Income vs. Rent Growth, Part 2

Last updated on Oct 4, 2024 3 min read

In the previous post, I looked at median gross rent and median household income growth from 2005 - 2023 according to the American Community Survey. But that analysis was only at the national level. What about the state level? Let’s find out.

Again, I’ll pull the median household income and median gross rent from the ACS. But this time I’ll select states. Additionally, I’ll switch to looking at median household income only for renter households. Finally, rather than looking at the full time series, I’ll only pull the starting and ending years.

state_hh_income <- get_ACS("NAME,B25119_003E","state",2005,2005,1)
state_hh_income2 <- get_ACS("B25119_003E","state",2023,2023,1)

state_hh_income$inc05 <- as.numeric(state_hh_income$B25119_003E)
state_hh_income2$inc23 <- as.numeric(state_hh_income2$B25119_003E)

state_hh_income <- cbind(state = state_hh_income$NAME,
                         inc05 = state_hh_income$inc05,
                         inc23 = state_hh_income2$inc23)

state_hh_income <- as_tibble(state_hh_income)

state_hh_income <- state_hh_income %>%
  mutate(inc_growth = as.numeric(inc23) / as.numeric(inc05) - 1)


state_hh_rent <- get_ACS("B25064_001E,NAME","state",2005,2005,1)
state_hh_rent2 <- get_ACS("B25064_001E","state",2023,2023,1)
state_hh_rent$rent05 <- as.numeric(state_hh_rent$B25064_001E)
state_hh_rent2$rent23 <- as.numeric(state_hh_rent2$B25064_001E)
state_hh_rent <- cbind(state = state_hh_rent$NAME,
                       rent05 = state_hh_rent$rent05,
                       rent23 = state_hh_rent2$rent23)
state_hh_rent <- as_tibble(state_hh_rent)
state_hh_rent <- state_hh_rent %>%
  mutate(rent_growth = as.numeric(rent23) / as.numeric(rent05) - 1    
  )

state_income_rent <- merge(state_hh_income, state_hh_rent, by = "state")
state_income_rent <- state_income_rent %>% 
  mutate(diff = rent_growth - inc_growth)

state_income_rent <- arrange(state_income_rent, desc(diff))
state_income_rent2 <- state_income_rent %>% 
  select(state, inc_growth, rent_growth, diff) %>% 
  mutate(across(c("inc_growth", "rent_growth", "diff"), function(x) (paste0(round(x, 4) * 100,"%"))))

States with the largest difference:

knitr::kable(head(state_income_rent2))
state inc_growth rent_growth diff
Arizona 90.42% 124.27% 33.85%
Florida 81.34% 112.48% 31.14%
Hawaii 66.36% 94.97% 28.62%
Delaware 45.8% 71.25% 25.45%
Nevada 63.83% 88.39% 24.56%
Wyoming 61.81% 86.22% 24.41%

Arizona and Florida had some of the fastest rent growth post-Covid, so this makes sense.

States with the smallest difference:

knitr::kable(tail(state_income_rent2))
state inc_growth rent_growth diff
47 Ohio 74.57% 65.42% -9.15%
48 Puerto Rico 55.94% 46.58% -9.37%
49 Vermont 86.61% 75.99% -10.62%
50 West Virginia 87.45% 75.98% -11.47%
51 Illinois 80.26% 68.66% -11.59%
52 District of Columbia 144.39% 128.85% -15.54%

Let’s redo the national analysis, but this time with the renter median household income.

us_hh_income <- get_ACS("B25119_003E","us",2005,2019,1)
us_hh_income2 <- get_ACS("B25119_003E","us",2021,2023,1)
us_hh_income <- rbind(us_hh_income, us_hh_income2)

us_rent <- get_ACS("B25064_001E","us",2005,2019,1)
us_rent2 <- get_ACS("B25064_001E","us",2021,2023,1)
us_rent <- rbind(us_rent, us_rent2)

us_hh_income$B25119_003E[1]
## [1] "28251"
us_hh_income <- us_hh_income %>%
  mutate(income_index = as.numeric(B25119_003E) * 100 / 28251)

rent_start <- us_rent$B25064_001E[1]
us_rent <- us_rent %>% 
  mutate(rent_index = as.numeric(B25064_001E) * 100/ 728)

rent_income <- inner_join(us_hh_income, us_rent, by = "year")

rent_income$Difference <- rent_income$rent_index - rent_income$income_index

total_change <- rent_income %>%
  select(rent_index, income_index, Difference)

total_change <- total_change[18,]
total_change2 <- total_change %>%
  mutate(across(everything(), function(x) (paste0(round(x, 2),"%"))))

knitr::kable(total_change2)
rent_index income_index Difference
193.13% 183.07% 10.06% 
df <- rent_income %>%
  select(year, rent_index, income_index) %>%
  rename(renter_income_index = income_index) %>% 
  gather(key = "variable", value = "value", -year)


x <- ggplot(df, aes(x = year, y = value)) +
  geom_line(aes(color = variable), size = 1.5) +
  scale_color_manual(values = c("Orange", "cyan3")) +
  labs(caption = "Source: American Community Survey \n @abibler.bsky.social",
       title =
         "Median Gross Rent vs. Median Household Income (Renters), \n 2005 = 100") +
  ylab("Index") +
  xlab("Year") +
  theme_minimal() +
  scale_y_continuous(breaks=(seq(100, 200, 25)), limits = c(100, 200)) +
  theme(legend.title = element_blank(),
        panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank(),
        plot.title = element_text(size = 18, face = "bold"))
x

We can see that this time the difference is “only” 10%, and now the difference seems to be more due to the post-Covid rent spike.

Adam Bibler
Adam Bibler