Retirement Age Men in the United States increased to 66.83 Years in 2025 from 66.67 Years in 2024. This dataset provides - United States Retirement Age Men - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Retirement Age Women in the United States increased to 66.83 Years in 2025 from 66.67 Years in 2024. This dataset provides - United States Retirement Age Women - actual values, historical data, forecast, chart, statistics, economic calendar and news.

This dataset provides values for RETIREMENT AGE MEN reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

United States CSI: Savings: Change in Comfortable Retirement: 5Yrs Ago: Don’t Know data was reported at 0.000 % in May 2018. This stayed constant from the previous number of 0.000 % for Apr 2018. United States CSI: Savings: Change in Comfortable Retirement: 5Yrs Ago: Don’t Know data is updated monthly, averaging 1.000 % from Dec 1997 (Median) to May 2018, with 246 observations. The data reached an all-time high of 3.000 % in Mar 2002 and a record low of 0.000 % in May 2018. United States CSI: Savings: Change in Comfortable Retirement: 5Yrs Ago: Don’t Know data remains active status in CEIC and is reported by University of Michigan. The data is categorized under Global Database’s USA – Table US.H026: Consumer Sentiment Index: Savings & Retirement. Table 19: Change in Likelihood of Comfortable Retirement The question was: Compared with 5 years ago, do you think the chances that you (and your husband/wife) will have a comfortable retirement have gone up, gone down, or remained about the same?

United States CSI: Savings: Adequate Retirement Income Probability: 50% data was reported at 11.000 % in May 2018. This records an increase from the previous number of 10.000 % for Apr 2018. United States CSI: Savings: Adequate Retirement Income Probability: 50% data is updated monthly, averaging 15.000 % from Dec 1997 (Median) to May 2018, with 246 observations. The data reached an all-time high of 21.000 % in Jan 2004 and a record low of 10.000 % in Apr 2018. United States CSI: Savings: Adequate Retirement Income Probability: 50% data remains active status in CEIC and is reported by University of Michigan. The data is categorized under Global Database’s USA – Table US.H026: Consumer Sentiment Index: Savings & Retirement. The question was: What do you think the chances are that when you retire, your income from Social Security and job pensions will be adequate to maintain your living standards?

This dataset sets forth the Police Retirement System holdings (both equity and fixed income) of the identified pension/retirement system as of the close of the fiscal year.

United States CSI: Savings: Adequate Retirement Income Probability: 51-74% data was reported at 7.000 % in May 2018. This stayed constant from the previous number of 7.000 % for Apr 2018. United States CSI: Savings: Adequate Retirement Income Probability: 51-74% data is updated monthly, averaging 6.000 % from Dec 1997 (Median) to May 2018, with 246 observations. The data reached an all-time high of 10.000 % in Dec 2012 and a record low of 3.000 % in Nov 2009. United States CSI: Savings: Adequate Retirement Income Probability: 51-74% data remains active status in CEIC and is reported by University of Michigan. The data is categorized under Global Database’s USA – Table US.H026: Consumer Sentiment Index: Savings & Retirement. The question was: What do you think the chances are that when you retire, your income from Social Security and job pensions will be adequate to maintain your living standards?

About this dataset

Context

The dataset tabulates the Palm Coast population by age cohorts (Children: Under 18 years; Working population: 18-64 years; Senior population: 65 years or more). It lists the population in each age cohort group along with its percentage relative to the total population of Palm Coast. The dataset can be utilized to understand the population distribution across children, working population and senior population for dependency ratio, housing requirements, ageing, migration patterns etc.

Key observations

The largest age group was 18 to 64 years with a poulation of 48,210 (52.93% of the total population). Source: U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Age cohorts:

• Under 18 years
• 18 to 64 years
• 65 years and over

Variables / Data Columns

• Age Group: This column displays the age cohort for the Palm Coast population analysis. Total expected values are 3 groups ( Children, Working Population and Senior Population).
• Population: The population for the age cohort in Palm Coast is shown in the following column.
• Percent of Total Population: The population as a percent of total population of the Palm Coast is shown in the following column.

