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.

Context

The Federal Reserve sets interest rates to promote conditions that achieve the mandate set by the Congress — high employment, low and stable inflation, sustainable economic growth, and moderate long-term interest rates. Interest rates set by the Fed directly influence the cost of borrowing money. Lower interest rates encourage more people to obtain a mortgage for a new home or to borrow money for an automobile or for home improvement. Lower rates encourage businesses to borrow funds to invest in expansion such as purchasing new equipment, updating plants, or hiring more workers. Higher interest rates restrain such borrowing by consumers and businesses.

Content

This dataset includes data on the economic conditions in the United States on a monthly basis since 1954. The federal funds rate is the interest rate at which depository institutions trade federal funds (balances held at Federal Reserve Banks) with each other overnight. The rate that the borrowing institution pays to the lending institution is determined between the two banks; the weighted average rate for all of these types of negotiations is called the effective federal funds rate. The effective federal funds rate is determined by the market but is influenced by the Federal Reserve through open market operations to reach the federal funds rate target. The Federal Open Market Committee (FOMC) meets eight times a year to determine the federal funds target rate; the target rate transitioned to a target range with an upper and lower limit in December 2008. The real gross domestic product is calculated as the seasonally adjusted quarterly rate of change in the gross domestic product based on chained 2009 dollars. The unemployment rate represents the number of unemployed as a seasonally adjusted percentage of the labor force. The inflation rate reflects the monthly change in the Consumer Price Index of products excluding food and energy.

Acknowledgements

The interest rate data was published by the Federal Reserve Bank of St. Louis' economic data portal. The gross domestic product data was provided by the US Bureau of Economic Analysis; the unemployment and consumer price index data was provided by the US Bureau of Labor Statistics.

Inspiration

How does economic growth, unemployment, and inflation impact the Federal Reserve's interest rates decisions? How has the interest rate policy changed over time? Can you predict the Federal Reserve's next decision? Will the target range set in March 2017 be increased, decreased, or remain the same?

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 Mortgage Fixed Rate: Mth Avg: 15 Year data was reported at 4.250 % pa in Oct 2018. This records an increase from the previous number of 4.080 % pa for Sep 2018. United States Mortgage Fixed Rate: Mth Avg: 15 Year data is updated monthly, averaging 5.680 % pa from Sep 1991 (Median) to Oct 2018, with 326 observations. The data reached an all-time high of 8.800 % pa in Jan 1995 and a record low of 2.660 % pa in Apr 2013. United States Mortgage Fixed Rate: Mth Avg: 15 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.

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.

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.

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: 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.

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

This dataset provides insights into the global housing market, covering various economic factors from 2015 to 2024. It includes details about property prices, rental yields, interest rates, and household income across multiple countries. This dataset is ideal for real estate analysis, financial forecasting, and market trend visualization.

📑 Column Descriptions

Factors Affecting USA National Home Prices:

Overview:

This dataset contains a comprehensive collection of indicators which dictate the housing prices in the United States.

1. US Mortgage Rates:

• The average interest rates on mortgage loans in the United States.
• Used to track the cost of borrowing for housing and its impact on the real estate market.

2. Gross Domestic Product (GDP):

• The total monetary value of all goods and services produced within the United States during a specified period.
• A fundamental measure of economic performance, reflecting the overall economic health and growth trends of the country.

3. Unemployment Rates:

• The percentage of the labor force that is currently unemployed and actively seeking employment.
• A crucial indicator of labor market health and economic stability, influencing government policies and social welfare programs.

4. FED Funds Rate:

• The interest rate at which depository institutions lend reserve balances to other depository institutions overnight, as set by the Federal Reserve.
• This rate is a primary tool for monetary policy, influencing borrowing costs and, subsequently, overall economic activity.

5. Population Growth:

• The annual rate at which the U.S. population is changing, reflecting births, deaths, and migration.
• Offers insights into demographic trends, which have implications for labor force, consumer markets, and social services planning.

6. Consumer Price Index (CPI):

• A measure of the average change over time in the prices paid by urban consumers for a market basket of consumer goods and services.
• A key indicator for assessing inflation or deflation, influencing consumer spending behavior and economic policy decisions.

