Methods and Data Sources
For each of the following indicators, the dashboard uses information from the U.S. Census Bureau American Community Survey (ACS) and decennial Census to provide data for all of these variables. The 2026 edition of the Housing Equity Indicators uses 2019 and 2024 five-year ACS data. Additional data sources used for a specific indicator are noted. OHCS has included analysis and visualizations of each indicator for every local government in Oregon with a population greater than 10,000. An additional 2026 update will include similar analysis and visualizations of each indicator for Tillamook County and Urban Unincorporated Lands (UULs) within the Portland Tri-County Area.
Indicator A) Housing outcomes
The ORS identifies this indicator as “Housing outcomes, such as cost burden and availability of housing units to own or to rent, and housing condition for various demographics, including race or ethnicity, disability status, English proficiency, and age.” For each Oregon city with a population of over 10,000, the dashboard provides data for:
- Availability of housing units to own or to rent is defined as the current number and rate of households that own or rent their homes, also known as tenure in the ACS.
-
Source: U.S. Census Bureau, American Community Survey, Table DP04
- Housing condition is defined as the quality or state of a living environment. OHCS identified housing units built before 1970, a lack of a complete kitchen, and a lack of complete plumbing as potential housing problems. This aligns with definitions of poor housing conditions used by the Census Bureau and the U.S. Department of Housing and Urban Development.
- Source: U.S. Census Bureau, American Community Survey Table DP04, B25049, and B25053
For both of these variables the dashboard provides information for the following demographics when the data is available, and the margins of error are within an acceptable range:
- Race or ethnicity is defined as the self-identified response to the American Community Survey (ACS). The U.S. The Census Bureau follows guidelines provided by the U.S. Office of Management and Budget to determine race and ethnicity categories. Information on the standards that guide ACS race and ethnicity surveys can be found
the census bureau's website.
-
Source: U.S. Census Bureau, American Community Survey, Table B18101
- Disability status is defined as the self-identified response to the American Community Survey (ACS). The U.S. Census Bureau includes six categories of disability status, including ambulatory, cognitive, self-care, hearing, vision, and independent living difficulties.
- Source: U.S. Census Bureau, American Community Survey, Table DP02
- English proficiency is defined as the self-identified response to the American Community Survey (ACS). The ability for an individual to speak English very well, not well, or not at all.
- Source: U.S. Census Bureau, American Community Survey, Table B01001
- Age is defined as the self-identified response to the American Community Survey (ACS) from under 5 years old to 85 years or older.
- Source: U.S. Census Bureau, American Community Survey, Table DP05
Note: OHCS has opted not to provide cross tabulations of these variables (e.g., cost burden by race and ethnicity) due to data quality concerns at the city level. A significant majority of variables cross tabulated at the city level have high margins of error that severely limit the usefulness of information for decision-making purposes. Where possible, OHCS has provided data disaggregated by race and ethnicity, but if margins of error are too high for disaggregation, data may be aggregated to compare White, Non-Hispanic and Black, Indigenous, and People of Color (BIPOC) populations (e.g., tenure by White, Non-Hispanic and BIPOC) in an attempt to balance information needs and data quality considerations. For the housing equity indicators dashboard, the BIPOC category is an aggregation of individuals who do not self-identify in the ACS as White, Non-Hispanic.
Similarly, OHCS will not utilize Comprehensive Housing Affordability Strategy (CHAS) data from the U.S. Department of Housing and Urban Development (HUD) due to high margins of error at the city level and release delays. While the CHAS data does include cross-tabulations of variables such as cost burden with disability status, race, and ethnicity, the margins of error at the city level are significant due to small sample sizes and would make comparisons between cities or planning within a city difficult. The CHAS dataset is also much older than the ACS. The most recent CHAS data is from 2022, compared to 2024 from the ACS, suggesting it is less descriptive of Oregon’s current state of affairs.
