The NYC Department of City Planning’s (DCP) Housing Database contains all NYC Department of Buildings (DOB) approved housing construction and demolition jobs filed or completed in NYC since January 1, 2010. It includes the three primary construction job types that add or remove residential units: new buildings, major alterations, and demolitions, and can be used to determine the change in legal housing units across time and space. Records in the Housing Database Project-Level Files are geocoded to the greatest level of precision possible, subject to numerous quality assurance and control checks, recoded for usability, and joined to other housing data sources relevant to city planners and analysts. Data are updated semiannually, at the end of the second and fourth quarters of each year. Please see DCP’s annual Housing Production Snapshot summarizing findings from the 21Q4 data release here. Additional Housing and Economic analyses are also available. The NYC Department of City Planning’s (DCP) Housing Database Unit Change Summary Files provide the net change in Class A housing units since 2010, and the count of units pending completion for commonly used political and statistical boundaries (Census Block, Census Tract, City Council district, Community District, Community District Tabulation Area (CDTA), Neighborhood Tabulation Area (NTA). These tables are aggregated from the DCP Housing Database Project-Level Files, which is derived from Department of Buildings (DOB) approved housing construction and demolition jobs filed or completed in NYC since January 1, 2010. Net housing unit change is calculated as the sum of all three construction job types that add or remove residential units: new buildings, major alterations, and demolitions. These files can be used to determine the change in legal housing units across time and space.

About this dataset

Context

The dataset tabulates the Bellefontaine Neighbors median household income by race. The dataset can be utilized to understand the racial distribution of Bellefontaine Neighbors income.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• Bellefontaine Neighbors, MO median household income breakdown by race betwen 2011 and 2021
• Median Household Income by Racial Categories in Bellefontaine Neighbors, MO (2021, in 2022 inflation-adjusted dollars)

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/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of Bellefontaine Neighbors median household income by race. You can refer the same here

About this dataset

Context

The dataset presents median household incomes for various household sizes in Bellefontaine Neighbors, MO, 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, Bellefontaine Neighbors did not include 5, or 7-person households. Across the different household sizes in Bellefontaine Neighbors the mean income is $61,519, and the standard deviation is $20,211. The coefficient of variation (CV) is 32.85%. This high CV indicates high relative variability, suggesting that the incomes vary significantly across different sizes of households.
• 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 $40,422. It then further increased to $93,094 for 6-person households, the largest household size for which the bureau reported a median household income.

https://i.neilsberg.com/ch/bellefontaine-neighbors-mo-median-household-income-by-household-size.jpeg" alt="Bellefontaine Neighbors, MO 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 Bellefontaine Neighbors median household income. You can refer the same here

These boundaries were developed by the Department of Planning based on 2020 Census data. Be aware that other organizations may use different neighborhood boundaries in their analyses.Demographics included are: race, ethnicity, gender, vacancy rate, homeowner status, family structure, and age.DATA DICTIONARY:

Field Name

Description

Name

Name of neighborhood statistical area

Population

Total population (P3)

White

White alone population (P3)

Blk_AfAm

Black or African American alone population (P3)

AmInd_AkNa

American Indian/Native Alaskan alone population (P3)

Asian

Asian alone population (P3)

NatHaw_Pac

Native Hawaiian and other Pacific Islander alone population (P3)

Other_Race

Some other race alone population (P3)

TwoOrMore

Two or more races population (P3)

Hisp_Lat

Hispanic or Latino population (P4)

Male

Male population (P12)

Female

Female population (P12)

Total_Units

Total housing units (H1)

Occ_Occupied

Occupied housing units (H3)

Occ_Vacant

Vacant housing units (H3)

Tenure_Owner

Owner-occupied units (H4)

Tenure_Renter

Renter-occupied units (H4)

Vacant_ForRent

Vacant units for rent (H5)

Vacant_ForSale

Vacant units for sale (H5)

Vacant_Other_All

All other vacant units (H5)

HH_Total

Total households (P16)

HH_Family

Total family households (P16)

HH_Married

Married couple family households (P16)

HH_OtherFamily

Other family households (P16)

