Displacement risk indicator showing how many households within the specified groups are facing severely housing cost burden (contributing more than 50% of monthly income toward housing costs).

Displacement risk indicator showing how many households within the specified groups are facing housing cost burden (contributing more than 30% of monthly income toward housing costs).

Analysis of ‘Rent Burden Greater than 50%’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/98bb7b6a-079b-4659-af21-27e5e167c432 on 27 January 2022.

--- Dataset description provided by original source is as follows ---

Displacement risk indicator showing how many households within the specified groups are facing severely housing cost burden (contributing more than 50% of monthly income toward housing costs).

--- Original source retains full ownership of the source dataset ---

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 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.

Analysis of ‘Rent Burden Greater than 30%’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/b00a909f-0194-420c-8595-65f4b2e539b2 on 27 January 2022.

--- Dataset description provided by original source is as follows ---

Displacement risk indicator showing how many households within the specified groups are facing housing cost burden (contributing more than 30% of monthly income toward housing costs) .

--- Original source retains full ownership of the source dataset ---

The housing affordability measure illustrates the relationship between income and housing costs. A household that spends 30% or more of its collective monthly income to cover housing costs is considered to be “housing cost-burden[ed].”[1] Those spending between 30% and 49.9% of their monthly income are categorized as “moderately housing cost-burden[ed],” while those spending more than 50% are categorized as “severely housing cost-burden[ed].”[2]

How much a household spends on housing costs affects the household’s overall financial situation. More money spent on housing leaves less in the household budget for other needs, such as food, clothing, transportation, and medical care, as well as for incidental purchases and saving for the future.

The estimated housing costs as a percentage of household income are categorized by tenure: all households, those that own their housing unit, and those that rent their housing unit.

Throughout the period of analysis, the percentage of housing cost-burdened renter households in Champaign County was higher than the percentage of housing cost-burdened homeowner households in Champaign County. All three categories saw year-to-year fluctuations between 2005 and 2023, and none of the three show a consistent trend. However, all three categories were estimated to have a lower percentage of housing cost-burdened households in 2023 than in 2005.

Data on estimated housing costs as a percentage of monthly income was sourced from the U.S. Census Bureau’s American Community Survey (ACS) 1-Year Estimates, which are released annually.

As with any datasets that are estimates rather than exact counts, it is important to take into account the margins of error (listed in the column beside each figure) when drawing conclusions from the data.

Due to the impact of the COVID-19 pandemic, instead of providing the standard 1-year data products, the Census Bureau released experimental estimates from the 1-year data in 2020. This includes a limited number of data tables for the nation, states, and the District of Columbia. The Census Bureau states that the 2020 ACS 1-year experimental tables use an experimental estimation methodology and should not be compared with other ACS data. For these reasons, and because data is not available for Champaign County, no data for 2020 is included in this Indicator.

For interested data users, the 2020 ACS 1-Year Experimental data release includes a dataset on Housing Tenure.

[1] Schwarz, M. and E. Watson. (2008). Who can afford to live in a home?: A look at data from the 2006 American Community Survey. U.S. Census Bureau.

[2] Ibid.

Sources: U.S. Census Bureau; American Community Survey, 2023 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using data.census.gov; (17 October 2024).; U.S. Census Bureau; American Community Survey, 2022 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using data.census.gov; (22 September 2023).; U.S. Census Bureau; American Community Survey, 2021 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using data.census.gov; (30 September 2022).; U.S. Census Bureau; American Community Survey, 2019 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using data.census.gov; (10 June 2021).; U.S. Census Bureau; American Community Survey, 2018 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using data.census.gov; (10 June 2021).;U.S. Census Bureau; American Community Survey, 2017 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (13 September 2018).; U.S. Census Bureau; American Community Survey, 2016 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (14 September 2017).; U.S. Census Bureau; American Community Survey, 2015 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (19 September 2016).; U.S. Census Bureau; American Community Survey, 2014 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2013 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2012 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2011 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2010 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2009 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2008 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; 16 March 2016).; U.S. Census Bureau; American Community Survey, 2007 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2006 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2005 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).

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.

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.

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. These dataset give the community of housing analysts the opportunity to use a consistent set of affordability measures. The most recent year HADS is available as a Public Use File (PUF) is 2013. For 2015 and beyond, HADS is only available as an IUF and can no longer be released on a PUF. Those seeking access to more recent data should reach to the listed point of contact.

