Home Ownership Rate in the United States decreased to 65.10 percent in the first quarter of 2025 from 65.70 percent in the fourth quarter of 2024. This dataset provides the latest reported value for - United States Home Ownership Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Graph and download economic data for Homeownership Rate in the United States (RHORUSQ156N) from Q1 1965 to Q1 2025 about homeownership, housing, rate, and USA.

Graph and download economic data for Homeownership Rate for the United States (USHOWN) from 1984 to 2024 about homeownership, housing, rate, and USA.

This dataset contains Iowa households by household type for State of Iowa, individual Iowa counties, Iowa places and census tracts within Iowa. Data is from the American Community Survey, Five Year Estimates, Table B11001. A household includes all the persons who occupy a housing unit as their usual place of residence. A housing unit is a house, an apartment, a mobile home, a group of rooms, or a single room that is occupied as separate living quarters. Household type includes All, All Family, Family - Married Couple, Family - All Single Householders, Family - Male Householder - No Wife Present, Family - Female Householder - No Husband Present, All Nonfamily, Nonfamily - Householder Living Alone, and Nonfamily - Householder Not Living Alone A family household is a household maintained by a householder who is in a family. A family group is defined as any two or more people residing together, and related by birth, marriage, or adoption. Householder refers to the person (or one of the people) in whose name the housing unit is owned or rented (maintained) or, if there is no such person, any adult member, excluding roomers, boarders, or paid employees. If the house is owned or rented jointly by a married couple, the householder may be either the husband or the wife.

Graph and download economic data for Homeownership Rates by Race and Ethnicity: Black Alone in the United States (BOAAAHORUSQ156N) from Q1 1994 to Q1 2025 about homeownership, African-American, rate, and USA.

The data on relationship to householder were derived from answers to Question 2 in the 2015 American Community Survey (ACS), which was asked of all people in housing units. The question on relationship is essential for classifying the population information on families and other groups. Information about changes in the composition of the American family, from the number of people living alone to the number of children living with only one parent, is essential for planning and carrying out a number of federal programs.

The responses to this question were used to determine the relationships of all persons to the householder, as well as household type (married couple family, nonfamily, etc.). From responses to this question, we were able to determine numbers of related children, own children, unmarried partner households, and multi-generational households. We calculated average household and family size. When relationship was not reported, it was imputed using the age difference between the householder and the person, sex, and marital status.

Household – A household includes all the people who occupy a housing unit. (People not living in households are classified as living in group quarters.) A housing unit is a house, an apartment, a mobile home, a group of rooms, or a single room that is occupied (or if vacant, is intended for occupancy) as separate living quarters. Separate living quarters are those in which the occupants live separately from any other people in the building and which have direct access from the outside of the building or through a common hall. The occupants may be a single family, one person living alone, two or more families living together, or any other group of related or unrelated people who share living arrangements.

Average Household Size – A measure obtained by dividing the number of people in households by the number of households. In cases where people in households are cross-classified by race or Hispanic origin, people in the household are classified by the race or Hispanic origin of the householder rather than the race or Hispanic origin of each individual.

Average household size is rounded to the nearest hundredth.

Comparability – The relationship categories for the most part can be compared to previous ACS years and to similar data collected in the decennial census, CPS, and SIPP. With the change in 2008 from “In-law” to the two categories of “Parent-in-law” and “Son-in-law or daughter-in-law,” caution should be exercised when comparing data on in-laws from previous years. “In-law” encompassed any type of in-law such as sister-in-law. Combining “Parent-in-law” and “son-in-law or daughter-in-law” does not represent all “in-laws” in 2008.

The same can be said of comparing the three categories of “biological” “step,” and “adopted” child in 2008 to “Child” in previous years. Before 2008, respondents may have considered anyone under 18 as “child” and chosen that category. The ACS includes “foster child” as a category. However, the 2010 Census did not contain this category, and “foster children” were included in the “Other nonrelative” category. Therefore, comparison of “foster child” cannot be made to the 2010 Census. Beginning in 2013, the “spouse” category includes same-sex spouses.

