Urban and Regional Migration Estimate (1/1/2010 – 9/30/2024)

This project provides 2 sets of migration estimates for the major US metro areas. The first series measures net migration of people to and from the urban neighborhoods of the metro areas. The second series covers all neighborhoods but breaks down net migration to other regions by four region types: (1) high-cost metros, (2) affordable, large metros, (3) midsized metros, and (4) small metros and rural areas.  These series were introduced in a Cleveland Fed District Data Brief entitled “Urban and Regional Migration Estimates: Will Your City Recover from the Pandemic?"

The migration estimates in this project are created with data from the Federal Reserve Bank of New York/Equifax Consumer Credit Panel (CCP). The CCP is a 5 percent random sample of the credit histories maintained by Equifax. The CCP reports the census block of residence for over 10 million individuals each quarter. Each month, Equifax receives individuals’ addresses, along with reports of debt balances and payments, from creditors (mortgage lenders, credit card issuers, student loan servicers, etc.). An algorithm maintained by Equifax considers all of the addresses reported for an individual and identifies the individual’s most likely current address. Equifax anonymizes the data before they are added to the CCP, removing names, addresses, and Social Security numbers (SSNs). In lieu of mailing addresses, the census block of the address is added to the CCP. Equifax creates a unique, anonymous identifier to enable researchers to build individuals’ panels. The panel nature of the data allows us to observe when someone has migrated and is living in a census block different from the one they lived in at the end of the preceding quarter. For more details about the CCP and its use in measuring migration, see Lee and Van der Klaauw (2010) and DeWaard, Johnson and Whitaker (2019).   

Definitions
 

Metropolitan area

The metropolitan areas in these data are combined statistical areas. This is the most aggregate definition of metro areas, and it combines Washington DC with Baltimore, San Jose with San Francisco, Akron with Cleveland, etc. Metro areas are combinations of counties that are tightly linked by worker commutes and other economic activity. All counties outside of metropolitan areas are tracked as parts of a rural commuting zone (CZ). CZs are also groups of counties linked by commuting, but CZ definitions cover all counties, both metropolitan and non-metropolitan. 

High-cost metropolitan areas

High-cost metro areas are those where the median list price for a house was more than $200 per square foot on average between April 2017 and April 2022. These areas include San Francisco-San Jose, New York, San Diego, Los Angeles, Seattle, Boston, Miami, Sacramento, Denver, Salt Lake City, Portland, and Washington-Baltimore.

Other Types of Regions
Metro areas with populations above 2 million and house price averages below $200 per square foot are categorized as affordable, large metros. Metro areas with populations between 500,000 and 2 million are categorized as mid-sized metros, regardless of house prices.  All remaining counties are in the small metro and rural category.

To obtain a metro area's total net migration, sum the four net migration values for the the four types of regions.
 

Urban neighborhood

Census tracts are designated as urban if they have a population density above 7,000 people per square mile. High density neighborhoods can support walkable retail districts and high-frequency public transportation. They are more likely to have the “street life” that people associate with living in an urban rather than a suburban area. The threshold of 7,000 people per square mile was selected because it was the average density in the largest US cities in the 1930 census. Before World War II, workplaces, shopping, schools and parks had to be accessible on foot. 

Tracts are also designated as urban if more than half of their housing units were built before WWII and they have a population density above 2,000 people per square mile. The lower population density threshold for the pre-war neighborhoods recognizes that many urban tracts have lost population since the 1960s. While the street grids usually remain, the area also needs sufficient density to support neighborhood establishments and continue to function as an urban neighborhood. 

Small cities and towns often have a few dense and walkable neighborhoods, but these tracts are not given an urban designation unless their metro area has at least 500,000 residents. Another defining characteristic of an urban neighborhood is that it places its residents close to amenities that can only be supported by the scale of a major metro, such as major league sports stadiums, professional theaters, museums, etc. 

Urban migration

To obtain net urban migration estimates, we count the number of people moving into the urban neighborhoods of the indicated metros and subtract the number of people moving out of the same urban neighborhoods. Negative values mean more people are leaving than arriving. The out-migration counts include people moving from the urban neighborhoods to suburbs in the same metro area or to any region outside the metro area. Similarly, the in-migration counts include people arriving in the urban neighborhoods from suburbs in the same metro area or any region outside the metro area. Local urban-to-urban moves are not included. 