HH_Male_NoSpouse

Male householder, no spouse present family household (P16)

HH_Female_NoSpouse

Female householder, no spouse present family household (P16)

HH_NonFamily

Total nonfamily households (P16)

HH_NonFamilyAlone

Householder living alone nonfamily households (P16)

HH_NonFamilyNotAlone

Householder not living alone nonfamily households (P16)

HH18_With18

Households with one or more people under 18 (P21)

HH18_FamilyWith18

Family households with one or more people under 18 (P21)

HH18_NonFamilyWith18

Nonfamily households with one or more people under 18 (P21)

HH18_No18

Households with no people under 18 (P21)

HH18_FamilyNo18

Family households with no people under 18 (P21)

HH18_NonFamilyNo18

Nonfamily households with no people under 18 (P21)

Age_U5

Population under 5 years (P12)

Age_5_9

Population age 5-9 (P12)

Age_10_14

Population age 10-14 (P12)

Age_15_17

Population age 15-17 (P12)

Age_18_21

Population age 18-21 (P12)

Age_22_24

Population age 22-24 (P12)

Age_25_29

Population age 25-29 (P12)

Age_30_34

Population age 30-34 (P12)

Age_35_39

Population age 35-39 (P12)

Age_40_44

Population age 40-44 (P12)

Age_45_49

Population age 45-49 (P12)

Age_50_54

Population age 50-54 (P12)

Age_55_59

Population age 55-59 (P12)

Age_60_64

Population age 60-64 (P12)

Age_65_69

Population age 65-69 (P12)

Age_70_74

Population age 70-74 (P12)

Age_75_79

Population age 75-79 (P12)

Age_80_84

Population age 80-84 (P12)

Age_85up

Population age 85 and up (P12)

Med_Age

Median age (P13)

Med_Age_Male

Median male age (P13)

Med_Age_Female

Median female age (P13)

To leave feedback or ask a question about this dataset, please fill out the following form: Neighborhood Statistical Area (NSA) Boundaries feedback form.

About this dataset

Context

The dataset presents the mean household income for each of the five quintiles in Bellefontaine Neighbors, MO, as reported by the U.S. Census Bureau. The dataset highlights the variation in mean household income across quintiles, offering valuable insights into income distribution and inequality.

Key observations

• Income disparities: The mean income of the lowest quintile (20% of households with the lowest income) is 10,367, while the mean income for the highest quintile (20% of households with the highest income) is 169,915. This indicates that the top earners earn 16 times compared to the lowest earners.
• *Top 5%: * The mean household income for the wealthiest population (top 5%) is 359,970, which is 211.85% higher compared to the highest quintile, and 3472.27% higher compared to the lowest quintile.

Content

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

Income Levels:

• Lowest Quintile
• Second Quintile
• Third Quintile
• Fourth Quintile
• Highest Quintile
• Top 5 Percent

Variables / Data Columns

• Income Level: This column showcases the income levels (As mentioned above).
• Mean Household Income: Mean household income, in 2022 inflation-adjusted dollars for the specific income level.

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 Bellefontaine Neighbors median household income. You can refer the same here

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).

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...

About this dataset

Context

The dataset presents the distribution of median household income among distinct age brackets of householders in New Chicago. Based on the latest 2018-2022 5-Year Estimates from the American Community Survey, it displays how income varies among householders of different ages in New Chicago. It showcases how household incomes typically rise as the head of the household gets older. The dataset can be utilized to gain insights into age-based household income trends and explore the variations in incomes across households.

Key observations: Insights from 2022

In terms of income distribution across age cohorts, in New Chicago, the median household income stands at $87,560 for householders within the 25 to 44 years age group, followed by $64,818 for the 45 to 64 years age group. Notably, householders within the 65 years and over age group, had the lowest median household income at $42,022.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Age groups classifications include:

• Under 25 years
• 25 to 44 years
• 45 to 64 years
• 65 years and over

Variables / Data Columns

• Age Of The Head Of Household: This column presents the age of the head of household
• Median Household Income: Median household income, in 2022 inflation-adjusted dollars for the specific age group

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 New Chicago median household income by age. You can refer the same here

The estimated median household income and estimated median family income are two separate measures: every family is a household, but not every household is a family. According to the U.S. Census Bureau definitions of the terms, a family “includes a householder and one or more people living in the same household who are related to the householder by birth, marriage, or adoption,”[1] while a household “includes all the people who occupy a housing unit,” including households of just one person[2]. When evaluated together, the estimated median household income and estimated median family income provide a thorough picture of household-level economics in Champaign County.

