Abstract (en): The Home Mortgage Disclosure Act (HMDA): Loan Application Register (LAR) and Transmittal Sheet (TS) Raw Data, 2011 contains information collected in calendar year 2010. The HMDA, enacted by Congress in 1975, requires most mortgage lenders located in metropolitan areas to report data about their housing-related lending activity. The HMDA data were collected from 7,923 lending institutions and cover approximately 16.3 million home purchase and home improvement loans and refinancings, including loan originations, loan purchases, and applications that were denied, incomplete, or withdrawn. The Private Mortgage Insurance Companies (PMIC) data refer to applications for mortgage insurance to insure home purchase mortgages and to insure mortgages to refinance existing obligations. Part 1, HMDA Transmittal Sheet (TS), and Part 4, PMIC Transmittal Sheet (TS), include information submitted by reporting institutions with the Loan Application Register (LAR), such as the reporting institution's name, address, and Tax ID. Part 2, HMDA Reporter Panel, and Part 5, PMIC Reporter Panel, contain information on all institutions that reported data for activity year 2010. Part 3, HMDA MSA Offices, and Part 6, PMIC MSA Offices, contain information on all metropolitan statistical areas in the data. Parts 7 through 799 contain HMDA and PMIC Loan Application Register (LAR) files at the national level, at the agency level, and by MSA/MD. With some exceptions, for each transaction the institution reported data about the loan (or application), such as the type and amount of the loan made (or applied for) and, in limited circumstances, its price, the disposition of the application, such as whether it was denied or resulted in an origination of a loan, the property to which the loan relates, such as its type (single-family versus multi-family), and location (including the census tract), the sale of the loan, if it was sold, and the applicant's and co-applicant's ethnicity, race, sex, and income. The data are not weighted and do not contain any weight variables. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Created variable labels and/or value labels.; Checked for undocumented or out-of-range codes.. Home purchase and home improvement loans and refinancings (or applications) lended or insured by financial institutions in the United States that were required to report HMDA data in 2011. Smallest Geographic Unit: city HMDA data were collected from 7,923 depository and nondepository institutions that were required to report HMDA data if they met the law's criteria for coverage. Generally, whether a lender is covered by HMDA depended on the lender's asset size, its location, and whether it is in the business of residential mortgage lending. PMIC data were collected from eight mortgage insurance companies that insured home purchase mortgages and to insure mortgages to refinance existing obligations. For more information about how respondents reported, please refer to A Guide to HMDA Reporting. 2016-07-25 The study title and collection dates have been revised to reflect the 2010 activity year, with data reported in 2011. Filesets 1 through 6 have also been replaced to correct the study year. For datasets 7 through 799, ICPSR is releasing the original deposited data files in the condition they were received, along with SPSS, Stata, and SAS setup files. Additional information about the HMDA can be found at the Home Mortgage Disclosure Act Web site. A glossary of terms used in the data can be found at Home Mortgage Disclosure Act Glossary.

The Pre-1990 HMDA Aggregation Data were prepared annually during this period by the FFIEC on behalf of institutions reporting HMDA data. The Aggregation Data consists of home purchase and home improvement loans that a depository institution originated or purchased during each calendar year. The collected HMDA data were individually aggregated up to the tract level by the reporting depository institution and submitted accordingly to the FFIEC. Individual records are the summary of loan activity for the specified respondent for the indicated census tract except when the census tract numbers were either 888888 or 999999. The 888888 tract records are the sum of all loan activity by the reporter outside of the MSA being reported, but not appearing in any other MSA report. The 999999 tract records are the consolidated county summary data for loans made in untracted counties or counties with 1980 total population less than 30,000. The 1988 and 1989 Aggregation Data files include aggregated data from nondepository institutions, specifically mortgage banking subsidiaries of bank holding companies.

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.

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 purpose of the SEPHER data set is to allow for testing, assessing and generating new analysis and metrics that can address inequalities and climate injustice. The data set was created by Tedesco, M., C. Hultquist, S. E. Char, C. Constantinides, T. Galjanic, and A. D. Sinha.

SEPHER draws upon four major source datasets: CDC Social Vulnerability Index, FEMA National Risk Index, Home Mortgage Disclosure Act, and Evictions datasets. The data from these source datasets have been merged, cleaned, and standardized and all of the variables documented in the data dictionary.