OHCS has instead replicated a number of descriptive data variables from the ACS related to housing, population, and income, which can help cities understand the populations living in their communities and the challenges they face. These surveys are ultimately what is utilized to inform the CHAS. The variables included on the Housing Outcomes section of the Housing Equity Indicators Dashboard for local governments in Oregon with a population of 10,000 or greater are:
Housing Data
- Household Tenure: The number and percentage of households that are Homeowners or Renters (B25003)
- Household Tenure by Age Group (B25007)
- Household Tenure for White and BIPOC Households (B25003, B25003H)
- Median Home Value (DP04)
- Poor Housing Condition (B25048, B25052): This is shown as the number of housing units lacking complete plumbing or kitchen facilities
- Structures Built Pre-1970 (DP04)
Population Data
- Population by Race and Ethnicity (DP05)
- Population by Age (B01001)
- White, Non-Hispanic Population by Age (B01001H)
- BIPOC Population by Age (B01001, B01001H)
- Population Aged 75 or Older with a Disability (B18101)
- Population with Limited English Proficiency (DP02): This is defined in the ACS as people who self-identify as speaking English “less than very well”.
- Household Types (B11001): Number and Percentage of households that are in Family or Nonfamily households
Income Data
- Median Household Income by Race and Ethnicity (S1901, B19013B - I)
- Median Household Income by Age (B19049)
- Population Below Poverty Level by Race and Ethnicity (S1701)
- Population Below Poverty Level by Age (S1701)
- Population with a Disability Below and Above the Poverty Level (B23024)
Aging Data
The department has defined an additional “Aging Typology” indicator to measure aging across Oregon.
The Aging Typology indicator measures the extent and pace of population aging across Oregon cities. OHCS uses this indicator to compare communities’ current and projected aging dynamics and to identify where aging pressures may be more or less pronounced. The 2026 update incorporates both the previously developed elderly ratio and additional Age Dependency Ratios from ACS tables to provide a more complete picture of local age structure.
1. Population Projections
OHCS projected city populations to 2030 by 5‑year age group using observed demographic trends between 2010 and 2020. Projections follow the same cohort‑change method used in prior years and are applied uniformly across all incorporated cities.
2. Elderly Ratio (70+)
For each city, OHCS calculated an elderly ratio for 2020 and 2030. The elderly ratio is defined as:
- Population ages 70 and older
divided by
- Population ages 20 to 69
This measure provides a focused view of the relationship between older adults and the core working‑age population.
3. Typology Assignment
Cities are assigned to one of four Aging Typology categories and ranked based on:
-
Their
current elderly ratio (2020)
-
Their projected rate of change in the elderly ratio (2020–2030)
The four typology categories remain unchanged:
-
High Current and High Projected Cities with an above‑average elderly ratio in 2020 and a faster‑than‑average projected increase through 2030. These cities are generally at the peak of their aging cycle.
-
High Current and Low Projected Cities with an above‑average elderly ratio in 2020 and a slower‑than‑average projected increase. These cities are generally in the late stages of aging.
-
Low Current and High Projected Cities with a below‑average elderly ratio in 2020 but a faster‑than‑average projected increase. These cities are typically in the early stages of aging.
-
Low Current and Low Projected Cities with a below‑average elderly ratio in 2020 and a slower‑than‑average projected increase. These places generally experience minimal aging effects due to stable in‑migration or growth in younger households.
4. ACS Method Age Dependency Ratios (New in 2026)
To complement the elderly ratio and provide additional context for aging patterns, OHCS now reports Age Dependency Ratios for each city. These ratios describe the number of dependents for every 100 working‑age adults (18–64), using ACS Table S0101.
- Total Age Dependency Ratio
- ACS Method Elderly Dependency Ratio (65+)
- ACS Method Child Dependency Ratio (under 18)
These ratios do not determine typology categories, but they are published on the dashboard to provide a fuller understanding of local population structure.
Indicator B) Housing types produced and land efficiency - Partial
The ORS described this indicator as displaying “housing types produced and overall land efficiency of existing and new housing” for each city with a population of over 10,000.
This is an important indicator that can help cities understand the number of units per buildable residential acre within their city and how built up or sprawling the units within a city are. However, there is no standardized measure of land efficiency using data that is available and accurate for each city across the state.
In 2025, OHCS and DLCD began to administer a survey with Portland State University’s Population Research Center (PRC) on a revised version of the Annual Housing Units and Population Survey (AHUPS) that was administered by PRC to all jurisdictions subject to OHNA to collect data on housing permitting and production activity. The survey details housing types produced between January 2024 and December 2024. The “Housing Production” survey data is now displayed on the 2026 OHNA Housing Equity Indicators dashboard, but this is only one component of this indicator and does not tell the full story of land efficiency.
PRC asked for data on the following housing types of produced units in the survey:
Category: Single-unit – “Single-unit” is a category consisting of detached single-unit dwellings and accessory dwelling units.
