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.

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Overview:

Explore the dataset and potentially gain valuable insight into your data science project through interesting features. The dataset was developed for a portfolio optimization graduate project I was working on. The goal was to the monetize risk of company deleveraging by associated with changes in economic data. Applications of the dataset may include. To see the data in action visit my analytics page. Analytics Page & Dashboard and to access all 295,000+ records click here.

• Mortgage-Backed Securities
• Geographic Business Investment
• Real Estate Analysis

For any questions, you may reach us at research_development@goldenoakresearch.com. For immediate assistance, you may reach me on at 585-626-2965. Please Note: the number is my personal number and email is preferred

Statistical Themes:

Note: in total there are 75 fields the following are just themes the fields fall under Home Owner Costs: Sum of utilities, property taxes.

• Second Mortgage: Households with a second mortgage statistics.
• Home Equity Loan: Households with a Home equity Loan statistics.
• Debt: Households with any type of debt statistics.
• Mortgage Costs: Statistics regarding mortgage payments, home equity loans, utilities and property taxes
• Home Owner Costs: Sum of utilities, property taxes statistics
• Gross Rent: Contract rent plus the estimated average monthly cost of utility features
• Gross Rent as Percent of Income Gross rent as the percent of income very interesting
• High school Graduation: High school graduation statistics.
• Population Demographics: Population demographic statistics.
• Age Demographics: Age demographic statistics.
• Household Income: Total income of people residing in the household.
• Family Income: Total income of people related to the householder.

Sources, if you wish to get the data your self :)

2012-2016 ACS 5-Year Documentation was provided by the U.S. Census Reports. Retrieved May 2, 2018, from

• Census estimate, error, and location data
• Census location information

Access All 325,258 Location of Our Most Complete Database Ever:

Providing you the potential to monetize risk and optimize your investment portfolio through quality economic features at unbeatable price. Access all 295,000+ records on an incredibly small scale, see links below for more details:

• Full Dataset: View Full Dataset
• Real Estate Research: View Research

HMDA requires many Financial Institutions (FI)s to maintain, report, and publicly disclose information about applications for and originations of mortgage loans. HMDA s purposes are to provide the public and public officials with sufficient information to enable them to determine whether institutions are serving the housing needs of the communities and neighborhoods in which they are located, to assist public officials in distributing public sector investments in a manner designed to improve the private investment environment, and to assist in identifying possible discriminatory lending patterns and enforcing antidiscrimination statutes. This publicly-available data asset contains HMDA data collected in or after 2017 and has been modified to protect the privacy of individuals whose information is present in the dataset.

Context

This data set includes customers who have paid off their loans, who have been past due and put into collection without paying back their loan and interests, and who have paid off only after they were put in collection. The financial product is a bullet loan that customers should pay off all of their loan debt in just one time by the end of the term, instead of an installment schedule. Of course, they could pay off earlier than their pay schedule.

Content

Loan_id A unique loan number assigned to each loan customers

Loan_status Whether a loan is paid off, in collection, new customer yet to payoff, or paid off after the collection efforts

Principal Basic principal loan amount at the origination

terms Can be weekly (7 days), biweekly, and monthly payoff schedule

Effective_date When the loan got originated and took effects

Due_date Since it’s one-time payoff schedule, each loan has one single due date

Paidoff_time The actual time a customer pays off the loan

Pastdue_days How many days a loan has been past due

Age, education, gender A customer’s basic demographic information

Single Family Purchase Loan Data 1999- 2020

  Single family purchase loan data that has had the PII removed; comprised of monthly snapshots Jan 2019-may 2020 About HUD Housing and Funding Allocation Data: Links to several different HUD datasets including CARES Act and Indian Housing Block Grant FY2020 allocations, and monthly single- and multi-family 2020 loan data with the PII removed. Other datasets contain sheltered/unsheltered/total homeless data by demographic, HUD Continuum of Care area, and State, shelter capacity by state yearly from 2007 to 2019, and American Community Survey 2014-2018 5-year county level estimates for median rent value.
  Geography Level: State, City, County, ZipItem Vintage: 1999-2020
  Update Frequency: N/AAgency: HUD (Multiple)Available File Type: Excel with PDF Supplement (All links go to same FHA dataset) 

  Return to Other Federal Agency Datasets Page

Company Information:

The data set is based upon https://www.kaggle.com/prateikmahendra/loan-data"> Lending Club Information . - TheIrish Dummy Banks is a peer to peer lending bank based in the ireland, in which bank provide funds for potential borrowers and bank earn a profit depending on the risk they take (the borrowers credit score). Irish Fake bank provides loan to their loyal customers. The complete data set is borrowed from Lending Club For more basic information about the company please check out the wikipedia article about the company. This dataset is copied and clean from kaggle but it has been changed. The any kind of similarity is just for learning purposes. I dont have any intention for Plagiarism I just like to be clear myself.

