The Housing Precarity Risk Model shows which communities are at risk of post-pandemic eviction, displacement, and long-term poverty. These maps are a conservative estimate meaning eviction and displacement may actually be higher in some areas depending on local and federal resources, recovery efforts, policies (or lack thereof), and market dynamics. We encourage you to engage with local stakeholders to achieve a more accurate understanding of local needs. We will continue to update these estimates as we collect more data and feedback.

The dashboard map features 6 different layers:

  • Housing Precarity Risk: a composite score of eviction risk, displacement vulnerability, and pandemic unemployment.
  • Eviction Risk: composite score for the top variables that relate to eviction lockouts. This estimate does not account for eviction notices and filings and therefore is a conservative estimate of eviction risk.
  • Displacement Vulnerability: location of communities with predominantly low-income households and/or ongoing displacement. Predominantly low-income households are also susceptible to displacement depending on local housing markets. Also, low-income displacement often precedes gentrification.
  • 2020 Unemployment: Estimated unemployment during the pandemic
  • Change in Unemployment from 2019 to 2020
  • Segregation: Neighborhood level typology showing which racial groups share of the population is greater than 10%.

If you believe a tract in your area is incorrectly labeled, let us know by filling out this form. This helps us improve our model.


The COVID-19 recession featured massive unemployment among low-wage workers, exacerbating already precarious housing conditions and putting millions more at risk of losing their homes. As the United States enters recovery and federal and local eviction moratoria near their end, it is unclear how many households will face eviction, displacement, or even homelessness. Therefore, it is important to understand who and where vulnerable groups live so that assistance can be directed appropriately. 

To help understand the extent of housing precariousness in the U.S., the Urban Displacement Project was funded by C3.AI to build the Housing Precarity Risk Model (HPRM). Precarity, defined as resilience to economic and environmental shocks (Pendall et al, 2012), is measured as a composite score of pre-pandemic low-income concentrations, low-income displacement and eviction lockout risk interacted with 2020 unemployment and COVID-19 infections. Our primary goal is to identify the most vulnerable neighborhoods (i.e. US Census tracts) that are in particularly urgent need of assistance and resources from local, state, and federal agencies. We also provide anti-displacement policy recommendations below. 

How to use these maps

The two maps above show which neighborhoods have the highest risk of displacement and eviction across 53 metropolitan areas with populations larger than 1 million people. The first tab is a National Estimation Map of our HPRM score on a scale of 0 to 9 (3 to 5 = higher risk, 6 to 9 = highest risk). The first layer of the map shows the distribution of our composite housing precarity index, and each of the subsequent layers represents one of the key indicators used to calculate the index: estimated pre-pandemic eviction risk (described below), pre-pandemic displacement risk, 2020 unemployment, and change in unemployment from 2019 to 2020. 

The second tab is a 2-City COVID and HPRM Map that displays the current state of housing precarity in the cities of Seattle and Chicago displaying actual eviction rates from 2017 and available COVID-19 infection data. The COVID-19 rate for 2020 is not part of the HPRM score but is included as an optional overlay to highlight where neighborhoods that were already highly vulnerable in 2019 have recently been struck by severe outbreaks of the pandemic. We also provide layers showing census tract segregation (labeled with racial groups that represent more than 10% of the total tract population) as well as tracts with student populations greater than 30%. Student populations can skew precarity results.


Below are several key findings that can be seen in the national maps.

  • 41% of all households in our 53 metro study area (26.9 million) live in neighborhoods where there is a high level of vulnerability to eviction or displacement
  • 52% of all renters live in neighborhoods with a high risk of displacement or eviction.
  • 73% of Black-headed and 63% of Latinx-headed renter households live in neighborhoods with high risk of displacement or eviction.
  • 74% of Black renters live in neighborhoods with a high risk of eviction.

