In 2023, the rate of homeownership among White people living in the United States was 74.3 percent. Comparatively, 45.7 percent of Black people owned a home in the same year.

Fannie Mae and Freddie Mac Loan-Level Dataset

Borrower Demographics, Loan-to-Value Ratios, and Census Tract Location

By Natarajan Krishnaswami [source]

About this dataset

The FHFA Public Use Databases provide an unprecedented look into the flow of mortgage credit and capital in America's communities. With detailed information about the income, race, gender and census tract location of borrowers, this database can help lenders, planners, researchers and housing advocates better understand how mortgages are acquired by Fannie Mae and Freddie Mac.

This data set includes 2009-2016 single-family property loan information from the Enterprises in combination with corresponding census tract information from the 2010 decennial census. It allows for greater granularity in examining mortgage acquisition patterns within each MSA or county by combining borrower/property characteristics, such as borrower's race/ethnicity; co-borrower demographics; occupancy type; Federal guarantee program (conventional/other versus FHA-insured); age of borrowers; loan purpose (purchase, refinance or home improvement); lien status; rate spread between annual percentage rate (APR) and average prime offer rate (APOR); HOEPA status; area median family income and more.

In addition to demographic data on borrowers and properties, this dataset also provides insight into affordability metrics such as median family incomes at both the MSA/county level as well as functional owner occupied bankrupt tracts using 2010 Census based geography while taking into account American Community Survey estimates available at January 1st 2016. This allows us to calculate metrics that are important for assessing inequality such as tract income ratios which measure what portion of an area’s median family income is made up by a single borrows earnings or the ratio between borrows annual income compared to an area’s average median family iincome for those year’s reporting period. Finally each record contains Enterprise Flags associated with whether loans were purchased my Fannie Mae or Freddie Mac indicating further insights regarding who is financing policies affecting undocumented immigrant labor access as well affordable housing legislation targeted towards first time home buyers

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How to use the dataset

This guide will provide you with all the information needed to use the Fannie Mae and Freddie Mac Loan-Level Dataset for 2016. The dataset contains loan-level data for both Fannie Mae and Freddie Mac, including loans acquired in 2016. It includes details such as homeowner demographics, loan-to-value ratio, census tract location, and affordability of mortgage.

The first step to using this dataset is understanding how it is organized. There are 38 fields that make up the loan level data set, making it easy to understand what is being looked at. For each field there is a description of what the field represents and potential values it can take on (i.e., if it’s an integer or float). Having an understanding of the different fields will help when querying certain data points or comparing/contrasting.

Once you understand what type of information is available in this dataset you can start to create queries or visualizations that compare trends across Fannie Mae & Freddie Mac loans made in 2016. Depending on your interest areas such as homeownership rates or income disparities certain statistics may be pulled from the dataset such as borrower’s Annual Income Ratio per area median family income by state code or a comparison between Race & Ethnicity breakdown between borrowers and co-borrowers from various states respective MSAs, among other possibilities based on your inquiries . Visualizations should then be created so that clear comparisons and contrasts could be seen more easily by other users who may look into this same dataset for additional insights as well .

After creating queries/visualization , you can dive deeper into research about corresponding trends & any biases seen within these datasets related within particular racial groupings compared against US Postal & MSA codes used within the 2010 Census Tract locations throughout the US respectively by further utilizing publicly available research material that looks at these subjects with regards housing policies implemented through out years one could further draw conclusions depending on their current inquiries

Research Ideas

• Use the dataset to analyze borrowing patterns based on race, nationality and gender, to better understand the links between minority groups and access to credit...

These data are part of NACJD's Fast Track Release and are distributed as they were received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except for the removal of direct identifiers. Users should refer to the accompanying readme file for a brief description of the files available with this collection and consult the investigator(s) if further information is needed.The data were obtained from one state prison system that was characterized by a diverse and rising prison population. This prison system housed more than 30,000 inmates across 15 institutions (14 men's facilities; 1 women's facility). The data contain information on inmates' placements into different housing units across all 15 state prison complexes, including designated maximum security, restrictive housing units. Inmates placed in restrictive housing were in lockdown the majority of the day, had limited work opportunities, and were closely monitored. These inmates were also escorted in full restraints within the institution. They experienced little recreational time, visitation and phone privileges, and few interactions with other inmates. The data contain information on inmates' housing placements, institutional misconduct, risk factors, demographic characteristics, criminal history, and offense information. These data provide information on every housing placement for each inmate, including the time spent in each placement, and the reasons documented by correctional staff for placing inmates in each housing unit. Demographic information includes inmate sex, race/ethnicity, and age. The collection contains 1 Stata data file "Inmate-Housing-Placements-Data.dta" with 16 variables and 124,942 cases.

