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.

Historical Prime Rates and Related Significant Events 1956-Present

Understanding Lending and Economic Trends

By Brandon Gadoci [source]

About this dataset

This dataset looks back at the history of lending rates from 1956 to present and investigates the effects of significant historical events on prime lending rate. The data, which was sourced from trusted sources, provides an insight into how major political and economic developments have influenced the cost of borrowing in different countries. By examining which events had an impact on interest rates and by how much, this dataset could prove invaluable for researchers looking to understand historical financial trends or for investors trying to understand past market behaviour. Take a step back in time with this comprehensive collection of lending data – it could be the key to unlocking greater insights into our financial history!

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How to use the dataset

This dataset contains historical prime rates from 1956 to present, as well as significant events that may have affected the prime lending rate. With this data, you can analyze changes in the average majority prime rate charged by banks and any events that may have contributed to this change.

To get started with this dataset, you'll want to make sure you understand the columns it contains: Year: This is the year of the data point. (Integer)
Average Majority Prime Rate Charged By Banks: This is average prime rate charged by banks in the majority of he year for a given time period. (Float)
Significant Events: Significant events that may have impacted or shifted the Prime Lending Rate during a certain period or throughout history. (String)

You can then use this information to begin exploring and comparing periods where there were drastic shifts inside of one year within this data set as it provides an overall view intoprime lending during these different times periods along with what plausible external or internal factors could’ve caused them. To do so, you can use descriptive statistics such a means and medians, along with graphing tools such as line charts and scatter plots to observe any correlations between fluctuations inPrime Lending Rates and Significant Events taking place concurrently at different points in time throughout history over six decades §§ when both economic states seem prosperous or abysmal for comparison purposes so we can identify driving forces behind certain trends inside our data set

Research Ideas

• Create a timeline visualization of major prime rate events in the US to show the influence of various political and economic factors on interest rates.
• Superimpose this data over monthly trends of mortgage and auto loan interest rates to illustrate the impact that movements in the prime lending rate have on consumer borrowing.
• Determine which banks currently offer loans with the lowest prime rates, by tracking historic trends against current market conditions for lenders

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: historical_prime rate.csv | Column name | Description | |:-------------------------------------------------|:---------------------------------------------------------------------------| | Year | Year of the average majority prime rate charged by banks. (Integer) | | Average majority prime rate charged by banks | The average majority prime rate charged by banks in a given year. (Float) | | Significant Events | Significant events that may have had an effect on the prime rate. (String) |

Acknowledgements

If you use this dataset in your research, please cr...

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.

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.

UK Mortgage Rates

Mortgage products in the united kingdom

By Jeff [source]

About this dataset

This dataset contains information on thousands of mortgage products available in the UK, including the interest rate, APR, revert rate, fees, and initial rate period. This data can be used to compare different mortgage products and find the best deal for your needs

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Featured Notebooks

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How to use the dataset

This dataset contains information on thousands of mortgage products available in the UK, including the interest rate, APR, revert rate, fees, and initial rate period.

To use this dataset, simply download it and then import it into your favorite spreadsheet program. You can then use the data to compare mortgage rates across different products and banks.

This dataset can be used to help you: - Compare mortgage rates from different banks - Find the best mortgage product for your needs - Understand how fees and other charges affect the overall cost of a mortgage

Research Ideas

• Analysing the different mortgage products available on the market
• Benchmarking against other products in order to get a competitive rate
• Finding products that have low fees and revert rates

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: UK_Mortgage_Rate.csv | Column name | Description | |:----------------------------|:----------------------------------------------------------------| | SKU | The product's SKU. (String) | | BANK_NAME | The name of the bank that offers the mortgage product. (String) | | MTG_PRODUCT_SUBTITLE | The subtitle of the mortgage product. (String) | | MTG_PRODUCT_TYPE_RAW | The raw product type of the mortgage product. (String) | | MTG_PRODUCT_YEARS | The number of years of the mortgage product. (Integer) | | MTG_INITIAL_RATE_PCT | The initial rate percentage of the mortgage product. (Float) | | MTG_APR_PCT | The APR percentage of the mortgage product. (Float) | | MTG_REVERT_RATE | The revert rate of the mortgage product. (Float) | | MTG_FEES_TOTAL | The total fees of the mortgage product. (Float) | | MTG_INITIAL_RATE_MONTHS | The initial rate months of the mortgage product. (Integer) | | SCAN_DATE | The date that the mortgage product was scanned. (Date) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Jeff.