The American Community Survey (ACS) Public Use Microdata Sample (PUMS) contains a sample of responses to the ACS. The ACS PUMS dataset includes variables for nearly every question on the survey, as well as many new variables that were derived after the fact from multiple survey responses (such as poverty status).Each record in the file represents a single person, or, in the household-level dataset, a single housing unit. In the person-level file, individuals are organized into households, making possible the study of people within the contexts of their families and other household members. Individuals living in Group Quarters, such as nursing facilities or college facilities, are also included on the person file. ACS PUMS data are available at the nation, state, and Public Use Microdata Area (PUMA) levels. PUMAs are special non-overlapping areas that partition each state into contiguous geographic units containing roughly 100,000 people each. ACS PUMS files for an individual year, such as 2019, contain data on approximately one percent of the United States population.

This study was undertaken to obtain information on the characteristics of gun ownership, gun-carrying practices, and weapons-related incidents in the United States -- specifically, gun use and other weapons used in self-defense against humans and animals. Data were gathered using a national random-digit-dial telephone survey. The respondents were comprised of 1,905 randomly-selected adults aged 18 and older living in the 50 United States. All interviews were completed between May 28 and July 2, 1996. The sample was designed to be a representative sample of households, not of individuals, so researchers did not interview more than one adult from each household. To start the interview, six qualifying questions were asked, dealing with (1) gun ownership, (2) gun-carrying practices, (3) gun display against the respondent, (4) gun use in self-defense against animals, (5) gun use in self-defense against people, and (6) other weapons used in self-defense. A "yes" response to a qualifying question led to a series of additional questions on the same topic as the qualifying question. Part 1, Survey Data, contains the coded data obtained during the interviews, and Part 2, Open-Ended-Verbatim Responses, consists of the answers to open-ended questions provided by the respondents. Information collected for Part 1 covers how many firearms were owned by household members, types of firearms owned (handguns, revolvers, pistols, fully automatic weapons, and assault weapons), whether the respondent personally owned a gun, reasons for owning a gun, type of gun carried, whether the gun was ever kept loaded, kept concealed, used for personal protection, or used for work, and whether the respondent had a permit to carry the gun. Additional questions focused on incidents in which a gun was displayed in a hostile manner against the respondent, including the number of times such an incident took place, the location of the event in which the gun was displayed against the respondent, whether the police were contacted, whether the individual displaying the gun was known to the respondent, whether the incident was a burglary, robbery, or other planned assault, and the number of shots fired during the incident. Variables concerning gun use by the respondent in self-defense against an animal include the number of times the respondent used a gun in this manner and whether the respondent was hunting at the time of the incident. Other variables in Part 1 deal with gun use in self-defense against people, such as the location of the event, if the other individual knew the respondent had a gun, the type of gun used, any injuries to the respondent or to the individual that required medical attention or hospitalization, whether the incident was reported to the police, whether there were any arrests, whether other weapons were used in self-defense, the type of other weapon used, location of the incident in which the other weapon was used, and whether the respondent was working as a police officer or security guard or was in the military at the time of the event. Demographic variables in Part 1 include the gender, race, age, household income, and type of community (city, suburb, or rural) in which the respondent lived. Open-ended questions asked during the interview comprise the variables in Part 2. Responses include descriptions of where the respondent was when he or she displayed a gun (in self-defense or otherwise), specific reasons why the respondent displayed a gun, how the other individual reacted when the respondent displayed the gun, how the individual knew the respondent had a gun, whether the police were contacted for specific self-defense events, and if not, why not.

The housing affordability measure illustrates the relationship between income and housing costs. A household that spends 30% or more of its collective monthly income to cover housing costs is considered to be “housing cost-burden[ed].”[1] Those spending between 30% and 49.9% of their monthly income are categorized as “moderately housing cost-burden[ed],” while those spending more than 50% are categorized as “severely housing cost-burden[ed].”[2]

How much a household spends on housing costs affects the household’s overall financial situation. More money spent on housing leaves less in the household budget for other needs, such as food, clothing, transportation, and medical care, as well as for incidental purchases and saving for the future.

