Graph and download economic data for Delinquency Rate on Single-Family Residential Mortgages, Booked in Domestic Offices, All Commercial Banks (DRSFRMACBS) from Q1 1991 to Q4 2024 about domestic offices, delinquencies, 1-unit structures, mortgage, family, residential, commercial, domestic, banks, depository institutions, rate, and USA.

Mortgage Application in the United States decreased by 2 percent in the week ending March 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.

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.

Single Family Refinance Loan Data 1999- 2020

  Single family refinance loan data that nan 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

Long term dataset showing the 30 year fixed rate mortgage average in the United States since 1971.

Abstract copyright UK Data Service and data collection copyright owner.The Opinions and Lifestyle Survey (formerly known as the ONS Opinions Survey or Omnibus) is an omnibus survey that began in 1990, collecting data on a range of subjects commissioned by both the ONS internally and external clients (limited to other government departments, charities, non-profit organisations and academia).Data are collected from one individual aged 16 or over, selected from each sampled private household. Personal data include data on the individual, their family, address, household, income and education, plus responses and opinions on a variety of subjects within commissioned modules. The questionnaire collects timely data for research and policy analysis evaluation on the social impacts of recent topics of national importance, such as the coronavirus (COVID-19) pandemic and the cost of living, on individuals and households in Great Britain. From April 2018 to November 2019, the design of the OPN changed from face-to-face to a mixed-mode design (online first with telephone interviewing where necessary). Mixed-mode collection allows respondents to complete the survey more flexibly and provides a more cost-effective service for customers. In March 2020, the OPN was adapted to become a weekly survey used to collect data on the social impacts of the coronavirus (COVID-19) pandemic on the lives of people of Great Britain. These data are held in the Secure Access study, SN 8635, ONS Opinions and Lifestyle Survey, Covid-19 Module, 2020-2022: Secure Access. From August 2021, as coronavirus (COVID-19) restrictions were lifting across Great Britain, the OPN moved to fortnightly data collection, sampling around 5,000 households in each survey wave to ensure the survey remains sustainable. The OPN has since expanded to include questions on other topics of national importance, such as health and the cost of living. For more information about the survey and its methodology, see the ONS OPN Quality and Methodology Information webpage.Secure Access Opinions and Lifestyle Survey dataOther Secure Access OPN data cover modules run at various points from 1997-2019, on Census religion (SN 8078), cervical cancer screening (SN 8080), contact after separation (SN 8089), contraception (SN 8095), disability (SNs 8680 and 8096), general lifestyle (SN 8092), illness and activity (SN 8094), and non-resident parental contact (SN 8093). See Opinions and Lifestyle Survey: Secure Access for details. Main Topics:Each month's questionnaire consists of two elements: core questions, covering demographic information, are asked each month together with non-core questions that vary from month to month. The non-core questions for this month were: Company Cars (Module 1a): questions about the number of company cars in the household; total mileage and total business mileage; age of car and value of car when new; engine size. Mortgage Arrears (Module 2): source of mortgage, if any; whether behind in payments, and if so reasons for falling behind. Also question on whether bought from a Right to Buy scheme. Investment (Module 7a): ownership of shares and income from shares, bank accounts and building society accounts. Overseas Transactions (Module 58): financial transactions (receipts or payments) made as a private individual in the past 12 months; value in pound sterling; currency of transaction; reasons for transaction. Youth Services (Module 76): young people aged 11-25 were asked about leisure time activities; whether belongs or goes to a youth club, youth centre, youth group or youth organisation, or takes part in any other youth service activity; whether has ever belonged to a youth organisation; types of groups belongs to and who runs them; how often attends; any voluntary organisations belongs to; type of youth project takes part in and who runs it; whether has taken part in running a youth organisation; attitudes toward the Youth Service; reasons for attending/not attending. GP Accidents (Module 78): accidents in previous three months that resulted in seeing a doctor or going to hospital; where accident happened; whether saw a GP or went straight to hospital. Arrears and Repossessions (Module 79): questions about mortgage arrears and repossessions or voluntary surrenders of accommodation as a result of falling behind with mortgage payments. Marital Status and Cohabitation (Module 90): marital status and marital history; reasons for getting married if living together before marrying; history of previous cohabitation relationships that did not lead to marriage. Buying With a Mortgage (Module 91): reasons for becoming an owner occupier; year present home was bought; purchase price and original amount borrowed; whether previously owned home; whether bought under right to buy scheme; whether re-mortgaged or extended amount borrowed; value of house now; mortgage repayments; assistance with mortgage interest from the Department of Social Security; mortgage arrears in past three years; whether has mortgage protection policy and if so whether has tried to draw on it in past three years; debts on loans, hire purchase or services; net income and sources of income of respondent and spouse; increase or decrease of income over last three years and reasons; whether has any difficulties in paying for housing at present. The data for module 90 are under embargo and are therefore not currently available.