<a src="https://en.wikipedia.org/wiki/Lending_Club"> Lending Club Information </a>

The central idea and coding is abstract from Kevin mark ham youtube video series, Introduction to machine learning with scikit-learn video series. You can find link under resources section.

Data Description

• LoanStatNew Description

• addr_state The state provided by the borrower in the loan application

• annual_inc The self-reported annual income provided by the borrower during registration.

• annual_inc_joint The combined self-reported annual income provided by the co-borrowers during registration

• application_type Indicates whether the loan is an individual application or a joint application with two co-borrowers

• collection_recovery_fee post charge off collection fee

• collections_12_mths_ex_med Number of collections in 12 months excluding medical collections

• delinq_2yrs The number of 30+ days past-due incidences of delinquency in the borrower's credit file for the past 2 years

• desc Loan description provided by the borrower

• dti A ratio calculated using the borrower’s total monthly debt payments on the total debt obligations, - - - excluding mortgage and the requested LC loan, divided by the borrower’s self-reported monthly income.

• dti_joint A ratio calculated using the co-borrowers' total monthly payments on the total debt obligations, - excluding mortgages and the requested LC loan, divided by the co-borrowers' combined self-reported monthly income

• earliest_cr_line The month the borrower's earliest reported credit line was opened

• emp_length Employment length in years. Possible values are between 0 and 10 where 0 means less than one year

• and 10 means ten or more years.

• emp_title The job title supplied by the Borrower when applying for the loan.*

• fico_range_high The upper boundary range the borrower’s FICO at loan origination belongs to.

• fico_range_low The lower boundary range the borrower’s FICO at loan origination belongs to.

• funded_amnt The total amount committed to that loan at that point in time.

• funded_amnt_inv The total amount committed by investors for that loan at that point in time.

• grade LC assigned loan grade

• home_ownership The home ownership status provided by the borrower during registration. Our values are: RENT, OWN, MORTGAGE, OTHER.

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.

Content

More details about each file are in the individual file descriptions.

Context

This is a dataset from the Federal Reserve hosted by the Federal Reserve Economic Database (FRED). FRED has a data platform found here and they update their information according to the frequency that the data updates. Explore the Federal Reserve using Kaggle and all of the data sources available through the Federal Reserve organization page!

• Update Frequency: This dataset is updated daily.

Acknowledgements

This dataset is maintained using FRED's API and Kaggle's API.

Cover photo by Andrew Neel on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

15 Year Mortgage Rate in the United States increased to 5.86 percent in July 10 from 5.80 percent in the previous week. This dataset includes a chart with historical data for the United States 15 Year Mortgage Rate.

The G.19 Statistical Release, Consumer Credit, reports outstanding credit extended to individuals for household, family, and other personal expenditures, excluding loans secured by real estate. Total consumer credit comprises two major types: revolving and nonrevolving. Revolving credit plans may be unsecured or secured by collateral and allow a consumer to borrow up to a prearranged limit and repay the debt in one or more installments. Credit card loans comprise most of revolving consumer credit measured in the G.19, but other types, such as prearranged overdraft plans, are also included. Nonrevolving credit is closed-end credit extended to consumers that is repaid on a prearranged repayment schedule and may be secured or unsecured. To borrow additional funds, the consumer must enter into an additional contract with the lender. Consumer motor vehicle and education loans comprise the majority of nonrevolving credit, but other loan types, such as boat loans, recreational vehicle loans, and personal loans, are also included. This statistical release is designated by OMB as a Principal Federal Economic Indicator (PFEI).

Analysis of ‘Loan Data’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/itssuru/loan-data on 28 January 2022.

--- Dataset description provided by original source is as follows ---

About the data and what to do…

publicly available data from LendingClub.com. Lending Club connects people who need money (borrowers) with people who have money (investors). Hopefully, as an investor you would want to invest in people who showed a profile of having a high probability of paying you back.

We will use lending data from 2007-2010 and be trying to classify and predict whether or not the borrower paid back their loan in full. You can download the data from here.

