Existing Home Sales in the United States decreased to 4000 Thousand in August from 4010 Thousand in July of 2025. This dataset provides the latest reported value for - United States Existing Home Sales - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Housing Starts in the United States decreased to 1307 Thousand units in August from 1429 Thousand units in July of 2025. This dataset provides the latest reported value for - United States Housing Starts - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

About this dataset

Context

The dataset presents median income data over a decade or more for males and females categorized by Total, Full-Time Year-Round (FT), and Part-Time (PT) employment in Mountain Home. It showcases annual income, providing insights into gender-specific income distributions and the disparities between full-time and part-time work. The dataset can be utilized to gain insights into gender-based pay disparity trends and explore the variations in income for male and female individuals.

Key observations: Insights from 2023

Based on our analysis ACS 2019-2023 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In Mountain Home, the median income for all workers aged 15 years and older, regardless of work hours, was $33,003 for males and $23,731 for females.

These income figures indicate a substantial gender-based pay disparity, showcasing a gap of approximately 28% between the median incomes of males and females in Mountain Home. With women, regardless of work hours, earning 72 cents to each dollar earned by men, this income disparity reveals a concerning trend toward wage inequality that demands attention in thecity of Mountain Home.

- Full-time workers, aged 15 years and older: In Mountain Home, among full-time, year-round workers aged 15 years and older, males earned a median income of $40,799, while females earned $38,645, resulting in a 5% gender pay gap among full-time workers. This illustrates that women earn 95 cents for each dollar earned by men in full-time positions. While this gap shows a trend where women are inching closer to wage parity with men, it also exhibits a noticeable income difference for women working full-time in the city of Mountain Home.

Interestingly, when analyzing income across all roles, including non-full-time employment, the gender pay gap percentage was higher for women compared to men. It appears that full-time employment presents a more favorable income scenario for women compared to other employment patterns in Mountain Home.

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.

Gender classifications include:

• Male
• Female

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Variables / Data Columns

• Year: This column presents the data year. Expected values are 2010 to 2023
• Male Total Income: Annual median income, for males regardless of work hours
• Male FT Income: Annual median income, for males working full time, year-round
• Male PT Income: Annual median income, for males working part time
• Female Total Income: Annual median income, for females regardless of work hours
• Female FT Income: Annual median income, for females working full time, year-round
• Female PT Income: Annual median income, for females working part time

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 Mountain Home median household income by race. You can refer the same here

About this dataset

Context

The dataset presents the detailed breakdown of the count of individuals within distinct income brackets, categorizing them by gender (men and women) and employment type - full-time (FT) and part-time (PT), offering valuable insights into the diverse income landscapes within Home Lake township. The dataset can be utilized to gain insights into gender-based income distribution within the Home Lake township population, aiding in data analysis and decision-making..

Key observations

• Employment patterns: Within Home Lake township, among individuals aged 15 years and older with income, there were 39 men and 31 women in the workforce. Among them, 20 men were engaged in full-time, year-round employment, while 15 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 5% fell within the income range of under $24,999, while 33.33% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 30% of men in full-time roles earned incomes exceeding $100,000, while none of women in full-time positions earned within this income bracket.
• Refer to the research insights for more key observations on more income brackets ( Annual income under $24,999, Annual income between $25,000 and $49,999, Annual income between $50,000 and $74,999, Annual income between $75,000 and $99,999 and Annual income above $100,000) and employment types (full-time year-round and part-time)

Content

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

Income brackets:

• $1 to $2,499 or loss
• $2,500 to $4,999
• $5,000 to $7,499
• $7,500 to $9,999
• $10,000 to $12,499
• $12,500 to $14,999
• $15,000 to $17,499
• $17,500 to $19,999
• $20,000 to $22,499
• $22,500 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 $54,999
• $55,000 to $64,999
• $65,000 to $74,999
• $75,000 to $99,999
• $100,000 or more

Variables / Data Columns

• Income Bracket: This column showcases 20 income brackets ranging from $1 to $100,000+..
• Full-Time Males: The count of males employed full-time year-round and earning within a specified income bracket
• Part-Time Males: The count of males employed part-time and earning within a specified income bracket
• Full-Time Females: The count of females employed full-time year-round and earning within a specified income bracket
• Part-Time Females: The count of females employed part-time and earning within a specified income bracket

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

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 Home Lake township median household income by race. You can refer the same here

Graph and download economic data for Median Sales Price of Houses Sold for the United States (MSPUS) from Q1 1963 to Q2 2025 about sales, median, housing, and USA.