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.

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 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.

Indonesia Banking Survey: Loan Interest Rate: Whole Year Estimation: in USD: Investment data was reported at 6.566 % in Mar 2025. This records an increase from the previous number of 6.446 % for Dec 2024. Indonesia Banking Survey: Loan Interest Rate: Whole Year Estimation: in USD: Investment data is updated quarterly, averaging 6.330 % from Mar 2012 (Median) to Mar 2025, with 53 observations. The data reached an all-time high of 6.961 % in Sep 2023 and a record low of 4.454 % in Mar 2022. Indonesia Banking Survey: Loan Interest Rate: Whole Year Estimation: in USD: Investment data remains active status in CEIC and is reported by Bank Indonesia. The data is categorized under Indonesia Premium Database’s Business and Economic Survey – Table ID.SE003: Banking Survey: Interest Rate. [COVID-19-IMPACT]

The benchmark interest rate in Mexico was last recorded at 7.25 percent. This dataset provides - Mexico Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The interest rate set by the Federal Reserve is a crucial tool for promoting economic conditions that meet the mandate established by the United States Congress, which includes high employment, low and stable inflation, sustainable economic growth, and the moderation of long-term interest rates. The interest rates determined by the Fed directly influence the cost of credit, making financing either more accessible or more restrictive. When interest rates are low, there is a greater incentive for consumers to purchase homes through mortgages, finance automobiles, or undertake home renovations. Additionally, businesses are encouraged to invest in expanding their operations, whether by purchasing new equipment, modernizing facilities, or hiring more workers. Conversely, higher interest rates tend to curb such activity, discouraging borrowing and slowing economic expansion.

The dataset analyzed contains information on the economic conditions in the United States on a monthly basis since 1954, including the federal funds rate, which represents the percentage at which financial institutions trade reserves held at the Federal Reserve with each other in the interbank market overnight. This rate is determined by the market but is directly influenced by the Federal Reserve through open market operations to reach the established target. The Federal Open Market Committee (FOMC) meets eight times a year to determine the federal funds rate target, which has been defined within a range with upper and lower limits since December 2008.

Furthermore, real Gross Domestic Product (GDP) is calculated based on the seasonally adjusted quarterly rate of change in the economy, using chained 2009 dollars as a reference. The unemployment rate represents the seasonally adjusted percentage of the labor force that is unemployed. Meanwhile, the inflation rate is determined by the monthly change in the Consumer Price Index, excluding food and energy prices for a more stable analysis of core inflation.

The interest rate data was sourced from the Federal Reserve Bank of St. Louis' economic data portal, while GDP information was provided by the U.S. Bureau of Economic Analysis, and unemployment and inflation data were made available by the U.S. Bureau of Labor Statistics.

The analysis of this data helps to understand how economic growth, the unemployment rate, and inflation influence the Federal Reserve’s monetary policy decisions. Additionally, it allows for a study of the evolution of interest rate policies over time and raises the question of how predictable the Fed’s future decisions may be. Based on observed trends, it is possible to speculate whether the target range set in March 2017 will be maintained, lowered, or increased, considering the prevailing economic context and the challenges faced in conducting U.S. monetary policy.

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.

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]

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.

This table contains 38 series, with data starting from 1957 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (1 item: Canada), Rates (38 items: Bank rate; Chartered bank administered interest rates - prime business; Chartered bank - consumer loan rate; Forward premium or discount (-), United States dollars in Canada: 1 month; ...).

🏦 Synthetic Loan Approval Dataset

A Realistic, High-Quality Dataset for Credit Risk Modelling

🎯 Why This Dataset?

Most loan datasets on Kaggle have unrealistic patterns where:

1. ❌ Credit scores don't matter
2. ❌ Approval logic is backwards
3. ❌ Models learn nonsense patterns

Unlike most loan datasets available online, this one is built on real banking criteria from US and Canadian financial institutions. Drawing from 3 years of hands-on finance industry experience, the dataset incorporates realistic correlations and business logic that reflect how actual lending decisions are made. This makes it perfect for data scientists looking to build portfolio projects that showcase not just coding ability, but genuine understanding of credit risk modelling.