Top 10 Most Precarious Metros (weighted by households):

  1. Las Vegas-Henderson-Paradise, NV
  2. New Orleans-Metairie, LA
  3. Detroit-Warren-Dearborn, MI
  4. Providence-Warwick, RI-MA
  5. Buffalo-Cheektowaga, NY
  6. Memphis, TN-MS-AR
  7. Cleveland-Elyria, OH
  8. Philadelphia-Camden-Wilmington, PA-NJ-DE-MD
  9. New York-Newark-Jersey City, NY-NJ-PA
  10. Los Angeles-Long Beach-Anaheim, CA

The full city ranking list and other statistics can be found here

Post-pandemic policy recommendations

These maps not only show immediate precarity, but also highlight the neighborhoods that are likely to experience long-lasting economic consequences of the pandemic including increased poverty and homelessness. In preparation for an expiring national eviction moratorium and an unprecedented wave of evictions, policymakers need to develop strategies that prevent displacement in combination with the products of our Housing Precarity Risk Model (HPRM) project. 

Our research shows a direct link between housing precarity and racial inequality due to the legacies of segregation and discrimination that has forced Black, Indigenous, and Persons of Color to live in precarious conditions. Housing policy is deeply tied to the racial wealth gap, which has been exacerbated by the COVID-19 pandemic. Key to a racially equitable housing strategy is going beyond addressing discrimination to combat our nation’s pervasive structural racism. Affirmatively Furthering Fair Housing (AFFH), the HUD mandate reaffirmed by the Biden Administration that will go into effect on July 31st, requires HUD grantees to meaningfully engage in fair housing planning and provides technical assistance and support  in developing such plans.Jurisdictions should apply a racial lens to their policies and anticipate AFFH’s reinstatement in developing their anti-displacement frameworks.

The “Three P’s” anti-displacement framework—protection, preservation, and production—is widely used in California and comprehensively addresses housing unaffordability.

  • Protection – Protections against displacement provide an immediate defense for tenants facing the oncoming eviction crisis. Tenant protection policies include just cause for eviction, rental assistance, and right to counsel in eviction proceedings.
  • Preservation – Preservation of existing affordable housing is critical to keeping neighborhoods affordable and stable, especially amidst the economic turmoil of the COVID-19 pandemic. Preservation policies include community land trusts, rent stabilization, and right of first refusal.
  • Production – Producing new, permanently affordable housing proactively establishes neighborhood affordability. Affordable housing production policies include land banks, mandatory inclusionary housing, and upzoning high income areas.

The following table presents state and local level anti-displacement policies that we recommend in response to the expiration of the national eviction moratorium:

Eviction moratoriumExtending the eviction moratorium would immediately stave off the wave of evictions as well as give governments time to enact and manage other anti-displacement policies.
Just causeAlso known as good cause, just cause policies place restrictions on justifiable grounds for eviction. Under just cause, landlords cannot evict tenants without cause; instead, landlords can typically only evict tenants if they fail to pay rent or violate other terms of the lease. Just cause also establishes a set of specific procedures for eviction that landlords must follow.
Rent stabilizationRent stabilization protects tenants from excessive rent increases while allowing landlords a reasonable return on their investments. The policy is most effective without vacancy decontrol and typically requires the creation of a rent board to decide on yearly rent adjustments.
Right of first refusalWhen a property is up for sale, the right of first refusal grants nonprofits and/or tenants an exclusive period to try to purchase a property without competition of private buyers, thus supporting the acquisition of property for long-term affordability. The policy is similar to the Community Opportunity to Purchase Act (COPA) and Tenant Opportunity to Purchase Act (TOPA).
Emergency rental assistanceUsing federal COVID-19 funds, temporary rental assistance should be offered to at-risk landlords and tenants. Importantly, the temporary rental assistance should have minimal eligibility barriers, should apply to back rent, and should have targeted outreach to at-risk renters.
Land bankA land bank is a public authority or nonprofit organization that holds and repurposes land for public good. Land can be acquired from tax foreclosures, donations, and vacant public land. The land can then be transferred to local nonprofits and community land trusts.
Provide immediate necessities to homeless personsAnti-displacement plans should integrate anti-homelessness strategies. Immediate relief includes implementing harm-reductionist techniques, funding more case workers, and providing tangible necessities like food and hygiene products.
Community land trust (CLT)Jurisdictions should support and fund community land trusts (CLTs), which maintain permanently affordable land as a public good. Compatible with other social housing models like limited-equity cooperatives and mutual housing associations.
Hotel conversionsWhen areas have significant numbers of financially distressed hotels, they should enact a policy that converts them into dedicated housing for low-income people and people experiencing homelessness. Policy may require a zoning override provision.
Eviction record expungementSealing eviction records helps people who experienced eviction find housing and prevent furnishers and credit reporting agencies from aggregating eviction data and blacklisting tenants.
Housing trust fundNecessary to anti-displacement strategies is dedicating local funds to affordable housing and anti-displacement programs. In addition, jurisdictions should develop a plan to raise funds for the trust, such as through linkage fees or taxes.
Right to counselIn eviction cases, landlords almost always have legal representation while tenants do not. Cities should guarantee an attorney to tenants for all eviction cases, a policy which has proven effectiveness in resulting in better terms for the tenant.