Detached single-unit dwelling – A detached single-unit dwelling is not defined in statute. For the purposes of reporting, a “detached single-unit dwelling” is any primary, detached residential unit on an individual lot or parcel, excluding lots or parcels created through a middle housing land division under ORS 92.031.
Accessory dwelling unit (ADU) – ORS 197A.425 defines an “accessory dwelling unit” as “an interior, attached or detached residential structure that is used in connection with or that is accessory to a single-[unit] dwelling.” If an interior, attached, or detached residential structure is accessory to other housing types, it may still be reported as an ADU but should not categorized with those units described in ORS 197A.425.
Category: Middle housing – ORS 197A.420 defines “middle housing” as “duplexes, triplexes, quadplexes, cottage clusters, and townhouses.”
Duplex – OAR Chapter 660, Division 046 defines a “duplex” as “two attached dwelling units on a lot or parcel. A Medium or Large City may define a Duplex to include two detached dwelling units on a Lot or Parcel.” For the purposes of reporting, report any two units (excluding ADUs) on a lot or parcel. This includes duplexes on lots or parcels created through a middle housing land division under ORS 92.031. Lots or parcels created through a middle housing land division do not change the underlying middle housing type. Report duplexes on lots or parcels created through a middle housing land division as a duplex, not any other housing type.
Triplex – OAR Chapter 660, Division 046 defines a “triplex” as “three attached dwelling units on a lot or parcel. A Large City may define a Triplex to include any configuration of three detached or attached dwelling units on one Lot or Parcel.” For the purposes of reporting, report any three units (excluding ADUs) on a lot or parcel. This includes triplexes on lots or parcels created through a middle housing land division under ORS 92.031. Lots or parcels created through a middle housing land division do not change the underlying middle housing type. Report triplexes on lots or parcels created through a middle housing land division as a triplex, not any other housing type.
Quadplex – OAR Chapter 660, Division 046 defines a “quadplex” as “four attached dwelling units on a lot or parcel. A Large City may define a Quadplex to include any configuration of three detached or attached dwelling units on one Lot or Parcel.” For the purposes of reporting, report any four units (excluding ADUs) on a lot or parcel. This includes quadplexes on lots or parcels created through a middle housing land division under ORS 92.031. Lots or parcels created through a middle housing land division do not change the underlying middle housing type. Report quadplexes on lots or parcels created through a middle housing land division as a quadplex, not any other housing type.
Townhouse – ORS 197A.420 defines a “townhouse” as “a dwelling unit constructed in a row of two or more attached units, where each dwelling unit is located on an individual lot or parcel and shares at least one common wall with an adjacent unit.” Townhouses are sometimes known as "attached single-unit dwellings" or "row houses."
Cottage Cluster – ORS 197A.420 defines a “cottage cluster” as a “grouping of no fewer than four detached housing units per acre with a footprint of less than 900 square feet each and that include a common courtyard.”
Category: Multi-unit – “Multi-unit” is a category consisting of multi-unit dwellings with five or more units as well as dwellings consisting of single-room occupancies, and homes in manufactured dwelling parks.
Multi-unit dwelling (5+ units) – A multi-unit dwelling is not defined in statute. For the purposes of reporting, a “multi-unit dwelling” is five or more units on an individual lot or parcel, not including middle housing, single room occupancies, or manufactured dwelling parks.
Single room occupancy – ORS 197A.430 defines a “single room occupancy” as “a residential development with no fewer than four attached units that are independently rented and lockable and provide living and sleeping space for the exclusive use of an occupant, but require that the occupant share sanitary or food preparation facilities with other units in the occupancy.”
Manufactured dwelling park – ORS 446.003 defines a “manufactured dwelling park” as “any place where four or more manufactured dwellings or prefabricated structures, as defined in ORS 455.010, that are relocatable and more than eight and one-half feet wide, are located within 500 feet of one another on a lot, tract or parcel of land under the same ownership, the primary purpose of which is to rent or lease space or keep space for rent or lease to any person for a charge or fee paid or to be paid for the rental or lease or use of facilities or to offer space free in connection with securing the trade or patronage of such person.” Manufactured dwelling parks do not include manufactured dwelling subdivisions or cottage clusters containing manufactured homes. Dwelling units in a manufactured home park can include both manufactured and prefabricated homes.