Both estimated median household income and estimated median family income were higher in 2023 than in 2005. The changes in estimated median household income and estimated median family income between 2022 and 2023 were not statistically significant. Estimated median family income is consistently higher than estimated median household income, largely due to the definitions of each term, and the types of household that are measured and are not measured in each category.

Median income data 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. 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 datasets on Median Household Income in the Past 12 Months (in 2020 Inflation-Adjusted Dollars) and Median Family Income in the Past 12 Months (in 2020 Inflation-Adjusted Dollars).

[1] U.S. Census Bureau. (Date unknown). Glossary. “Family Household.” (Accessed 19 April 2016).

[2] U.S. Census Bureau. (Date unknown). Glossary. “Household.” (Accessed 19 April 2016).

Sources: U.S. Census Bureau; American Community Survey, 2023 American Community Survey 1-Year Estimates, Table S1903; 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 S1903; generated by CCRPC staff; using data.census.gov; (18 September 2023).; U.S. Census Bureau; American Community Survey, 2021 American Community Survey 1-Year Estimates, Table S1903; generated by CCRPC staff; using data.census.gov; (3 October 2022).; U.S. Census Bureau; American Community Survey, 2019 American Community Survey 1-Year Estimates, Table S1903; generated by CCRPC staff; using data.census.gov; (7 June 2021).; U.S. Census Bureau; American Community Survey, 2018 American Community Survey 1-Year Estimates, Table S1903; generated by CCRPC staff; using data.census.gov; (7 June 2021).;U.S. Census Bureau; American Community Survey, 2017 American Community Survey 1-Year Estimates, Table S1903; 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 S1903; 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 S1903; 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 S1903; 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 S1903; 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 S1903; 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 S1903; 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 S1903; 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 S1903; 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 S1903; 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 S1903; 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 S1903; 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 S1903; generated by CCRPC staff; using American FactFinder; (16 March 2016).

About this dataset

Context

The dataset presents the distribution of median household income among distinct age brackets of householders in Cashion Community. Based on the latest 2018-2022 5-Year Estimates from the American Community Survey, it displays how income varies among householders of different ages in Cashion Community. It showcases how household incomes typically rise as the head of the household gets older. The dataset can be utilized to gain insights into age-based household income trends and explore the variations in incomes across households.

Key observations: Insights from 2022

In terms of income distribution across age cohorts, in Cashion Community, where there exist only two delineated age groups, the median household income is $72,888 for householders within the 25 to 44 years age group, compared to $64,644 for the 65 years and over age group.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Age groups classifications include:

• Under 25 years
• 25 to 44 years
• 45 to 64 years
• 65 years and over

Variables / Data Columns

• Age Of The Head Of Household: This column presents the age of the head of household
• Median Household Income: Median household income, in 2022 inflation-adjusted dollars for the specific age group

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 Cashion Community median household income by age. You can refer the same here

Property currently or historically owned and managed by the City of Chicago. Information provided in the database, or on the City’s website generally, should not be used as a substitute for title research, title evidence, title insurance, real estate tax exemption or payment status, environmental or geotechnical due diligence, or as a substitute for legal, accounting, real estate, business, tax or other professional advice. The City assumes no liability for any damages or loss of any kind that might arise from the reliance upon, use of, misuse of, or the inability to use the database or the City’s web site and the materials contained on the website. The City also assumes no liability for improper or incorrect use of materials or information contained on its website. All materials that appear in the database or on the City’s web site are distributed and transmitted "as is," without warranties of any kind, either express or implied as to the accuracy, reliability or completeness of any information, and subject to the terms and conditions stated in this disclaimer.

