A dataset containing vacancy rates and other values related to housing for tracts in Baltimore; Stamford, CT; and New Haven, CT. The data comes from the 2022 American Community Survey (ACS). This was updated to include all tracts in Maryland, though Maryland tracts not in Baltimore city will have NA in the city column.
Format
A data frame with 1529 rows and 12 variables:
- state
Character. The state FIPS code.
- city
Character. The city name.
- county
Character. The 3-digit county FIPS code.
- county_name
Character. Full county name.
- geoid
Character. The tract FIPS code.
- total_units
Numeric. The total number of housing units.
- vacants
Numeric. The number of vacant housing units.
- med_rent
Numeric. The median rent for renter-occupied housing units.
- med_housing_value
Numeric. The median housing value for owner-occupied housing units.
- vacancy_rate
Numeric. The vacancy rate.
- area_sqmi
Numeric. The area in square miles.
- housing_density
Numeric. The housing density (total units per square mile).
Source
Calculated from US Census Bureau. American Community Survey 2022 5-year estimates. Calculated by Camille with the tidycensus and cwi packages.
Examples
head(vacant)
#> # A tibble: 6 × 12
#> state city county county_name geoid total_units vacants med_rent
#> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 24 NA 001 Allegany County 24001000600 1574 200 606
#> 2 24 NA 001 Allegany County 24001001100 857 167 590
#> 3 24 NA 001 Allegany County 24001000800 1033 247 939
#> 4 24 NA 001 Allegany County 24001000100 2245 668 904
#> 5 24 NA 001 Allegany County 24001000200 1617 227 858
#> 6 24 NA 001 Allegany County 24001000700 1769 307 722
#> # ℹ 4 more variables: med_housing_value <dbl>, vacancy_rate <dbl>,
#> # area_sqmi <dbl>, housing_density <dbl>