The estimated housing costs as a percentage of household income are categorized by tenure: all households, those that own their housing unit, and those that rent their housing unit.

Throughout the period of analysis, the percentage of housing cost-burdened renter households in Champaign County was higher than the percentage of housing cost-burdened homeowner households in Champaign County. All three categories saw year-to-year fluctuations between 2005 and 2023, and none of the three show a consistent trend. However, all three categories were estimated to have a lower percentage of housing cost-burdened households in 2023 than in 2005.

Data on estimated housing costs as a percentage of monthly income was sourced from the U.S. Census Bureau’s American Community Survey (ACS) 1-Year Estimates, which are released annually.

As with any datasets that are estimates rather than exact counts, it is important to take into account the margins of error (listed in the column beside each figure) when drawing conclusions from the data.

Due to the impact of the COVID-19 pandemic, instead of providing the standard 1-year data products, the Census Bureau released experimental estimates from the 1-year data in 2020. This includes a limited number of data tables for the nation, states, and the District of Columbia. The Census Bureau states that the 2020 ACS 1-year experimental tables use an experimental estimation methodology and should not be compared with other ACS data. For these reasons, and because data is not available for Champaign County, no data for 2020 is included in this Indicator.

For interested data users, the 2020 ACS 1-Year Experimental data release includes a dataset on Housing Tenure.

[1] Schwarz, M. and E. Watson. (2008). Who can afford to live in a home?: A look at data from the 2006 American Community Survey. U.S. Census Bureau.

[2] Ibid.

Sources: U.S. Census Bureau; American Community Survey, 2023 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using data.census.gov; (17 October 2024).; U.S. Census Bureau; American Community Survey, 2022 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using data.census.gov; (22 September 2023).; U.S. Census Bureau; American Community Survey, 2021 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using data.census.gov; (30 September 2022).; U.S. Census Bureau; American Community Survey, 2019 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using data.census.gov; (10 June 2021).; U.S. Census Bureau; American Community Survey, 2018 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using data.census.gov; (10 June 2021).;U.S. Census Bureau; American Community Survey, 2017 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (13 September 2018).; U.S. Census Bureau; American Community Survey, 2016 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (14 September 2017).; U.S. Census Bureau; American Community Survey, 2015 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (19 September 2016).; U.S. Census Bureau; American Community Survey, 2014 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2013 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2012 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2011 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2010 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2009 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2008 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; 16 March 2016).; U.S. Census Bureau; American Community Survey, 2007 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2006 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2005 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).

This data collection contains a single concatenated file that merges common variables for respondents from two separate surveys, including 1,241 respondents from AMERICAN'S USE OF TIME, 1965-1966 (ICPSR 7254), and 812 respondents from TIME USE IN ECONOMIC AND SOCIAL ACCOUNTS, 1975-1976 (ICPSR 7580), for a total of 2,053 respondents. The sample was restricted to match the design of the earlier study, so the merged file includes data for individual Americans between 19 and 65 years of age living in cities with a population between 30,000 and 280,000, and in households that had at least one adult employed in a non-farming occupation. Two general types of information were gathered in both studies: sociodemographic background characteristics and time use data for a 24-hour period. The 1965-1966 time use data were obtained from a diary of activities kept by the respondent over a 24-hour period, and the 1975-1976 data were collected in face-to-face interviews. In both cases, the sociodemographic data also were gathered from personal interviews. The merged file contains sociodemographic background data that includes age, sex, race, relationship to head of household, occupation, marital status, number and age of children in household, homeowner/renter status, residence tenure, number of paid household help, number of books owned, church/religious preferences, highest level of education attained, whether raised on a farm, and income level. The time use data in the merged file chronicles activities such as work outside the home, household/domestic work, child care, obtaining goods and services, personal care needs, education and professional training, organization involvement, entertainment/social activities, sports/active leisure, and passive leisure.

