Midterm

Exploring the cost of rent in Nashville-area ZIP codes

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

Map of Nashville Zip Codes

# 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")

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")

# 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))

# Showing the data as a table

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

FMR_Nash_table

# 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))

# Showing the data as a table

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

FMR_Nash_table

#Map

# 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_Nash_Map

FMR_Nash_Map <- st_as_sf(FMR_Nash_Map)

# Making the map

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

# Showing the map

Rent_Category_Map

#API

census_api_key("93315bc9312a299da799361cc702a29db484d7cd")
Census_Data <- get_acs(
  geography = "zcta",
  variables = c("DP04_0047", "DP04_0045"),
  year = 2023,
  survey = "acs5",
  output = "wide",
  geometry = FALSE
)
Census_Data <- Census_Data %>% 
  rename(c("Rentals" = "DP04_0047E",
           "Rentals_MOE" = "DP04_0047M",
           "Households" = "DP04_0045E",
           "Households_MOE" = "DP04_0045M"))

glimpse(Census_Data)

# Merging Rent_Category_Map and Census_Data

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, "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

Midterm

Emmett Landers

2025-03-07