In 2023, there were approximately **** million housing cost burdened households among renters in the United States. A household is considered to be moderately burdened when the housing costs exceed 30 percent of the family income. Severely burdened households, on the other hand, spend more than 50 percent of their income on rent.

In 2021, there were approximately 21.6 million housing cost burdened renter households in the United States, with close to 12 million being severely burdened. About six million households with an annual income below 15,000 U.S. dollars were severely burdened. A household is considered to be moderately cost burdened when the housing costs exceed 30 percent of the family income. Severely burdened households, on the other hand, spend over 50 percent of their income on rent.

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.

This layer shows housing costs as a percentage of household income, by census tracts in the City of Los Angeles. This contains the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. Income is based on earnings in past 12 months of survey.

This map shows housing costs as a percentage of household income. Severe housing cost burden is described as when over 50% of income in a household is spent on housing costs. For renters it is over 50% of household income going towards gross rent (contract rent plus tenant-paid utilities). Miami, Florida accounts for the having the highest population of renters with severe housing burden costs.The map's topic is shown by tract and county centroids. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. Income is based on earnings in past 12 months of survey. Current Vintage: 2015-2019ACS Table(s): B25070, B25091Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 10, 2020National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis map can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases. Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For census tracts, the water cutouts are derived from a subset of the 2010 AWATER (Area Water) boundaries offered by TIGER. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

This U.S. Census Bureau American Community Survey (ACS) five-year estimates data set includes information about rent cost burden levels, calculated as gross rent as a percentage of household income in the past 12 months, in a number of geographic areas ranging from statewide to census tract. The data set includes median gross rent data from 2009-2016.

This statistic shows the share of income spent on rent by 22 to 30 year olds in the United States as of 2018, by generation. Millennials aged 22 to 30 years old, had the largest rent burden and spent 45 percent of their income on rent.

This data layer depicts, by census tracts, gross rent as a percentage of household income in the past 12 months for the San Francisco Bay Region. The source data, from the United States Census Bureau, has been reprocessed by the Metropolitan Transportation Commission.

To produce this feature set, the Metropolitan Transportation Commission downloaded American Community Survey (ACS) table B25070 to create a feature set representing rent as a percentage of household income by the following categories: ● Rent less than 30% of household income ● Rent is 30.0% to 49.9% of household income ● Rent is greater than or equal to 50% of household income

The resulting attribute table had all margin of error fields deleted, percentage fields added, county code field added, jurisdiction name added, and the source field names were changed.

The source table used to develop this feature service is from the United States Census Bureau, 2015-2019 American Community Survey 5-Year Estimates and can be downloaded from https://data.census.gov/cedsci/table?q=B25070%3A%20GROSS%20RENT%20AS%20A%20PERCENTAGE%20OF%20HOUSEHOLD%20INCOME%20IN%20THE%20PAST%2012%20MONTHS&g=0400000US06%241500000&tid=ACSDT5Y2019.B25070

In April 2020, almost half of night-time economy sector businesses in the UK were receiving no support from landlords during the COVID-19 outbreak. Out of the share of businesses receiving rent relief, the most common option was deferred rent agreement with landlord, with 31 percent of the total number of respondents.

Graph and download economic data for Burdened Households (5-year estimate) in Franklin County, OH (DP04ACS039049) from 2010 to 2023 about Franklin County, OH; burdened; Columbus; OH; households; 5-year; and USA.

Housing affordability is a major concern for many Los Angeles County residents. Housing constitutes the single largest monthly expense for most people. Renters are more susceptible than homeowners to high housing costs, especially if they live in a community without rent control or other tenant protection policies. Compared to homeowners, renters are also more likely to experience housing burden or housing instability and have a higher risk for homelessness.For more information about the Community Health Profiles Data Initiative, please see the initiative homepage.

The Housing Affordability Data System (HADS) is a set of files derived from the 1985 and later national American Housing Survey (AHS) and the 2002 and later Metro AHS. This system categorizes housing units by affordability and households by income, with respect to the Adjusted Median Income, Fair Market Rent (FMR), and poverty income. It also includes housing cost burden for owner and renter households. These files have been the basis for the worst case needs tables since 2001. The data files are available for public use, since they were derived from AHS public use files and the published income limits and FMRs. We are providing these files give the community of housing analysts the opportunity to use a consistent set of affordability measures.This data set appears to not be upated after 2013

Residential conduct, satisfaction with residential area and housing desires of residents of a newly established settlement in Bochum.

