Midterm

Rent in the Nashville area

The cost of renting a home in the Nashville area ranges from $1,270 for a tiny studio apartment on the area’s western fringe to $4,260 for a four-bedroom home in the posh Oak Hill neighborhood, the latest federal data show.

Below is a breakdown of estimated rents by rental home size for each of 80 major ZIP codes in the Nashville area, followed by a table of summary data for two-bedroom rents and a clickable rent data map. Data come from the U.S. Department of Housing and Urban Development’s Small-Area Fair Market Rent program.

Rent information by ZIP code

Nashville-area FMR, by size and ZIP
ZIP	Studio	BR1	BR2	BR3	BR4	ZIP_Average	Rent_Category
37069	2380	2470	2740	3460	4260	3062	Above average
37135	2380	2470	2740	3460	4260	3062	Above average
37220	2380	2470	2740	3460	4260	3062	Above average
37179	2350	2440	2700	3410	4200	3020	Above average
37201	2260	2350	2600	3280	4040	2906	Above average
37027	2220	2300	2550	3220	3960	2850	Above average
37219	2170	2260	2500	3160	3890	2796	Above average
37065	2150	2230	2470	3120	3840	2762	Above average
37068	2150	2230	2470	3120	3840	2762	Above average
37067	2120	2200	2440	3080	3790	2726	Above average
37215	2080	2160	2390	3020	3720	2674	Above average
37205	2070	2150	2380	3010	3700	2662	Above average
37014	2070	2150	2380	3000	3700	2660	Above average
37204	2040	2120	2350	2970	3650	2626	Above average
37122	2030	2100	2330	2940	3620	2604	Above average
37064	1980	2060	2280	2880	3540	2548	Above average
37221	1950	2020	2240	2830	3480	2504	Above average
37037	1940	2010	2230	2820	3470	2494	Above average
37174	1840	1890	2220	2810	3230	2398	Above average
37086	1820	1890	2090	2640	3250	2338	Above average
37214	1780	1850	2050	2590	3190	2292	Above average
37153	1760	1830	2020	2560	3140	2262	Above average
37203	1740	1810	2000	2530	3110	2238	Above average
37046	1700	1770	2000	2550	3030	2210	Above average
37209	1700	1770	1960	2480	3050	2192	Above average
37013	1680	1740	1930	2440	3000	2158	Above average
37128	1680	1740	1930	2440	3000	2158	Above average
37212	1680	1740	1930	2440	3000	2158	Above average
37208	1670	1730	1920	2430	2980	2146	Below average
37213	1670	1740	1920	2420	2980	2146	Below average
37206	1640	1710	1890	2390	2940	2114	Below average
37216	1640	1710	1890	2390	2940	2114	Below average
37228	1640	1710	1890	2390	2940	2114	Below average
37011	1630	1690	1870	2360	2910	2092	Below average
37024	1630	1690	1870	2360	2910	2092	Below average
37062	1630	1690	1870	2360	2910	2092	Below average
37070	1630	1690	1870	2360	2910	2092	Below average
37116	1630	1690	1870	2360	2910	2092	Below average
37129	1630	1690	1870	2360	2910	2092	Below average
37202	1630	1690	1870	2360	2910	2092	Below average
37222	1630	1690	1870	2360	2910	2092	Below average
37224	1630	1690	1870	2360	2910	2092	Below average
37229	1630	1690	1870	2360	2910	2092	Below average
37232	1630	1690	1870	2360	2910	2092	Below average
37236	1630	1690	1870	2360	2910	2092	Below average
37238	1630	1690	1870	2360	2910	2092	Below average
37240	1630	1690	1870	2360	2910	2092	Below average
37243	1630	1690	1870	2360	2910	2092	Below average
37246	1630	1690	1870	2360	2910	2092	Below average
37076	1570	1630	1810	2290	2810	2022	Below average
37138	1550	1610	1780	2250	2770	1992	Below average
37211	1550	1610	1780	2250	2770	1992	Below average
37217	1550	1610	1780	2250	2770	1992	Below average
37072	1520	1580	1750	2210	2720	1956	Below average
37089	1520	1580	1750	2210	2720	1956	Below average
37131	1520	1580	1750	2210	2720	1956	Below average
37133	1520	1580	1750	2210	2720	1956	Below average
37090	1500	1560	1730	2180	2680	1930	Below average
37060	1470	1540	1710	2170	2620	1902	Below average
37167	1450	1510	1670	2110	2600	1868	Below average
37115	1440	1500	1660	2100	2580	1856	Below average
37210	1430	1490	1650	2080	2560	1842	Below average
37127	1410	1460	1620	2050	2520	1812	Below average
37218	1410	1460	1620	2050	2520	1812	Below average
37143	1400	1450	1610	2030	2500	1798	Below average
37189	1370	1420	1570	1980	2440	1756	Below average
37085	1350	1410	1550	1960	2420	1738	Below average
37015	1330	1390	1540	1940	2390	1718	Below average
37130	1300	1350	1490	1880	2320	1668	Below average
37132	1300	1350	1490	1880	2320	1668	Below average
38476	1270	1310	1470	1860	2280	1638	Below average
37020	1270	1300	1460	1890	2240	1632	Below average
37207	1270	1320	1460	1840	2270	1632	Below average
37025	1270	1300	1460	1840	2240	1622	Below average
37080	1270	1300	1460	1840	2240	1622	Below average
37118	1270	1300	1460	1840	2240	1622	Below average
37149	1270	1300	1460	1840	2240	1622	Below average
37160	1270	1300	1460	1840	2240	1622	Below average
37180	1270	1300	1460	1840	2240	1622	Below average
38401	1270	1300	1460	1840	2240	1622	Below average