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 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.

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.

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.

The Priority Neighborhoods dataset is a part of the City of Oakland Department of Transportation's (OakDOT's) Geographic Equity Toolbox. The Priority Neighborhoods GIS dataset relies upon demographic data from the American Community Survey (ACS). This dataset assigns each census tract in Oakland a numerical priority value and a quantile from lowest and highest, as determined by the following seven weighted demographic factors (with weights in brackets "[XX%]"): • People of Color [25%] • Low-income Households (<50% of Area Median Income for a 4-person household) [25%] • People with Disability [10%] • Seniors 65 Years and Over [10%] • Single Parent Families [10%] • Severely Rent-Burdened Households [10%] • Low Educational Attainment (less than a bachelor's degree) [10%]

This dataset was last updated in October 2024 with data from the 2022 5-year (i.e., averaged from 2018 through 2022) American Community Survey (ACS). The ACS is managed by the United States Census Bureau; learn more about the ACS at: https://www.census.gov/programs-surveys/acs.

See the online map and read the methodology at: https://www.oaklandca.gov/resources/oakdot-geographic-equity-toolbox. This dataset is maintained by the OakDOT Race and Equity Team; learn more about the team at: https://www.oaklandca.gov/topics/oakdot-race-and-equity-team.

Field Descriptions: • TRACT: Census Tract Number • QUINTILE: Priority Quintile (calculated) • PLAN_AREA: OakDOT Planning Area • POPULATION: Population (average from 2018 through 2022) • PCT_POC: Percent People of Color • PCT_INC: Percent Low Income • PCT_SRB: Percent Severely Rent-Burdened • PCT_PWD: People with a Disability • PCT_SENIOR: Percent Seniors • PCT_SPH: Percent Single Parent Households • PCT_EDU: Percent Low Educational Attainment • RAT_POC: Ratio of People of Color (compared to Citywide average) • RAT_INC: Ratio of Low Income (compared to Citywide average) • RAT_SRB: Ratio of Severely Rent-Burdened (compared to Citywide average) • RAT_PWD: Ratio of People with a Disability (compared to Citywide average) • RAT_SENIOR: Ratio of Seniors (compared to Citywide average) • RAT_SPH: Ratio of Single Parent Households (compared to Citywide average) • RAT_EDU: Ratio of Low Educational Attainment (compared to Citywide average) • RAT_SCORE: Priority Ratio (compared to Citywide average) • ALAND: Land Area in square feet

City of Oakland, Department of Transportation (OakDOT) 250 Frank H. Ogawa Plaza, Suite 4314 | Oakland, CA 94612

Los Angeles Index of Displacement PressureThe Los Angeles Index of Displacement Pressure combines measures that past research efforts and our own original research have shown correlate with future change and displacement pressure. Created in 2015/2016, the index primarily uses data from 2012-2015.These seven measures are applied at the Census Tract level for tracts where >=40% of households earn less than the City's median income. The measures are grouped into two classes: change factors and displacement pressure factors.Change factor measures are those that suggest future revitalization is likely due to investment, projected housing price gains, and proximity to recently changed areas. On the other hand, displacement pressure factors capture areas with a high concentration of existing residents who may have difficulty absorbing massive rent increases that often accompany revitalization. The Los Angeles Index of Displacement Pressure captures the intersection between these two classes.Change Measures Transportation InvestmentMeasure 1: Distance to current rail stations (within a 1/2 mile radius. Tracts beyond 1/2 mile receive no score for this measure). Source: LA MetroMeasure 2: Distance to rail stations under construction/recently opened in 2016 (within a 1/2 mile radius. Tracts beyond 1/2 mile receive no score for this measure)Source: LA Metro Proximity to Rapidly Changing NeighborhoodsMeasure 3: Distance to the closest "top tier" changing neighborhood, as defined by the Los Angeles Index of Neighborhood Change (within a 1 mile radius. Tracts beyond 1 mile receive no score for this measure)Source: The Los Angeles Index of Neighborhood Change Housing MarketMeasure 4: Change in housing price projections from 2015 to 2020 Source: ESRI Community Analyst Displacement Pressure FactorsMeasure 5: Percent of households that rentSource: American Community Survey, Five-Year Estimate, 2014Measure 6: Percent of households that are extremely rent burdened (pay >=50% of household income on rent)Source: American Community Survey, Five-Year Estimate, 2014Measure 7: The number of affordable properties and housing units that are due to expire by 2023.Source: The Los Angeles Housing Element, 2012Date updated: April 7, 2018Refresh rate: Never - Historical data

