About the Opportunity Zones Data

Opportunity Zones Explorer  | About the Data | Archive

The current list of designated Opportunity Zones can be found on the Department of the Treasury’s website. This interactive tool will be updated continually as Treasury updates the list of designated Opportunity Zones.

The underlying data and methodology are from the Urban Institute.

The Federal Reserve Bank of St. Louis has created this interactive tool as a way to visualize the data. The map allows one to see where there is investment and gentrification risk at the county level. The list below allows for zooming in on individual census tracts.

The following text is from the Urban Institute and describes the methodology they used to determine investment score and gentrification risk.

Commercial Lending

To develop this measurement, Urban Institute used 2011‒15 CoreLogic data of loans to commercial, industrial, agricultural and exempt properties geocoded to 2010 census tracts, excluding single loans that totaled $100 million or more. Urban Institute summed the total investment over the five-year period at the census tract level and created an annual average. They divided that average investment amount by the number of workers employed in the tract to create an investment-per-employee ratio. They obtained employment data from the Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics Workplace Area Characteristics at the census block level and then aggregated them to the census tract level. We calculated this measure only for census tracts with 200 or more employees.

Multifamily Lending

Urban Institute developed a measure of multifamily lending using 2011–15 CoreLogic data of loans to multifamily properties (five or more units) coded to 2010 census tracts. They excluded single loans that totaled $100 million or more. They summed the total investment over the five-year period at the census tract level and created an annual average. They divided that average investment amount by the number of multifamily units in the tract to create an investment-per-multifamily-unit ratio. They obtained the tract multifamily unit data from the 2011–15 American Community Survey. This measure was calculated only for census tracts with 200 or more multifamily units.

Single-family Lending

Urban Institute compiled a single-family lending measure for 2011 through 2015 using tract-level Home Mortgage Disclosure Act records. They considered only home purchase loans. They took the average amount and total number of home purchase loans per tract to arrive at estimated average total loans over the five-year period. They divided the average annual amount by the total number of single-family units in each tract to arrive at an average annual level of Home Mortgage Disclosure Act single-family lending per single-family unit. This measure was calculated only for census tracts with 200 or more units of single-family housing as obtained from the 2011–15 American Community Survey.

Small-business Lending

Urban Institute compiled a small-business measure at the tract level for the years 2011 through 2015. They obtained lender-level Community Reinvestment Act loan amounts for small businesses from the annual aggregate Community Reinvestment Act data files for 2011 through 2015, available through the Federal Financial Institutions Examination Council. They excluded likely credit card loans by dropping records if the average size of their loans made under $100,000 was less than $10,000. Collapsing five years of data by tract, we arrived at a total sum, which we used to obtain the average annual amount of Community Reinvestment Act lending. They then divided this amount by the number of small-business employees in each tract. They considered any private-sector employee working at a firm with up to 19 employees as an employee of a small business. They obtained these data from the Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics Workplace Area Characteristics at the census block level and then aggregated them to the census tract.

They created z-scores for each component measure (commercial, multifamily, single-family and small business) unless the tract did not meet the cutoff criteria. They averaged the z-scores to create a composite investment score. If a tract did not meet the eligibility threshold for a given category, it was averaged based on the other categories. Then, looking at only LICs and contiguous tracts, we ranked tract z-scores relative to other LIC and contiguous tract scores within the same state or territory. They then created a decile ranking of the composite z-scores for all eligible LIC and continuous tracts, meaning that each LICs and contiguous tract has a ranking from 1 (low) to 10 (high).

Displacement Risk

Designated tracts that have gentrified may need federal investment support less than other tracts that have not. Gentrification is challenging to measure at a national level, and ideally would incorporate local data. View a compilation of studies.

To help inform the decision of which tracts will maximize the return on public investment, they created a flag for tracts that have experienced high levels of socioeconomic change. But local knowledge will be needed to validate, verify and modify the information presented.

Tracts received a socioeconomic change flag if they were more than 1 standard deviation above the mean of all national census tracts on the composite socioeconomic change index they developed. This index was composed of four indicators measuring the change in their respective values between 2000 and 2016. They obtained all 2012–16 data from the 2012–16 American Community Survey, and they obtained all 2000 data from the 2000 Decennial Census.

They included the following four measures in these calculations:

  • Percentage point change in the share of residents with a bachelor’s degree or higher
  • Dollar change in median family income
  • Percentage point change in the share of non-Hispanic white residents (which, for example, can help to explain difference in assets, not just incomes)
  • Change in average housing burden

They created the housing burden measure by calculating z-scores for two housing measures and averaging them: (1) change in the tract’s median home value divided by change in the metropolitan statistical area’s median household income, and (2) change in the tract’s median gross rent divided by change in the metropolitan statistical area’s median household income. For rural areas outside metropolitan or micropolitan statistical areas, they used the county median household income instead. Any tracts with fewer than 100 units of rental-occupied housing were scored by the home value measure alone, and any tracts with fewer than 100 units of owner-occupied housing were scored by the rent measure alone.

To receive a score in the first three demographic indicators (educational attainment, median family income and race), tracts had to have at least 100 residents. They created z-scores for each of these three factors and then averaged them with the housing burden z-score. They calculated this score for all US tracts, not just LICs and contiguous tracts. Any tract 1 standard deviation or more above the mean is flagged as a “1,” having experienced sizable socioeconomic change across these dimensions.