CDC Social Vulnerability Index

CDC Social Vulnerability Index (SVI) dataset is a dataset prepared for the Centers for Disease Control and Prevention for the purpose of assessing the degree of social vulnerability of American communities to natural hazards and anthropogenic events. It contains data on 15 social factors taken or derived from Census reports as well as rankings of each tract based on these individual factors, groups of factors corresponding to four related themes (Socioeconomic, Household Composition & Disability, Minority Status & Language, and Housing Type & Transportation) and overall. The data is available for the years 2000, 2010, 2014, 2016, and 2018.

FEMA National Risk Index

The National Risk Index (NRI) dataset compiled by the Federal Emergency Management Agency (FEMA) consists of historic natural disaster data from across the United States at a tract-level. The dataset includes information about 18 natural disasters including earthquakes, tsunamis, wildfires, volcanic activity and many others. Each disaster is detailed out in terms of its frequency, historic impact, potential exposure, expected annual loss and associated risk. The dataset also includes some summary variables for each tract including the total expected loss in terms of building loss, human loss and agricultural loss, the population of the tract, and the area covered by the tract. It finally includes a few more features to characterize the population such as social vulnerability rating and community resilience.

Home Mortgage Disclosure Act

The Home Mortgage Disclosure Act (HMDA) dataset contains loan-level data for home mortgages including information on applications, denials, approvals, and institution purchases. It is managed and expanded annually by the Consumer Financial Protection Bureau based on the data collected from financial institutions. The dataset is used by public officials to make decisions and policies, uncover lending patterns and discrimination among mortgage applicants, and investigate if lenders are serving the housing needs of the communities. It covers the period from 2007 to 2017.

Evictions

The Evictions dataset is compiled and managed by the Eviction Lab at Princeton University and consists of court records related to eviction cases in the United States between 2000 and 2016. Its purpose is to estimate the prevalence of court-ordered evictions and compare eviction rates among states, counties, cities, and neighborhoods. Besides information on eviction filings and judgments, the dataset includes socioeconomic and real estate data for each tract including race/ethnic origin, household income, poverty rate, property value, median gross rent, rent burden, and others.

Abstract

The purpose of the SEPHER data set is to allow for testing, assessing and generating new analysis and metrics that can address inequalities and climate injustice. The data set was created by Tedesco, M., C. Hultquist, S. E. Char, C. Constantinides, T. Galjanic, and A. D. Sinha.

Methodology

SEPHER draws upon four major source datasets: CDC Social Vulnerability Index, FEMA National Risk Index, Home Mortgage Disclosure Act, and Evictions datasets. The data from these source datasets have been merged, cleaned, and standardized and all of the variables documented in the data dictionary.

CDC Social Vulnerability Index

CDC Social Vulnerability Index (SVI) dataset is a dataset prepared for the Centers for Disease Control and Prevention for the purpose of assessing the degree of social vulnerability of American communities to natural hazards and anthropogenic events. It contains data on 15 social factors taken or derived from Census reports as well as rankings of each tract based on these individual factors, groups of factors corresponding to four related themes (Socioeconomic, Household Composition & Disability, Minority Status & Language, and Housing Type & Transportation) and overall. The data is available for the years 2000, 2010, 2014, 2016, and 2018.

FEMA National Risk Index

The National Risk Index (NRI) dataset compiled by the Federal Emergency Management Agency (FEMA) consists of historic natural disaster data from across the United States at a tract-level. The dataset includes information about 18 natural disasters including earthquakes, tsunamis, wildfires, volcanic activity and many others. Each disaster is detailed out in terms of its frequency, historic impact, potential exposure, expected annual loss and associated risk. The dataset also includes some summary variables for each tract including the total expected loss in terms of building loss, human loss and agricultural loss, the population of the tract, and the area covered by the tract. It finally includes a few more features to characterize the population such as social vulnerability rating and community resilience.

Home Mortgage Disclosure Act

The Home Mortgage Disclosure Act (HMDA) dataset contains loan-level data for home mortgages including information on applications, denials, approvals, and institution purchases. It is managed and expanded annually by the Consumer Financial Protection Bureau based on the data collected from financial institutions. The dataset is used by public officials to make decisions and policies, uncover lending patterns and discrimination among mortgage applicants, and investigate if lenders are serving the housing needs of the communities. It covers the period from 2007 to 2017.

Evictions

The Evictions dataset is compiled and managed by the Eviction Lab at Princeton University and consists of court records related to eviction cases in the United States between 2000 and 2016. Its purpose is to estimate the prevalence of court-ordered evictions and compare eviction rates among states, counties, cities, and neighborhoods. Besides information on eviction filings and judgments, the dataset includes socioeconomic and real estate data for each tract including race/ethnic origin, household income, poverty rate, property value, median gross rent, rent burden, and others.