent in 2022. That can be explained with the accelerated house price growth in recent years and the higher interest rates, eroding housing affordability. Within Europe, the Netherlands counts as one of the countries with the highest mortgage debt among private individuals. This has a political background as the Dutch tax system allowed homeowners to deduct interest paid on mortgage from pre-tax income for a maximum period of thirty year, essentially allowing for income support for homeowners. In the Netherlands, this system is known as hypotheekrenteaftrek.

This dataset contains the predicted prices of the asset 🏠 From Memecoins to Mortgage Loans over the next 16 years. This data is calculated initially using a default 5 percent annual growth rate, and after page load, it features a sliding scale component where the user can then further adjust the growth rate to their own positive or negative projections. The maximum positive adjustable growth rate is 100 percent, and the minimum adjustable growth rate is -100 percent.

Canada Conventional Mortgage: 5 Years: Weekly data was reported at 6.490 % pa in 07 May 2025. This stayed constant from the previous number of 6.490 % pa for 30 Apr 2025. Canada Conventional Mortgage: 5 Years: Weekly data is updated weekly, averaging 5.700 % pa from Jan 2000 (Median) to 07 May 2025, with 1323 observations. The data reached an all-time high of 8.750 % pa in 31 May 2000 and a record low of 4.640 % pa in 12 Jul 2017. Canada Conventional Mortgage: 5 Years: Weekly data remains active status in CEIC and is reported by Bank of Canada. The data is categorized under Global Database’s Canada – Table CA.M005: Conventional Mortgage Rate. [COVID-19-IMPACT]

This dataset contains the predicted prices of the asset Advanced Mortgage & Reserve over the next 16 years. This data is calculated initially using a default 5 percent annual growth rate, and after page load, it features a sliding scale component where the user can then further adjust the growth rate to their own positive or negative projections. The maximum positive adjustable growth rate is 100 percent, and the minimum adjustable growth rate is -100 percent.

Mortgage interest rates worldwide varied greatly in June 2025, from less than ******percent in many European countries to as high as ***percent in Turkey. The average mortgage rate in a country depends on the central bank's base lending rate and macroeconomic indicators such as inflation and forecast economic growth. Since 2022, inflationary pressures have led to rapid increases in mortgage interest rates. Which are the leading mortgage markets? An easy way to estimate the importance of the mortgage sector in each country is by comparing household debt depth, or the ratio of the debt held by households compared to the county's GDP. In 2024, Switzerland, Australia, and Canada had some of the highest household debt to GDP ratios worldwide. While this indicator shows the size of the sector relative to the country’s economy, the value of mortgages outstanding allows to compare the market size in different countries. In Europe, for instance, the United Kingdom, Germany, and France were the largest mortgage markets by outstanding mortgage lending. Mortgage lending trends in the U.S. In the United States, new mortgage lending soared in 2021. This was largely due to the growth of new refinance loans that allow homeowners to renegotiate their mortgage terms and replace their existing loan with a more favorable one. Following the rise in interest rates, the mortgage market cooled, and refinance loans declined.

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.

Mortgage Rates: Hot Economic Conjecture Puts the Squeeze on Homebuyers

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