About this dataset

Context

The dataset presents median household incomes for various household sizes in Garrett Park, MD, as reported by the U.S. Census Bureau. The dataset highlights the variation in median household income with the size of the family unit, offering valuable insights into economic trends and disparities within different household sizes, aiding in data analysis and decision-making.

Key observations

• Of the 7 household sizes (1 person to 7-or-more person households) reported by the census bureau, Garrett Park did not include 6, or 7-person households. Across the different household sizes in Garrett Park the mean income is $232,250, and the standard deviation is $55,997. The coefficient of variation (CV) is 24.11%. This high CV indicates high relative variability, suggesting that the incomes vary significantly across different sizes of households. Please note that the U.S. Census Bureau uses $250,001 as a JAM value to report incomes of $250,000 or more. In the case of Garrett Park, there were 2 household sizes where the JAM values were used. Thus, the numbers for the mean and standard deviation may not be entirely accurate and have a higher possibility of errors. However, to obtain an approximate estimate, we have used a value of $250,001 as the income for calculations, as reported in the datasets by the U.S. Census Bureau.
• In the most recent year, 2021, The smallest household size for which the bureau reported a median household income was 1-person households, with an income of $140,325. It then further increased to $270,229 for 5-person households, the largest household size for which the bureau reported a median household income.

https://i.neilsberg.com/ch/garrett-park-md-median-household-income-by-household-size.jpeg" alt="Garrett Park, MD median household income, by household size (in 2022 inflation-adjusted dollars)">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Household Sizes:

• 1-person households
• 2-person households
• 3-person households
• 4-person households
• 5-person households
• 6-person households
• 7-or-more-person households

Variables / Data Columns

• Household Size: This column showcases 7 household sizes ranging from 1-person households to 7-or-more-person households (As mentioned above).
• Median Household Income: Median household income, in 2022 inflation-adjusted dollars for the specific household size.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Garrett Park median household income. You can refer the same here

Graph and download economic data for Consumer Unit Characteristics: Percent Homeowner by Age: from Age 25 to 34 (CXUHOMEOWNLB0403M) from 1990 to 2023 about consumer unit, age, homeownership, 25 years +, percent, and USA.

Graph and download economic data for Average Sales Price of Houses Sold for the United States (ASPUS) from Q1 1963 to Q1 2025 about sales, housing, and USA.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units and the group quarters population for states and counties..Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2023 American Community Survey 1-Year Estimates.ACS data generally reflect the geographic boundaries of legal and statistical areas as of January 1 of the estimate year. For more information, see Geography Boundaries by Year..Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..Users must consider potential differences in geographic boundaries, questionnaire content or coding, or other methodological issues when comparing ACS data from different years. Statistically significant differences shown in ACS Comparison Profiles, or in data users' own analysis, may be the result of these differences and thus might not necessarily reflect changes to the social, economic, housing, or demographic characteristics being compared. For more information, see Comparing ACS Data..Households not paying cash rent are excluded from the calculation of median gross rent..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

According to United States census data, 91.2 percent of all U.S. households reported having some form internet subscription in 2022. This was up from 90.3 percent of households in 2021.

