Mortgage Application in the United States increased by 0.20 percent in the week ending November 21 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.

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.

Mortgage Originations in the United States increased to 512.15 Billion USD in the third quarter of 2025 from 458.28 Billion USD in the second quarter of 2025. This dataset includes a chart with historical data for the United States Mortgage Originations.

Average Mortgage Size in the United States increased to 381.40 Thousand USD in October from 379.11 Thousand USD in September of 2025. This dataset includes a chart with historical data for the United States Average Mortgage Size.

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.

Most of the text in this description originally appeared on the Mapping Inequality Website. Robert K. Nelson, LaDale Winling, Richard Marciano, Nathan Connolly, et al., “Mapping Inequality,” American Panorama, ed. Robert K. Nelson and Edward L. Ayers, "HOLC staff members, using data and evaluations organized by local real estate professionals--lenders, developers, and real estate appraisers--in each city, assigned grades to residential neighborhoods that reflected their "mortgage security" that would then be visualized on color-coded maps. Neighborhoods receiving the highest grade of "A"--colored green on the maps--were deemed minimal risks for banks and other mortgage lenders when they were determining who should received loans and which areas in the city were safe investments. Those receiving the lowest grade of "D," colored red, were considered "hazardous." Conservative, responsible lenders, in HOLC judgment, would "refuse to make loans in these areas [or] only on a conservative basis." HOLC created area descriptions to help to organize the data they used to assign the grades. Among that information was the neighborhood's quality of housing, the recent history of sale and rent values, and, crucially, the racial and ethnic identity and class of residents that served as the basis of the neighborhood's grade. These maps and their accompanying documentation helped set the rules for nearly a century of real estate practice. " HOLC agents grading cities through this program largely "adopted a consistently white, elite standpoint or perspective. HOLC assumed and insisted that the residency of African Americans and immigrants, as well as working-class whites, compromised the values of homes and the security of mortgages. In this they followed the guidelines set forth by Frederick Babcock, the central figure in early twentieth-century real estate appraisal standards, in his Underwriting Manual: "The infiltration of inharmonious racial groups ... tend to lower the levels of land values and to lessen the desirability of residential areas." These grades were a tool for redlining: making it difficult or impossible for people in certain areas to access mortgage financing and thus become homeowners. Redlining directed both public and private capital to native-born white families and away from African American and immigrant families. As homeownership was arguably the most significant means of intergenerational wealth building in the United States in the twentieth century, these redlining practices from eight decades ago had long-term effects in creating wealth inequalities that we still see today. Mapping Inequality, we hope, will allow and encourage you to grapple with this history of government policies contributing to inequality." Data was copied from the Mapping Inequality Website for communities in Western Pennsylvania where data was available. These communities include Altoona, Erie, Johnstown, Pittsburgh, and New Castle. Data included original and georectified images, scans of the neighborhood descriptions, and digital map layers. Data here was downloaded on June 9, 2020.

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...

Context

The dataset is from the U.S. Small Business Administration (SBA)

The U.S. SBA was founded in 1953 on the principle of promoting and assisting small enterprises in the U.S. credit market (SBA Overview and History, US Small Business Administration (2015)). Small businesses have been a primary source of job creation in the United States; therefore, fostering small business formation and growth has social benefits by creating job opportunities and reducing unemployment.

There have been many success stories of start-ups receiving SBA loan guarantees such as FedEx and Apple Computer. However, there have also been stories of small businesses and/or start-ups that have defaulted on their SBA-guaranteed loans.

Content

Shape of the data: 899164 rows and 27 columns

Data Dictionary

Description of the first two digits of NAICS.

Acknowledgements

Original data set id from “Should This Loan be Approved or Denied?”: A Large Dataset with Class Assignment Guidelines. by: Min Li, Amy Mickel & Stanley Taylor

To link to this article: https://doi.org/10.1080/10691898.2018.1434342

Inspiration

Good luck with predictions!