The following columns were added 4/14/2023:

• Sales Status
• Sale Offering Status
• Sale Offering Reason
• Square Footage - City Estimate
• Land Value (2022) -- Note: The year will change over time.

The following columns were added 3/19/2024:

• Application Use
• Grouped Parcels
• Application Deadline
• Offer Round
• Application URL

This layer includes the Master Plan neighborhoods in Detroit as of May 2015.

Direct link: Short-Term Rental Eligibility Dataset

DATASET CONTEXT

Boston's ordinance on short-term rentals is designed to incorporate the growth of the home-share industry into the City's work to create affordable housing for all residents. We want to preserve housing for residents while allowing Bostonians to benefit from this new industry. Starting on on January 1, 2019, short-term rentals in Boston will need to register with the City of Boston.

Eligibility for every unit in the City of Boston is dependant on the following six criteria:

• No affordability covenant restrictions
• Compliance with housing laws and codes
• No violations of laws regarding short-term rental use
• Owner occupied
• Two- or three-family dwelling
• Residential use classification

The Short-Term Rental Eligibility Dataset leverages information, wherever possible, about these criteria. For additional details and information about these criteria, please visit https://www.boston.gov/short-term-rentals.

ABOUT THIS DATASET

ATTENTION: The Short-Term Rental Eligibility Dataset is now available for residents and landlords to determine their registration eligibility.

NOTE: These data are refreshed on a nightly basis.

In June 2018, a citywide ordinance established new guidelines and regulations for short-term rentals in Boston. Registration opened January 1, 2019. The Short-Term Rental Eligibility Dataset was created to help residents, landlords, and City officials determine whether a property is eligible to be registered as a short-term rental.

The Short-Term Rental Eligibility Dataset currently joins data from the following datasets:

• Live Street Address Management (SAM) Addresses
• Assessing Online
• Code Enforcement - Building and Property
• Problem Properties
• Income-Restricted Properties
• STR Registration Data

HOW TO DETERMINE ELIGIBILITY FOR SHORT-TERM RENTAL REGISTRATION

1. ** Open** the Short-Term Rental Eligibility Dataset. In the dataset's search bar, enter the address of the property you are seeking to register.

2. Find the row containing the correct address and unit of the property you are seeking. This is the information we have for your unit.

3. Look at the columns marked as “Home-Share Eligible,” “Limited-Share Eligible,” and “Owner-Adjacent Eligible.”

   A “yes” under any of these columns means your unit IS eligible for registration under that short-term rental type. Click here for a description of short-term rental types.

   A “no” under any of these columns means your unit is NOT eligible for registration under that short-term rental type. Click here for a description of short-term rental types.

4. If your unit has a “yes” under “Home-Share Eligible,” “Limited-Share Eligible,” or “Owner-Adjacent Eligible,” you can register your unit here.

WHY IS MY UNIT LISTED AS “NOT ELIGIBLE”?

If you find that your unit is listed as NOT eligible, and you would like to understand more about why, you can use the Short-Term Rental Eligibility Dataset to learn more. The following columns measure each of the six eligibility criteria in the following ways:

1. No affordability covenant restrictions

   • A “yes” in the “Income Restricted” column tells you that the unit is marked as income restricted and is NOT eligible.

The “Income Restricted” column measures whether the unit is subject to an affordability covenant, as reported by the Department of Neighborhood Development and/or the Boston Planning and Development Agency.
For questions about affordability covenants, contact the Department of Neighborhood Development.

1. Compliance with housing laws and codes

   • A “yes” in the “Problem Properties” column tells you that this unit is considered a “Problem Property” by the Problem Properties Task Force and is NOT eligible.

Learn more about how “Problem Properties” are defined here.

* A **“yes”** in the **“Problem Property Owner”** column tells you that the owner of this unit also owns a “Problem Property,” as reported by the Problem Properties Task Force. 

Owners with any properties designated as a Problem Property are NOT eligible.

No unit owned by the owner of a “Problem Property” may register a short-term rental.
Learn more about how “Problem Properties” are defined here.