Topics: 1. Survey part: time of moving and first impression of the new city settlement; today´s judgement on the new municipal area; difficulties adjusting; judgement on the building surface and paint colors; neighborhood contacts; judgement on group facilities in the building; judgement on building management; judgement on child-orientation of the settlement as well as of the residence; detailed judgement on the plan of the residence as well as of all rooms; judgement on the basic equipment of the residence and attitude to greater amenities; judgement on the floor and temperature of the residence; perceived noise pollution from co-residents or stairway and elevator; comparison of current residence with earlier residence; amount of rent and subjectively perceived rent burden; recept of rent aid; time expended and means of transport used for the way to work; judgement on the location of Hustadt relative to the university and the center of Bochum; frequency of trips to the center of town and visits to cultural facilities or events; shopping habits; judgement on the distance of the buildings from each other and the ´openness´ of the residence to view from outside; general judgement on the external appearance of the building; satisfaction with life in Hustadt; inclination to move.

1. Observation part: The interviewer conducted an intensive observation of the residence in part during the interview but primarily not until after the end of the actual interview. Recorded were: type, style and extent of room furniture; number of books and pictures in the living room; books in other rooms; detailed recording of all pieces of furniture in every room of the residence.

Demography: age; family composition; number of children; age of children; age and number of siblings; household income; household size; characteristics of spouse; self-assessment of social class.

This layer shows housing costs as a percentage of household income. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. Income is based on earnings in past 12 months of survey. This layer is symbolized to show the percent of renter households that spend 30.0% or more of their household income on gross rent (contract rent plus tenant-paid utilities). To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B25070, B25091 Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

House prices capture the financial burden of purchasing a dwelling, and their development over time is measured by a (real) house price index. The evolution of rental prices can be monitored over time by the (real) rent price index. Alternatively, house prices can be compared to income (price-to-income ratio) as a measure of the affordability of owning a dwelling. If the price-to-income ratio is above (below) their long-term average, house prices are considered to be overvalued (undervalued). Meanwhile, the OECD database on regional house price indices shows how house price developments vary across regions and cities within countries (for further discussion, see the OECD National and Regional House Price Indices Database, as well as OECD, 2020a).

Housing costs can represent a substantial financial burden to households, especially low-income households. The median of the ratio of housing costs over income gives an indication of the financial pressure that households face from housing costs. Another common measure of housing affordability presented in this indicator is the housing cost overburden rate, which measures the proportion of households or population that spend more than 40% of their disposable income on housing costs (in line with Eurostat methodology). For a discussion of different measures of housing affordability and their advantages and limits, please see indicator HC1.5 Overview of affordable housing indicators in the OECD Affordable Housing Database. For policy measures aiming to support households with housing costs, please see indicators in the PH2, PH3 and PH4 series. Housing costs can refer to: (1) a narrow definition based on rent and mortgage costs (principal repayment and mortgage interest); or (2) a wider definition that also includes the costs of mandatory services and charges, regular maintenance and repairs, taxes and utilities, which are referred to as “total housing costs” below. Housing costs are considered as a share of household disposable income, which includes social transfers (such as housing allowances) and excludes taxes. Income is equivalised for household size based on a common equivalence elasticity (the square root of household size) which implies that a household’s economic needs increase less than proportionally with its size. Housing costs refer to the primary residence. The data presented here are based on household survey microdata and concern national household or population level data.

Rental Management System Market Outlook

The global rental management system market size was valued at approximately USD 2.8 billion in 2023 and is projected to reach USD 7.4 billion by 2032, growing at a CAGR of 11.2% during the forecast period. This growth is primarily driven by the increasing adoption of digital solutions in the real estate sector to streamline operations, enhance customer experience, and improve management efficiency. Factors such as the rising demand for rental properties, advancements in cloud technology, and the need for centralized property management are expected to significantly contribute to the market's expansion.

The growth of the rental management system market can be attributed to several key factors. Firstly, the increasing urbanization and migration to metropolitan areas have resulted in a surge in demand for rental properties. With an expanding urban population, property managers and landlords are seeking efficient ways to manage their growing portfolios, which has led to the adoption of rental management systems. These systems offer features such as automated rent collection, maintenance scheduling, and tenant screening, thereby reducing the administrative burden on property managers and enhancing tenant satisfaction.