Category: Other – “Other” is a category consisting of units that do not fall in any of the other reporting categories (i.e. single-unit, middle housing, multi-unit).
Boats, RVs, vans, etc. – This category captures structures that do not readily fall into other housing type or construction typology specified elsewhere in the report. For the purposes of reporting, these structures do not count as dwelling units, however they are reported and tracked to paint a full picture of the housing outcomes of Oregonians.
Indicator C) New housing units built to standards
The ORS described this indicator as displaying “new housing units built to standards, as defined by the Department of Consumer and Business Services by rule, relating to accessibility and visitability.”
This is an important indicator that can help cities understand the share of their housing stock that is available to people with disabilities, an important component of housing choice.
New in 2026, data for this indicator is reported by local governments via the PRC-administered AHUPS survey, which asked questions about accessibility and visitability for newly produced housing during the period of January 2024 to December 2024. The survey did not collect data on existing units, which is an element that OHCS, DLCD, and our partners will continue to work towards collecting for greater visibility into the complete stock of housing meeting these standards.
PRC asked for data on the following types of produced housing units in the survey:
Fully accessible unit (UFAS) – Fully accessible units refer to those units complying with the Uniform Federal Accessibility Standards (UFAS), 24 C.F.R. § 40, Appendix A
“Accessible units” refer to those units complying with accessible standards pursuant to the American National Standards Institute (ANSI) code A117.1-2009, Accessible and Usable Buildings and Facilities.
Type A units (ANSI) – “Type A units” refer to those units complying with Type A standards pursuant to the American National Standards Institute (ANSI) code A117.1-2009, Accessible and Usable Buildings and Facilities.
Type B units (ANSI) – “Type B units” refer to those units complying with Type B standards pursuant to the American National Standards Institute (ANSI) code A117.1-2009, Accessible and Usable Buildings and Facilities.
Type C units (ANSI) – “Type C units” refer to visitable units complying with Type C standards pursuant to the American National Standards Institute (ANSI) code A117.1-2009, Accessible and Usable Buildings and Facilities.
Indicator D) Risk of gentrification and displacement
The ORS does not define this indicator. OHCS has adopted the Anti-Displacement and Gentrification Toolkit and related materials that were developed by Dr. Lisa Bates, Dr. Marisa Zapata, and Seyoung Sung at Portland State University. The toolkit was formally adopted in September 2021 by DLCD to help cities with over 10,000 residents create housing needs analysis stemming from HB2003 (2019).
As described by DLCD, “the toolkit walks practitioners through a series of analyses that consider aspects of housing need that are not typically part of a HNA, including assessing current housing dynamics and spatial vulnerabilities for Black, Indigenous, and People of Color (BIPOC), low-income, and renter households. From here, the toolkit provides a methodology to characterize specific neighborhood typologies depending on the unique housing characteristics of that neighborhood. These typologies can then be used to inform which specific HPS tools, actions, and strategies are best served to mitigate negative externalities, such as gentrification and displacement, within neighborhoods.”
A wide range of data variables are aggregated at the census tract and city level and then compared to one another to determine the potential characteristics of a given neighborhood. A list of the established indicators and data variables is included.
Purpose and approach: Census tracts are grouped into six neighborhood typologies to describe area characteristics and support city decision‑making; tract‑level data are evaluated relative to the city to assign a typology.
Indicator 1 – Neighborhood Change:
Variables: Median Household Income (MHI) change; renter household change; education change; BIPOC change.
Indicator 2 – Income Profile:
Variables: Low‑income households; household income.
Indicator 3 – Vulnerable People:
Variables: BIPOC; limited English proficiency; persons with disabilities; female‑headed households; 65 years and older.
Indicator 4 – Precarious Housing:
Variables: Multifamily housing units; housing units built before 1970.
Indicator 5 – Housing Market Activity:
Variables: Median rent; rent change; median home value; home value change.
The data source for each of the indicators above is the American Community Survey (ACS), multiple tables.
Using the above data, census tracts are sorted into six different neighborhood typologies which can help describe the characteristics of a given area. This is an effort to provide cities with the ability to make informed decisions about how to address housing problems that may be occurring in parts of their community. Data at the census tract level is compared to their respective cities, and based on those relationships, a typology is assigned. Tract level typologies are provided.
