Home Mortgage Disclosure Act (HMDA) requires many FIs 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.

The Home Mortgage Disclosure Act (HMDA) database (Consumer Financial Protection Bureau, 2022) has compiled mortgage lending data since 1981, but the collection and dissemination methods have changed over time (Federal Financial Institutions Examination Council, 2018), creating barriers to conducting longitudinal analyses. This HMDA Longitudinal Dataset (HLD) organizes and standardizes information across different eras of HMDA data collection between 1981 and 2021, enabling such analysis. This collection contains two types of datasets: 1) HMDA aggregated data by census tract for each decade and 2) HMDA aggregated data by census tract for individual years. Items for analysis include borrower income values, mortgages by loan type (e.g., conventional, Federal Housing Administration (FHA), Veterans Affairs (VA), refinances), and mortgages by borrower race and gender.

The data layer was developed by the Research & Analytics Group of the Atlanta Regional Commission, using data from using Home Mortgage Disclosure Act (HMDA) data to show mortgage loan applications, originations, denials, and applicant income, for 2023, in the Atlanta Region.A Beginner's Guide to HMDA DataMore info at https://ffiec.cfpb.gov/

Nationwide HMDA data, 2018-2021. Cleaned to record only accepted mortgages for primary residence, owner-occupied, single-family dwellings. Source: https://ffiec.cfpb.gov/data-browser/data/2018?category=nationwide

Code to create dataset available at https://github.com/nkacher/HMDA_age

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)

From landing page:FHFA establishes annual single-family and multifamily housing goals for mortgages purchased by Fannie Mae and Freddie Mac. The Enterprise Housing Goals include separate categories for single-family mortgages on housing that is affordable to low-income and very low-income families, as well as refinanced mortgages for low-income borrowers. FHFA also establishes separate annual goals for multifamily housing. Loans that are eligible for housing goals credit are mortgages on owner-occupied housing with one to four units. The mortgages must be conventional, conforming mortgages, defined as mortgages that are not insured or guaranteed by the Federal Housing Administration or another government agency and with principal balances that do not exceed the conforming loan limits for Enterprise mortgages. This page provides data on Enterprise performance and activity related to the single-family housi​​ng goals. A full glossary of terms is provided below. Single-Family Enterprise Mortgage Acquisitions: Race and Ethnicity Data The new housing goals data tables provide insight on the racial and ethnic composition of loans acquired by the Enterprises that are eligible for housing goals credit. FHFA has provided the racial and ethnic distribution of the Enterprises' acquisitions across each of the current single-family housing goals categories. ​ Single-Family Housing Goal Loan Segments: State-Level Data FHFA is publishing state-level data for each single-family goal loan purchase and refinance segment. It is important to note that FHFA does not set state-level targets but only at the national level. These tables provide the Enterprises' share in each state along with the market share, as calculated by FHFA using the 'static' HMDA data for each year to determine Enterprise housing goals performance each year. It is important to note that HMDA state-level data are impacted by the number of HMDA-exempt reporters in each state. For more information on HMDA reporting requirements, visit the CFPB HMDA Reporting Requirements page.Low-Income Census Tracts, Minority Census Tracts and Designated Disaster Areas Data The Federal Housing Enterprises Financial Safety and Soundness Act of 1992 (Safety and Soundness Act) provides for the establishment of single-family and multifamily goals each year, including a single-family purchase money mortgage goal for families residing in low-income areas. The Safety and Soundness Act defines "low-income area" for the single-family low-income areas home purchase goal as: Census tracts or block numbering areas in which the median income does not exceed 80 percent of area median income (AMI). In addition, for the purposes of this goal, "families residing in low-income areas" also include: Families with income not greater than 100 percent of AMI who reside in minority census tracts. Families with income not greater than 100 percent of AMI who reside in designated disaster areas. ​A "minority census tract" is a census tract that has a minority population of at least 30 percent and a median income of less than 100 percent of the AMI. A "low-income census tract" is census tract in which the median income does not exceed 80 percent of the AMI. Designated disaster areas are identified by FHFA based on the three most recent years' declarations by the Federal Emergency Management Agency (FEMA), where individual assistance payments were authorized by FEMA. A map of census tracts identified as minority census tracts in 2024 can be ​found here. A map of census tracts identified as low-income census tracts in 2024 can be found here. ​Learn more about low-income census tracts, minority census tracts, and designated disaster areas.