North Carolina Population and Housing Statistics

Demographic and Housing Trends in North Carolina

By Matthew Schnars [source]

About this dataset

This comprehensive dataset provides a well-detailed and robust statistical representation of various characteristics related to the population and housing conditions of North Carolina. The dataset originates from NC LINC (Log Into North Carolina), a critical data allocation platform that focuses on sharing information regarding diverse aspects of the state’s overall demographics, socio-economic conditions, education, and employment background.

The dataset highlights a variety of facets such as population estimates by age group, race or ethnic group encompassing multiple demographic groups across different geographic areas within the state including counties and municipalities. Utilizing this expansive set of data could prove instrumental for researchers looking into demographic trends, market estimation studies or any other analysis requiring population certifications.

Revolving around Housing Statistics in North Carolina, this dataset also gives a complete perspective about various ypes of residences available throughout the region. Availability types include both renter-occupied housing units along with owned homes, providing an encapsulating vision into the home ownership versus rental situation in North Carolina. In conjunction with providing insight into occupancy details for vacant homes.

An intriguing section included within these datasets is congregated ethnicity-based data spread across numerous age-groups which can assist research based out on diverse cultures dwelling within this area.

Overall, this dataset constitutes an essential resource for stakeholders interested in understanding demographic transformations over time or gaining insights into housing availability situations across different localities in North Carolina State to inform urban planning strategies and policies beneficially impacting residents’ lives directly

How to use the dataset

This dataset offers a broad range of demographic and housing data for North Carolina, making it an ideal resource for those interested in demographic trends, urban planning, social science research, real estate and economic studies. Here's how to get the most out of it:

• Interpretation: Determine what each column represents in terms of demographic and housing attributes. Familiarize yourself with the unique characteristics that each column represents such as population size, race categories, gender distributions etc.

• Comparison Studies: Analyze different locations within North Carolina by comparing figures across rows (geographic units). This can provide insight on socio-economic disparities or geographical preferences among residents.

• Temporal Analysis: Although the dataset doesn't contain specific dates or timeframes directly related to these statistics, you can cross-reference with external datasets from different years to conduct temporal analysis procedures such as observing the growth rates in population or changes in housing statistics.

• Joining Data: Combine this dataset with other relevant datasets like education levels or crime rates which may not be available here but could add multidimensional value when conducting thorough analyses.

• Correlation Studies: Perform correlation studies between different columns e.g., is there a strong correlation between population density and number of occupied houses? Such insights may be valuable for multiple sectors including real estate investment or policy-making purposes.

• Map Visualization: Use geographic tools to map data based on counties/townships providing visual perspectives over raw number comparisons which could potentially lead to more nuanced interpretations of demographic distributions across North Carolina

• Predictive Modelling/Forecasting: Based on historic figures available through this database develop models which predict future trends within demographics & housing sector

8: Presentation/Communication Tool: Whether you're delivering a presentation about social class disparities in NC regions or just curious about where populations are densest versus where there are more mobile homes vs homes owned freely -hamarize and display data in an easy-to-understand format.

Before diving deep, always remember to clean the dataset by eliminating duplicates, filling NA values aptly, and verifying the authenticity of the data. Furthermore, always respect privacy & comply with data regulation policies while handling demographic databases

Research Ideas

• Urban Planning: This dataset can be a val...

The IPUMS Contextual Determinants of Health (CDOH) data series provides access to measures of disparities, policies, and counts, by state or county, for historically marginalized populations in the United States including Black, Asian, Hispanic/Latina/o/e/x, and LGBTQ+ persons, and women. The IPUMS CDOH data are made available through ICPSR/DSDR for merging with the National Couples' Health and Time Study (NCHAT), United States, 2020-2021 (ICPSR 38417) by approved restricted data researchers. All other researchers can access the IPUMS CDOH data via the IPUMS CDOH website. Unlike other IPUMS products, the CDOH data are organized into multiple categories related to Race and Ethnicity, Sexual and Gender Minority, Gender, and Politics. The measures were created from a wide variety of data sources (e.g., IPUMS NHGIS, the Census Bureau, the Bureau of Labor Statistics, the Movement Advancement Project, and Myers Abortion Facility Database). Measures are currently available for states or counties from approximately 2015 to 2020. The Race and Ethnicity measure in this release is an indicator of homeownership inequity, which includes the ratio between the proportion of householders identifying as White alone, not Hispanic or Latino, who own (as opposed to renting) their home and the proportion of householders identifying as a different race/ethnic group who own their home. Three ratios are provided for Black, Asian, and Hispanic groups. To work with the IPUMS CDOH data, researchers will need to use the variable MATCH_ID to merge the data in DS1 with NCHAT surveys within the virtual data enclave (VDE).