Another significant growth driver for the rental management system market is the advancement in cloud computing technology. The shift towards cloud-based solutions has revolutionized the property management industry by offering scalable, flexible, and cost-effective solutions. Cloud-based rental management systems enable real-time access to data, seamless integration with other software, and enhanced security features. This has made it easier for property managers to manage properties remotely and provide timely services to tenants, thereby improving operational efficiency and reducing costs.

In this evolving landscape, the integration of a Smart Property Management System is becoming increasingly crucial. These systems leverage advanced technologies to offer a holistic approach to property management, enabling property managers to automate various processes and gain insights through data analytics. By incorporating IoT devices and AI-driven tools, smart property management systems can monitor energy usage, enhance security, and provide predictive maintenance alerts. This not only improves operational efficiency but also enhances tenant satisfaction by ensuring a seamless living experience. As the demand for digital solutions grows, the adoption of smart property management systems is expected to rise, further driving the market's expansion.

Furthermore, the growing awareness about the benefits of rental management systems among property managers, real estate agents, and landlords is fueling market growth. These stakeholders are increasingly recognizing the value of digital solutions in automating repetitive tasks, improving communication with tenants, and ensuring regulatory compliance. The ability to generate detailed reports and analytics through rental management systems is also helping property managers make informed decisions and optimize their operations. As a result, the adoption of these systems is expected to continue rising, contributing to market expansion.

Regionally, North America holds a significant share of the rental management system market due to the high adoption rate of advanced technologies and the presence of major market players. The region's well-established real estate sector and the increasing demand for rental properties in urban areas are driving market growth. Europe and the Asia Pacific are also witnessing substantial growth, with the latter expected to experience the highest CAGR during the forecast period. The rapid urbanization, increasing disposable income, and growing awareness about digital solutions in countries like China and India are key factors propelling the market in the Asia Pacific region.

Component Analysis

The rental management system market by component is segmented into software and services. The software segment comprises various types of rental management software, including property management software, tenant management software, and lease management software. This segment is witnessing significant growth due to the increasing need for automation and digitization in property management. Property management software is particularly popular as it offers comprehensive solutions for managing various aspects of renta

This data set is EMBARGOED until noon ET Tuesday, June 29. This data is intended for print publication on or after June 29. A story will be filed under the slug US--Virus Outbreak-Rental Assistance.

Overview

The Center for Public Integrity in collaboration with the AP has collected detailed statistics from about 70 agencies that administered rental assistance programs in 2020 with money from the Coronavirus Relief Fund, part of the CARES Act. These figures show how much money these agencies planned to spend on rental assistance and how much they actually spent. We also have data showing how many households received assistance and how many applications were submitted.

An additional data sheet shows how much money was allocated for rental assistance from all sources (not just CRF money) per renter-occupied household in 2020 and statewide eviction rates in 2016, the latest available data from the Eviction Lab at Princeton University.

Findings

• All together, about 70 state and local agencies set aside $2.7 billion from the Coronavirus Relief Fund in 2020 for rental assistance programs. More than $425 million of that — 16 percent — was either reallocated for other COVID-related expenditures or remained unspent by March 31, 2021.
• Four states with comparatively high historic eviction rates allocated very little for renters in 2020. These states — Georgia, West Virginia, South Carolina and Oklahoma — are run by Republican governors.
• Onerous requisites, such as lengthy forms and extensive documentation requirements, prevented many applicants from accessing rental assistance funds. In New York, for example, tenants had to prove they were rent-burdened before the pandemic to qualify for assistance in the first round of funding; only $47.5 million wound up getting spent on rental assistance, out of $100 million allocated by the state legislature.

Methodology

The Center for Public Integrity started with a spreadsheet produced by the National Low Income Housing Coalition that showed every known rental assistance program in the United States as of Dec. 23, 2020. The detailed spreadsheet included how much money had been allocated per program and the source of those funds. Public Integrity isolated only the programs that were categorized as being funded by the Coronavirus Relief Fund, which was part of the CARES Act. We focused on the CRF because it was the largest single source of rental assistance in 2020. We contacted more than 70 agencies to find out how much money they wound up spending.