analyze the american community survey (acs) with r and monetdb experimental. think of the american community survey (acs) as the united states' census for off-years - the ones that don't end in zero. every year, one percent of all americans respond, making it the largest complex sample administered by the u.s. government (the decennial census has a much broader reach, but since it attempts to contact 100% of the population, it's not a sur vey). the acs asks how people live and although the questionnaire only includes about three hundred questions on demography, income, insurance, it's often accurate at sub-state geographies and - depending how many years pooled - down to small counties. households are the sampling unit, and once a household gets selected for inclusion, all of its residents respond to the survey. this allows household-level data (like home ownership) to be collected more efficiently and lets researchers examine family structure. the census bureau runs and finances this behemoth, of course. the dow nloadable american community survey ships as two distinct household-level and person-level comma-separated value (.csv) files. merging the two just rectangulates the data, since each person in the person-file has exactly one matching record in the household-file. for analyses of small, smaller, and microscopic geographic areas, choose one-, three-, or fiv e-year pooled files. use as few pooled years as you can, unless you like sentences that start with, "over the period of 2006 - 2010, the average american ... [insert yer findings here]." rather than processing the acs public use microdata sample line-by-line, the r language brazenly reads everything into memory by default. to prevent overloading your computer, dr. thomas lumley wrote the sqlsurvey package principally to deal with t his ram-gobbling monster. if you're already familiar with syntax used for the survey package, be patient and read the sqlsurvey examples carefully when something doesn't behave as you expect it to - some sqlsurvey commands require a different structure (i.e. svyby gets called through svymean) and others might not exist anytime soon (like svyolr). gimme some good news: sqlsurvey uses ultra-fast monetdb (click here for speed tests), so follow the monetdb installation instructions before running this acs code. monetdb imports, writes, recodes data slowly, but reads it hyper-fast . a magnificent trade-off: data exploration typically requires you to think, send an analysis command, think some more, send another query, repeat. importation scripts (especially the ones i've already written for you) can be left running overnight sans hand-holding. the acs weights generalize to the whole united states population including individuals living in group quarters, but non-residential respondents get an abridged questionnaire, so most (not all) analysts exclude records with a relp variable of 16 or 17 right off the bat. this new github repository contains four scripts: 2005-2011 - download all microdata.R create the batch (.bat) file needed to initiate the monet database in the future download, unzip, and import each file for every year and size specified by the user create and save household- and merged/person-level replicate weight complex sample designs create a well-documented block of code to re-initiate the monet db server in the future fair warning: this full script takes a loooong time. run it friday afternoon, commune with nature for the weekend, and if you've got a fast processor and speedy internet connection, monday morning it should be ready for action. otherwise, either download only the years and sizes you need or - if you gotta have 'em all - run it, minimize it, and then don't disturb it for a week. 2011 single-year - analysis e xamples.R run the well-documented block of code to re-initiate the monetdb server load the r data file (.rda) containing the replicate weight designs for the single-year 2011 file perform the standard repertoire of analysis examples, only this time using sqlsurvey functions 2011 single-year - variable reco de example.R run the well-documented block of code to re-initiate the monetdb server copy the single-year 2011 table to maintain the pristine original add a new age category variable by hand add a new age category variable systematically re-create then save the sqlsurvey replicate weight complex sample design on this new table close everything, then load everything back up in a fresh instance of r replicate a few of the census statistics. no muss, no fuss replicate census estimates - 2011.R run the well-documented block of code to re-initiate the monetdb server load the r data file (.rda) containing the replicate weight designs for the single-year 2011 file match every nation wide statistic on the census bureau's estimates page, using sqlsurvey functions click here to view these four scripts for more detail about the american community survey (acs), visit: < ul> the us census...