This dataset encompasses deterministic consumer demographics, collected from over 150,000 triple-opt-in first-party US Daily Active Users (DAU). Included are age, gender, ethnicity, location, employment, education, income, pet ownership, having kids/children, relationship status, military status, number of people in household, car ownership vs lease, small business owner, spanish TV viewership as a proxy for acculturation, and having kids under 18 in the home.

The RAND Center for Population Health and Health Disparities (CPHHD) Data Core Series is composed of a wide selection of analytical measures, encompassing a variety of domains, all derived from a number of disparate data sources. The CPHHD Data Core's central focus is on geographic measures for census tracts, counties, and Metropolitan Statistical Areas (MSAs) from two distinct geo-reference points, 1990 and 2000. The current study, Decennial Census Abridged, has two cross-sectional datasets, one longitudinal (interpolated) dataset, and one longitudinal (extrapolated) dataset containing a large number and variety of population and housing characteristics-related measures. These data are summarized at five different geographic levels: tract, county (FIPS), county (Geographic), MSA (Geographic), and state. The following types of measures constructed from the Census Bureau Population and Housing Characteristics data are included in the data for this collection: housing characteristics (stock, quality, ownership, costs, expenditures, occupancy, etc.), crowding (housing and population density), urbanicity, racial and ethnic composition, language, nationality, and citizenship. Further measures cover family/household structure, transportation, educational attainment, labor force, employment status, disabilities, income, poverty, and demographics (e.g., age, gender, and race).

Yearly statewide and by-Continuum of Care total counts of individuals receiving homeless response services by age group, race, gender, veteran status, and disability status.

This data comes from the Homelessness Data Integration System (HDIS), a statewide data warehouse which compiles and processes data from all 44 California Continuums of Care (CoC)—regional homelessness service coordination and planning bodies. Each CoC collects data about the people it serves through its programs, such as homelessness prevention services, street outreach services, permanent housing interventions and a range of other strategies aligned with California’s Housing First objectives.

The dataset uploaded reflects the 2024 HUD Data Standard Changes. Previously, Race and Ethnicity were separate files but are now combined.

Information updated as of 11/13/2025.

Distribution of purchase mortgages by gender for U.S. home buyers in 2024, showing market share across different gender categories including Male, Female, Not Provided, Not Applicable, and Both

Distribution of purchase mortgages by race for U.S. home buyers in 2024, showing market share across different racial and ethnic groups including White, Black or African American, Asian, and other categories

Average mortgage loan amounts by gender for U.S. home buyers in 2024

Demographic data for San Mateo County's residents by Census Tract. Includes population, race, Hispanic ethnicity, gender, age groups, household, family, and housing information. This data is from the 2010 United States Census Summary File 1 (SF1).

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

Census data for cities and Census Designated Places in San Mateo County. Includes population, race, Hispanic ethnicity, gender, age groups, household, family, and housing information. This data is from the 2010 United States Census Summary File 1 (SF1).

The Multi-City Study of Urban Inequality was designed to broaden the understanding of how changing labor market dynamics, racial attitudes and stereotypes, and racial residential segregation act singly and in concert to foster contemporary urban inequality. This data collection comprises data for two surveys: a survey of households and a survey of employers. Multistage area probability sampling of adult residents took place in four metropolitan areas: Atlanta (April 1992-September 1992), Boston (May 1993-November 1994), Detroit (April-September 1992), and Los Angeles (September 1993-August 1994). The combined four-city data file in Part 1 contains data on survey questions that were asked in households in at least two of the four survey cities. Questions on labor market dynamics included industry, hours worked per week, length of time on job, earnings before taxes, size of employer, benefits provided, instances of harassment and discrimination, and searching for work within particular areas of the metropolis in which the respondent resided. Questions covering racial attitudes and attitudes about inequality centered on the attitudes and beliefs that whites, Blacks, Latinos, and Asians hold about one another, including amount of discrimination, perceptions about wealth and intelligence, ability to be self-supporting, ability to speak English, involvement with drugs and gangs, the fairness of job training and educational assistance policies, and the fairness of hiring and promotion preferences. Residential segregation issues were studied through measures of neighborhood quality and satisfaction, and preferences regarding the racial/ethnic mix of neighborhoods. Other topics included residence and housing, neighborhood characteristics, family income structure, networks and social functioning, and interviewer observations. Demographic information on household respondents was also elicited, including length of residence, education, housing status, monthly rent or mortgage payment, marital status, gender, age, race, household composition, citizenship status, language spoken in the home, ability to read and speak English, political affiliation, and religion. The data in Part 2 represent a telephone survey of current business establishments in Atlanta, Boston, Detroit, and Los Angeles carried out between spring 1992 and spring 1995 to learn about hiring and vacancies, particularly for jobs requiring just a high school education. An employer size-weighted, stratified, probability sample (approximately two-thirds of the cases) was drawn from regional employment directories, and a probability sample (the other third of the cases) was drawn from the current or most recent employer reported by respondents to the household survey in Part 1. Employers were queried about characteristics of their firms, including composition of the firm's labor force, vacant positions, the person most recently hired and his or her salary, hours worked per week, educational qualifications, promotions, the firm's recruiting and hiring methods, and demographic information for the respondent, job applicants, the firm's customers, and the firm's labor force, including age, education, race, and gender.

