Sourcing & Cleaning House Price & Income Data # 1
# Home data is sourced from Zillow database for "For-Sale" houses
# Get working directory
wd <- getwd()
# Create data path
data_dir <- file.path(wd, "Project")
# Read "For-Sale" home prices by month from Zillow
zillow_data <- read_csv(file.path(data_dir,"Median_List_Price_Monthly_Smooth_All_Homes.csv"),show_col_types=F)
# Step 1: Clean data (remove NA states)
zillow_cleaned <- zillow_data %>%
filter(!is.na(StateName))
# Step 2: Pivot from wide to long (dates were stored as separate col)
zillow_long <- zillow_cleaned %>%
pivot_longer(
cols = matches("^\\d{4}-\\d{2}-\\d{2}$"), # selects all columns that look like dates
names_to = "Date",
values_to = "MedianPrice"
) %>%
mutate(Date = as.Date(Date))
# Step 3: Restrict date range to [2022, 2024]
zillow_filtered <- zillow_long %>%
filter(format(Date, "%Y") >= 2022 & format(Date, "%Y") < 2025)
# Step 4: Take median price by year
zillow_by_year_state <- zillow_filtered %>%
mutate(Year = year(Date)) %>%
group_by(StateName, Year) %>%
summarise(Median_Yearly_Price = median(MedianPrice, na.rm = TRUE)) %>%
ungroup()
## `summarise()` has grouped output by 'StateName'. You can override using the
## `.groups` argument.
# Income data is sourced by Bureau of Labor Statistics, utilizing code 00-0000 which refers to All Occupations
# Read the Excel file
Income_2022 <- read_excel(file.path(getwd(), "Project", "all_data_M_2022.xlsx"))
Income_2023 <- read_excel(file.path(getwd(), "Project", "all_data_M_2023.xlsx"))
Income_2024 <- read_excel(file.path(getwd(), "Project", "all_data_M_2024.xlsx"))
# Filter the data
Income_2022_data <- Income_2022 %>%
filter(OCC_CODE == "00-0000") %>%
mutate(AREA = as.numeric(AREA)) %>%
filter(AREA >= 1, AREA <= 56) #2022
Income_2022_data <- Income_2022_data %>%
filter(AREA_TITLE != "District of Columbia")
Income_2023_data <- Income_2023 %>%
filter(OCC_CODE == "00-0000") %>%
mutate(AREA = as.numeric(AREA)) %>%
filter(AREA >= 1, AREA <= 56) #2023
Income_2023_data <- Income_2023_data %>%
filter(AREA_TITLE != "District of Columbia")
Income_2024_data <- Income_2024 %>%
filter(OCC_CODE == "00-0000") %>% #00-0000 All Occupations
mutate(AREA = as.numeric(AREA)) %>%
filter(AREA >= 1, AREA <= 56) #2024
Income_2024_data <- Income_2024_data %>%
filter(AREA_TITLE != "District of Columbia")
Merging House Price & Income Data # 2
# Select relevant cols from income data
income_2022_clean <- Income_2022_data %>%
select(StateName = PRIM_STATE, Median_Income = A_MEDIAN) %>%
mutate(Year = 2022)
income_2023_clean <- Income_2023_data %>%
select(StateName = PRIM_STATE, Median_Income = A_MEDIAN) %>%
mutate(Year = 2023)
income_2024_clean <- Income_2024_data %>%
select(StateName = PRIM_STATE, Median_Income = A_MEDIAN) %>%
mutate(Year = 2024)
# Combine income dataset
all_income <- bind_rows(income_2022_clean, income_2023_clean, income_2024_clean)
# Combine median state income and median home income at state level
affordability_data <- left_join(zillow_by_year_state, all_income, by = c("StateName", "Year")) #2024 CO will have NA for Median Income
Calculating Affordability Ratio # 3
# Convert to numerical
affordability_data <- affordability_data %>%
mutate(
Median_Yearly_Price = as.numeric(Median_Yearly_Price),
Median_Income = as.numeric(Median_Income)
)
# Use CO 2023 income as a baseline for CO 2024 income
co_2023_income <- affordability_data$Median_Income[affordability_data$StateName == "CO" & affordability_data$Year == 2023]
