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.

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.

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.

Switzerland Mortgage Rate: Fixed: by Maturity: 10 Years data was reported at 1.779 % pa in Sep 2018. This records an increase from the previous number of 1.697 % pa for Aug 2018. Switzerland Mortgage Rate: Fixed: by Maturity: 10 Years data is updated monthly, averaging 2.290 % pa from Jan 2008 (Median) to Sep 2018, with 129 observations. The data reached an all-time high of 4.700 % pa in Jun 2008 and a record low of 1.520 % pa in Sep 2016. Switzerland Mortgage Rate: Fixed: by Maturity: 10 Years data remains active status in CEIC and is reported by Swiss National Bank. The data is categorized under Global Database’s Switzerland – Table CH.M005: Mortgage Rates.

France Mortgage Rate: Avg: Consumer: Up to 1 Year data was reported at 3.780 % in Mar 2025. This records an increase from the previous number of 3.750 % for Feb 2025. France Mortgage Rate: Avg: Consumer: Up to 1 Year data is updated monthly, averaging 3.120 % from Jan 2003 (Median) to Mar 2025, with 267 observations. The data reached an all-time high of 5.380 % in Dec 2008 and a record low of 1.160 % in Feb 2022. France Mortgage Rate: Avg: Consumer: Up to 1 Year data remains active status in CEIC and is reported by Banque de France. The data is categorized under Global Database’s France – Table FR.M007: Mortgage Rate. http://www.banque-france.fr/gb/stat_conjoncture/series/statmon/html/statmon.htm [COVID-19-IMPACT]

Switzerland Mortgage Rate: Fixed: by Maturity: 5 Years data was reported at 1.252 % pa in Sep 2018. This records an increase from the previous number of 1.201 % pa for Aug 2018. Switzerland Mortgage Rate: Fixed: by Maturity: 5 Years data is updated monthly, averaging 1.580 % pa from Jan 2008 (Median) to Sep 2018, with 129 observations. The data reached an all-time high of 4.500 % pa in Jun 2008 and a record low of 1.170 % pa in May 2017. Switzerland Mortgage Rate: Fixed: by Maturity: 5 Years data remains active status in CEIC and is reported by Swiss National Bank. The data is categorized under Global Database’s Switzerland – Table CH.M005: Mortgage Rates.

Context

Have you ever wondered how lenders use various factors such as credit score, annual income, the loan amount approved, tenure, debt-to-income ratio etc. and select your interest rates?

The process, defined as ‘risk-based pricing’, uses a sophisticated algorithm that leverages different determining factors of a loan applicant. Selection of significant factors will help develop a prediction algorithm which can estimate loan interest rates based on clients’ information. On one hand, knowing the factors will help consumers and borrowers to increase their credit worthiness and place themselves in a better position to negotiate for getting a lower interest rate. On the other hand, this will help lending companies to get an immediate fixed interest rate estimation based on clients information. Here, your goal is to use a training dataset to predict the loan rate category (1 / 2 / 3) that will be assigned to each loan in our test set.

You can use any combination of the features in the dataset to make your loan rate category predictions. Some features will be easier to use than others.

Acknowledgements

https://datahack.analyticsvidhya.com/contest/janatahack-machine-learning-for-banking/True/#ProblemStatement

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.

Greece Lending Rate: Outstanding Amount (OA): Households: Mortgage Loans: Over 1 and Up to 5 Years data was reported at 4.556 % pa in Sep 2018. This records an increase from the previous number of 4.554 % pa for Aug 2018. Greece Lending Rate: Outstanding Amount (OA): Households: Mortgage Loans: Over 1 and Up to 5 Years data is updated monthly, averaging 4.574 % pa from Sep 2002 (Median) to Sep 2018, with 193 observations. The data reached an all-time high of 6.580 % pa in May 2003 and a record low of 3.353 % pa in Jul 2016. Greece Lending Rate: Outstanding Amount (OA): Households: Mortgage Loans: Over 1 and Up to 5 Years data remains active status in CEIC and is reported by Bank of Greece. The data is categorized under Global Database’s Greece – Table GR.M005: Lending Rates.