Created for the 2023-2025 State of Black Los Angeles County (SBLA) interactive report. To learn more about this effort, please visit the report home page at https://ceo.lacounty.gov/ardi/sbla/. For more information about the purpose of this data, please contact CEO-ARDI. For more information about the configuration of this data, please contact ISD-Enterprise GIS. Table Name Indicator Name Universe Timeframe Source Race Notes Source URL

homeownership_pct % Homeownership Occupied Housing Units 2016-2020 American Community Survey - Table B25003B-I Race alone; White is Non-Hispanic White https://data.census.gov/cedsci/table?g=0500000US06037&tid=ACSDT5Y2020.B25003

renters_pct % Renters Occupied Housing Units 2016-2020 American Community Survey - Table B25003B-I Race alone; White is Non-Hispanic White https://data.census.gov/cedsci/table?g=0500000US06037&tid=ACSDT5Y2020.B25003

mean_home_value Mean Home Value Households 2021 Public Use Microdata Sample (PUMS) All races are Non-Hispanic LA County eGIS-Demography

accepted_mortgage_pct Accepted Mortgate Rate Mortgage Applications 2021 Home Mortgage Disclosure Act HMDA categories - https://files.consumerfinance.gov/f/documents/cfpb_reportable-hmda-data_regulatory-and-reporting-overview-reference-chart-2019.pdf https://ffiec.cfpb.gov/data-browser/data/2021

rent_burden_pct Rent Burdened Renter Households 2019 California Housing Partnership All races are Non-Hispanic https://chpc.net/housingneeds/?view=37.405074,-119.26758,5&county=California,Los+Angeles&group=housingneed&chart=shortfall|current,cost-burden|current,cost-burden-re|current,homelessness,historical-rents,vacancy,asking-rents|2022,budgets|2021,funding|current,state-funding,lihtc|2010:2021:historical,rhna-progress,multifamily-production

rent_burden_severe_pct Severely Rent Burdened Renter Households 2019 California Housing Partnership All races are Non-Hispanic https://chpc.net/housingneeds/?view=37.405074,-119.26758,5&county=California,Los+Angeles&group=housingneed&chart=shortfall|current,cost-burden|current,cost-burden-re|current,homelessness,historical-rents,vacancy,asking-rents|2022,budgets|2021,funding|current,state-funding,lihtc|2010:2021:historical,rhna-progress,multifamily-production

eviction_per_100_hh Eviction Rate Renter Households 2014-2017 The Eviction Lab at Princeton University

https://data-downloads.evictionlab.org/#data-for-analysis/

homeless_count Homeless Count Population excluding Long Beach, Glendale, and Pasadena 2022 LAHSA

https://www.lahsa.org/documents?id=6545-2022-greater-los-angeles-homeless-count-deck

homeless_homeless_pct % Homeless Population Population excluding Long Beach, Glendale, and Pasadena 2022 LAHSA

https://www.lahsa.org/documents?id=6545-2022-greater-los-angeles-homeless-count-deck

homeless_county_pct % County Population Population excluding Long Beach, Glendale, and Pasadena 2022 LAHSA

https://www.lahsa.org/documents?id=6545-2022-greater-los-angeles-homeless-count-deck

unable_pay_mortgage_rent% Delayed or Were Unable to Pay Mortgage or Rent in the past 2 Years Households 2018 LAC Health Survey https://www.publichealth.lacounty.gov/ha/HA_DATA_TRENDS.htm

homeless_ever% Who Reported Ever Being Homeless or Not Having Their Own Place to Live or Sleep in the past Five Years Adults 2018 LAC Health Survey https://www.publichealth.lacounty.gov/ha/HA_DATA_TRENDS.htm