Nashville Housing Market Analysis

Nashvilles two-bedroom rents show widespread unaffordability across the metro area.

Housing costs for two-bedroom units in the greater Nashville area have risen sharply, with nearly 50 ZIP codes considered unaffordable for typical single-parent households, and only a small fraction remaining within an affordable range.

The analysis combines HUD’s 2026 SAFMR rent estimates with ACS 2022 data to estimate the number of rental households and total households by ZIP code. The map below shows two-bedroom rent levels, shaded from light to dark pink, and detail windows provide rental and total household estimates for each ZIP code. This evaluation highlights which neighborhoods may offer more accessible housing and which are likely to place higher cost burdens on families, helping planners, policymakers, and residents make informed decisions. —

Load Packages

if (!require("tidyverse")) install.packages("tidyverse")
if (!require("gt")) install.packages("gt")
if (!require("leaflet")) install.packages("leaflet")
if (!require("leafpop")) install.packages("leafpop")
if (!require("sf")) install.packages("sf")
if (!require("RColorBrewer")) install.packages("RColorBrewer")
if (!require("classInt")) install.packages("classInt")
if (!require("scales")) install.packages("scales")
if (!require("htmlwidgets")) install.packages("htmlwidgets")
if (!require("tidycensus")) install.packages("tidycensus")

library(tidyverse)
library(gt)
library(sf)
library(leaflet)
library(leafpop)
library(RColorBrewer)
library(classInt)
library(scales)
library(htmlwidgets)
library(tidycensus)

Define Nashville 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"
)

Download HUD SAFMR Rent Data

download.file(
"https://www.huduser.gov/portal/datasets/fmr/fmr2026/fy2026_safmrs.xlsx",
"rent.xlsx",
mode = "wb"
)

Read & Clean Rent Data

FMR_Area <- readxl::read_xlsx(
path = "rent.xlsx",
.name_repair = "universal"
)

FMR_Area <- FMR_Area %>%
transmute(
ZIP = ZIP.Code,
Studio = SAFMR.0BR,
BR1 = SAFMR.1BR,
BR2 = SAFMR.2BR,
BR3 = SAFMR.3BR,
BR4 = SAFMR.4BR
) %>%
filter(ZIP %in% ZIPList) %>%
distinct()

Download ZIP Code Shapefile

if(!file.exists("zipmap.zip")){
download.file(
"https://www2.census.gov/geo/tiger/GENZ2020/shp/cb_2020_us_zcta520_500k.zip",
"zipmap.zip",
mode = "wb"
)
}

unzip("zipmap.zip", overwrite = TRUE)

Load ZIP Code Map

ZIPMap <- st_read("cb_2020_us_zcta520_500k.shp") %>%
  transmute(ZIP = ZCTA5CE20) %>%
  filter(ZIP %in% ZIPList)

## Reading layer `cb_2020_us_zcta520_500k' from data source 
##   `C:\Users\miaru\OneDrive\Documents\cb_2020_us_zcta520_500k.shp' 
##   using driver `ESRI Shapefile'
## Simple feature collection with 33791 features and 7 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -176.6967 ymin: -14.37374 xmax: 145.8304 ymax: 71.34122
## Geodetic CRS:  NAD83

Join Rent Data to Zip Map

MapData <- ZIPMap %>%
  left_join(FMR_Area, by = "ZIP")

Get ACS Rental Data

ACS_Area <- get_acs(
  geography = "zcta",
  variables = c(
    Rentals = "B25032_013",
    TotalHH = "B11001_001"
  ),
  year = 2022
) %>%
  select(GEOID, variable, estimate, moe) %>%
  pivot_wider(
    names_from = variable,
    values_from = c(estimate, moe)
  ) %>%
  transmute(
    ZIP = GEOID,
    Rentals_E = estimate_Rentals,
    Rentals_M = moe_Rentals,
    TotalHH_E = estimate_TotalHH,
    TotalHH_M = moe_TotalHH
  ) %>%
  filter(ZIP %in% ZIPList)

Join ACS Data to MapData

MapData <- MapData %>%
  left_join(ACS_Area, by = "ZIP")