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 Palm Coast Population by Age. You can refer the same here

Important Note: This item is in mature support as of September 2023 and will be retired in December 2025. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version.The USGS Protected Areas Database of the United States (PAD-US) is the official inventory of public parks and other protected open space. The spatial data in PAD-US represents public lands held in trust by thousands of national, state and regional/local governments, as well as non-profit conservation organizations.GAP 1 and 2 areas are primarily managed for biodiversity, GAP 3 are managed for multiple uses including conservation and extraction, GAP 4 no known mandate for biodiversity protection. Provides a general overview of protection status including management designations. PAD-US is published by the U.S. Geological Survey (USGS) Science Analytics and Synthesis (SAS), Gap Analysis Project (GAP). GAP produces data and tools that help meet critical national challenges such as biodiversity conservation, recreation, public health, climate change adaptation, and infrastructure investment. See the GAP webpage for more information about GAP and other GAP data including species and land cover.The USGS Protected Areas Database of the United States (PAD-US) classifies lands into four GAP Status classes:GAP Status 1 - Areas managed for biodiversity where natural disturbances are allowed to proceedGAP Status 2 - Areas managed for biodiversity where natural disturbance is suppressedGAP Status 3 - Areas protected from land cover conversion but subject to extractive uses such as logging and miningGAP Status 4 - Areas with no known mandate for protectionIn the United States, areas that are protected from development and managed for biodiversity conservation include Wilderness Areas, National Parks, National Wildlife Refuges, and Wild & Scenic Rivers. Understanding the geographic distribution of these protected areas and their level of protection is an important part of landscape-scale planning. Dataset SummaryPhenomenon Mapped: Areas protected from development and managed to maintain biodiversity Coordinate System: Web Mercator Auxiliary SphereExtent: 50 United States plus Puerto Rico, the US Virgin Islands, the Northern Mariana Islands and other Pacific Ocean IslandsVisible Scale: 1:1,000,000 and largerSource: USGS Science Analytics and Synthesis (SAS), Gap Analysis Project (GAP) PAD-US version 3.0Publication Date: July 2022Attributes included in this layer are: CategoryOwner TypeOwner NameLocal OwnerManager TypeManager NameLocal ManagerDesignation TypeLocal DesignationUnit NameLocal NameSourcePublic AccessGAP Status - Status 1, 2, or 3GAP Status DescriptionInternational Union for Conservation of Nature (IUCN) Description - I: Strict Nature Reserve, II: National Park, III: Natural Monument or Feature, IV: Habitat/Species Management Area, V: Protected Landscape/Seascape, VI: Protected area with sustainable use of natural resources, Other conservation area, UnassignedDate of EstablishmentThe source data for this layer are available here. What can you do with this Feature Layer?Feature layers work throughout the ArcGIS system. Generally your work flow with feature layers will begin in ArcGIS Online or ArcGIS Pro. Below are just a few of the things you can do with a feature service in Online and Pro.ArcGIS OnlineAdd this layer to a map in the map viewer. The layer is limited to scales of approximately 1:1,000,000 or larger but a vector tile layer created from the same data can be used at smaller scales to produce a webmap that displays across the full range of scales. The layer or a map containing it can be used in an application.Change the layer’s transparency and set its visibility rangeOpen the layer’s attribute table and make selections and apply filters. Selections made in the map or table are reflected in the other. Center on selection allows you to zoom to features selected in the map or table and show selected records allows you to view the selected records in the table.Change the layer’s style and filter the data. For example, you could set a filter for Gap Status Code = 3 to create a map of only the GAP Status 3 areas.Add labels and set their propertiesCustomize the pop-upArcGIS ProAdd this layer to a 2d or 3d map. The same scale limit as Online applies in ProUse as an input to geoprocessing. For example, copy features allows you to select then export portions of the data to a new feature class. Note that many features in the PAD-US database overlap. For example wilderness area designations overlap US Forest Service and other federal lands. Any analysis should take this into consideration. An imagery layer created from the same data set can be used for geoprocessing analysis with larger extents and eliminates some of the complications arising from overlapping polygons.Change the symbology and the attribute field used to symbolize the dataOpen table and make interactive selections with the mapModify the pop-upsApply Definition Queries to create sub-sets of the layerThis layer is part of the Living Atlas of the World that provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.

