In 2021, roughly **** percent of housing units in the United States had severe housing problems. From a state perspective, West Virginia recorded the lowest share of severe housing problems, averaging ** percent. On the other hand, over one in five houses in several states had severe problems. California and Hawaii recorded the highest percentage of severe housing problems, with over ** percent of housing units qualifying as such. Severe housing problems such as a lack of complete kitchen facilities, lack of plumbing facilities, overcrowding, or severely cost-burdened occupants directly impact one's health.

This dataset shows the Department of Housing Preservation and Development (HPD) complaints and information about problems associated with complaints.

The U.S. Department of Housing and Urban Development (HUD) periodically receives "custom tabulations" of Census data from the U.S. Census Bureau that are largely not available through standard Census products. These datasets, known as "CHAS" (Comprehensive Housing Affordability Strategy) data, demonstrate the extent of housing problems and housing needs, particularly for low income households. The primary purpose of CHAS data is to demonstrate the number of households in need of housing assistance. This is estimated by the number of households that have certain housing problems and have income low enough to qualify for HUD’s programs (primarily 30, 50, and 80 percent of median income). CHAS data provides counts of the numbers of households that fit these HUD-specified characteristics in a variety of geographic areas. In addition to estimating low-income housing needs, CHAS data contributes to a more comprehensive market analysis by documenting issues like lead paint risks, "affordability mismatch," and the interaction of affordability with variables like age of homes, number of bedrooms, and type of building.This dataset is a special tabulation of the 2016-2020 American Community Survey (ACS) and reflects conditions over that time period. The dataset uses custom HUD Area Median Family Income (HAMFI) figures calculated by HUD PDR staff based on 2016-2020 ACS income data. CHAS datasets are used by Federal, State, and Local governments to plan how to spend, and distribute HUD program funds. To learn more about the Comprehensive Housing Affordability Strategy (CHAS), visit: https://www.huduser.gov/portal/datasets/cp.html, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. To learn more about the American Community Survey (ACS), and associated datasets visit: https://www.census.gov/programs-surveys/acs Data Dictionary: DD_ACS 5-Year CHAS Estimate Data by County Date of Coverage: 2016-2020

Specific Housing Problems, 2019

description: This dataset is a county level summary of housing problems of low income households. Low income households (; abstract: This dataset is a county level summary of housing problems of low income households. Low income households (

This dataset is a county level summary of housing problems of low income households. Low income households (

The U.S. Department of Housing and Urban Development (HUD) periodically receives custom tabulations of data from the U.S. Census Bureau that are largely not available through standard Census products. These data, known as the CHAS data (Comprehensive Housing Affordability Strategy), demonstrate the extent of housing problems and housing needs, particularly for low income households. The CHAS data are used by local governments to plan how to spend HUD funds, and may also be used by HUD to distribute grant funds

This statistic shows the issues pertaining to housing quality in Great Britain (UK), according to British adults responding to a housing survey in 2014, by their employment status. Of respondents, the most satisfied with their housing were those with an income of 55,000 British pounds and more: 65 percent of that group reported that none of the issues applied to their homes.

Pathways to Removing Obstacles to Housing (PRO Housing) Pathways to Removing Obstacles to Housing, or PRO Housing, is a competitive grant program being administered by HUD. PRO Housing seeks to identify and remove barriers to affordable housing production and preservation.

Under the Need rating factor, applicants will be awarded ten (10) points if their application primarily serves a ‘priority geography’. Priority geography means a geography that has an affordable housing need greater than a threshold calculation for one of three measures. The threshold calculation is determined by the need of the 90th-percentile jurisdiction (top 10%) for each factor as computed comparing only jurisdictions with greater than 50,000 population. Threshold calculations are done at the county and place level and applied respectively to county and place applicants. An application can also quality as a priority geography if it serves a geography that scores in the top 5% of its State for the same three measures. The measures are as follows:

Affordable housing not keeping pace, measured as (change in population 2019-2009 divided by 2009 population) – (change in number of units affordable and available to households at 80% HUD Area Median Family Income (HAMFI) 2019-2009 divided by units affordable and available at 80% HAMFI 2009). Insufficient affordable housing, measured as number of households at 80% HAMFI divided by number of affordable and available units for households at 80% HAMFI. Widespread housing cost burden or substandard housing, measured as number of households with housing problems at 100% HAMFI divided by number of households at 100% HAMFI. Housing problems is defined as: cost burden of at least 50%, overcrowding, or substandard housing.

For more information on Pro Housing, please visit: https://www.hud.gov/program_offices/comm_planning/pro_housing

Dataset

This dataset was created by Ananth Reddy

Released under CC0: Public Domain

Contents

Persons living with housing problems, by select housing-vulnerable populations and affordability, suitability, adequacy and core housing need indicators, Canada. Vulnerable population refers to persons belonging, or perceived as belonging, to groups that are in a disadvantaged position or marginalized.