Regional migration

The regional migration estimates count the people who move between different metro areas or between metro areas and rural commuting zones. Local within-metro movers are not included. The estimates of regional moves include everyone who moves to another region, making no distinction between urban/suburban neighborhoods.

Citation: If using the data, please cite Whitaker, Stephan D. 2023. “Urban and Regional Migration Estimates: Will Your City Recover from the Pandemic?” Federal Reserve Bank of Cleveland, Cleveland Fed District Data Brief. https://doi.org/10.26509/frbc-ddb-20230803

Geographic Coverage:    United States, Combined Statistical Areas, Metropolitan areas, Metro areas

Time Period(s):    1/1/2010 – 9/30/2024

Universe:    US residents with a Social Security Number (SSN) and a credit history.  Approximately 90 percent of adults in the US have credit histories.  Coverage phases in for birth cohorts between the ages of 18 and 23 as individuals make their first applications for credit.  Recent immigrants and people who do not borrow from reporting lenders (credit card issuers, mortgage lenders, student loan servicers, etc.) are not covered.

Data Type(s):  administrative records data; census/enumeration data

Methodology

Sampling:   At the creation of the Federal Reserve Bank of New York/Equifax Consumer Credit Panel (CCP), five random pairs of digits were selected.  Borrowers in the Equifax credit histories are included in the CCP if the last two digits of their Social Security Number matches one of the five random pairs of digits.  The random digits are never redrawn, and SSNs rarely change, so once a borrower appears in the CCP, they will continue to appear each quarter.   The last four digits of SSNs are assigned sequentially within states as applications for SSNs are processed, so these numbers are also effectively random. The matching process brings in 5 percent of all first-time borrowers in each quarter, maintaining the sample's national representativeness.  People exit the panel if they die or pass several years with no reported credit activity.

Data Source:   Federal Reserve Bank of New York/Equifax Consumer Credit Panel, American Community Survey, National Association of Realtors

Geographic Unit:   Census Tract, Combined Statistical Areas (Metro Areas)

The following publications cite the data in this project.

• Whitaker, Stephan D. “Urban and Regional Migration Estimates: Will Your City Recover from the Pandemic?” Cleveland Fed District Data Briefs. Federal Reserve Bank of Cleveland, August 3, 2023. https://doi.org/10.26509/frbc-ddb-20230803.

The following publications relate in an unspecified way to the data in this project.

• Brevoort, Kenneth P., Philipp Grimm, and Michelle Kambara. “Credit Invisibles and the Unscored.” Cityscape 18, no. 2 (July 1, 2016): 9–34. https://www.huduser.gov/portal/periodicals/cityscpe/vol18num2/ch1.pdf.

• DeWaard, Jack, Janna Johnson, and Stephan Whitaker. “Internal Migration in the United States: A Comprehensive Comparative Assessment of the Consumer Credit Panel.” Demographic Research 41 (October 11, 2019): 953–1006. https://doi.org/10.4054/demres.2019.41.33.

• Lee, Donghoon, and Wilbert van der Klaauw. “An Introduction to the FRBNY Consumer Credit Panel.” FRB of New York Staff Report, no. 479 (2010). https://doi.org/10.2139/ssrn.1719116.

• Whitaker, Stephan D. “Did the COVID-19 Pandemic Cause an Urban Exodus?” Cleveland Fed District Data Briefs. Federal Reserve Bank of Cleveland, February 5, 2021. https://doi.org/10.26509/frbc-ddb-20210205.

• Whitaker, Stephan D. “Migrants from High-Cost, Large Metro Areas during the COVID-19 Pandemic, Their Destinations, and How Many Could Follow.” Cleveland Fed District Data Briefs. Federal Reserve Bank of Cleveland, March 25, 2021. https://doi.org/10.26509/frbc-ddb-20210325.

• Whitaker, Stephan D. “Population, Migration, and Generations in Urban Neighborhoods.” Economic Commentary  (Federal Reserve Bank of Cleveland), May 14, 2019, 1–5. https://doi.org/10.26509/frbc-ec-201908.

• Whitaker, Stephan D. “The Demographics of Urban Migrants Since the Pandemic.” Cleveland Fed District Data Briefs. Federal Reserve Bank of Cleveland, January 18, 2024. https://doi.org/10.26509/frbc-ddb-20240118.