* The **“Open Violation Count”** column tells you how many open violations the unit has. Units with **any open** violations are NOT eligible. Violations counted include: violations of the sanitary, building, zoning, and fire code; stop work orders; and abatement orders. 

NOTE: Violations written before 1/1/19 that are still open will make a unit NOT eligible until these violations are resolved.
If your unit has an open violation, visit these links to appeal your violation(s) or pay your code violation fine(s).

* The **“Violations in the Last 6 Months”** column tells you how many violations the unit has received in the last six months. Units with **three or more** violations, whether open or closed, are NOT eligible. 

NOTE: Only violations written on or after 1/1/19 will count against this criteria.
If your unit has an open violation, visit these links to appeal your violation(s) or pay your code violation fine(s).

How to comply with housing laws and codes:
Have an open violation? Visit these links to appeal your violation(s) or pay your code violation fine(s).
Have questions about problem properties? Visit Neighborhood Service’s Problem Properties site.
a legal restriction that prohibits the use of the unit as a Short-Term Rental under condominium bylaws.
Units with legal restrictions found upon investigation are NOT eligible.

If the investigation of a complaint against the unit yields restrictions of the nature detailed above, we will mark the unit with a “yes” in this column. Until such complaint-based investigations begin, all units are marked with “no.”
NOTE: Currently no units have a “legally restricted” designation.
Limited-Share
If you are the owner-occupant of a unit and you have not filed for Residential Tax Exemption, you can still register your unit by proving owner-occupancy. It is recommended that you submit proof of residency in your short-term rental registration application to expedite the process of proving owner-occupancy (see “Primary Residence Evidence” section).

* **“Building Owner-Occupied”** measures whether the building has a single owner AND is owner occupied. A “no” in this column indicates that the unit is NOT eligible for an owner-adjacent short-term rental. 

If you believe your building occupancy data is incorrect, please contact the Assessing Department.

1. Two- or three-family dwelling

   • The “Units in Building” column tells you how many units are in the building. Owner-Adjacent units are only allowed in two- to three-family buildings; therefore, four or more units in this column will mark the unit as NOT eligible for an Owner-Adjacent Short-Term Rental.

   • A “no” in the “Building Single Owner” column tells you that the owner of this unit does not own the entire building and is NOT eligible for an Owner-Adjacent Short-Term Rental.

   If you believe your building occupancy data is incorrect, please contact the Assessing Department.
   R4

   If you believe your building occupancy data is incorrect, please contact the Assessing Department.

Visit this site for more information on unit eligibility criteria.

About this dataset

Context

The dataset presents a breakdown of households across various income brackets in Many, LA, as reported by the U.S. Census Bureau. The Census Bureau classifies households into different categories, including total households, family households, and non-family households. Our analysis of U.S. Census Bureau American Community Survey data for Many, LA reveals how household income distribution varies among these categories. The dataset highlights the variation in number of households with income, offering valuable insights into the distribution of Many households based on income levels.

Key observations

• For Family Households: In Many, the majority of family households, representing 26.43%, earn Less than $10,000, showcasing a substantial share of the community families falling within this income bracket. Conversely, the minority of family households, comprising 0.0%, have incomes falling $45,000 to $49,999, representing a smaller but still significant segment of the community.
• For Non-Family Households: In Many, the majority of non-family households, accounting for 34.44%, have income Less than $10,000, indicating that a substantial portion of non-family households falls within this income bracket. On the other hand, the minority of non-family households, comprising 0.0%, earn $45,000 to $49,999, representing a smaller, yet notable, portion of non-family households in the community.