S&P Case-Shiller Housing Price Index (USA):

• Measures changes in the prices of residential real estate properties over time, offering insight into the health and trends of the housing market in the United States.
• Crucial for assessing the state of the housing market, including property values, trends, and their impact on the broader economy.

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.

The benchmark interest rate in Japan was last recorded at 0.50 percent. This dataset provides - Japan Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

United States Mortgage Adjustable Rate: Mth Avg: 5 Year data was reported at 4.110 % pa in Nov 2018. This records an increase from the previous number of 4.080 % pa for Oct 2018. United States Mortgage Adjustable Rate: Mth Avg: 5 Year data is updated monthly, averaging 3.540 % pa from Jan 2005 (Median) to Nov 2018, with 167 observations. The data reached an all-time high of 6.360 % pa in Jul 2006 and a record low of 2.610 % pa in May 2013. United States Mortgage Adjustable Rate: Mth Avg: 5 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.

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.

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.

Here's an expanded description for your Kaggle dataset:

Credit Risk Prediction Dataset

This dataset provides detailed financial, demographic, and loan-specific information on individuals, making it ideal for building predictive models to assess credit risk and loan default probability. Designed to reflect key variables commonly used in credit risk analysis, it includes a variety of indicators that can help researchers and practitioners build robust models for predicting loan outcomes.

Features:

1. person_age: The age of the individual in years. Age can often be a proxy for life stage, impacting income stability and financial behaviors.
2. person_income: Annual income of the individual, in dollars. Income level can influence an individual's ability to manage loan repayments, making it a crucial variable in credit scoring.
3. person_home_ownership: The type of home ownership, which indicates the individual's housing situation, such as RENT, OWN, or MORTGAGE. This factor can indirectly signal economic stability.
4. person_emp_length: Length of employment in months, representing job stability. Longer employment history might correlate with income stability and reduced risk.
5. loan_intent: The primary purpose of the loan, categorized as PERSONAL, EDUCATION, MEDICAL, etc. Understanding the intent behind loan requests helps in assessing loan repayment likelihood, as different loan types have unique risk profiles.
6. loan_grade: A categorical variable representing the risk grade assigned to the loan, with grades from A (low risk) to D (high risk). Loan grades summarize an assessment of default risk and are often used as input in financial modeling.
7. loan_amnt: The total amount of the loan requested by the individual, in dollars. Loan size often correlates with risk, as higher loan amounts might present greater repayment challenges.
8. loan_int_rate: The interest rate assigned to the loan, indicating the cost of borrowing. Higher interest rates might imply higher risk, as they are typically assigned to riskier profiles.
9. loan_status: A binary indicator of loan repayment status, where 1 indicates loan default and 0 indicates successful repayment. This serves as the target variable for predictive modeling tasks.
10. loan_percent_income: The ratio of the loan amount to the individual’s income, providing an income-relative measure of debt burden. Higher values may indicate greater financial strain.
11. cb_person_default_on_file: A categorical variable indicating if the individual has previously defaulted (Y for Yes, N for No). This historical record can be an essential predictor of future credit risk.
12. cb_person_cred_hist_length: The length of the individual's credit history in years, with longer histories potentially indicating more experience with managing credit.

Dataset Overview:

• Total Records: 32,581 entries, representing a diverse sample of credit-seeking individuals.
• Missing Values: Certain columns, specifically person_emp_length and loan_int_rate, contain missing values, which may need to be addressed in preprocessing.

Use Cases:

This dataset is suited for several applications, including:

1. Predictive Modeling: Build classification models to predict loan defaults. Techniques like logistic regression, decision trees, random forests, or gradient boosting models (XGBoost, AdaBoost) can be employed, with loan_status as the target variable.
2. Credit Scoring Research: Analyze how different demographics, employment, and financial factors contribute to credit risk, supporting development in areas like financial technology, credit scoring, and risk management.
3. Feature Engineering and Optimization: Leverage this dataset for advanced feature engineering and hyperparameter optimization experiments, focusing on improving model accuracy and interpretability.
4. Impact Analysis: Investigate the impact of specific factors such as credit history length or loan intent on default risk, helping refine risk assessment criteria.
5. Data Science Projects: This dataset is ideal for end-to-end machine learning projects that cover data cleaning, feature selection, model training, and evaluation, suitable for beginners and professionals alike.

