Here’s a quick map of the U.S. Department of Housing and Urban Development’s current two-bedroom Fair Market Rent estimates for the four Rutherford County ZIP codes closest to MTSU’s campus. Below the map, tables show estimates for all rental unit sizes at present, five years ago, and the change since five years ago.

Current two-bedroom Fair Market Rents for MTSU-area ZIP codes
FMR five years ago
ZIP Zero_BR_5YearsAgo One_BR_5YearsAgo Two_BR_5YearsAgo Three_BR_5YearsAgo Four_BR_5YearsAgo
37127 $850 $900 $1,070 $1,400 $1,720
37128 $1,030 $1,100 $1,300 $1,700 $2,080
37129 $930 $990 $1,180 $1,540 $1,890
37130 $840 $890 $1,060 $1,380 $1,700
37132 $840 $890 $1,060 $1,380 $1,700
FMR this year
ZIP Zero_BR_ThisYear One_BR_ThisYear Two_BR_ThisYear Three_BR_ThisYear Four_BR_ThisYear
37127 $1,410 $1,460 $1,620 $2,050 $2,520
37128 $1,680 $1,740 $1,930 $2,440 $3,000
37129 $1,630 $1,690 $1,870 $2,360 $2,910
37130 $1,300 $1,350 $1,490 $1,880 $2,320
37132 $1,300 $1,350 $1,490 $1,880 $2,320
Change
ZIP Zero_BR_Change One_BR_Change Two_BR_Change Three_BR_Change Four_BR_Change
37127 $560 $560 $550 $650 $800
37128 $650 $640 $630 $740 $920
37129 $700 $700 $690 $820 $1,020
37130 $460 $460 $430 $500 $620
37132 $460 $460 $430 $500 $620

This R script extracted, analyzed, formatted and displayed the data. If you would like to learn how to use R for data-driven reporting, consider enrolling in my Data Skills for Media Professionals course at MTSU.

# Installing required packages

if (!require("dplyr")) install.packages("dplyr")
if (!require("tidyverse")) install.packages("tidyverse")
if (!require("mapview")) install.packages("mapview")
if (!require("tidycensus")) install.packages("tidycensus")
if (!require("sf")) install.packages("sf")
if (!require("openxlsx")) install.packages("openxlsx")
if (!require("scales")) install.packages("scales")
if (!require("gt")) install.packages("gt")
if (!require("gtExtras")) install.packages("gtExtras")

library(dplyr)
library(tidyverse)
library(ggplot2) #From the tidyverse package
library(readr) #From the tidyverse package
library(tidycensus)
library(sf)
library(mapview)
library(openxlsx)
library(scales)
library(gt)
library(gtExtras)

options(tigris_use_cache = TRUE)
options(scipen = 999)

# Getting the map data

ZIPList <- c("37127",
             "37128",
             "37129",
             "37130",
             "37132")

ZCTAMap <- get_acs(geography = "zcta",
                   variables = c(Population = "B01001_001"),
                   year = 2020,
                   survey = "acs5",
                   output = "wide",
                   geometry = TRUE)

# Filtering and formatting the map data

RCMap <- ZCTAMap[ZCTAMap$GEOID %in% ZIPList,]

RCMap <- rename(RCMap, ZIP = GEOID)

# For FMR URLs, see:
# https://www.huduser.gov/portal/datasets/fmr/smallarea/index.html#2023_data
# https://www.huduser.gov/portal/datasets/fmr/smallarea/index.html#2018_data

FMRNow = read.xlsx("https://www.huduser.gov/portal/datasets/fmr/fmr2025/fy2025_safmrs.xlsx",sheet=1)
FMRNow <- FMRNow[FMRNow$ZIP.Code %in% ZIPList,]%>%
  distinct(.keep_all = TRUE)

# FMRBefore = read.xlsx("https://www.huduser.gov/portal/datasets/fmr/fmr2020/fy2020_safmrs_rev.xlsx",sheet=1)
# FMRBefore <- FMRBefore[FMRBefore$ZIP.Code %in% ZIPList,]%>%
#   distinct(.keep_all = TRUE)
# This is the original code
# Had to write custom code for the 2020 FMR data
# Because HUD changed the variable names.
# Custome code is below -- Ken, May 17, 2025

FMRBefore = read.xlsx("https://www.huduser.gov/portal/datasets/fmr/fmr2020/fy2020_safmrs_rev.xlsx",sheet=1)
FMRBefore <- FMRBefore[FMRBefore$zcta %in% ZIPList,]%>%
  distinct(.keep_all = TRUE) %>% 
  rename(ZIP.Code = zcta,
         SAFMR.0BR = safmr_0br,
         SAFMR.1BR = safmr_1br,
         SAFMR.2BR = safmr_2br,
         SAFMR.3BR = safmr_3br,
         SAFMR.4BR = safmr_4br)