This dataset contains the American Community Survey (ACS) five-year estimates for Norfolk, Virginia. According to the United States Census Bureau, the ACS is the premier source for detailed population and housing information about communities and the nation. Every year, the Census Bureau conducts a survey and creates estimates for demographic categories such as income, employment, poverty, race, ethnicity, housing, age, gender, internet access, vehicle access, and other topics. For census tracts, 5-year estimates are generated and released to the public. This dataset includes five-year estimates released in 2020 for census tracts in Norfolk, VA and will be updated annually with each new release of five-year estimates.

In 2023, there were an estimated ******* white homeless people in the United States, the most out of any ethnicity. In comparison, there were around ******* Black or African American homeless people in the U.S. How homelessness is counted The actual number of homeless individuals in the U.S. is difficult to measure. The Department of Housing and Urban Development uses point-in-time estimates, where employees and volunteers count both sheltered and unsheltered homeless people during the last 10 days of January. However, it is very likely that the actual number of homeless individuals is much higher than the estimates, which makes it difficult to say just how many homeless there are in the United States. Unsheltered homeless in the United States California is well-known in the U.S. for having a high homeless population, and Los Angeles, San Francisco, and San Diego all have high proportions of unsheltered homeless people. While in many states, the Department of Housing and Urban Development says that there are more sheltered homeless people than unsheltered, this estimate is most likely in relation to the method of estimation.

