Here are the 2025 fair market rent estimates for the four ZIP codes surrounding MTSU’s campus.
MTSU-area fair market rents, 2025 | |||||
ZIP | Studio | One | Two | Three | Four |
---|---|---|---|---|---|
37127 | 1410 | 1460 | 1620 | 2050 | 2520 |
37128 | 1680 | 1740 | 1930 | 2440 | 3000 |
37129 | 1630 | 1690 | 1870 | 2360 | 2910 |
37130 | 1300 | 1350 | 1490 | 1880 | 2320 |
# Installing and loading required packages
if (!require("tidyverse"))
install.packages("tidyverse")
if (!require("gtExtras"))
install.packages("gtExtras")
library(tidyverse)
library(gtExtras)
library(readxl)
# Downloading data from:
# https://www.huduser.gov/portal/datasets/fmr/fmr2025/fy2025_safmrs.xlsx
download.file("https://www.huduser.gov/portal/datasets/fmr/fmr2025/fy2025_safmrs.xlsx", "rent.xlsx", mode = "wb")
# Reading the downloaded Excel file into a data frame called FMR
FMR <- read_xlsx(path = "rent.xlsx", .name_repair = "universal")
# Making a list of Rutherford County ZIP codes
ZIPList <- c(
"37127",
"37128",
"37129",
"37130"
)
# Filtering for Rutherford ZIP codes and
# selecting columns of interest
FMR_RuCo <- 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_RuCo) <- c("ZIP",
"Studio",
"One",
"Two",
"Three",
"Four")
# Showing the data as a table
FMR_RuCo_table <- gt(FMR_RuCo) %>%
tab_header("MTSU-area fair market rents, 2025") %>%
cols_align(align = "left") %>%
gt_theme_pff()
FMR_RuCo_table