Green – Affordable and Vulnerable:
Income Profile: Low
Vulnerable People: Yes
Precarious Housing: Yes
Housing Market Activity: No
Neighborhood Demographic Change: No
Yellow – Early Gentrification:
Income Profile: Low
Vulnerable People: Yes
Precarious Housing: Yes
Housing Market Activity: Yes
Neighborhood Demographic Change: No
Orange – Active Gentrification:
Income Profile: Low
Vulnerable People: Yes
Precarious Housing: Yes
Housing Market Activity: Yes
Neighborhood Demographic Change: Yes
Red – Late Gentrification:
Income Profile: High
Vulnerable People: Yes
Precarious Housing: No
Housing Market Activity: Yes
Neighborhood Demographic Change: Yes
Blue – Becoming Exclusive:
Income Profile: High
Vulnerable People: No
Precarious Housing: No
Housing Market Activity: Yes
Neighborhood Demographic Change: Yes
Purple – Advanced Exclusive:
Income Profile: High
Vulnerable People: No
Precarious Housing: No
Housing Market Activity: Higher home value and rent
Neighborhood Demographic Change: No
Grey – Unassigned: Limited evidence of observable gentrification or displacement risk. No typology has been assigned.
White – Missing Data: This tract could not be fully classified because at least one required indicator was missing. There was not enough complete data to assign a final gentrification typology.
The Anti-Displacement and Gentrification Toolkit then provides potential policy actions and intervention strategies to help address problems associated with a given typology.
View a full
walkthrough of the methodology, typologies, and actions for a city.
Indicator E) Housing segregation by race and income
The ORS does not define this indicator. OHCS’ definition evaluates segregation and concentration by race and income at the census tract and city level. Spatial concentration and segregation measure the imbalance of low-income, high-income, BIPOC, and white populations within and between OHNA cities. Specifically, it measures areas with a significant concentration of specific income and/or racial demographics while simultaneously missing (or excluding) other income and racial demographics.
This methodology attempts to highlight communities that are overrepresented in a given area but isolated or segregated from others. These are neighborhoods where housing segregation occurs and should be evaluated during planning. However, this is only a starting point - no single indicator can capture the systematic and historical nuances of a given neighborhood within a city.
For instance, a census tract that is sorted into the “High-Income White Neighborhood” typology would not only have a significant share of wealthier and white households but would have a small share of BIPOC and low-income households. To determine the segregation or concentration of low-income households (those earning less than $50,000 per year), high-income households (those earning more than $150,000 per year, white (non-Hispanic), and BIPOC communities, a location quotient is calculated.
Each census tract is compared to both its respective city and the average of all cities with a population of 10,000 or greater (“OHNA cities”). A location quotient of 0.8 or below is considered “low,” between 0.8 and 1.2 is “average,” and above 1.2 is “concentrated.”
- A location quotient score below 0.8 has a
low tract descriptor.
- A location quotient score between 0.8 and 1.2 has an
average tract descriptor.
- A location quotient score above 1.2 has a
concentrated tract descriptor.
For example, if a census tract in Bend is made up of 52% low-income households, it is divided by the share of low-income households in the city of Bend (28.3%) and the average share of low-income households for all OHNA cities (31.6%). The location quotient for the tract is 1.84 compared with Bend and 1.64 for the all-city average. In either case, low-income households are overrepresented in the tract and would be considered concentrated (above the 1.2 threshold).
Location quotient scores are then used to sort census tracts into typologies that describe not only what households are represented in an area but which are excluded. A tract must have a flag for both the city and all-city categories to meet typology requirements. An exception to this is the white descriptor due to the racial makeup of Oregon. If a census tract is made up of more than 90% households, it meets the typology for a “white” tract. Concentration of college students was also added as a typology. This is because colleges can significantly impact housing markets, often increasing competition especially for more affordable rentals. On mapping tools, race and ethnicity take precedence over college, though. If a census tract flagged as both a white and college neighborhood, only “white” would appear in the typology.
The low-income typology is defined as the tract is concentrated compared to both city and all city for low-income households and scored low on both high-income categories.
The mixed-income typology is defined as the tract is scored as average in both income categories compared to both city and all city.
The high-income typology is defined as the tract is concentrated in both high-income categories and scored low on low-income in both categories.
The white typology is defined as the tract is concentrated in White, Non-Hispanic compared to both city and all city and scored low on BIPOC in both categories.
The BIPOC typology is defined as the tract is concentrated in BIPOC compared to both city and all city and scored low on white in both categories.
The college typology is defined as more than 10% of tract residents are enrolled in post-secondary education.