This data set represents American Community Survey (ACS) 2014-2018 tract information related to Equity Priority Communities (EPCs) for Plan Bay Area 2050+.The Plan Bay Area 2050+ Equity Priority Communities incorporate EPCs identified with 2014-2018 ACS data, as well as EPCs identified with 2018-2022 ACS data into a single consolidated map of Plan Bay Area 2050+ Equity Priority Communities.This data set was developed using American Community Survey 2014-2018 data for eight variables considered.This data set represents all tracts within the San Francisco Bay Region, and contains attributes for the eight Metropolitan Transportation Commission (MTC) Equity Priority Communities tract-level variables for exploratory purposes. Equity Priority Communities are defined by MTC Resolution No. 4217-Equity Framework for Plan Bay Area 2040.As part of the development of the [DRAFT] Equity Priority Communities - Plan Bay Area 2050+ features, the source Census tracts had portions that overlapped either the Pacific Ocean or San Francisco Bay removed. The result is this feature set has fewer Census tracts than the unclipped tract source data.Plan Bay Area 2050+ Equity Priority Communities (tract geography) are based on eight ACS 2014-2018 (ACS 2018) tract-level variables:People of Color (70% threshold)Low-Income (less than 200% of Federal poverty level, 28% threshold)Level of English Proficiency (12% threshold)Seniors 75 Years and Over (8% threshold)Zero-Vehicle Households (15% threshold)Single-Parent Households (18% threshold)People with a Disability (12% threshold)Rent-Burdened Households (14% threshold)If a tract exceeds both threshold values for Low-Income and People of Color shares OR exceeds the threshold value for Low-Income AND also exceeds the threshold values for three or more variables, it is a EPC.Detailed documentation on the production of this feature set can be found in the MTC Equity Priority Communities project documentation.

This data set represents all urbanized tracts within the San Francisco Bay Region, and contains attributes for the eight Metropolitan Transportation Commission (MTC) Equity Priority Communities (EPC) tract-level variables for exploratory purposes. These features were formerly referred to as Communities of Concern (CoC).MTC 2018 Equity Priority Communities (tract geography) is based on eight ACS 2012-2016 tract-level variables: Persons of Color (70% threshold) Low-Income (less than 200% of Fed. poverty level, 30% threshold) Level of English Proficiency (12% threshold) Elderly (10% threshold) Zero-Vehicle Households (10% threshold) Single Parent Households (20% threshold)Disabled (12% threshold) Rent-Burdened Households (15% threshold) If a tract exceeds both threshold values for Low-Income and Person of Color shares OR exceeds the threshold value for Low-Income AND also exceeds the threshold values for three or more variables, it is a EPC.Detailed documentation on the production of this feature set can be found in the MTC Equity Priority Communities project documentation.

Plan Bay Area 2050 utilized this single data layer to inform the Plan Bay Area 2050 Equity PriorityCommunities (EPC).

This data set was developed using American Community Survey (ACS) 2014-2018 data for eight variables considered.

This data set represents all tracts within the San Francisco Bay Region and contains attributes for the eight Metropolitan Transportation Commission (MTC) Equity Priority Communities tract-level variables for exploratory purposes. These features were formerly referred to as Communities of Concern.

Plan Bay Area 2050 Equity Priority Communities (tract geography) are based on eight ACS 2014-2018 (ACS 2018) tract-level variables:

People of Color (70% threshold) Low-Income (less than 200% of Federal poverty level, 28% threshold) Level of English Proficiency (12% threshold) Seniors 75 Years and Over (8% threshold) Zero-Vehicle Households (15% threshold) Single-Parent Households (18% threshold) People with a Disability (12% threshold) Rent-Burdened Households (14% threshold)

If a tract exceeds both threshold values for Low-Income and People of Color shares OR exceeds thethreshold value for Low-Income AND also exceeds the threshold values for three or more variables, it is a EPC.

Detailed documentation on the production of this feature set can be found in the MTC Equity Priority Communities project documentation.