Merged <- merge(FMRBefore,FMRNow, by = "ZIP.Code")
keepvars <- c("ZIP.Code",
              "SAFMR.0BR.x",
              "SAFMR.1BR.x",
              "SAFMR.2BR.x",
              "SAFMR.3BR.x",
              "SAFMR.4BR.x",
              "SAFMR.0BR.y",
              "SAFMR.1BR.y",
              "SAFMR.2BR.y",
              "SAFMR.3BR.y",
              "SAFMR.4BR.y")
Merged <- Merged[keepvars]

colnames(Merged) <- c("ZIP",
                      "Studio_5YearsAgo",
                      "One_bedroom_5YearsAgo",
                      "Two_bedroom_5YearsAgo",
                      "Three_bedroom_5YearsAgo",
                      "Four_bedroom_5YearsAgo",
                      "Studio_ThisYear",
                      "One_bedroom_ThisYear",
                      "Two_bedroom_ThisYear",
                      "Three_bedroom_ThisYear",
                      "Four_bedroom_ThisYear")

Merged <- Merged %>% 
  mutate(Studio_change = Studio_ThisYear - Studio_5YearsAgo,
         One_bedroom_change = One_bedroom_ThisYear - One_bedroom_5YearsAgo,
         Two_bedroom_change = Two_bedroom_ThisYear - Two_bedroom_5YearsAgo,
         Three_bedroom_change = Three_bedroom_ThisYear - Three_bedroom_5YearsAgo,
         Four_bedroom_change = Four_bedroom_ThisYear - Four_bedroom_5YearsAgo)

Merged <- Merged %>% 
  mutate(Zero_BR_5YearsAgo = dollar(Studio_5YearsAgo),
         One_BR_5YearsAgo = dollar(One_bedroom_5YearsAgo),
         Two_BR_5YearsAgo = dollar(Two_bedroom_5YearsAgo),
         Three_BR_5YearsAgo = dollar(Three_bedroom_5YearsAgo),
         Four_BR_5YearsAgo = dollar(Four_bedroom_5YearsAgo),
         Zero_BR_ThisYear = dollar(Studio_ThisYear),
         One_BR_ThisYear = dollar(One_bedroom_ThisYear),
         Two_BR_ThisYear = dollar(Two_bedroom_ThisYear),
         Three_BR_ThisYear = dollar(Three_bedroom_ThisYear),
         Four_BR_ThisYear = dollar(Four_bedroom_ThisYear),
         Zero_BR_Change = dollar(Studio_change),
         One_BR_Change = dollar(One_bedroom_change),         
         Two_BR_Change = dollar(Two_bedroom_change),
         Three_BR_Change = dollar(Three_bedroom_change),
         Four_BR_Change = dollar(Four_bedroom_change),)
         
RCMap_plus <- RCMap %>% 
  left_join(Merged, by = c("ZIP" = "ZIP"))

mapviewOptions(basemaps.color.shuffle = FALSE)

ZIPMap <- mapview(RCMap_plus, zcol = "Two_BR_ThisYear",
                  layer.name = "ZIP Codes",
                  popup = FALSE)

ZIPMap

# Making a .kml Map File
st_write(RCMap_plus,"FMRMap.kml", append = FALSE)

# Making a CSV data file
RCMap_plus_data <- st_drop_geometry(RCMap_plus, drop = TRUE)
write_csv(RCMap_plus_data,"FMRData.csv")

# Making tables

FMRBeforeTable <- Merged %>% 
  select(ZIP,
         Zero_BR_5YearsAgo,
         One_BR_5YearsAgo,
         Two_BR_5YearsAgo,
         Three_BR_5YearsAgo,
         Four_BR_5YearsAgo)

Table1 <- FMRBeforeTable %>%
  gt() %>%
  tab_header(
    title = "FMR five years ago") %>% 
  gt_theme_538()

FMRNowTable <- Merged %>% 
  select(ZIP,
         Zero_BR_ThisYear,
         One_BR_ThisYear,
         Two_BR_ThisYear,
         Three_BR_ThisYear,
         Four_BR_ThisYear)

Table2 <- FMRNowTable  %>%
  gt() %>%
  tab_header(
    title = "FMR this year") %>% 
gt_theme_538()

FMRChangeTable <- Merged %>% 
  select(ZIP,
         Zero_BR_Change,
         One_BR_Change,
         Two_BR_Change,
         Three_BR_Change,
         Four_BR_Change)

Table3 <- FMRChangeTable  %>%
  gt() %>%
  tab_header(
    title = "Change") %>% 
gt_theme_538()

Table1
Table2
Table3