Map of Two-Bedroom Fair Market Rent

pal <- colorNumeric(
  palette = "RdPu",
  domain = MapData$BR2
)

leaflet(MapData) %>%
  addProviderTiles("CartoDB.Positron") %>%
  addPolygons(
    fillColor = ~pal(BR2),
    weight = 1,
    color = "white",
    fillOpacity = 0.7,
    popup = ~paste0(
      "<b>ZIP:</b> ", ZIP,
      "<br><b>Studio:</b> $", Studio,
      "<br><b>1BR:</b> $", BR1,
      "<br><b>2BR:</b> $", BR2,
      "<br><b>3BR:</b> $", BR3,
      "<br><b>4BR:</b> $", BR4,
      "<br><br><b>Rental Homes:</b> ", Rentals_E,
      "<br><b>Rental MOE:</b> ", Rentals_M,
      "<br><br><b>Total Households:</b> ", TotalHH_E,
      "<br><b>Total HH MOE:</b> ", TotalHH_M
    )
  ) %>%
  addLegend(
    pal = pal,
    values = ~BR2,
    title = "Two Bedroom Fair Market Rent",
    position = "bottomright"
  )

ZIP Code Affordability Table

# Define affordability threshold (example: 2BR rent <= $1400 is affordable)
MapData <- MapData %>%
  mutate(Affordability = ifelse(BR2 <= 1400, "Affordable", "Unaffordable"))

# Summarize
Summary <- MapData %>%
  st_set_geometry(NULL) %>%    # drop geometry for summarise
  group_by(Affordability) %>%
  summarise(
    Count = n(),
    Minimum = min(BR2, na.rm = TRUE),
    Average = round(mean(BR2, na.rm = TRUE)),
    Maximum = max(BR2, na.rm = TRUE)
  )

# Display with gt
Summary_Table <- gt(Summary) %>%
  tab_header(title = "ZIP Code Affordability") %>%
  cols_align(align = "left")

Summary_Table

Affordability	Count	Minimum	Average	Maximum
ZIP Code Affordability
Affordable	9	1320	1332	1390
Unaffordable	54	1450	1906	2600

# Subset MapData for the table
Data_From_Map <- MapData %>%
  st_set_geometry(NULL) %>%   # remove geometry
  select(ZIP, BR2, Rentals_E, TotalHH_E, Affordability)

# Subset MapData for the table
Data_From_Map <- MapData %>%
  st_set_geometry(NULL) %>%   # remove geometry
  select(ZIP, BR2, Rentals_E, TotalHH_E, Affordability)

# Create gt table
Data_From_Map_Table <- gt(Data_From_Map) %>%
  tab_header(title = "Data") %>%
  cols_align(align = "left")

Data_From_Map_Table

ZIP	BR2	Rentals_E	TotalHH_E	Affordability
Data
37064	2150	5035	23199	Unaffordable
37232	1770	0	0	Unaffordable
37072	1630	4138	13159	Unaffordable
37015	1460	1471	7347	Unaffordable
37090	1640	2081	6590	Unaffordable
37167	1640	8454	21855	Unaffordable
37179	2440	813	5570	Unaffordable
37153	1920	276	1883	Unaffordable
37013	1870	15919	36255	Unaffordable
37201	2360	452	885	Unaffordable
37025	1320	130	2172	Affordable
37206	1840	6453	12862	Unaffordable
37037	2180	290	2864	Unaffordable
37046	1880	161	2458	Unaffordable
37213	1830	13	13	Unaffordable
37067	2250	6236	13057	Unaffordable
37228	1730	1480	1492	Unaffordable
37160	1320	4817	13127	Affordable
37180	1320	259	1452	Affordable
37122	2200	4111	23409	Unaffordable
37128	1800	8900	26411	Unaffordable
37130	1470	12252	23323	Unaffordable
37238	1770	0	0	Unaffordable
37174	2050	4317	17221	Unaffordable
38401	1320	7003	25448	Affordable
37020	1360	140	1890	Affordable
37027	2440	3922	22708	Unaffordable
37210	1580	5337	7344	Unaffordable
37129	1800	6797	21677	Unaffordable
37115	1620	10268	17954	Unaffordable
37209	1950	10128	18570	Unaffordable
37086	1990	3637	12449	Unaffordable
37076	1780	7512	17136	Unaffordable
37219	2440	882	1158	Unaffordable
37080	1320	527	3028	Affordable
38476	1390	24	300	Affordable
37014	2250	0	1307	Unaffordable
37203	1990	9564	11834	Unaffordable
37204	2280	3077	6935	Unaffordable
37189	1480	258	1567	Unaffordable
37135	2600	421	7240	Unaffordable
37207	1450	6960	16034	Unaffordable
37205	2230	3727	11955	Unaffordable
37212	1820	4007	6938	Unaffordable
37060	1600	95	975	Unaffordable
37062	1690	870	4951	Unaffordable
37211	1780	15376	30135	Unaffordable
37214	2000	5623	14475	Unaffordable
37218	1580	1808	5945	Unaffordable
37220	2470	225	2410	Unaffordable
37085	1520	55	1878	Unaffordable
37215	2200	2588	10260	Unaffordable
37216	1810	2466	8597	Unaffordable
37217	1780	6660	12468	Unaffordable
37069	2600	787	6811	Unaffordable
37127	1560	1688	6988	Unaffordable
37132	1470	0	0	Unaffordable
37143	1490	222	1517	Unaffordable
37221	2200	5282	18787	Unaffordable
37149	1320	76	911	Affordable
37138	1730	1991	9485	Unaffordable
37208	1880	5888	8670	Unaffordable
37118	1320	0	494	Affordable