For the second set of data, we used the same NLIHC spreadsheet but tallied all allocations — regardless of funding source — for each state and divided that number by the U.S. Census Bureau’s estimate of renter-occupied households in each state (from American Community Survey table S2502, five-year estimate, 2015 to 2019). We also included the statewide eviction rates as of 2016 (the most recent available) published by the Eviction Lab.

Included Data

• 1-crf_programs.csv: Details for every known rental assistance program as of Dec. 23, 2020, that was funded by the Coronavirus Relief Fund.

  Columns A through E were collected by the National Low Income Housing Coalition, and they show the following: Geographic Level; State; City/County/Locality, if applicable; Program Name; and Administering Agency.

  Columns F through L were collected by Public Integrity and the Associated Press. Those columns show amount of CRF money set aside in 2020, the amount spent on rental assistance by March 31, 2021, the amount reallocated or unspent by March 31, 2021, the percent unspent by March 31, 2021, the number of households that received assistance by March 31, 2021, the number of applications received by March 31, 2021, and notes about the data.

• 2-state_totals.csv: State-level totals for all known allocated rental assistance funding, regardless of funding source, along with the number of renter-occupied households in each state from 2015 to 2019, the statewide eviction rate as of 2016, and the amount of allocated rental assistance funding per renter-occupied household.

Caveats

• If your city, county or state does not appear to have allocated any money for rental assistance, check with your local and state officials or your governor’s office to find out if a program was indeed administered. We can’t ensure that every program wound up on the NLIHC’s original list.
• We only focused on programs that were categorized as being part of the Coronavirus Relief Fund, but your city, county or state may have had a rental assistance program with other sources of funding.
• The per-capita funding figures do not necessarily indicate that those state governments administered rental assistance programs. Some — or all — of that per-capita money could have been administered by local governments. For example, Georgia did not administer a statewide rental assistance program in 2020; the per-capita figure for Georgia reflects money spent in that state by local governments. For a full list of programs, visit NLIHC’s broader list at https://docs.google.com/spreadsheets/d/1hLfybfo9NydIptQu5wghUpKXecimh3gaoqT7LU1JGc8/edit#gid=1851738141.
• Eviction rates were not available for these four states in 2016: Alaska, Arkansas, North Dakota and South Dakota.

Attribution

“According to data obtained by the Center for Public Integrity, The Associated Press and the National Low Income Housing Coalition”.

Contact reporter Sarah Kleiner at skleiner@publicintegrity.org.

Abstract

The purpose of the SEPHER data set is to allow for testing, assessing and generating new analysis and metrics that can address inequalities and climate injustice. The data set was created by Tedesco, M., C. Hultquist, S. E. Char, C. Constantinides, T. Galjanic, and A. D. Sinha.

Methodology

SEPHER draws upon four major source datasets: CDC Social Vulnerability Index, FEMA National Risk Index, Home Mortgage Disclosure Act, and Evictions datasets. The data from these source datasets have been merged, cleaned, and standardized and all of the variables documented in the data dictionary.

CDC Social Vulnerability Index

CDC Social Vulnerability Index (SVI) dataset is a dataset prepared for the Centers for Disease Control and Prevention for the purpose of assessing the degree of social vulnerability of American communities to natural hazards and anthropogenic events. It contains data on 15 social factors taken or derived from Census reports as well as rankings of each tract based on these individual factors, groups of factors corresponding to four related themes (Socioeconomic, Household Composition & Disability, Minority Status & Language, and Housing Type & Transportation) and overall. The data is available for the years 2000, 2010, 2014, 2016, and 2018.

FEMA National Risk Index

The National Risk Index (NRI) dataset compiled by the Federal Emergency Management Agency (FEMA) consists of historic natural disaster data from across the United States at a tract-level. The dataset includes information about 18 natural disasters including earthquakes, tsunamis, wildfires, volcanic activity and many others. Each disaster is detailed out in terms of its frequency, historic impact, potential exposure, expected annual loss and associated risk. The dataset also includes some summary variables for each tract including the total expected loss in terms of building loss, human loss and agricultural loss, the population of the tract, and the area covered by the tract. It finally includes a few more features to characterize the population such as social vulnerability rating and community resilience.