Content

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

Income Levels:

• Less than $10,000
• $10,000 to $14,999
• $15,000 to $19,999
• $20,000 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $59,999
• $60,000 to $74,999
• $75,000 to $99,999
• $125,000 to $149,999
• $150,000 to $199,999
• $200,000 or more

Variables / Data Columns

• Income Level: The income level represents the income brackets ranging from Less than $10,000 to $200,000 or more in Many, LA (As mentioned above).
• All Households: Count of households for the specified income level
• % All Households: Percentage of households at the specified income level relative to the total households in Many, LA
• Family Households: Count of family households for the specified income level
• % Family Households: Percentage of family households at the specified income level relative to the total family households in Many, LA
• Non-Family Households: Count of non-family households for the specified income level
• % Non-Family Households: Percentage of non-family households at the specified income level relative to the total non-family households in Many, LA

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 Many median household income. You can refer the same here

This dataset was developed by the Research & Analytics Group at the Atlanta Regional Commission using data from the U.S. Census Bureau.For a deep dive into the data model including every specific metric, see the Infrastructure Manifest. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics.Naming conventions:Prefixes: None Countp Percentr Ratem Mediana Mean (average)t Aggregate (total)ch Change in absolute terms (value in t2 - value in t1)pch Percent change ((value in t2 - value in t1) / value in t1)chp Change in percent (percent in t2 - percent in t1)s Significance flag for change: 1 = statistically significant with a 90% CI, 0 = not statistically significant, blank = cannot be computed Suffixes: _e19 Estimate from 2014-19 ACS_m19 Margin of Error from 2014-19 ACS_00_v19 Decennial 2000, re-estimated to 2019 geography_00_19 Change, 2000-19_e10_v19 2006-10 ACS, re-estimated to 2019 geography_m10_v19 Margin of Error from 2006-10 ACS, re-estimated to 2019 geography_e10_19 Change, 2010-19The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent. The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2015-2019). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available. For further explanation of ACS estimates and margin of error, visit Census ACS website.Source: U.S. Census Bureau, Atlanta Regional CommissionDate: 2015-2019Data License: Creative Commons Attribution 4.0 International (CC by 4.0)Link to the manifest: https://www.arcgis.com/sharing/rest/content/items/3d489c725bb24f52a987b302147c46ee/data

About this dataset

Context

The dataset tabulates the median household income in Cashion Community. It can be utilized to understand the trend in median household income and to analyze the income distribution in Cashion Community by household type, size, and across various income brackets.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• Cashion Community, TX Median Household Income Trends (2010-2021, in 2022 inflation-adjusted dollars)
• Median Household Income Variation by Family Size in Cashion Community, TX: Comparative analysis across 7 household sizes
• Income Distribution by Quintile: Mean Household Income in Cashion Community, TX
• Cashion Community, TX households by income brackets: family, non-family, and total, in 2022 inflation-adjusted dollars

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/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of Cashion Community median household income. You can refer the same here

About this dataset

Context

The dataset presents the distribution of median household income among distinct age brackets of householders in Bellefontaine Neighbors. Based on the latest 2017-2021 5-Year Estimates from the American Community Survey, it displays how income varies among householders of different ages in Bellefontaine Neighbors. It showcases how household incomes typically rise as the head of the household gets older. The dataset can be utilized to gain insights into age-based household income trends and explore the variations in incomes across households.

Key observations: Insights from 2021

In terms of income distribution across age cohorts, in Bellefontaine Neighbors, the median household income stands at $59,810 for householders within the 45 to 64 years age group, followed by $46,589 for the 25 to 44 years age group. Notably, householders within the 65 years and over age group, had the lowest median household income at $44,085.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2022-inflation-adjusted dollars.

Age groups classifications include:

• Under 25 years
• 25 to 44 years
• 45 to 64 years
• 65 years and over

Variables / Data Columns

• Age Of The Head Of Household: This column presents the age of the head of household
• Median Household Income: Median household income, in 2022 inflation-adjusted dollars for the specific age group

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 Bellefontaine Neighbors median household income by age. You can refer the same here

About this dataset

Context

The dataset presents a breakdown of households across various income brackets in May township, Washington County, Minnesota, as reported by the U.S. Census Bureau. The Census Bureau classifies households into different categories, including total households, family households, and non-family households. Our analysis of U.S. Census Bureau American Community Survey data for May township, Washington County, Minnesota reveals how household income distribution varies among these categories. The dataset highlights the variation in number of households with income, offering valuable insights into the distribution of May township households based on income levels.