📊 Dataset Overview

🔑 Key Features

Identifier:

-Customer ID (unique identifier for each application)

Demographics:

-Age, Occupation Status, Years Employed

Financial Profile:

-Annual Income, Credit Score, Credit History Length -Savings/Assets, Current Debt

Credit Behaviour:

-Defaults on File, Delinquencies, Derogatory Marks

Loan Request:

-Product Type, Loan Intent, Loan Amount, Interest Rate

Calculated Ratios:

-Debt-to-Income, Loan-to-Income, Payment-to-Income

💡 What Makes This Dataset Special?

1️⃣ Real-World Approval Logic The dataset implements actual banking criteria: - DTI ratio > 50% = automatic rejection - Defaults on file = instant reject - Credit score bands match real lending thresholds - Employment verification for loans ≥$20K

2️⃣ Realistic Correlations - Higher income → Better credit scores - Older applicants → Longer credit history - Students → Lower income, special treatment for small loans - Loan intent affects approval (Education best, Debt Consolidation worst)

3️⃣ Product-Specific Rules - Credit Cards: More lenient, higher limits - Personal Loans: Standard criteria, up to $100K - Line of Credit: Capped at $50K, manual review for high amounts

4️⃣ Edge Cases Included - Young applicants (age 18) building first credit - Students with thin credit files - Self-employed with variable income - High debt-to-income ratios - Multiple delinquencies

🎓 Perfect For - Machine Learning Practice: Binary classification with real patterns - Credit Risk Modelling: Learn actual lending criteria - Portfolio Projects: Build impressive, explainable models - Feature Engineering: Rich dataset with meaningful relationships - Business Analytics: Understand financial decision-making

📈 Quick Stats

Approval Rates by Product - Credit Card: 60.4% more lenient) - Personal Loan: 46.9 (standard) - Line of Credit: 52.6% (moderate)

Loan Intent (Best → Worst Approval Odds) 1. Education (63% approved) 2. Personal (58% approved) 3. Medical/Home (52% approved) 4. Business (48% approved) 5. Debt Consolidation (40% approved)

Credit Score Distribution - Mean: 644 - Range: 300-850 - Realistic bell curve around 600-700

Income Distribution - Mean: $50,063 - Median: $41,608 - Range: $15K - $250K

🎯 Expected Model Performance

With proper feature engineering and tuning: - Accuracy: 75-85% - ROC-AUC: 0.80-0.90 - F1-Score: 0.75-0.85

Important: Feature importance should show: 1. Credit Score (most important) 2. Debt-to-Income Ratio 3. Delinquencies 4. Loan Amount 5. Income

If your model shows different patterns, something's wrong!

🏆 Use Cases & Projects

Beginner - Binary classification with XGBoost/Random Forest - EDA and visualization practice - Feature importance analysis

Intermediate - Custom threshold optimization (profit maximization) - Cost-sensitive learning (false positive vs false negative) - Ensemble methods and stacking

Advanced - Explainable AI (SHAP, LIME) - Fairness analysis across demographics - Production-ready API with FastAPI/Flask - Streamlit deployment with business rules

⚠️ Important Notes

This is SYNTHETIC Data - Generated based on real banking criteria - No real customer data was used - Safe for public sharing and portfolio use

Limitations - Simplified approval logic (real banks use 100+ factors) - No temporal component (no time series) - Single country/currency assumed (USD) - No external factors (economy, market conditions)

Educational Purpose This dataset is designed for: - Learning credit risk modeling - Portfolio projects - ML practice - Understanding lending criteria

NOT for: - Actual lending decisions - Financial advice - Production use without validation

🤝 Contributing

Found an issue? Have suggestions? - Open an issue on GitHub - Suggest i...

Monthly and long-term China Interest Rate data: historical series and analyst forecasts curated by FocusEconomics.

This comprehensive dataset encapsulates a wide array of information regarding home mortgage activities in Utah from 2018 to 2022. It includes detailed data points such as loan types, purposes, amounts, and applicant demographics. Key metrics like loan-to-value ratios, interest rates, and applicant credit scores offer deep insights into the housing loan market. Additionally, it covers varied loan characteristics, property values, and applicant details, reflecting the dynamics of Utah's mortgage landscape. This rich dataset is invaluable for analyzing trends, understanding market behaviors, and examining the impact of financial policies in Utah's real estate sector.