Nashville-Area Rent Data

This chart represents rent in the Nashville-area organized by zip, as well as size. It also classifies which areas are below or above rent average on the far right.

Nasvhille-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
37014 2070 2150 2380 3000 3700 2660 Above Average
37205 2070 2150 2380 3010 3700 2662 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
37020 1270 1300 1460 1890 2240 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
37207 1270 1320 1460 1840 2270 1632 Below Average
38401 1270 1300 1460 1840 2240 1622 Below Average
38476 1270 1310 1470 1860 2280 1638 Below Average

Below is a summarized table of the chart above, focusing primarily on two bedroom stats by rent category. It informs you of how many of your options are above or below average as well. Not nearly as specific as the chart above, but it does help give you an idea.

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

Below is an interactive map of the Nashville-area. The shades of green represent the average rent of that zip code. Click on the area to find out more information on that specific zip code. Includes not only apartments, but also rental housing.

This is all of the R script that went into making these charts and maps possible.

# 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")

# Calculate the Zip_Average rent
FMR_Nash <- FMR_Nash %>%
  mutate(Zip_Average = rowMeans(select(., Studio, BR1, BR2, BR3, BR4), na.rm = TRUE))

# Define rent categories based on Zip_Average
FMR_Nash <- FMR_Nash %>%
  mutate(rent_category = case_when(
    Zip_Average < 2149 ~ "Below Average",
    Zip_Average >= 2150 ~ "Above Average"
  ))

# Order columns and sort by Studio rent in descending order
FMR_Nash <- FMR_Nash %>%
  select(ZIP, Studio, BR1, BR2, BR3, BR4, Zip_Average, rent_category) %>%
  arrange(desc(Studio))

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

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

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

Summary_BR2_table

FMR_Nash <- FMR %>%
  filter(ZIP.Code %in% ZIPList) %>%
  select(ZIP.Code, SAFMR.0BR, SAFMR.1BR, SAFMR.2BR, SAFMR.3BR, SAFMR.4BR) %>%
  distinct()
colnames(FMR_Nash) <- c("ZIP", "Studio", "BR1", "BR2", "BR3", "BR4")

# Download and read ZIP code shapefile
download.file("https://www2.census.gov/geo/tiger/GENZ2020/shp/cb_2020_us_zcta520_500k.zip", "ZCTAs2020.zip")
unzip("ZCTAs2020.zip")
ZCTAMap <- read_sf("cb_2020_us_zcta520_500k.shp")

census_api_key("60e19f7ff69b7368570b2a6560506673a1e4017c")

Census_Data <- get_acs(
  geography = "zcta",
  variables = c("B25003_003","B25003_003M","B25003_001","B25003_001M"),
  year = 2022,
  survey = "acs5",
  output = "wide",
  geometry = FALSE
)

Census_Data <- Census_Data %>%
  rename(Rentals = B25003_003E, 
         Rentals_MOE = B25003_003M, 
         Households = B25003_001E,
         Households_MOE = B25003_001M)

# Calculate Zip_Average in FMR_Nash before joining with the shapefile
FMR_Nash_Map <- FMR_Nash %>%
  mutate(ZIP_Average = rowMeans(select(., Studio, BR1, BR2, BR3, BR4), na.rm = TRUE))

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

FMR_Nash_Map <- left_join(FMR_Nash_Map, ZCTAMap, by = c("ZIP" = "ZCTA5CE20")) %>%
  select(ZIP, Studio, BR1, BR2, BR3, BR4, ZIP_Average, geometry) %>%
  st_as_sf()

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

ZIP_Map <- mapview(
  FMR_Nash_Map,
  zcol = "ZIP_Average",
  col.regions = brewer.pal(9, "Greens"),
  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")

  )
)

ZIP_Map