Key observations

• For Family Households: In May township, the majority of family households, representing 35.8%, earn $200,000 or more, showcasing a substantial share of the community families falling within this income bracket. Conversely, the minority of family households, comprising 0.0%, have incomes falling $10,000 to $14,999, representing a smaller but still significant segment of the community.
• For Non-Family Households: In May township, the majority of non-family households, accounting for 19.29%, have income $75,000 to $99,999, indicating that a substantial portion of non-family households falls within this income bracket. On the other hand, the minority of non-family households, comprising 0.0%, earn $10,000 to $14,999, representing a smaller, yet notable, portion of non-family households in the community.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Income Levels:

• Less than $10,000
• $10,000 to $14,999
• $15,000 to $19,999
• $20,000 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $59,999
• $60,000 to $74,999
• $75,000 to $99,999
• $125,000 to $149,999
• $150,000 to $199,999
• $200,000 or more

Variables / Data Columns

• Income Level: The income level represents the income brackets ranging from Less than $10,000 to $200,000 or more in May township, Washington County, Minnesota (As mentioned above).
• All Households: Count of households for the specified income level
• % All Households: Percentage of households at the specified income level relative to the total households in May township, Washington County, Minnesota
• Family Households: Count of family households for the specified income level
• % Family Households: Percentage of family households at the specified income level relative to the total family households in May township, Washington County, Minnesota
• Non-Family Households: Count of non-family households for the specified income level
• % Non-Family Households: Percentage of non-family households at the specified income level relative to the total non-family households in May township, Washington County, Minnesota

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 May township median household income. You can refer the same here

About this dataset

Context

The dataset tabulates the Cashion Community household income by gender. The dataset can be utilized to understand the gender-based income distribution of Cashion Community income.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• Cashion Community, TX annual median income by work experience and sex dataset : Aged 15+, 2010-2022 (in 2022 inflation-adjusted dollars)
• Cashion Community, TX annual income distribution by work experience and gender dataset (Number of individuals ages 15+ with income, 2021)

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/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of Cashion Community income distribution by gender. You can refer the same here

About this dataset

Context

The dataset presents a breakdown of households across various income brackets in Edson, Wisconsin, as reported by the U.S. Census Bureau. The Census Bureau classifies households into different categories, including total households, family households, and non-family households. Our analysis of U.S. Census Bureau American Community Survey data for Edson, Wisconsin reveals how household income distribution varies among these categories. The dataset highlights the variation in number of households with income, offering valuable insights into the distribution of Edson town households based on income levels.

Key observations

• For Family Households: In Edson town, the majority of family households, representing 20.61%, earn $100,000 to $124,999, showcasing a substantial share of the community families falling within this income bracket. Conversely, the minority of family households, comprising 0.0%, have incomes falling $60,000 to $74,999, representing a smaller but still significant segment of the community.
• For Non-Family Households: In Edson town, the majority of non-family households, accounting for 30.15%, have income $75,000 to $99,999, indicating that a substantial portion of non-family households falls within this income bracket. On the other hand, the minority of non-family households, comprising 0.0%, earn $60,000 to $74,999, representing a smaller, yet notable, portion of non-family households in the community.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Income Levels:

• Less than $10,000
• $10,000 to $14,999
• $15,000 to $19,999
• $20,000 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $59,999
• $60,000 to $74,999
• $75,000 to $99,999
• $125,000 to $149,999
• $150,000 to $199,999
• $200,000 or more

Variables / Data Columns

• Income Level: The income level represents the income brackets ranging from Less than $10,000 to $200,000 or more in Edson, Wisconsin (As mentioned above).
• All Households: Count of households for the specified income level
• % All Households: Percentage of households at the specified income level relative to the total households in Edson, Wisconsin
• Family Households: Count of family households for the specified income level
• % Family Households: Percentage of family households at the specified income level relative to the total family households in Edson, Wisconsin
• Non-Family Households: Count of non-family households for the specified income level
• % Non-Family Households: Percentage of non-family households at the specified income level relative to the total non-family households in Edson, Wisconsin

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 Edson town median household income. You can refer the same here