Two-bedroom rent summary table

Two-bedroom stats, by rent category
Rent_Category	Count	Minimum	Average	Maximum
Above average	28	1930	2309	2740
Below average	52	1460	1713	1920

Clickable data map

Code:

# Getting and loading required packages

if (!require("tidyverse"))
  install.packages("tidyverse")
if (!require("openxlsx"))
  install.packages("openxlsx")
if (!require("gtExtras"))
  install.packages("gtExtras")
if (!require("leafpop"))
  install.packages("leafpop")
if (!require("sf"))
  install.packages("sf")
if (!require("mapview"))
  install.packages("mapview")
if (!require("RColorBrewer"))
  install.packages("RColorBrewer")
if (!require("tidycensus"))
  install.packages("tidycensus")

library(tidyverse)
library(openxlsx)
library(gtExtras)
library(readxl)
library(sf)
library(mapview)
library(leafpop)
library(RColorBrewer)
library(tidycensus)

# Reading data from:
# https://www.huduser.gov/portal/datasets/fmr/fmr2025/fy2025_safmrs.xlsx
# Note that you are downloading the 2025 data. We have been working with 2024 data.
# The data frame should have 51,899 observations of 18 variables

download.file(
  "https://www.huduser.gov/portal/datasets/fmr/fmr2025/fy2025_safmrs.xlsx",
  "rent.xlsx",
  mode = "wb"
)

FMR <- read_xlsx(path = "rent.xlsx", .name_repair = "universal")

# Making a list of Nashville-area ZIP codes

ZIPList <- c(
  "37135",
  "37215",
  "37064",
  "37060",
  "37014",
  "37122",
  "37027",
  "37046",
  "37221",
  "37153",
  "37210",
  "37202",
  "37024",
  "37218",
  "37062",
  "37179",
  "37025",
  "37206",
  "37065",
  "37214",
  "37067",
  "37246",
  "37068",
  "37167",
  "37069",
  "37189",
  "37070",
  "37204",
  "37072",
  "37208",
  "37076",
  "37212",
  "37080",
  "37216",
  "37085",
  "37020",
  "37086",
  "38476",
  "37089",
  "37160",
  "37090",
  "37174",
  "37115",
  "37180",
  "37116",
  "37201",
  "37118",
  "37203",
  "37015",
  "37205",
  "37127",
  "37207",
  "37128",
  "37209",
  "37129",
  "37211",
  "37130",
  "37213",
  "37220",
  "37037",
  "37222",
  "37217",
  "37228",
  "37219",
  "37232",
  "37013",
  "37131",
  "37224",
  "37132",
  "37229",
  "37133",
  "37236",
  "37238",
  "37240",
  "37243",
  "37138",
  "38401",
  "37143",
  "37011",
  "37149"
)

# Filtering for Nashville-area ZIP codes and
# selecting columns of interest
# FMR_Nash data frame should have 80 observations of six variables

FMR_Nash <- FMR %>%
  filter(ZIP.Code %in% ZIPList) %>%
  select(ZIP.Code, SAFMR.0BR, SAFMR.1BR, SAFMR.2BR, SAFMR.3BR, SAFMR.4BR) %>%
  distinct()