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:

New Home Sales in the United States increased to 676 Thousand units in February from 664 Thousand units in January of 2025. This dataset provides the latest reported value for - United States New Home Sales - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Median price paid for residential property in England and Wales, by property type and administrative geographies. Annual data.

Find the original dataset here

Pandas EDA with Plotly using this dataset here

Paycheck Protection Program (PPP) Loan Data – Key Aspects

SBA Values Transparency, Protecting Taxpayer Funds, and Protecting Proprietary Information of Small Businesses

In releasing PPP loan data to the public, SBA is maintaining a balance between providing transparency to American taxpayers and protecting small businesses’ confidential business information, such as payroll, and personally identifiable information. Small businesses are the driving force of American economic stability and are essential to America’s economic rebound from the pandemic. SBA is committed to ensuring that any release of PPP loan data does not harm small businesses or their employees.

PPP Is A Delegated Loan Making Process

PPP loans are not made by SBA. PPP loans are made by lending institutions and then guaranteed by SBA. Accordingly, borrowers apply to lenders and self-certify that they are eligible for PPP loans. The self- certification includes a good faith certification that the borrower has economic need requiring the loan and a certification that the borrower has applied the affiliation rules and is a small business, among other certifications The lender then reviews the borrower’s application, and if all the paperwork is in order, approves the loan and submits it to SBA.

PPP Loan Data Is Not Indicative of Loan Forgiveness or Program Compliance

A small business or non-profit organization that is listed in the publicly released data has been approved for a PPP loan by a delegated lender. However, the lender’s approval does not reflect a determination by SBA that the borrower is eligible for a PPP loan or entitled to loan forgiveness. All PPP loans are subject to SBA review and all loans over $2 million will automatically be reviewed. The fact that a borrower is listed in the data as having a PPP loan does not mean that SBA has determined that the borrower complied with program rules or is eligible to receive a PPP loan and loan forgiveness. Further, a small business’s receipt of a PPP loan should not be interpreted as an endorsement of the small business’ business activity or business model.

Cancelled Loans Do Not Appear In The PPP Loan Data

The public PPP data includes only active loans. Loans that were cancelled for any reason are not included in the public data release.

PPP Loan Demographic Data Is Voluntarily Submitted

PPP loan data reflects the information borrowers provided to their lenders in applying for PPP loans. SBA can make no representations about the accuracy or completeness of any information that borrowers provided to their lenders. Not all borrowers provided all information. For example, approximately 75% of all PPP loans did not include any demographic information because that information was not provided by the borrowers. SBA is working to collect more demographic information from borrowers to better understand which small businesses are benefiting from PPP loans. The loan forgiveness application expressly requests demographic information for borrowers.

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).

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

The Housing and Economic Recovery Act of 2008 (HERA) requires the Federal Housing Finance Agency (FHFA) to submit an annual report to Congress on the collateral pledged to the FHLBanks, including an analysis of collateral by type and by Bank district.3 FHFA’s Report on Collateral Pledged to Federal Home Loan Banks provides the required information as well as additional analysis of data on the types and amounts of collateral pledged to the Banks to secure advances and other collateralized products offered by the Banks to their members. The information in this report uses data collected through a quarterly data collection conducted by FHFA’s Division of Federal Home Loan Bank Regulation (DBR).