Here are what the columns represent:

credit.policy: 1 if the customer meets the credit underwriting criteria of LendingClub.com, and 0 otherwise. purpose: The purpose of the loan (takes values "credit_card", "debt_consolidation", "educational", "major_purchase", "small_business", and "all_other"). int.rate: The interest rate of the loan, as a proportion (a rate of 11% would be stored as 0.11). Borrowers judged by LendingClub.com to be more risky are assigned higher interest rates. installment: The monthly installments owed by the borrower if the loan is funded. log.annual.inc: The natural log of the self-reported annual income of the borrower. dti: The debt-to-income ratio of the borrower (amount of debt divided by annual income). fico: The FICO credit score of the borrower. days.with.cr.line: The number of days the borrower has had a credit line. revol.bal: The borrower's revolving balance (amount unpaid at the end of the credit card billing cycle). revol.util: The borrower's revolving line utilization rate (the amount of the credit line used relative to total credit available). inq.last.6mths: The borrower's number of inquiries by creditors in the last 6 months. delinq.2yrs: The number of times the borrower had been 30+ days past due on a payment in the past 2 years. pub.rec: The borrower's number of derogatory public records (bankruptcy filings, tax liens, or judgments).

--- Original source retains full ownership of the source dataset ---

Context

There's a story behind every dataset and here's your opportunity to share yours.

Content

What's inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too.

Acknowledgements

We wouldn't be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

Monthly Multifamily Insured Loan Data 2020

  Multifamily insured loan data that has had the PII removed, as of August 31, 2020 (updated monthly) About HUD Housing and Funding Allocation Data: Links to several different HUD datasets including CARES Act and Indian Housing Block Grant FY2020 allocations, and monthly single- and multi-family 2020 loan data with the PII removed. Other datasets contain sheltered/unsheltered/total homeless data by demographic, HUD Continuum of Care area, and State, shelter capacity by state yearly from 2007 to 2019, and American Community Survey 2014-2018 5-year county level estimates for median rent value.
  Geography Level: State, City, ZipItem Vintage: 2020
  Update Frequency: N/AAgency: HUD (Multiple)Available File Type: Excel with PDF Supplement (All links go to same FHA dataset) 

  Return to Other Federal Agency Datasets Page

Home Loans in the United Kingdom increased to 2054 GBP Million in May from -776 GBP Million in April of 2025. This dataset provides - United Kingdom Mortgage Lending- actual values, historical data, forecast, chart, statistics, economic calendar and news.

New Mortgage Lending Statistics. Published by Central Bank of Ireland. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).These data relate to new mortgage lending on residential property in Ireland on an annual basis. Data relates to those institutions [(banks and non-bank mortgage lenders)] who issue at least €50 million of new mortgage lending in a six-month period and are subsequently required to submit loan-level information to the Central Bank for the purposes of the macroprudential mortgage measures. The value and volume of new lending is provided, by borrower type, along with the distribution of lending by Loan-to-value and Loan-to-income ratio. Average characteristics are also provided. These data do not constitute official statistics. These data are published to support transparency and understanding of market developments....

Monthly Multifamily Terminated Loan Data 2020

  Multifamily terminated loan data that has had the PII removed, as of August 31, 2020 (updated monthly) About HUD Housing and Funding Allocation Data: Links to several different HUD datasets including CARES Act and Indian Housing Block Grant FY2020 allocations, and monthly single- and multi-family 2020 loan data with the PII removed. Other datasets contain sheltered/unsheltered/total homeless data by demographic, HUD Continuum of Care area, and State, shelter capacity by state yearly from 2007 to 2019, and American Community Survey 2014-2018 5-year county level estimates for median rent value.
  Geography Level: State, City, ZipItem Vintage: 2020
  Update Frequency: N/AAgency: HUD (Multiple)Available File Type: Excel with PDF Supplement (All links go to same FHA dataset) 