This hosted feature layer has been published in RI State Plane Feet NAD 83.The RI Neighborhood Stabilization Program (NSP) Mapping analysis was performed to assist the Office of Housing and Community Development in identifying target areas with both a Foreclosure Rate (Block Group Level) >=6.5% and a Subprime Loan percentage rate >= 1.4% (Zip Code Level). Based on these criteria the following communities were identified as containing such target areas: Central Falls, Cranston, Cumberland, East Providence, Johnston, North Providence, Pawtucket, Providence, Warwick, West Warwick, and Woonsocket. Federal funding, under the Housing and Economic Recovery Act of 2008 (HERA), Neighborhood Stabilization Program (NSP), totaling $19.6 will be expended in these NSP Target Areas to assist in the rehabilitation and redevelopment of abandoned and foreclosed homes, stabilizing communities.The State of Rhode Island distributes funds allocated, giving priority emphasis and consideration to those areas with the greatest need, including those areas with - 1) Highest percentage of home foreclosures; 2) Highest percentage of homes financed by subprime mortgage loans; and 3) Anticipated increases in rate of foreclosure. The RI Office of Housing and Community Development, with the assistance of Rhode Island Housing, utilized the following sources to meet the above requirements. 1) U.S. Department of Housing & Urban Development (HUD) developed foreclosure data to assist grantees in identification of Target Areas. The State utilized HUD's predictive foreclosure rates to identify those areas which are likely to face a significant rise in the rate of home foreclosures. HUD's methodology factored in Home Mortgage Disclosure Act, income, unemployment, and other information in its calculation. The results were analyzed and revealed a high level of consistency with other needs data available. 2) The State obtained subprime mortgage loan information from the Federal Reserve Bank of Boston. Though the data does not include all mortgages, and was only available at the zip code level rather than Census Tract, findings were generally consistent with other need categories. This data was joined to the Foreclosure dataset in order to select areas with both a Foreclosure Rate >=6.5% and a Subprime Loan Rate >=1.4%. 3) The State also obtained, from the Warren Group, actual local foreclosure transaction records. The Warren Group is a source for real estate and banking news and transaction data throughout New England. This entity has analyzed local deed records in assembling information presented. The data set was normalized due to potential limitations. An analysis revealed a high level of consistency with HUD-predictive foreclosure rates.

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.

Context

Financial institutions incur significant losses due to the default of vehicle loans. This has led to the tightening up of vehicle loan underwriting and increased vehicle loan rejection rates. The need for a better credit risk scoring model is also raised by these institutions. This warrants a study to estimate the determinants of vehicle loan default. A financial institution has hired you to accurately predict the probability of loanee/borrower defaulting on a vehicle loan in the first EMI (Equated Monthly Instalments) on the due date. Following Information regarding the loan and loanee are provided in the datasets: Loanee Information (Demographic data like age, Identity proof etc.) Loan Information (Disbursal details, loan to value ratio etc.) Bureau data & history (Bureau score, number of active accounts, the status of other loans, credit history etc.) Doing so will ensure that clients capable of repayment are not rejected and important determinants can be identified which can be further used for minimising the default rates.

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.

AGNC: Will Mortgage REIT Thrive in a Rising Rate Environment?

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

Small Business Administration Loan Guarantee (SBA)

1.5 Million Loan Guarantees Across 7(a) and 504 Programs

By Noah Brod [source]

About this dataset

The Small Business Administration (SBA) Loan Guarantee Data provides a comprehensive look at loans that were approved by the SBA from January 1, 1990 to December 31, 1999. This dataset offers insight into roughly 1.5 million approved loans, including details such as the loan amount, interest rate, project county, and more.

This data was collected as part of an FOIA request and is updated quarterly for up-to-date information. It should be noted that the SBA is not a direct lender but rather a guarantor of the loan which is made by either a bank or non-bank lender.

The dataset includes detailed fields such as AsOfDate, Program Type, Gross Approval Amounts and Initial Interest Rates; Fanchise Codes and County Information; Delivery Method and Status Reports; Congressional Districts involved in financing these loans; Jobs Supported as part of each loan; Billing Information related to ChargeOff Dates and Amounts; SBADistrict Office associated with individual loan approvals ;and more!

This unique pool of data can offer invaluable insights into the mechanisms behind small business lending throughout the nineties in America – showing how much has changed since then but also how some trends remain consistent over time. The Small Business Administration Loan Guarantee Data can help shine light on important topics such as demographic disparities among borrowers or regional differences between approving offices - increasing our understanding of American business practices overall!

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For more datasets, click here.

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Research Ideas

• Using NaicsCode, initialize a visual representation of the most popular types of businesses that receive SBA loan ensures to get a better sense of which industries are the biggest uses for this financing program.
• Calculating Loan Status data over a period of time to analyse trends in terms of loan defaults and how much money is disbursed vs charged off.
• Examining GrossApproval and SBAGuarterNeedApproval data to determine which zipcodes or states have received more funding from the SBA and apply this information in aid targeting certain areas as part of govermental stimulus packages during tough economic times

Acknowledgements

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

License

Unknown License - Please check the dataset description for more information.

Columns

File: 7a_504_FOIA%20Data%20Dictionary.csv

File: FOIA%20-%207(a)(FY1991-FY1999).csv | Column name | Description | |:--------------------------|:-------------------------------------------------------------| | AsOfDate | Date the data was last updated. (Date) | | Program | Type of loan program. (String) | | BorrName | Name of the borrower. (String) | | BorrStreet | Street address of the borrower. (String) | | BorrCity | City of the borrower. (String) | | BorrState | State of the borrower. (String) | | BorrZip | Zip code of the borrower. (String) | | BankName | Name of the bank. (String) | | BankStreet | Street address of the bank. (String) | | BankCity | City of the bank. (String) | | BankState | State of the bank. (String) | | BankZip | Zip code of the bank. (String) | | GrossApproval | Total amount of the loan approved. (Number) | | SBAGuaranteedApproval | Amount of the loan guaranteed by the SBA. (Number) | | ApprovalDate | Date the loan was approved. (Date) | | ApprovalFiscalYear | Fiscal year the loan was approved. (Number) | | FirstDisbursementDate | Date the loan was disbursed. (Date) | | DeliveryMethod | Method of delivery for the loan. (String) | | subpgmdesc | Description of the loan program. (String) ...