Important Note: This item is in mature support as of June 2024 and will be retired in December 2026. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version. Areas protected from conversion include areas that are permanently protected and managed for biodiversity such as Wilderness Areas and National Parks. In addition to protected lands, portions of areas protected from conversion includes multiple-use lands that are subject to extractive uses such as mining, logging, and off-highway vehicle use. These areas are managed to maintain a mostly undeveloped landscape including many areas managed by the Bureau of Land Management and US Forest Service.The Protected Areas Database of the United States classifies lands into four GAP Status classes. This layer displays lands managed for biodiversity conservation (GAP Status 1 and 2) and multiple-use lands (GAP Status 3). Dataset SummaryPhenomenon Mapped: Protected and multiple-use lands (GAP Status 1, 2, and 3)Units: MetersCell Size: 30.92208102 metersSource Type: ThematicPixel Type: 8-bit unsigned integerData Coordinate System: WGS 1984Mosaic Projection: Web Mercator Auxiliary SphereExtent: 50 United States plus Puerto Rico, the US Virgin Islands, Guam, Northern Mariana Islands and American Samoa.Source: USGS National Gap Analysis Program PAD-US version 3.0Publication Date: July 2022ArcGIS Server URL: https://landscape10.arcgis.com/arcgis/This layer displays protected areas from the Protected Areas Database of the United States version 3.0 created by the USGS National Gap Analysis Program. This layer displays areas managed for biodiversity where natural disturbances are allowed to proceed or are mimicked by management (GAP Status 1), areas managed for biodiversity where natural disturbance is suppressed (GAP Status 2), and multiple-use lands where extract activities are allowed (GAP Status 3). The source data for this layer are available here. A feature layer published from this dataset is also available.The polygon vector layer was converted to raster layers using the Polygon to Raster Tool using the National Elevation Dataset 1 arc second product as a snap raster.The service behind this layer was published with 8 functions allowing the user to select different views of the service. Other layers created from this service using functions include:USA Protected AreasUSA Unprotected AreasUSA Protected Areas - Gap Status 1-4USA Protected Areas - Gap Status 1USA Protected Areas - Gap Status 2USA Protected Areas - Gap Status 3USA Protected Areas - Gap Status 4What can you do with this layer? This layer is suitable for both visualization and analysis across the ArcGIS system. This layer can be combined with your data and other layers from the ArcGIS Living Atlas of the World in ArcGIS Online and ArcGIS Pro to create powerful web maps that can be used alone or in a story map or other application.Because this layer is part of the ArcGIS Living Atlas of the World it is easy to add to your map:In ArcGIS Online, you can add this layer to a map by selecting Add then Browse Living Atlas Layers. A window will open. Type "Protected from Land Cover Conversion" in the search box and browse to the layer. Select the layer then click Add to Map.In ArcGIS Pro, open a map and select Add Data from the Map Tab. Select Data at the top of the drop down menu. The Add Data dialog box will open on the left side of the box, expand Portal if necessary, then select Living Atlas. Type "Protected from Land Cover Conversion" in the search box, browse to the layer then click OK.In ArcGIS Pro you can use the built-in raster functions to create custom extracts of the data. Imagery layers provide fast, powerful inputs to geoprocessing tools, models, or Python scripts in Pro.The ArcGIS Living Atlas of the World provides an easy way to explore many other beautiful and authoritative maps on hundreds of topics like this one.

About this dataset

Context

The dataset tabulates the Solano County population by age cohorts (Children: Under 18 years; Working population: 18-64 years; Senior population: 65 years or more). It lists the population in each age cohort group along with its percentage relative to the total population of Solano County. The dataset can be utilized to understand the population distribution across children, working population and senior population for dependency ratio, housing requirements, ageing, migration patterns etc.

Key observations

The largest age group was 18 to 64 years with a poulation of 275,113 (61.02% of the total population). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

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

Age cohorts:

• Under 18 years
• 18 to 64 years
• 65 years and over

Variables / Data Columns

• Age Group: This column displays the age cohort for the Solano County population analysis. Total expected values are 3 groups ( Children, Working Population and Senior Population).
• Population: The population for the age cohort in Solano County is shown in the following column.
• Percent of Total Population: The population as a percent of total population of the Solano County is shown in the following column.

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 Solano County Population by Age. 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 Chesterfield. Based on the latest 2019-2023 5-Year Estimates from the American Community Survey, it displays how income varies among householders of different ages in Chesterfield. 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 2023

In terms of income distribution across age cohorts, in Chesterfield, householders within the 45 to 64 years age group have the highest median household income at $189,652, followed by those in the 25 to 44 years age group with an income of $153,133. Meanwhile householders within the 65 years and over age group report the second lowest median household income of $92,465. Notably, householders within the under 25 years age group, had the lowest median household income at $58,438.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 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 2023 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 Chesterfield median household income by age. You can refer the same here

Most health insurance in the USA is provided by employers until eligibility for public health insurance (Medicare) begins at age 65. Retiring before 65 exposes workers who lack retiree health insurance coverage to the risk of catastrophic medical expenditure. We solve and estimate a dynamic model of the employment behavior of older married couples that includes risky medical expenditure and health insurance. Parameter estimates imply that the risk-reducing feature of health insurance can account for about half of the observed association between retiree health insurance and employment for married men, but can account for only one tenth of the much larger observed association for married women. Policy simulations imply very small effects on employment of changing the age of eligibility for Medicare from 65 to 67.