Home Mortgage Disclosure Act

The Home Mortgage Disclosure Act (HMDA) dataset contains loan-level data for home mortgages including information on applications, denials, approvals, and institution purchases. It is managed and expanded annually by the Consumer Financial Protection Bureau based on the data collected from financial institutions. The dataset is used by public officials to make decisions and policies, uncover lending patterns and discrimination among mortgage applicants, and investigate if lenders are serving the housing needs of the communities. It covers the period from 2007 to 2017.

Evictions

The Evictions dataset is compiled and managed by the Eviction Lab at Princeton University and consists of court records related to eviction cases in the United States between 2000 and 2016. Its purpose is to estimate the prevalence of court-ordered evictions and compare eviction rates among states, counties, cities, and neighborhoods. Besides information on eviction filings and judgments, the dataset includes socioeconomic and real estate data for each tract including race/ethnic origin, household income, poverty rate, property value, median gross rent, rent burden, and others.

US Census Bureau American Community Survey 2013-2017 Estimates in the Keys the Valley Region for Population, Households, Tenure, Cost Burden, Poverty, and Age of Housing Stock.

The American Community Survey (ACS) is a nationwide survey designed to provide communities with reliable and timely social, economic, housing, and demographic data every year. Because the ACS is based on a sample, rather than all housing units and people, ACS estimates have a degree of uncertainty associated with them, called sampling error. In general, the larger the sample, the smaller the level of sampling error. Data associated with a small town will have a greater degree of error than data associated with an entire county. To help users understand the impact of sampling error on data reliability, the Census Bureau provides a “margin of error” for each published ACS estimate. The margin of error, combined with the ACS estimate, give users a range of values within which the actual “real-world” value is likely to fall.

Single-year and multiyear estimates from the ACS are all “period” estimates derived from a sample collected over a period of time, as opposed to “point-in-time” estimates such as those from past decennial censuses. For example, the 2000 Census “long form” sampled the resident U.S. population as of April 1, 2000. The estimates here were derived from a sample collected over time from 2013-2017.

Data Dictionary - Population, Households, Tenure, Cost Burden, Poverty, Age of Housing Stock

·
Population: Total Population (B01003)

·
Households: Total number of households (B25003)

·
OwnHH: Total number of owner-occupied households (B25003)

·
RentHH: Total number of renter-occupied households (B25003)

·
TotalU: Total number of housing units (B25001)

·
VacantU: Total number of vacant units (B25004)

·
SeasRecOcU: Total number of Seasonal/Recreational/Occasionally Occupied Units (B25004)

·
ForSale: Total number of units currently for sale (B25004)

·
ForRent: Total number of units currently for rent (B25004)

·
MedianHI: Median Household Income (B25119)

·
OwnHH3049: Total number of owner-occupied households spending 30-49% of their income on housing costs. (B25095)

·
POwnHH3049: Percentage of owner-occupied households spending 30-49% of their income on housing costs. (B25095)

·
OwnHH50: Total number of severely cost-burdened owner-occupied households – those spending 50% or more of their income on housing costs. (B25095)

·
POwnHH50: Percentage of severely cost-burdened owner-occupied households – those spending 50% or more of their income on housing costs. (B25095)

·
OwnHH_CB: Total number of owner-occupied, cost-burdened households - those who spend 30% or more of their income on housing costs. (B25095)

·
POwnHH_CB: Percentage of owner-occupied, cost-burdened households - those who spend 30% or more of their income on housing costs. (B25095)

·
RenHH3049: Total number of renter-occupied households spending 30-49% of their income on housing costs. (B25070)

·
PRenHH3049: Percentage of renter-occupied households spending 30-49% of their income on housing costs. (B25070)

·
RenHH50: Total number of severely cost-burdened renter-occupied households – those spending 50% or more of their income on housing costs. (B25070)

·
PRenHH50: Percentage of severely cost-burdened renter-occupied households – those spending 50% or more of their income on housing costs. (B25070)

·
RenHH_CB: Total number of renter-occupied, cost-burdened households - those who spend 30% or more of their income on housing costs. (B25070)

·
PRenHH_CB: Percentage of renter-occupied, cost-burdened households - those who spend 30% or more of their income on housing costs. (B25070)

·
Poverty: Population below poverty level. (B17001)

·
PPoverty: Percentage of population below poverty level. Note poverty status (above or below) is not determined for the entire population. (B17001)

·
MYearBuilt: Median structure year of construction. (B25035)