Collection of data from the 2011 Population Census related to the place of residence. The following tables are included in the collection: A. PROXIMITY1. POPULATION RECORDED BY DISTRICT, GENDER AND AGE, 2011A2. HOUSING, FOUNDATIONS AND POPULATION RECORDED BY DISTRICT AND GENDER, 2011A3. RESIDENCES, HOUSING, FOUNDATIONS AND POPULATION REGISTERED BY DISTRICT, 2011A4. POPULATION RECORDED BY DISTRICT, YEAR OF AGE AND GENDER, 2011A5. TOTAL POPULATION DEPENDENCY INDEX, BY DISTRICT AND URBAN/RURAL AREA, 2011A6. POPULATION RECORDED BY LANGUAGE, GENDER, DISTRICT AND URBAN/RURAL AREA, 2011 B. URBAN/RURAL AREAB1. POPULATION RECORDED BY DISTRICT, URBAN/RURAL AREA, GENDER AND AGE, 2011B2. HOUSING, FOUNDATIONS AND POPULATION REGISTERED BY DISTRICT, URBAN/RURAL AREA AND GENDER, 2011B3. RESIDENTIAL, HOUSING, FOUNDATIONS AND POPULATION RECORDED BY DISTRICT AND URBAN/RURAL AREA, 2011B4. POPULATION RECORDED, BY DISTRICT AND URBAN/RURAL AREA, GENDER AND MIDDLE AGE, 2011B5. Population registered in the housing, rural and urban/rural area, race and age, 2011B6a. Employees (15 years old and above) at level of formation, place of residence (urban/rural area) and economic activity, 2011 — total B6b. Employees (15 years old and above) at level of form, place of residence (urban/rural area) and economic activity, 2011 — man6c. Employees (15 years old and above) at level of form, place of residence (urban/rural area) and economic activity, 2011 — WomenB7a. Employees (15 years old and above) at level of form, place of residence (urban/rural area) and occupation, 2011 — total 7b. Employees (15 years old and above) at the level of formation, place of residence (urban/rural area) and professional, 2011 — Mansb7c. EMPLOYEES (15 YEARS OLD AND ABOVE) AT LEVEL OF FORMATION, PLACE OF RESIDENCE (URBAN/RURAL AREA) AND OCCUPATION, 2011 — WOMENB8. POPULATION DENSITY RECORDED BY AGE, DISTRICT AND URBAN/RURAL AREA, 2011 C. PUBLICATION/COMMUNITY/ENORY1. POPULATION RECORDED BY DISTRICT, CITY/COMMUNITY, PARISH, GENDER AND AGE, 2011C2. HOUSING, INSTITUTIONS AND POPULATION REGISTERED BY DISTRICT, CITY/COMMUNITY, PARISH AND GENDER, 2011C3. RESIDENCES, HOUSING, FOUNDATIONS AND POPULATION RECORDED BY DISTRICT, CITY/COMMUNITY AND QUARTERS, 2011C4. POPULATION RECORDED BY DISTRICT, CITY/COMMUNITY, PARISH, GENDER AND MIDDLE AGE, 2011C5. Population recorded by size of municipality/community, gender and provinity, 2011C6a. Financially active population, workers and unemployed (15 years old and above) by economic activity and place of residence (province, municipality/community) — total, 2011C6b. Financially active population, workers and unemployed (15 years old and above) by economic activity and place of residence (province, municipality/community) — men, 2011c6c. ECONOMICALLY ACTIVE POPULATION, WORKERS AND UNEMPLOYED (15 YEARS OLD AND ABOVE) BY ECONOMIC ACTIVITY AND PLACE OF RESIDENCE (PROVINCE, PUBLIC/COMMUNITY) — WOMEN, 2011C7. TOTAL POPULATION DEPENDENCY INDEX BY DISTRICT, CITY/COMMUNITY AND QUARTERS, 2011C8. POPULATION DENSITY RECORDED BY AGE, DISTRICT, CITY/COMMUNITY AND QUARTERS, 2011 D. POSTAL CODEX1. RESIDENTIAL, HOUSING AND POPULATION RECORDED BY POSTAL CODE, DISTRICT, AND CITY/COMMUNITY, 2011D2. HOUSES, HOUSING AND POPULATION RECORDED ACCORDING TO POSTAL CODE 2011D3. POPULATION RECORDED BY POSTAL CODE, DISTRICT, CITY/COMMUNITY, NATIONALITY AND GENDER, 2011D4. POPULATION RECORDED BY POSTAL CODE, NATIONALITY AND GENDER, 2011D5. POPULATION RECORDED BY POSTAL CODE AND AGE, 2011D6. POPULATION RECORDED BY POSTAL CODE, GENDER AND MIDDLE AGE, 2011D7. TOTAL POPULATION DEPENDENCY INDEX BY POSTAL CODE, 2011

Metropolitan and Micropolitan Statistical Areas (metro and micro areas) are geographic entities defined by the Office of Management and Budget (OMB) for use by Federal statistical agencies in collecting, tabulating, and publishing Federal statistics. The term "Core Based Statistical Area" (CBSA) is a collective term for both metro and micro areas. A metro area contains a core urban area of 50,000 or more population, and a micro area contains an urban core of at least 10,000 (but less than 50,000) population. Each metro or micro area consists of one or more counties and includes the counties containing the core urban area, as well as any adjacent counties that have a high degree of social and economic integration (as measured by commuting to work) with the urban core.Metro and micro areas were downloaded from the U.S. Census Bureau's TIGER/Line Shapefiles. The WV GIS Technical Center added population and demographic attributes (Area, Total Population, FIPS codes, Race, Age, Gender, Housing, Families) from U.S. Census Bureau American Fact Finder. Coodinate System: NAD_1983_UTM_Zone_17N

Mortgage approval rates by gender for U.S. home buyers in 2024, showing approval percentages across different gender categories including Male, Female, Not Provided, Not Applicable, and Both

The U.S. Census Bureau, in collaboration with five federal agencies, launched the Household Pulse Survey to produce data on the social and economic impacts of Covid-19 on American households. The Household Pulse Survey was designed to gauge the impact of the pandemic on employment status, consumer spending, food security, housing, education disruptions, and dimensions of physical and mental wellness.

The survey was designed to meet the goal of accurate and timely weekly estimates. It was conducted by an internet questionnaire, with invitations to participate sent by email and text message. The sample frame is the Census Bureau Master Address File Data. Housing units linked to one or more email addresses or cell phone numbers were randomly selected to participate, and one respondent from each housing unit was selected to respond for him or herself. Estimates are weighted to adjust for nonresponse and to match Census Bureau estimates of the population by age, gender, race and ethnicity, and educational attainment. All estimates shown meet the NCHS Data Presentation Standards for Proportions.