## Code

# Load Packages
library(tidyverse)
library(gt)
library(sf)
library(leaflet)
library(leafpop)
library(RColorBrewer)
library(classInt)
library(scales)
library(htmlwidgets)
library(tidycensus)
library(readxl)

# Define Nashville 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"
)

# Read rent data
FMR_Area <- readxl::read_xlsx("rent.xlsx", .name_repair="universal") %>%
  transmute(
    ZIP = ZIP.Code,
    Studio = SAFMR.0BR,
    BR1 = SAFMR.1BR,
    BR2 = SAFMR.2BR,
    BR3 = SAFMR.3BR,
    BR4 = SAFMR.4BR
  ) %>%
  filter(ZIP %in% ZIPList) %>%
  distinct()

# Load ZIP shapefile
ZIPMap <- st_read("cb_2020_us_zcta520_500k.shp") %>%
  transmute(ZIP = ZCTA5CE20) %>%
  filter(ZIP %in% ZIPList)

MapData <- ZIPMap %>% left_join(FMR_Area, by="ZIP")

# Get ACS rental data
ACS_Area <- get_acs(
  geography="zcta",
  variables=c(Rentals="B25032_013", TotalHH="B11001_001"),
  year=2022
) %>%
  select(GEOID, variable, estimate, moe) %>%
  pivot_wider(names_from=variable, values_from=c(estimate, moe)) %>%
  transmute(
    ZIP = GEOID,
    Rentals_E = estimate_Rentals,
    Rentals_M = moe_Rentals,
    TotalHH_E = estimate_TotalHH,
    TotalHH_M = moe_TotalHH
  ) %>%
  filter(ZIP %in% ZIPList)

MapData <- MapData %>% left_join(ACS_Area, by="ZIP")

# Map
pal <- colorNumeric(palette="RdPu", domain=MapData$BR2)

leaflet(MapData) %>%
  addProviderTiles("CartoDB.Positron") %>%
  addPolygons(
    fillColor = ~pal(BR2),
    weight = 1,
    color = "white",
    fillOpacity = 0.7,
    popup = ~paste0(
      "<b>ZIP:</b> ", ZIP,
      "<br><b>Studio:</b> $", Studio,
      "<br><b>1BR:</b> $", BR1,
      "<br><b>2BR:</b> $", BR2,
      "<br><b>3BR:</b> $", BR3,
      "<br><b>4BR:</b> $", BR4,
      "<br><br><b>Rental Homes:</b> ", Rentals_E,
      "<br><b>Total Households:</b> ", TotalHH_E
    )
  )

# Affordability summary
MapData <- MapData %>%
  mutate(Affordability = ifelse(BR2 <= 1400, "Affordable", "Unaffordable"))

Summary <- MapData %>%
  st_set_geometry(NULL) %>%
  group_by(Affordability) %>%
  summarise(
    Count = n(),
    Minimum = min(BR2, na.rm=TRUE),
    Average = round(mean(BR2, na.rm=TRUE)),
    Maximum = max(BR2, na.rm=TRUE)
  )

gt(Summary)

# Data table
Data_From_Map <- MapData %>%
  st_set_geometry(NULL) %>%
  select(ZIP, BR2, Rentals_E, TotalHH_E, Affordability)

gt(Data_From_Map)

```

Midterm Assignment

Mia Ruiz

2026-03-07