Text source: https://www.huduser.gov/portal/publications/hsgfin/addi.html In recognition of the fact that a lack of savings is the most significant barrier to homeownership for most low-income families1, Congress passed the American Dream Downpayment Act of 2003, which established the American Dream Downpayment Initiative (ADDI). The ADDI program was designed to provide assistance with downpayments, closing costs, and, if necessary, rehabilitation work done in conjunction with a home purchase. This formula-based program disburses assistance through a network of Participating Jurisdictions (PJs) in all 50 states and affords them significant flexibility in designing homebuyer programs to meet the needs of their communities. Established as part of the HOME program,2 ADDI is a prime example of direct federal assistance to promote low-income homeownership. In recent years there have been growing concerns that many new low-income homeowners have had difficulty maintaining homeownership.3 To address these concerns in the context of the ADDI program, the Fiscal Year 2006 U.S. Senate Report on the Transportation, Treasury and HUD Appropriations Bill directed the U.S. Department of Housing and Urban Development (HUD) to report on the foreclosure and delinquency rate of households who received downpayment assistance through ADDI.4 This report has been developed in response to this congressional mandate. Due to the limited program history of ADDI, and since HOME-assisted homebuyers are quite similar to those assisted by the ADDI, this study jointly estimates annual foreclosure and delinquency rates for both HOME- and ADDI-assisted borrowers who purchased homes during the period from 2001 through 2005.5 While all HOME/ADDI-assisted borrowers were included in the analysis, in order to have the results be representative of the ADDI program, the sample of PJs was limited to those that were eligible for an allocation of ADDI funds in 2004, the year in which the largest number of PJs were eligible. The primary objective of the study, which addresses the congressional inquiry, is to provide an estimate of the foreclosure and delinquency rates among HOME/ADDI-assisted homebuyers. HUD was also interested in an analysis of the reasons behind these outcomes. Thus, a secondary objective of this study is to analyze the factors associated with variations in delinquency and default rates. 1 See, for example, U. S. Department of Housing and Urban Development, Barriers to Minority Homeownership, July 17, 2002, and Herbert et al., Homeownership Gaps Among Low-Income and Minority Borrowers and Neighborhoods, U.S. Department of Housing and Urban Development, March 2005. 2 Created under Title II of the National Affordable Housing Act of 1990, the HOME program is designed to provide affordable housing to low-income households, expand the capacity of nonprofit housing providers, and strengthen the ability of state and local governments to develop and implement affordable housing strate-gies tailored to local needs and priorities. 3 See, for example, Dean Baker, "Who's Dreaming?: Homeownership Among Low-Income Families," Center for Eco-nomic and Policy Research, Washington, DC, January 2005. 4 Throughout our discussion the terms "default" and "foreclosure" are used to refer to the same outcome where homeowners lose their home in foreclosure. 5 Foreclosure and delinquency rates for 2000 are not included here as the data was not consistent enough to produce valid estimations. This report is based in part on surveys of participating jurisdictions.

About this dataset

Context

The dataset presents median household incomes for various household sizes in Harveys Lake, PA, as reported by the U.S. Census Bureau. The dataset highlights the variation in median household income with the size of the family unit, offering valuable insights into economic trends and disparities within different household sizes, aiding in data analysis and decision-making.

Key observations

• Of the 7 household sizes (1 person to 7-or-more person households) reported by the census bureau, Harveys Lake did not include 6, or 7-person households. Across the different household sizes in Harveys Lake the mean income is $129,252, and the standard deviation is $84,094. The coefficient of variation (CV) is 65.06%. This high CV indicates high relative variability, suggesting that the incomes vary significantly across different sizes of households. Please note that the U.S. Census Bureau uses $250,001 as a JAM value to report incomes of $250,000 or more. In the case of Harveys Lake, there were 1 household sizes where the JAM values were used. Thus, the numbers for the mean and standard deviation may not be entirely accurate and have a higher possibility of errors. However, to obtain an approximate estimate, we have used a value of $250,001 as the income for calculations, as reported in the datasets by the U.S. Census Bureau.
• In the most recent year, 2021, The smallest household size for which the bureau reported a median household income was 1-person households, with an income of $45,564. It then further increased to $270,229 for 5-person households, the largest household size for which the bureau reported a median household income.

https://i.neilsberg.com/ch/harveys-lake-pa-median-household-income-by-household-size.jpeg" alt="Harveys Lake, PA median household income, by household size (in 2022 inflation-adjusted dollars)">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Household Sizes:

• 1-person households
• 2-person households
• 3-person households
• 4-person households
• 5-person households
• 6-person households
• 7-or-more-person households

Variables / Data Columns

• Household Size: This column showcases 7 household sizes ranging from 1-person households to 7-or-more-person households (As mentioned above).
• Median Household Income: Median household income, in 2022 inflation-adjusted dollars for the specific household size.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Harveys Lake median household income. You can refer the same here

About this dataset

Context

The dataset presents median household incomes for various household sizes in Rolling Hills Estates, CA, as reported by the U.S. Census Bureau. The dataset highlights the variation in median household income with the size of the family unit, offering valuable insights into economic trends and disparities within different household sizes, aiding in data analysis and decision-making.