Abstract copyright UK Data Service and data collection copyright owner. The CORE datasets contained in the study cover annual official statistics on new lettings of the stock owned by local authorities and private registered providers of social housing in England, as well as sales of the social stock owned by private registered providers. For each year, data is structured into five datasets, four based on type of letting (social rent general needs and supported needs, and affordable rent general needs and supported needs) and one based on sales by private registered providers. All datasets are based on administrative data collected via the COntinuous REcording of Lettings and Sales (CORE) system. It is a regulatory requirement for providers registered with the Homes and Communities Agency to supply the data. For those who are not registered, submissions are voluntary. Local authorities have participated in CORE since 2004-5 on a voluntary basis. In the first year, only 24% of stock-holding local authorities participated, but the number of authorities participating has steadily increased, with all authorities submitting some data for 2013-14. Weighting is applied to adjust for non-response by local authorities for social rent datasets and imputation is also carried out to address item level non-response of key data on tenant characteristic, for both local authorities and private registered providers. The three datasets for affordable rent are not weighted or imputed. The sales dataset is imputed, with details of the imputations contained within the data dictionary. The collection of social housing lettings and sales data allows for a better understanding of the socio-economic and demographic make-up of affordable housing customers by tenure and of local housing markets and affordable housing products. These data are used by central government to inform national housing policy and by local government to inform their Strategic Housing Market Assessments. The data are also used by academics, researchers, charities and the wider public to understand social housing issues. Further information may be found on the GOV.UK Social housing lettings and Social housing sales webpages. Latest editionFor the 11th edition (August 2023), data and documentation for CORE Lettings for 2018/19-2021/22 have been added to the study. Sales data and documentation for 2018/19-2020/21 have been replaced, and 2021/22 added. End User Licence, Special Licence and Secure Access datasets The CORE datasets are available at three access levels, depending on the level of detail in the data. For the standard End User Licence (EUL) version (SN 7603) the geographic level of the data is set at Government Office Region (GOR). Letting and voiding dates are provided at month and year only; age variables are top-coded at 90 years; income, benefits, earnings, charge and shortfall variables are banded to disguise unique values; landlords are grouped into coded categories.For the Special Licence access (SL) version (SN 7604), the geographic level is set at Local Authority. The SL data have more restrictive access conditions than those made available under the standard EUL. Prospective users of the SL version will need to complete an extra application form and demonstrate to the data owners exactly why they need access to the additional variables in order to get permission to use that version. For Secure Access (SN 7686) the full CORE datasets are available, with some key variables recoded. Prospective users of the Secure Access version will need to fulfil additional requirements, including completion of face-to-face training and agreement to further stringent access conditions.

About this dataset

Context

The dataset presents median income data over a decade or more for males and females categorized by Total, Full-Time Year-Round (FT), and Part-Time (PT) employment in Home Brook township. It showcases annual income, providing insights into gender-specific income distributions and the disparities between full-time and part-time work. The dataset can be utilized to gain insights into gender-based pay disparity trends and explore the variations in income for male and female individuals.

Key observations: Insights from 2021

Based on our analysis ACS 2017-2021 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In Home Brook township, the median income for all workers aged 15 years and older, regardless of work hours, was $35,468 for males and $18,315 for females.

These income figures highlight a substantial gender-based income gap in Home Brook township. Women, regardless of work hours, earn 52 cents for each dollar earned by men. This significant gender pay gap, approximately 48%, underscores concerning gender-based income inequality in the township of Home Brook township.

- Full-time workers, aged 15 years and older: In Home Brook township, among full-time, year-round workers aged 15 years and older, males earned a median income of $61,342, while females earned $58,099, resulting in a 5% gender pay gap among full-time workers. This illustrates that women earn 95 cents for each dollar earned by men in full-time positions. While this gap shows a trend where women are inching closer to wage parity with men, it also exhibits a noticeable income difference for women working full-time in the township of Home Brook township.

