Here is how we ranked the best cities to live in after coronavirus


We recently released our ranking of the US cities to live in after the novel coronavirus pandemic using the most recent data from 2018 to 2020.

For our ranking, we used nine different metrics about a metro area’s economy, population density, work life, and housing. To do this, we used datasets from government and academic research. After compiling our data, we calculated a z-score for each metro on each of the nine metrics compare the values on the same scale. We then summed each of the z-scores together to create an overall index for each metro area that we then ranked to get our list of the 30 best cities to move to.

When compiling our overall score, we took the additive inverse of the z-score for five of the metrics where a lower value should lead to a higher score: population density, unemployment rate, housing costs, commute time, and cost-of-living.

Here are the sources for each of our nine measures used for each metro area:

  • Population density, 2019: To calculate the population density of each metro area, we used the land area in square miles from the US Census Bureau’s gazetteer file and the Bureau’s estimates for the metro area’s 2019 population size.
  • Educational attainment, 2018: We used the share of people who are age 25 and over who have at least a bachelor’s degree from the US Census Bureau.
  • Unemployment rate, February 2020: To get a sense of the strength of each metro area’s labour market before the coronavirus pandemic and state lockdowns, we used seasonally-adjusted February unemployment rates from the US Bureau of Labour Statistics.
  • Housing affordability, 2018: We found the share of residents who have a mortgage, do not have a mortgage, and who are renters where less than 30% of their household income goes toward housing costs, a standard metric for housing affordability. We weighted these values by the number of households of each of the three types of units in the metro area. This data came from the US Census Bureau‘s 1-year American Community Survey.
  • Housing costs, 2018: Similarly to housing affordability, we calculated a weighted average of monthly median housing costs for homeowners with a mortgage, homeowners without a mortgage, and non-homeowners who rent using the same housing dataset from the US Census Bureau that we used to calculate housing affordability. For both the affordability and cost metrics, we excluded units where housing costs as a percentage of household income could not be calculated due to data availability from the Bureau.
  • Share of jobs that can be done remotely: We used a dataset created by researchers Jonathan Dingel and Brent Neiman from The University of Chicago. They recently calculated the share of jobs that are feasible to do from home for each metro area using occupational data and characteristics from the Bureau of Labour Statistics and the Occupational Information Network (O*NET).
  • Commute time, 2018: We used the daily average time to work in minutes from the US Census Bureau‘s 1-year American Community Survey to calculate the average time to and from work per week.
  • Cost of living, 2018: For our cost of living, we used the regional price parity from the Bureau of Economic Analysis, which looks at the price of goods and services in each metro area compared to the national average. So a metro area with a value over 100 means the metro area has a greater real price parity than the US average.
  • Total spending per pupil (elementary-secondary), 2018: To look at school spending, we used total spending per person in public elementary and secondary schools from the US Census Bureau’s Annual Survey of School System Finances dataset. Because some metro areas have more than one school district, we used the value from the school district with the highest enrollment in each metro area.