# Assign it to 2024
affordability_data$Median_Income[affordability_data$StateName == "CO" & affordability_data$Year == 2024] <- co_2023_income
# Affordability Ratio
affordability_data <- affordability_data %>%
mutate(Affordability_Ratio = Median_Yearly_Price / Median_Income)
# Get map data as a data.frame
states_map <- map_data("state")
# Match state names
affordability_data_clean <- affordability_data %>%
mutate(region = tolower(state.name[match(StateName, state.abb)]))
State Ranking by Affordability Ratio # 4
# Group and rank state by affordability ratio
ranked_data <- affordability_data_clean %>%
select(StateName, region, Year, Median_Income, Median_Yearly_Price, Affordability_Ratio) %>%
arrange(Year, Affordability_Ratio) %>%
group_by(Year) %>%
mutate(Rank = row_number()) %>%
ungroup()
# @parm: Dataset used for filtering
# @parm: Year to filter by
# Generates a table graphic of rankings of state AR based on year. Ref -> https://r-graph-gallery.com/package/gt.html
create_affordability_gt_table <- function(data, year) {
data %>%
filter(Year == year) %>%
select(Rank, region, StateName, Median_Income, Median_Yearly_Price, Affordability_Ratio) %>%
gt() %>%
tab_header(
title = "Housing Affordability Rankings by State",
subtitle = paste("Year:", year, "— Lower Ratio Means More Affordable")
) %>%
fmt_currency(
columns = c(Median_Income, Median_Yearly_Price),
currency = "USD"
) %>%
fmt_number(
columns = Affordability_Ratio,
decimals = 2
) %>%
cols_label(
region = "State (Full)",
StateName = "State (Abbrev)",
Median_Income = "Median Income",
Median_Yearly_Price = "Median House Price",
Affordability_Ratio = "Affordability Ratio"
)
}
create_affordability_gt_table(ranked_data, 2022)
| Housing Affordability Rankings by State |
| Year: 2022 — Lower Ratio Means More Affordable |
| Rank |
State (Full) |
State (Abbrev) |
Median Income |
Median House Price |
Affordability Ratio |
| 1 |
illinois |
IL |
$47,480.00 |
$130,891.50 |
2.76 |
| 2 |
new york |
NY |
$52,470.00 |
$190,958.00 |
3.64 |
| 3 |
kansas |
KS |
$41,870.00 |
$152,783.00 |
3.65 |
| 4 |
ohio |
OH |
$44,750.00 |
$171,300.00 |
3.83 |
| 5 |
iowa |
IA |
$44,350.00 |
$171,567.00 |
3.87 |
| 6 |
pennsylvania |
PA |
$45,790.00 |
$189,483.00 |
4.14 |
| 7 |
michigan |
MI |
$45,500.00 |
$191,758.50 |
4.21 |
| 8 |
west virginia |
WV |
$37,770.00 |
$161,125.00 |
4.27 |
| 9 |
indiana |
IN |
$42,100.00 |
$187,700.00 |
4.46 |
| 10 |
missouri |
MO |
$42,310.00 |
$190,108.00 |
4.49 |
| 11 |
nebraska |
NE |
$44,100.00 |
$212,358.50 |
4.82 |
| 12 |
oklahoma |
OK |
$39,100.00 |
$190,165.00 |
4.86 |
| 13 |
arkansas |
AR |
$37,270.00 |
$184,566.50 |
4.95 |
| 14 |
minnesota |
MN |
$48,760.00 |
$244,117.00 |
5.01 |
| 15 |
wisconsin |
WI |
$45,650.00 |
$231,608.50 |
5.07 |
| 16 |
north dakota |
ND |
$47,410.00 |
$247,333.00 |
5.22 |
| 17 |
louisiana |
LA |
$38,970.00 |
$207,566.50 |
5.33 |
| 18 |
kentucky |
KY |
$40,180.00 |
$217,067.00 |
5.40 |
| 19 |
georgia |
GA |
$42,890.00 |
$234,550.00 |
5.47 |
| 20 |
mississippi |
MS |
$36,100.00 |
$197,783.00 |
5.48 |
| 21 |
texas |
TX |
$43,460.00 |
$247,933.50 |
5.70 |
| 22 |
maryland |
MD |
$51,420.00 |
$294,608.00 |
5.73 |
| 23 |
alabama |
AL |
$38,470.00 |
$221,130.00 |
5.75 |
| 24 |
new jersey |
NJ |
$51,080.00 |
$300,300.00 |
5.88 |
| 25 |
virginia |
VA |
$48,290.00 |
$287,566.50 |
5.95 |
| 26 |