Link: https://datausa.io/map/?level=county&key=severe_housing_problems

Comprehensive Housing Affordability Strategy (CHAS) data documenting the extent of housing problems and housing needs, particularly for low income households, at the Place level. This is estimated by the number of households that have certain housing problems and have income low enough to qualify for HUD’s programs (primarily 30, 50, and 80 percent of median income).

This statistic shows the level of satisfaction with housing conditions among the British population in the United Kingdom (UK) as of 2014, by broad region. Among respondents living in Greater London, approximately 19 percent complained about accommodation being too expensive and 25 percent complained about lack of space. In Scotland, 58 percent of respondents reported that none of these issues applied to their homes.

The Department of Housing Preservation and Development (HPD) records complaints that are made by the public for conditions which violate the New York City Housing Maintenance Code (HMC) or the New York State Multiple Dwelling Law (MDL).

Each year, the U.S. Department of Housing and Urban Development (HUD) receives custom tabulations of American Community Survey (ACS) data from the U.S. Census Bureau. These data, known as the "CHAS" data (Comprehensive Housing Affordability Strategy), demonstrate the extent of housing problems and housing needs, particularly for low income households. The CHAS data are used by local governments to plan how to spend HUD funds, and may also be used by HUD to distribute grant funds. For more background on the CHAS data, including data documentation and a list of updates and corrections to previously released data, click here: https://www.huduser.gov/portal/datasets/cp/CHAS/bg_chas.htmlOriginal Data sourced from: https://www.huduser.gov/portal/datasets/cp.html

This table contains data on the percent of households paying more than 30% (or 50%) of monthly household income towards housing costs for California, its regions, counties, cities/towns, and census tracts. Data is from the U.S. Department of Housing and Urban Development (HUD), Consolidated Planning Comprehensive Housing Affordability Strategy (CHAS) and the U.S. Census Bureau, American Community Survey (ACS). The table is part of a series of indicators in the [Healthy Communities Data and Indicators Project of the Office of Health Equity] Affordable, quality housing is central to health, conferring protection from the environment and supporting family life. Housing costs—typically the largest, single expense in a family's budget—also impact decisions that affect health. As housing consumes larger proportions of household income, families have less income for nutrition, health care, transportation, education, etc. Severe cost burdens may induce poverty—which is associated with developmental and behavioral problems in children and accelerated cognitive and physical decline in adults. Low-income families and minority communities are disproportionately affected by the lack of affordable, quality housing. More information about the data table and a data dictionary can be found in the Attachments.

Comprehensive Housing Affordability Strategy (CHAS) data documenting the extent of housing problems and housing needs, particularly for low income households, at the County level. This is estimated by the number of households that have certain housing problems and have income low enough to qualify for HUD’s programs (primarily 30, 50, and 80 percent of median income).

The U.S. Department of Housing and Urban Development (HUD) periodically receives "custom tabulations" of Census data from the U.S. Census Bureau that are largely not available through standard Census products. These datasets, known as "CHAS" (Comprehensive Housing Affordability Strategy) data, demonstrate the extent of housing problems and housing needs, particularly for low income households. The primary purpose of CHAS data is to demonstrate the number of households in need of housing assistance. This is estimated by the number of households that have certain housing problems and have income low enough to qualify for HUD’s programs (primarily 30, 50, and 80 percent of median income). CHAS data provides counts of the numbers of households that fit these HUD-specified characteristics in a variety of geographic areas. In addition to estimating low-income housing needs, CHAS data contributes to a more comprehensive market analysis by documenting issues like lead paint risks, "affordability mismatch," and the interaction of affordability with variables like age of homes, number of bedrooms, and type of building. This dataset is a special tabulation of the 2016-2020 American Community Survey (ACS) and reflects conditions over that time period. The dataset uses custom HUD Area Median Family Income (HAMFI) figures calculated by HUD PDR staff based on 2016-2020 ACS income data. CHAS datasets are used by Federal, State, and Local governments to plan how to spend, and distribute HUD program funds. To learn more about the Comprehensive Housing Affordability Strategy (CHAS), visit: https://www.huduser.gov/portal/datasets/cp.html, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. To learn more about the American Community Survey (ACS), and associated datasets visit: https://www.census.gov/programs-surveys/acs Data Dictionary: DD_ACS 5-Year CHAS Estimate Data by State Date of Coverage: 2016-2020

Overview

The House Price Prediction Challenge, will test your regression skills by designing an algorithm to accurately predict the house prices in India. Accurately predicting house prices can be a daunting task. The buyers are just not concerned about the size(square feet) of the house and there are various other factors that play a key role to decide the price of a house/property. It can be extremely difficult to figure out the right set of attributes that are contributing to understanding the buyer's behavior as such. This dataset has been collected across various property aggregators across India. The dataset provides the 12 influencing factors your role as a data scientist is to predict the prices as accurately as possible.

You will get a lot of room for feature engineering and mastering advanced regression techniques such as Random Forest, Deep Neural Nets, and various other ensembling techniques.

Data Description:

Train.csv - 29451 rows x 12 columns Test.csv - 68720 rows x 11 columns Sample Submission - Acceptable submission format. (.csv/.xlsx file with 68720 rows)