Switzerland Mortgage Rate: Fixed: by Maturity: 3 Years data was reported at 1.121 % pa in Sep 2018. This records an increase from the previous number of 1.109 % pa for Aug 2018. Switzerland Mortgage Rate: Fixed: by Maturity: 3 Years data is updated monthly, averaging 1.300 % pa from Jan 2008 (Median) to Sep 2018, with 129 observations. The data reached an all-time high of 4.380 % pa in Jun 2008 and a record low of 1.090 % pa in May 2017. Switzerland Mortgage Rate: Fixed: by Maturity: 3 Years data remains active status in CEIC and is reported by Swiss National Bank. The data is categorized under Global Database’s Switzerland – Table CH.M005: Mortgage Rates.

Switzerland Mortgage Rate: Fixed: by Maturity: 7 Years data was reported at 1.487 % pa in Sep 2018. This records an increase from the previous number of 1.412 % pa for Aug 2018. Switzerland Mortgage Rate: Fixed: by Maturity: 7 Years data is updated monthly, averaging 1.910 % pa from Jan 2008 (Median) to Sep 2018, with 129 observations. The data reached an all-time high of 4.610 % pa in Jun 2008 and a record low of 1.320 % pa in Sep 2016. Switzerland Mortgage Rate: Fixed: by Maturity: 7 Years data remains active status in CEIC and is reported by Swiss National Bank. The data is categorized under Global Database’s Switzerland – Table CH.M005: Mortgage Rates.

The benchmark interest rate in Sweden was last recorded at 2 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: 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.

Sources: OECD (2021), OECD Inter-Country Input-Output Database, https://oe.cd/icio; International Monetary Fund (IMF), Statistics Department Questionnaire; IMF staff calculations.Category: Climate FinanceData series:Carbon Footprint of Bank Loans (Based on emission intensities)Carbon Footprint of Bank Loans (Based on emission intensities - normalized)Carbon Footprint of Bank Loans (Based on emission multipliers)Carbon Footprint of Bank Loans (Based on emission multipliers - normalized)Metadata:For relevant literature see Guan, Rong, Haitao Zheng, Jie Hu, Qi Fang, and Ruoen Ren. 2017. “The Higher Carbon Intensity of Loans, the Higher Non-Performing Loan Ratio: The Case of China.” Sustainability 9 (4) (April 22): 667. https://dx.doi.org/10.3390/su9040667.Methodology:The IMF has developed the Carbon Footprint of Bank Loans (CFBL) indicator for selected countries. CFBL indicator requires (i) deposit takers’ domestic loans by industry data, and (ii) the estimation of carbon emission factors (CEFs) by industry.The IMF has conducted a data collection exercise to obtain deposit takers’ domestic loans by industry data. The CEFs are calculated based on (i) direct metric tons of carbon emissions from fuel consumption per million $US of output by country and industry (CO2 emission intensities), and (ii) direct and indirect carbon emissions from fuel consumption per million $US of output by country (CO2 emission multipliers). The output multipliers and carbon emission intensities for 66 countries and 45 industries are sourced from the OECD Input-Output Database. Direct and indirect carbon emission factors are calculated by multiplying the Leontief inverse (also known as input-output multipliers) from the OECD World Input-Output Table by the carbon emissions from fuel consumption intensities.CFBL indicator is obtained by multiplying domestic loans to a specific industry by their corresponding carbon emission factors, summing over all industries and dividing the final result by total domestic loans. For a limited number of countries, updated CFBL information until 2018 will be posted in due course. CFBL is an experimental indicator. The index requires a nuanced reading. For instance, a sharp increase in the share of a brown industry in the deposit takers’ loans portfolio may create a negative impact on this indicator in the short term, but longer term results could diverge significantly if these loans were allocated for transition to low carbon environment or for continuing unsustainable brown activities. The emission coefficients applied to loans related to the emissions of the industry and not the emissions resulting from the consumption of the goods the industry produces. Also, the estimation methodology has a number of limitations. First, class level ISIC data could be more appropriate for the CFBL estimation, as it offers more detailed information to evaluate carbon footprint by industry. However, carbon emission factors are not available at this granularity. Also, the ISIC structure is not fully aligned with the needs of climate finance.Second, the granularity of the deposit takers’ domestic loans by industry data availability is not fully consistent across jurisdictions. It is not possible to obtain the loans by industry data at the same level of granularity from all participating countries. Third, the country coverage is limited as carbon intensity factors are available for only 66 countries. Fourth, input-output multipliers have limiting assumptions. Input-output multipliers are static (i.e., assume that there is a fixed input structure and fixed ratios for production for each industry) and do not take into account supply-side constraints or budget constraints. Please see additional information in this link.