Interestingly, when analyzing income across all roles, including non-full-time employment, the gender pay gap percentage was higher for women compared to men. It appears that full-time employment presents a more favorable income scenario for women compared to other employment patterns in Home Brook township.

https://i.neilsberg.com/ch/home-brook-township-mn-income-by-gender.jpeg" alt="Home Brook Township, Minnesota gender based income disparity">

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.

Gender classifications include:

• Male
• Female

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Variables / Data Columns

• Year: This column presents the data year. Expected values are 2010 to 2022
• Male Total Income: Annual median income, for males regardless of work hours
• Male FT Income: Annual median income, for males working full time, year-round
• Male PT Income: Annual median income, for males working part time
• Female Total Income: Annual median income, for females regardless of work hours
• Female FT Income: Annual median income, for females working full time, year-round
• Female PT Income: Annual median income, for females working part time

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 Home Brook township median household income by gender. You can refer the same here

The CORE datasets contained in the study cover annual official statistics on new lettings of the stock owned by local authorities and private registered providers of social housing in England, as well as sales of the social stock owned by private registered providers. For each year, data is structured into five datasets, four based on type of letting (social rent general needs and supported needs, and affordable rent general needs and supported needs) and one based on sales by private registered providers. All datasets are based on administrative data collected via the COntinuous REcording of Lettings and Sales (CORE) system. It is a regulatory requirement for providers registered with the Homes and Communities Agency to supply the data. For those who are not registered, submissions are voluntary. Local authorities have participated in CORE since 2004-5 on a voluntary basis. In the first year, only 24% of stock-holding local authorities participated, but the number of authorities participating has steadily increased, with all authorities submitting some data for 2013-14. Weighting is applied to adjust for non-response by local authorities for social rent datasets and imputation is also carried out to address item level non-response of key data on tenant characteristics, for both local authorities and private registered providers. The three datasets for affordable rent are not weighted or imputed. The sales dataset is imputed, with more information on the imputations within the data dictionary.

The collection of social housing lettings and sales data allows for a better understanding of the socio-economic and demographic make-up of affordable housing customers by tenure and of local housing markets and affordable housing products. These data are used by central government to inform national housing policy and by local government to inform their Strategic Housing Market Assessments. The data are also used by academics, researchers, charities and the wider public to understand social housing issues.

Further information may be found on the GOV.UK "https://www.gov.uk/government/collections/rents-lettings-and-tenancies" title="Social housing lettings"> Social housing lettings and "https://www.gov.uk/government/collections/social-housing-sales-including-right-to-buy-and-transfers" title="Social housing sales"> Social housing sales webpages.

Latest edition
For the 11th edition (August 2023), data and documentation for CORE Lettings for 2018/19-2021/22 have been added to the study. Sales data and documentation for 2018/19-2020/21 have been replaced, and 2021/22 added.

End User Licence, Special Licence and Secure Access datasets
The CORE datasets are available at three access levels, depending on the level of detail in the data.

• For the standard End User Licence (EUL) version (SN 7603) the geographic level of the data is set at Government Office Region (GOR). Letting and voiding dates are provided at month and year only; age variables are top-coded at 90 years; income, benefits, earnings, charge and shortfall variables are banded to disguise unique values; landlords are grouped into coded categories.
• For the Special Licence access (SL) version (SN 7604), the geographic level is set at Local Authority. The SL data have more restrictive access conditions than those made available under the standard EUL. Prospective users of the SL version will need to complete an extra application form and demonstrate to the data owners exactly why they need access to the additional variables in order to get permission to use that version.
• For Secure Access (SN 7686) the full CORE datasets are available, with some key variables recoded. Prospective users of the Secure Access version will need to fulfil additional requirements, including completion of face-to-face training and agreement to further stringent access conditions.

House Price Index YoY in the United States decreased to 2.30 percent in July from 2.70 percent in June of 2025. This dataset includes a chart with historical data for the United States FHFA House Price Index YoY.

This table contains data described by the following dimensions (Not all combinations are available): Geography (1 items: Canada ...).

Average House Prices in Canada decreased to 686800 CAD in September from 687600 CAD in August of 2025. This dataset includes a chart with historical data for Canada Average House Prices.

30 Year Mortgage Rate in the United States decreased to 6.27 percent in October 16 from 6.30 percent in the previous week. This dataset includes a chart with historical data for the United States 30 Year Mortgage Rate.