Dataset Highlights:

This dataset captures a wide range of indicators pertinent to creditworthiness and loan repayment behaviors. With a mix of continuous, categorical, and target variables, it enables a rich exploration of credit risk dynamics. The dataset can be used as a comprehensive tool for those looking to understand and predict credit risk or as a learning resource for aspiring data scientists interested in financial modeling and risk analysis.

Mortgage Pricing Engine Market Outlook

According to our latest research, the global mortgage pricing engine market size reached USD 1.85 billion in 2024, reflecting robust demand for advanced pricing automation solutions across the mortgage sector. The market is projected to grow at a CAGR of 8.3% during the forecast period, reaching USD 3.64 billion by 2033. This growth is fueled by the increasing digital transformation initiatives in the banking and financial services sector, the rising complexity of mortgage products, and the need for real-time, accurate pricing to enhance both compliance and customer experience.

One of the primary factors driving the expansion of the mortgage pricing engine market is the accelerating pace of digitalization within the financial services industry. Lenders and financial institutions are increasingly adopting mortgage pricing engines to automate the traditionally manual and error-prone process of mortgage rate calculation. This shift not only streamlines operations but also ensures that pricing remains competitive and compliant with evolving regulations. The integration of advanced analytics and artificial intelligence within these engines enables lenders to analyze vast datasets quickly, offering personalized rates and improving the overall decision-making process. As consumer expectations for faster and more transparent mortgage approvals rise, the adoption of mortgage pricing engines is becoming indispensable for institutions aiming to maintain a competitive edge.

Another significant growth factor is the ever-evolving regulatory landscape, which necessitates the use of sophisticated technology to ensure compliance. Mortgage pricing engines are designed to automatically incorporate regulatory updates, helping lenders avoid costly penalties and reputational damage. This capability is particularly crucial in regions where regulatory requirements are frequently updated or vary significantly between jurisdictions. The ability to provide audit trails and ensure transparency in pricing calculations further enhances the appeal of these solutions. As regulators continue to emphasize consumer protection and fair lending practices, the demand for robust, compliant mortgage pricing engines is expected to surge.

Furthermore, the growing complexity and diversity of mortgage products have made manual pricing unsustainable for most lenders. With multiple loan types, fluctuating interest rates, and borrower-specific criteria, the need for dynamic and flexible pricing tools has never been greater. Mortgage pricing engines enable lenders to quickly adapt to market changes, optimize pricing strategies, and offer tailored solutions to different customer segments. This flexibility not only improves profitability but also enhances customer satisfaction by offering more relevant and competitive loan options. As the mortgage landscape continues to evolve, the role of pricing engines in supporting innovation and agility will only increase.

From a regional perspective, North America continues to dominate the mortgage pricing engine market, accounting for the largest share in 2024. This leadership is underpinned by the region's advanced financial infrastructure, high adoption of digital technologies, and stringent regulatory environment. Europe follows closely, driven by increasing digitalization in banking and growing demand for efficient mortgage origination solutions. Meanwhile, the Asia Pacific region is emerging as a high-growth market, supported by rapid urbanization, expanding middle class, and rising homeownership rates. Latin America and the Middle East & Africa are also witnessing steady adoption, although market penetration remains lower compared to developed regions. As financial institutions worldwide seek to modernize their lending processes, regional dynamics will continue to shape the evolution of the global mortgage pricing engine market.

Component Analysis

The mortgage pricing engine market is segmented by component into software and services, each playing a vital role in the overall ecosystem. The software segment is the primary revenue generator, accounting for a significant portion of the market in 2024. Mortgage pricing engine software provides the core functionality required for real-time rate calculation, compliance checks, and integration with other banking systems. The demand for advanced software solutions is being driven by the need for seamless, automated work

United States Mortgage Adjustable Rate: Wk Ending: 5 Year: Margin data was reported at 2.770 % pa in 29 Nov 2018. This records an increase from the previous number of 2.760 % pa for 22 Nov 2018. United States Mortgage Adjustable Rate: Wk Ending: 5 Year: Margin data is updated weekly, averaging 2.740 % pa from Jan 2005 (Median) to 29 Nov 2018, with 726 observations. The data reached an all-time high of 2.800 % pa in 04 May 2006 and a record low of 0.740 % pa in 19 Apr 2012. United States Mortgage Adjustable Rate: Wk Ending: 5 Year: Margin 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.