Key observations

• Of the 7 household sizes (1 person to 7-or-more person households) reported by the census bureau, Rolling Hills Estates did not include 6, or 7-person households. Across the different household sizes in Rolling Hills Estates the mean income is $193,873, and the standard deviation is $88,205. The coefficient of variation (CV) is 45.50%. This high CV indicates high relative variability, suggesting that the incomes vary significantly across different sizes of households. Please note that the U.S. Census Bureau uses $250,001 as a JAM value to report incomes of $250,000 or more. In the case of Rolling Hills Estates, there were 2 household sizes where the JAM values were used. Thus, the numbers for the mean and standard deviation may not be entirely accurate and have a higher possibility of errors. However, to obtain an approximate estimate, we have used a value of $250,001 as the income for calculations, as reported in the datasets by the U.S. Census Bureau.
• In the most recent year, 2021, The smallest household size for which the bureau reported a median household income was 1-person households, with an income of $57,648. It then further increased to $270,229 for 5-person households, the largest household size for which the bureau reported a median household income.

https://i.neilsberg.com/ch/rolling-hills-estates-ca-median-household-income-by-household-size.jpeg" alt="Rolling Hills Estates, CA median household income, by household size (in 2022 inflation-adjusted dollars)">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Household Sizes:

• 1-person households
• 2-person households
• 3-person households
• 4-person households
• 5-person households
• 6-person households
• 7-or-more-person households

Variables / Data Columns

• Household Size: This column showcases 7 household sizes ranging from 1-person households to 7-or-more-person households (As mentioned above).
• Median Household Income: Median household income, in 2022 inflation-adjusted dollars for the specific household size.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Rolling Hills Estates median household income. You can refer the same here

About this dataset

Context

The dataset presents median household incomes for various household sizes in Hoquiam, WA, as reported by the U.S. Census Bureau. The dataset highlights the variation in median household income with the size of the family unit, offering valuable insights into economic trends and disparities within different household sizes, aiding in data analysis and decision-making.

Key observations

• Of the 7 household sizes (1 person to 7-or-more person households) reported by the census bureau, all of the household sizes were found in Hoquiam. Across the different household sizes in Hoquiam the mean income is $89,552, and the standard deviation is $81,653. The coefficient of variation (CV) is 91.18%. This high CV indicates high relative variability, suggesting that the incomes vary significantly across different sizes of households. Please note that the U.S. Census Bureau uses $250,001 as a JAM value to report incomes of $250,000 or more. In the case of Hoquiam, there were 1 household sizes where the JAM values were used. Thus, the numbers for the mean and standard deviation may not be entirely accurate and have a higher possibility of errors. However, to obtain an approximate estimate, we have used a value of $250,001 as the income for calculations, as reported in the datasets by the U.S. Census Bureau.
• In the most recent year, 2021, The smallest household size for which the bureau reported a median household income was 1-person households, with an income of $31,144. It then further increased to $270,229 for 7-person households, the largest household size for which the bureau reported a median household income.

https://i.neilsberg.com/ch/hoquiam-wa-median-household-income-by-household-size.jpeg" alt="Hoquiam, WA median household income, by household size (in 2022 inflation-adjusted dollars)">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Household Sizes:

• 1-person households
• 2-person households
• 3-person households
• 4-person households
• 5-person households
• 6-person households
• 7-or-more-person households

Variables / Data Columns

• Household Size: This column showcases 7 household sizes ranging from 1-person households to 7-or-more-person households (As mentioned above).
• Median Household Income: Median household income, in 2022 inflation-adjusted dollars for the specific household size.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Hoquiam median household income. You can refer the same here