The benchmark interest rate in China was last recorded at 3.10 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.

Existing Home Sales in the United States increased to 4260 Thousand in February from 4090 Thousand in January of 2025. This dataset provides the latest reported value for - United States Existing Home Sales - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Introduction

The dataset comprises over 10,000 chat conversations, each focusing on specific BFSI-related topics. Each conversation provides a detailed interaction between a call center agent and a customer, capturing real-life scenarios and language nuances.

Topic Diversity

The chat dataset covers a wide range of conversations on BFSI topics, ensuring that the dataset is comprehensive and relevant for training and fine-tuning models for various BFSI use cases. It offers diversity in terms of conversation topics, chat types, and outcomes, including both inbound and outbound chats with positive, neutral, and negative outcomes.

Language Variety & Nuances

The conversations in this dataset capture the diverse language styles and expressions prevalent in Swedish BFSI interactions. This diversity ensures the dataset accurately represents the language used by Swedish speakers in BFSI contexts.

The dataset encompasses a wide array of language elements, including:

This linguistic authenticity ensures that the dataset equips researchers and developers with a comprehensive understanding of the intricate language patterns, cultural references, and communication styles inherent to Swedish BFSI interactions.

Conversational Flow and Interaction Types

The dataset includes a broad range of conversations, from simple inquiries to detailed discussions, capturing the dynamic nature of BFSI customer-agent interactions.

Each of these conversations contains various aspects of conversation flow like:

This structured and varied conversational flow enables the creation of advanced NLP models that can effectively manage and respond to a wide range of customer service scenarios.

Data Format and Structure

These data depict the 117th Congressional Districts and their representatives for the United States. Congressional districts are the 435 areas from which members are elected to the U.S. House of Representatives. After the apportionment of congressional seats among the states, which is based on decennial census population counts, each state with multiple seats is responsible for establishing congressional districts for the purpose of electing representatives. Each congressional district is to be as equal in population to all other congressional districts in a state as practicable. The boundaries and numbers shown for the congressional districts are those specified in the state laws or court orders establishing the districts within each state.

Python script used to examine how the marketing of properties explains neighborhood racial and income change using historical public remarks in real estate listings from Multiple Listing Services (MLS) collected and curated by CoreLogic.The primary dataset used for this research consists of 158,253 geocoded real estate listings for single-family homes in Mecklenburg County, North Carolina between 2001 and 2020. The historical MLS data which include public remarks is proprietary and can be obtained through purchase agreement with CoreLogic. The MLS is not publicly available and only available for members of the National Association of Realtors. Public remarks for homes currently listed for sale can be collected from online real estate websites such as Zillow, Trulia, Realtor.com, Redfin, and others.Since we cannot share this data, users need to, before running the script provided here, run the script provided by Nilsson and Delmelle (2023) which can be accessed here: https://doi.org/10.6084/m9.figshare.20493012.v1. This in order to get a fabricated/mock dataset of classified listings called classes_mock.csv. The article associated with Nilsson and Delmelle's (2023) script can be accessed here: https://www.tandfonline.com/doi/abs/10.1080/13658816.2023.2209803The user can then run the code together with the data provided here to estimate the threshold models together with data derived from the publicly available HMDA data. To compile a historical data set of loan/application records (LAR) for the user's own study are, the user will need to download data from the following websites:https://ffiec.cfpb.gov/data-publication/snapshot-national-loan-level-dataset/2022 (2017-forward)https://www.ffiec.gov/hmda/hmdaproducts.htm (2007-2016)https://catalog.archives.gov/search-within/2456161?limit=20&levelOfDescription=fileUnit&sort=naId:asc (for data prior to 2007)

The benchmark interest rate in Sweden was last recorded at 2.25 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.