This data set provides recent market information on corporate loan transactions (339 records based on 187 agreements) sourced from filings by US SEC Registrants in all non-financial industries for the First Quarter 2021 (Q1 2021). Of the 339 records there are 8 loan transactions with credit rating equal to or higher than A-, 55 records in the BBB letter category, 47 records in the BB letter category, 46 records in the B letter category, 11 records that are rated at less than B-, and 172 records that are not rated. These 339 corporate loan transactions span the 11 GICS Sectors as follows: Energy 12; Materials 38; Industrials 50; Consumer Discretionary 86; Consumer Staples 20; Healthcare 31; Financials (mainly REITS, excludes FIs) 50; Information Technology 14; Communication Services 8; Utilities 20; and Real Estate 10. The following categories of data are described as follows: 1) Filing Information (with 7 data fields) includes the Filing Company Name, Filing Date, Filing Form and Type of Agreement (credit agreement, amended and restated credit agreement, or an amendment to a credit agreement), and Filing Company’s Central Index Key (CIK), SIC Code and GICS Sector Name; 2) Borrower and Other Loan Participant Details (6 data fields) includes the Primary Borrower’s Name, the Primary Borrower’s country of incorporation, the name(s) of additional borrowers, the name(s) of guarantors, the relationship of the guarantor(s) to the primary borrower, the name(s) of the lead arranger(s)/lender(s); 3) Credit Rating Information (17 data fields) includes publicly available S&P credit ratings and Moody’s credit ratings, if available, as at the date of the credit agreement for Primary Borrower, Parent, Issue and the respective dates of the ratings; 4) Loan Characteristics (16 data fields) includes whether the loan is a revolver or term loan, senior secured or senior unsecured, or other asset class. Also includes data on the loan amount, currency, pricing notes, collateral type, and types of financial covenants; and 5) Loan Pricing Details (11 data fields) includes the type of reference interest rate (e.g., LIBOR), the lending margin (or the fixed rate), commitment fee, annual (facility) fee, and other types of fees, as well as notes and other information related to the pricing. This data/information would assist any company’s finance & treasury department in negotiating pricing with banks/lenders or a multinational corporation’s international tax department to price its intercompany loans for transfer pricing purposes. CUFTanalytics has a database of over 14,000 records from corporate loan transactions from January 2009 to present day.

Note: IDs starting with IBRDB and IBRDG are "Guarantees" and the rest are "Loans". The loan types are as follows: NPL (Non-Pool Loan), CPL (Currency Pool Loan), SCPD (Single Currency Pool Loan - US), SCPM (Single Currency Pool Loan - EU), SCPY (Single Currency Pool Loan - YE), SCL (Single Currency Loan), FSL (Fixed Spread Loan), BLNR (IBRD Regular B-Loans), GURB (IBRD Guarantees), BLNC (IBRD Contingency B Loan), and GUBF (IBRD Guarantee Facility). The International Bank for Reconstruction and Development (IBRD) loans are public and publicly guaranteed debt extended by the World Bank Group. IBRD loans are made to, or guaranteed by, countries / economies that are members of IBRD. IBRD may also make loans to IFC. IBRD lends at market rates. Data are in U.S. dollars calculated using historical rates. This dataset contains the latest available snapshot of the Statement of Loans. The World Bank complies with all sanctions applicable to World Bank transactions.

Multifamily Portfolio datasets (section 8 contracts) - The information has been compiled from multiple data sources within FHA or its contractors. HUD oversees more than 22,000 privately owned multifamily properties, and more than 1.4 million assisted housing units. These homes were originally financed with FHA-insured or Direct Loans and many are supported with Section 8 or other rental assistance contracts. Our existing stock of affordable rental housing is a critical resource for seniors and families who otherwise would not have access to safe, decent places to call home.

Co-Branded Credit Card Defaults

Analyzing Application and Credit History Data

By Amit Kishore [source]

About this dataset

This dataset provides insights into the predictability of co-branded credit card default in a retail network of a company. With over [x] columns of data, this dataset contains information ranging from applicants' demographics and credit scores to their limits and payment history. This comprehensive dataset was constructed with the goal of understanding how demographic factors influence credit risk and ultimately, co-branded credit card default rates. From age to income, marital status to educational background, each variable is used to create an understanding of the risks associated with applicants taking out co-branded cards in the retail network. Additionally, get an inside look at current trends in loan application behavior — see how often customers use loan or have applied for new cards over set time intervals — as well as monthly payments and query history. Use this unique dataset to develop an improved model for predicting credit card default that could help financial institutions assess potential cusotmers more accuracyly!