  Return to Other Federal Agency Datasets Page

There is a newer and more authoritative version of this layer here! It is owned by the University of Richmond's Digital Scholarship Lab and contains data on many more cities.The Home Owners' Loan Corporation (HOLC) was created in the New Deal Era and trained many home appraisers in the 1930s. The HOLC created a neighborhood ranking system infamously known today as redlining. Local real estate developers and appraisers in over 200 cities assigned grades to residential neighborhoods. These maps and neighborhood ratings set the rules for decades of real estate practices. The grades ranged from A to D. A was traditionally colored in green, B was traditionally colored in blue, C was traditionally colored in yellow, and D was traditionally colored in red. A (Best): Always upper- or upper-middle-class White neighborhoods that HOLC defined as posing minimal risk for banks and other mortgage lenders, as they were "ethnically homogeneous" and had room to be further developed.B (Still Desirable): Generally nearly or completely White, U.S. -born neighborhoods that HOLC defined as "still desirable" and sound investments for mortgage lenders.C (Declining): Areas where the residents were often working-class and/or first or second generation immigrants from Europe. These areas often lacked utilities and were characterized by older building stock.D (Hazardous): Areas here often received this grade because they were "infiltrated" with "undesirable populations" such as Jewish, Asian, Mexican, and Black families. These areas were more likely to be close to industrial areas and to have older housing.Banks received federal backing to lend money for mortgages based on these grades. Many banks simply refused to lend to areas with the lowest grade, making it impossible for people in many areas to become homeowners. While this type of neighborhood classification is no longer legal thanks to the Fair Housing Act of 1968 (which was passed in large part due to the activism and work of the NAACP and other groups), the effects of disinvestment due to redlining are still observable today. For example, the health and wealth of neighborhoods in Chicago today can be traced back to redlining (Chicago Tribune). In addition to formerly redlined neighborhoods having fewer resources such as quality schools, access to fresh foods, and health care facilities, new research from the Science Museum of Virginia finds a link between urban heat islands and redlining (Hoffman, et al., 2020). This layer comes out of that work, specifically from University of Richmond's Digital Scholarship Lab. More information on sources and digitization process can be found on the Data and Download and About pages. This layer includes 7,148 neighborhoods spanning 143 cities across the continental United States. NOTE: As mentioned above, over 200 cities were redlined and therefore this is not a complete dataset of every city that experienced redlining by the HOLC in the 1930s. More cities are available in this feature layer from University of Richmond.Cities included in this layerAlabama: Birmingham, Mobile, MontgomeryCalifornia: Fresno, Los Angeles, Sacramento, San Diego, San Francisco, San Jose, StocktonColorado: DenverConnecticut: East Hartford, New Britain, New Haven, StamfordFlorida: Jacksonville, Miami, St. Petersburg, TampaGeorgia: Atlanta, Augusta, Chattanooga, Columbus, MaconIllinois: Aurora, Chicago, Decatur, Joliet, GaryIndiana: Evansville, Fort Wayne, Indianapolis, Gary, Muncie, South Bend, Terre HauteKansas: Greater Kansas City, WichitaKentucky: Lexington, LouisvilleLouisiana: New OrleansMassachusetts: Arlington, Belmont, Boston, Braintree, Brockton, Brookline, Cambridge, Chelsea, Dedham, Everett, Haverhill, Holyoke Chicopee, Lexington, Malden, Medford, Melrose, Milton, Needham, Newton, Quincy, Revere, Saugus, Somerville, Waltham, Watertown, Winchester, WinthropMaryland: BaltimoreMichigan: Battle Creek, Bay City, Detroit, Flint, Grand Rapids, Kalamazoo, Muskegon, Pontiac, Saginaw, ToledoMinnesota: Duluth, MinneapolisMissouri: Greater Kansas City, Springfield, St. Joseph, St. LouisNorth Carolina: Asheville, Charlotte, Durham, Greensboro, Winston SalemNew Hampshire: ManchesterNew Jersey: Atlantic City, Bergen Co., Camden, Essex County, Hudson County, TrentonNew York: Bronx, Brooklyn, Buffalo, Elmira, Binghamton/Johnson City, Lower Westchester Co., Manhattan, Niagara Falls, Poughkeepsie, Queens, Rochester, Staten Island, Syracuse, UticaOhio: Akron, Canton, Cleveland, Columbus, Dayton, Hamilton, Lima, Lorrain, Portsmouth, Springfield, Toledo, Warren, YoungstownOregon: PortlandPennsylvania: Altoona, Erie, Johnstown, New Castle, Philadelphia, PittsburghSouth Carolina: AugustaTennessee: Chattanooga, KnoxvilleTexas: DallasVirginia: Lynchburg, Norfolk, Richmond, RoanokeWashington: Seattle, Spokane, TacomaWisconsin: Kenosha, Milwaukee, Oshkosh, RacineWest Virginia: Charleston, WheelingAn example of a map produced by the HOLC of Philadelphia:

Content

This series has been discontinued and will no longer be updated. It was a duplicate of the following series, which will continue to be updated: https://fred.stlouisfed.org/series/WSHOMCB

The current face value of mortgage-backed obligations held by Federal Reserve Banks. These securities are guaranteed by Fannie Mae, Freddie Mac, or Ginnie Mae.

Context

This is a dataset from the Federal Reserve hosted by the Federal Reserve Economic Database (FRED). FRED has a data platform found here and they update their information according to the frequency that the data updates. Explore the Federal Reserve using Kaggle and all of the data sources available through the Federal Reserve organization page!

• Update Frequency: This dataset is updated daily.

• Observation Start: 2002-12-18

• Observation End : 2018-06-13

Acknowledgements

This dataset is maintained using FRED's API and Kaggle's API.

Cover photo by Rob Sarmiento on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

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.