About this dataset

Context

The dataset tabulates the Santa Fe population by age cohorts (Children: Under 18 years; Working population: 18-64 years; Senior population: 65 years or more). It lists the population in each age cohort group along with its percentage relative to the total population of Santa Fe. The dataset can be utilized to understand the population distribution across children, working population and senior population for dependency ratio, housing requirements, ageing, migration patterns etc.

Key observations

The largest age group was 18 to 64 years with a poulation of 50,861 (57.65% of the total population). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

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

Age cohorts:

• Under 18 years
• 18 to 64 years
• 65 years and over

Variables / Data Columns

• Age Group: This column displays the age cohort for the Santa Fe population analysis. Total expected values are 3 groups ( Children, Working Population and Senior Population).
• Population: The population for the age cohort in Santa Fe is shown in the following column.
• Percent of Total Population: The population as a percent of total population of the Santa Fe is shown in the following column.

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 Santa Fe Population by Age. You can refer the same here

This dataset provides values for RETIREMENT AGE WOMEN reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

About this dataset

Context

The dataset presents the distribution of median household income among distinct age brackets of householders in Essex County. Based on the latest 2019-2023 5-Year Estimates from the American Community Survey, it displays how income varies among householders of different ages in Essex County. 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 2023

In terms of income distribution across age cohorts, in Essex County, householders within the 45 to 64 years age group have the highest median household income at $125,742, followed by those in the 25 to 44 years age group with an income of $106,907. Meanwhile householders within the 65 years and over age group report the second lowest median household income of $64,344. Notably, householders within the under 25 years age group, had the lowest median household income at $62,124.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 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 2023 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 Essex County median household income by age. 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 Wahoo. Based on the latest 2019-2023 5-Year Estimates from the American Community Survey, it displays how income varies among householders of different ages in Wahoo. 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 2023

In terms of income distribution across age cohorts, in Wahoo, householders within the 25 to 44 years age group have the highest median household income at $117,328, followed by those in the 45 to 64 years age group with an income of $108,446. Meanwhile householders within the under 25 years age group report the second lowest median household income of $58,516. Notably, householders within the 65 years and over age group, had the lowest median household income at $42,402.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 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 2023 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 Wahoo median household income by age. You can refer the same here

In 2021, about 5.96 million people aged 65 years or older were living in California -- the most out of any state. In that same year, Florida, Texas, New York, and Pennsylvania rounded out the top five states with the most people aged 65 and over living there.

About this dataset

Context

The dataset tabulates the data for the Queen Creek, AZ population pyramid, which represents the Queen Creek population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Queen Creek, AZ, is 38.7.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Queen Creek, AZ, is 20.5.
• Total dependency ratio for Queen Creek, AZ is 59.1.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Queen Creek, AZ is 4.9.

Content

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

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group for the Queen Creek population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Queen Creek for the selected age group is shown in the following column.
• Population (Female): The female population in the Queen Creek for the selected age group is shown in the following column.
• Total Population: The total population of the Queen Creek for the selected age group is shown in the following column.

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 Queen Creek Population by Age. You can refer the same here

The 2015 American Housing Survey marks the first release of a newly integrated national sample and independent metropolitan area samples. The 2015 release features many variable name revisions, as well as the integration of an AHS Codebook Interactive Tool available on the U.S. Census Bureau We site. This data collection provides information on the characteristics of a national sample of housing units in 2015, including apartments, single-family homes, mobile homes, and vacant housing units. Data from the 15 largest metropolitan areas in the United States are included in the national sample survey (the AHS 2015 Metropolitan Data are also available as ICPSR 36805). The data are presented in three separate parts: Part 1, Household Record (Main Record), Part 2, Person Record, and Part 3, Project Record. Household Record data includes questions about household occupancy and tenure, household exterior and interior structural features, household equipment and appliances, housing problems, housing costs, home improvement, neighborhood features, recent moving information, income, and basic demographic information. The household record data also features four rotating topical modules: Arts and Culture, Food Security, Housing Counseling, and Healthy Homes. Person Record data includes questions about personal disabilities, income, and basic demographic information. Finally, the Project Record data includes questions about home improvement projects. Specific questions were asked about the types of projects, costs, funding sources, and year of completion.