Suggested Reading

COVID-19 Housing Policy Tools and Resources

Other COVID-19 Housing Policy Recommendations

Racial Equity in Housing

Social Housing


How we created the HPRM

The HPRM is based on the hypothesis that employment is the primary resource for maintaining housing. Therefore, pre-pandemic risk of forced migration through displacement or eviction was exacerbated by 2020 unemployment and COVID-19 infection. To calculate the HPRM score, we developed a point system for each variable of displacement, eviction, unemployment, and unemployment change and then summed the values to create the HPRM index. 

We use US Census data to estimate 2019 displacement using the Urban Displacement Project’s Gentrification and Displacement methodology. From this methodology, we use the neighborhood typologies of “low income/susceptible to displacement” to give tracts 1 point on the HPRM index and “ongoing displacement,” 2 points. Tracts without either of these two typologies received 0 points. 

Next, we use eviction data from the Eviction Study and The Eviction Lab to analyze eviction rates in our study areas. Because eviction data are sparse and largely unavailable, we developed a groundbreaking eviction risk estimation model that used complete eviction lockout data across 17 metros (Atlanta, Boston, Charleston, Charlotte, Chicago, Cleveland, Denver, Kansas City, Miami, Oklahoma City, Orlando, Raleigh, Richmond, Seattle Puget Sound, St. Louis, Tampa Bay, Virginia Beach) to extract the primary US Census variables that predict eviction. We controlled for over a hundred variables related to eviction using a Bayesian additive regression tree (BART) model, isolating the following primary tract factors: percent Black, rent, change in rent, percent with college degrees, percent of households with seniors, percent married, and building age. These variables proxy complex conditions that households face related to eviction risk. For example, percent Black is related to U.S. legacies of forced segregation and housing discrimination over time. We then calculated points for each tract based on the eviction rate for the related levels of each variable. For example, we found that tracts with less than a 9% Black population had, on average, less than 2% eviction rate (2% being the overall average eviction rate among our 17 metros of data) while tracts with 9% to 31% Black had an average 2% to 4% eviction rate, and above 31% had greater than 4% eviction rate. Therefore, tracts with a 0% to 9% Black population received 0 points, 9% to 31% received 1 point, and above 31% received 2 points. We then replicated this process with the other six variables and summed the tract scores together to create an eviction index. We then compared our index to actual eviction data to confirm our estimation. We found that tracts with 0 to 3 eviction points mostly had a less than 2% eviction rate, 4 to 5 points ranged with less than 2% to over 4% eviction rate, and tracts with 6 to 11 points had largely higher than 4% eviction rate. Using these results we then calculated this index for all tracts across our 53 metro areas. For the Seattle, San Francisco, and Chicago map, we relied on actual eviction data drawn from the Evictions Study and San Francisco eviction notice data portal. 

For 2020 unemployment data, we used tract level estimates developed by Catalyst based on PUMS and weekly unemployment insurance claims data. We used 2019 ACS 5-year tract level unemployment data to calculate change in unemployment from 2019 to 2020. Unemployment rates of less than 8% received 0 points, 8% to 14% received 1 point, and greater than 14% received 2 points. Unemployment change less than 8 percentage points between 2019 and 2020 received 0 points while greater than 8 percentage point change received 1 point. 

COVID-19 data were collected directly from the county health departments of San Francisco, California; Cook County, Illinois; King County, Washington; and Pierce County, Washington