# Renaming the columns

colnames(FMR_Nash) <- c("ZIP", "Studio", "BR1", "BR2", "BR3", "BR4")

########################### END OF HINT CODE ################

# Averaging estimates

FMR_Nash <- FMR_Nash %>%
  mutate(ZIP_Average = (Studio + BR1 + BR2 + BR3 + BR4) / 5)

# Sorting in descending order by ZIP_Average

FMR_Nash <- FMR_Nash %>%
  arrange(desc(ZIP_Average))

# Averaging ZIP_Average

Average_ZIP_Average <- mean(FMR_Nash$ZIP_Average)

# Categorizing by ZIP_Average

FMR_Nash <- FMR_Nash %>%
  mutate(
    Rent_Category = case_when(
      ZIP_Average > Average_ZIP_Average ~ "Above average",
      ZIP_Average == Average_ZIP_Average ~ "Average",
      ZIP_Average < Average_ZIP_Average ~ "Below average",
      .default = "Error"
    )
  )

# Showing the data as a table

FMR_Nash_table <- gt(FMR_Nash) %>%
  tab_header("Nashville-area FMR, by size and ZIP") %>%
  cols_align(align = "left") %>%
  gt_theme_538

FMR_Nash_table

# Grouping and summarizing

Summary_BR2 <- FMR_Nash %>%
  group_by(Rent_Category) %>%
  summarize(
    Count = n(),
    Minimum = min(BR2),
    Average = round(mean(BR2), 0),
    Maximum = max(BR2)
  )

# Making the table

Summary_BR2_table <- gt(Summary_BR2) %>%
  tab_header("Two-bedroom stats, by rent category") %>%
  cols_align(align = "left") %>%
  gt_theme_538

# Showing the table

Summary_BR2_table

# Downloading the ZIP code map file

download.file(
  "https://www2.census.gov/geo/tiger/GENZ2020/shp/cb_2020_us_zcta520_500k.zip",
  "ZCTAs2020.zip"
)

# Unzipping the ZIP code map file

unzip("ZCTAs2020.zip")

# Loading the ZIP code file into R as "ZCTAMap"

ZCTAMap <- read_sf("cb_2020_us_zcta520_500k.shp")

# Making ZIP a character variable

FMR_Nash$ZIP <- as.character(FMR_Nash$ZIP)

# Joining the files

FMR_Nash_Map <- left_join(FMR_Nash, ZCTAMap, by = c("ZIP" = "ZCTA5CE20"))

# Dropping unneeded columns

FMR_Nash_Map <- FMR_Nash_Map %>%
  select(-c(AFFGEOID20, GEOID20, NAME20, LSAD20, ALAND20, AWATER20))

# Converting FMR_RuCo_Map

FMR_Nash_Map <- st_as_sf(FMR_Nash_Map)

# Adding Census estimates of rental housing
# and total housing unit counts

# Transmitting API key

# census_api_key("PasteYourAPIKeyBetweenTheseQuoteMarks")

# Fetching the Census data

Census_Data <- get_acs(
  geography = "zcta",
  variables = c("DP04_0047", "DP04_0045"),
  year = 2023,
  survey = "acs5",
  output = "wide",
  geometry = FALSE
)

# Making better column names

Census_Data <- Census_Data %>%
  rename(
    c(
      "Rentals" = "DP04_0047E",
      "Rentals_MOE" = "DP04_0047M",
      "Households" = "DP04_0045E",
      "Households_MOE" = "DP04_0045M"
    )
  )

# A peek at the data

glimpse(Census_Data)

# Merging FMR_Nash_Map and Census_Data

FMR_Nash_Map <- left_join(FMR_Nash_Map, Census_Data, by = c("ZIP" = "GEOID"))

# Mapping by average rent (ZIP_Average)
# with custom color palette

ZIP_Map <- mapview(
  FMR_Nash_Map,
  zcol = "ZIP_Average",
  col.regions = brewer.pal(9, "Blues"),
  layer.name = "Average rent",
  popup = popupTable(
    FMR_Nash_Map,
    feature.id = FALSE,
    row.numbers = FALSE,
    zcol = c(
      "ZIP",
      "Studio",
      "BR1",
      "BR2",
      "BR3",
      "BR4",
      "Rentals",
      "Rentals_MOE",
      "Households",
      "Households_MOE"
    )
  )
)

# Showing the map

ZIP_Map