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How to use the dataset

This dataset aims to help predict co-branded credit card defaults in retail networks by providing a variety of information about the applicants. The dataset includes information such as age, gender, marital status, employment status, education level, monthly income and expenses, credit history length, number of loans and credit cards owned by the applicant, number of times they applied for loan/credit card inquiries and how many times they used each loan/credit card in the last months.
- In order to use this dataset effectively to predict co-branded credit card default rates in a retail network it is important to understand the data and how it's related each other. It is also important to consider any external factors that can influence an individual's likelihood of defaulting on a loan.
- The first step is to look at the descriptive statistics for each column so that we can get some idea as to what kind of values are seen most often among our data points and if there are any outliers present. This will give us an idea about which features may be most relevant when predicting defaults or if our model may need more contextual information from outside sources like socio-economic or political factors.
- Once we have identified any relevant features from our descriptive statistics analysis we'll then want to start exploring different ways these variables are related with one another and what kind of relationship these variables have with regards to defaults (both positively correlated/directly increase default risk plus negatively correlated/directly decrease default risk). This can be done through simple pair plots which show distribution and correlations between two given columns or triangular heatmaps which allow us explore correlations among multiple columns at once. Building upon these relationships further allows us then determine possible causes behind the observed correlations between different variable groups – allowing us get even more insight into why certain individuals are more likely than others be defaulters on their co-branded cards (whether it because they simply had bad luck or because there were larger systematic factors playing out).
- Having identified all relevant features from this data exploration process along with any external “background” data points - we finally move into constructing our machine learning models using appropriate algorithms suitable for predicting probability outcomes such as SVM or XGBoost tree ensembles etc.. When building out your ML model you’ll want ensure that all parameters necessary for accurate predictions have been included before deploying them on production systems so as not compromise neither customer privacy nor product quality standards set by regulatory authorities governing such models across countries globally

Research Ideas

• Using the given dataset to create a predictive model that can be used to identify customers at risk of defaulting on their co-branded credit cards. This could help determine which customers should be offered special incentives or strategies in order to reduce their risk of defaulting.
• Using the given dataset to create a financial health recommendation engine that analyzes customer’s existing credit cards and recommends other ways they can improve their financial situation (e.g., balance transfers, better rewards programs, etc.).
• Extracting insights from the data by...

Bank Lending Rate in the United States decreased to 7 percent in October from 7.25 percent in September of 2025. This dataset provides - United States Average Monthly Prime Lending Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Monthly foreclosures in Connecticut by county, 2008 through the present. Data updated monthly by the Connecticut Housing Finance Authority and tracked in the following dashboard: https://www.chfa.org/about-us/ct-monthly-housing-market-dashboard/.

CHFA has stopped maintaining the dashboard and associated datasets, and this dataset will no longer be updated as of 2022.

Monthly single-family home sales in Connecticut, 2001 through the present. Data updated monthly by the Connecticut Housing Finance Authority and tracked in the following dashboard: https://www.chfa.org/about-us/ct-monthly-housing-market-dashboard/.

CHFA has stopped maintaining the dashboard and associated datasets, and this dataset will no longer be updated as of 2022.

Housing Index in the United States decreased to 435.40 points in September from 435.60 points in August of 2025. This dataset provides the latest reported value for - United States House Price Index MoM Change - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Graph and download economic data for All-Transactions House Price Index for Connecticut (CTSTHPI) from Q1 1975 to Q3 2025 about CT, appraisers, HPI, housing, price index, indexes, price, and USA.

External Debt in Pakistan increased to 134971 USD Million in the second quarter of 2025 from 130179 USD Million in the first quarter of 2025. This dataset provides - Pakistan External Debt - actual values, historical data, forecast, chart, statistics, economic calendar and news.