For example, a low-income white neighborhood would be an area with a large share of low-income households, few high-income households, and more than 90% of the residents identified as white, non-Hispanic.
A high-income BIPOC neighborhood would be an area with a large share of high-income and BIPOC households and with few low-income and white households.
Finally, a low-income neighborhood would be an area with a large share of low-income households and few high-income households, did not have either more than 90% of residents as white, non-Hispanic or requirements for a BIPOC tract.
While this approach can help describe segregation and concentration by race and income within a city, it can struggle to capture typologies if a place is significantly different from the average OHNA city. For instance, if a city has a large number of high-income households throughout all of its tracts, it will flag as “concentrated” when compared to the average OHNA city. However, when the city’s tracts are compared to its own city, it may not appear as concentrated. This is due to the city having a significantly larger share of high-income households overall compared with the typical OHNA city. Since the comparators (individual city and OHNA city average) are significantly different, the results can vary as well.
To help address this, OHCS has provided comparisons between cities to take into consideration when planning. Cities are first sorted based on more general characteristics. A city is slotted into one of four categories based on racial diversity and low-income households. If a city falls below the OHNA city average in either category, it is considered less diverse and/or less low-income households. If a city is above the OHNA city average in either category, it is considered more diverse and/or more low-income households.
As an example, Beaverton is considered more diverse with less low-income households. This is because the city has a greater share of BIPOC communities and smaller number of low-income households when compared with the average OHNA city.
Additionally, the share of high or low-income concentrated tracts compared to the all-city average was calculated for each city. Then the mean and standard deviation was determined so that cities above one standard deviation were flagged to show that the overall city makeup is significantly different than the typical OHNA city.
Indicator G) Severe Rent Burdening
The ORS is clear on this indicator, specifying that the dashboard should display “Residential tenants who spend more than 50% of their household income on gross rent for housing.” For each local government in Oregon with a population of 10,000 or greater, the dashboard displays information on the share of renter households who spend more than 30% (rent burdened) or 50% (severely rent burdened) of their gross (pre-tax) income on gross rent, including utilities. To understand housing cost across the state, each local government was also placed into one of the following rent burden typologies:
Low severe rent burden; low rent burden city = the percentage of renters with severe burden in the selected city is below the median rate of severe burden in all OHNA cities, and the percentage of renters with rent burden in the selected city is below the median rate of rent burden in all OHNA cities.
Low severe rent burden; high rent burden city = the percentage of renters with severe burden in the selected city is below the median rate of severe burden in all OHNA cities and the percentage of renters with rent burden in the selected city is above the median rate of rent burden in all OHNA cities.
High severe rent burden; low rent burden city = the percentage of renters with severe burden in the selected city is above the median rate of severe burden in all OHNA cities, and the percentage of renters with rent burden in the selected city is below the median rate of rent burden in all OHNA cities.
High severe rent burden; high rent burden city = the percentage of renters with severe burden in the selected city is above the median rate of severe burden in all OHNA cities and the percentage of renters with rent burden in the selected city is above the median rate of rent burden in all OHNA cities.
Indicator H) Other measurable factors of indicators identified by the department
The ORS leaves this indicator open to OHCS’ discretion. Additional factors may be added here at a later date.
Future Work
OHCS hopes to continue building out the Housing Equity Dashboard in the future as more and better data becomes available. At present, indicator F identified in the ORS has insufficient data to be included in the dashboard. Indicator B is partially included.
Updated Indicator B) Housing types produced and land efficiency
The ORS described this indicator as displaying “housing types produced and overall land efficiency of existing and new housing” for each local government with a population of over 10,000.
In order to accurately capture this data and compare the land efficiency of new housing relative to existing housing stock, OHCS would need a well-managed address-level inventory of all housing developments containing precise measurements (i.e. square footage) of each property. This data could come from county assessor offices, but at the moment the data are incomplete and inconsistent from county to county. Improving this data quality is something OHCS, DLCD and other partners will continue to explore.
Future Indicator F) Environmentally just housing outcomes
The ORS described this indicator as displaying “Environmentally just housing outcomes, informed by the environmental justice mapping tool, developed by the Environmental Justice Council under ORS 182.555.”
Unfortunately, this dataset does not exist yet. However, OHCS is collaborating with the Environmental Justice Council to develop a “built environment” subcategory and will include the tool once it is publicly available.