north carolina |
NC |
$41,810.00 |
$255,133.00 |
6.10 |
| 27 |
maine |
ME |
$45,420.00 |
$284,225.00 |
6.26 |
| 28 |
connecticut |
CT |
$51,780.00 |
$324,933.50 |
6.28 |
| 29 |
new mexico |
NM |
$39,900.00 |
$271,750.00 |
6.81 |
| 30 |
south dakota |
SD |
$39,870.00 |
$280,100.00 |
7.03 |
| 31 |
south carolina |
SC |
$38,870.00 |
$274,316.50 |
7.06 |
| 32 |
tennessee |
TN |
$39,930.00 |
$293,816.50 |
7.36 |
| 33 |
delaware |
DE |
$47,150.00 |
$348,691.50 |
7.40 |
| 34 |
alaska |
AK |
$52,000.00 |
$386,483.00 |
7.43 |
| 35 |
vermont |
VT |
$47,320.00 |
$357,083.00 |
7.55 |
| 36 |
wyoming |
WY |
$45,450.00 |
$349,408.50 |
7.69 |
| 37 |
new hampshire |
NH |
$47,920.00 |
$371,300.00 |
7.75 |
| 38 |
arizona |
AZ |
$45,290.00 |
$371,667.00 |
8.21 |
| 39 |
washington |
WA |
$56,320.00 |
$463,290.00 |
8.23 |
| 40 |
florida |
FL |
$40,820.00 |
$349,983.50 |
8.57 |
| 41 |
rhode island |
RI |
$49,360.00 |
$431,450.00 |
8.74 |
| 42 |
massachusetts |
MA |
$58,450.00 |
$519,429.50 |
8.89 |
| 43 |
oregon |
OR |
$47,770.00 |
$463,667.00 |
9.71 |
| 44 |
california |
CA |
$49,740.00 |
$499,000.00 |
10.03 |
| 45 |
nevada |
NV |
$40,810.00 |
$433,300.00 |
10.62 |
| 46 |
idaho |
ID |
$40,060.00 |
$431,666.50 |
10.78 |
| 47 |
utah |
UT |
$44,470.00 |
$485,800.00 |
10.92 |
| 48 |
colorado |
CO |
$50,250.00 |
$557,698.00 |
11.10 |
| 49 |
montana |
MT |
$42,210.00 |
$570,558.00 |
13.52 |
| 50 |
hawaii |
HI |
$48,560.00 |
$931,083.50 |
19.17 |
create_affordability_gt_table(ranked_data, 2023)
| Housing Affordability Rankings by State |
| Year: 2023 — Lower Ratio Means More Affordable |
| Rank |
State (Full) |
State (Abbrev) |
Median Income |
Median House Price |
Affordability Ratio |
| 1 |
illinois |
IL |
$48,730.00 |
$144,608.00 |
2.97 |
| 2 |
new york |
NY |
$56,840.00 |
$211,000.00 |
3.71 |
| 3 |
iowa |
IA |
$46,460.00 |
$189,366.50 |
4.08 |
| 4 |
kansas |
KS |
$45,250.00 |
$186,291.50 |
4.12 |
| 5 |
ohio |
OH |
$46,690.00 |
$192,950.00 |
4.13 |
| 6 |
pennsylvania |
PA |
$47,430.00 |
$203,650.00 |
4.29 |
| 7 |
west virginia |
WV |
$39,770.00 |
$173,533.50 |
4.36 |
| 8 |
michigan |
MI |
$46,940.00 |
$217,556.00 |
4.63 |
| 9 |
indiana |
IN |
$45,470.00 |
$214,967.00 |
4.73 |
| 10 |
missouri |
MO |
$45,080.00 |
$213,133.50 |
4.73 |
| 11 |
arkansas |
AR |
$39,060.00 |
$196,367.00 |
5.03 |
| 12 |
oklahoma |
OK |
$41,480.00 |
$209,533.50 |
5.05 |
| 13 |
north dakota |
ND |
$48,830.00 |
$257,191.50 |
5.27 |
| 14 |
nebraska |
NE |
$46,440.00 |
$245,591.50 |
5.29 |
| 15 |
louisiana |
LA |
$41,320.00 |
$221,292.00 |
5.36 |
| 16 |
mississippi |
MS |
$37,500.00 |
$202,383.00 |
5.40 |
| 17 |
minnesota |
MN |
$50,880.00 |
$276,358.50 |
5.43 |
| 18 |
kentucky |
KY |
$43,730.00 |
$238,666.50 |
5.46 |
| 19 |
wisconsin |
WI |
$47,590.00 |
$263,117.00 |
5.53 |
| 20 |
maryland |
MD |
$55,810.00 |
$309,494.50 |
5.55 |
| 21 |
georgia |
GA |
$45,480.00 |
$261,950.00 |
5.76 |
| 22 |
texas |
TX |
$45,970.00 |
$267,158.50 |
5.81 |
| 23 |
alabama |
AL |
$41,350.00 |
$247,291.50 |
5.98 |
| 24 |
connecticut |
CT |
$56,130.00 |
$353,267.00 |
6.29 |
| 25 |
virginia |
VA |
$49,920.00 |
$316,200.00 |
6.33 |
| 26 |
north carolina |
NC |
$45,440.00 |
$291,316.50 |
6.41 |
| 27 |
new mexico |
NM |
$43,620.00 |
$285,333.00 |
6.54 |
| 28 |
new jersey |
NJ |
$54,860.00 |
$368,973.00 |
6.73 |
| 29 |
maine |
ME |
$47,590.00 |
$325,925.00 |
6.85 |
| 30 |
south carolina |
SC |
$42,220.00 |
$293,529.00 |
6.95 |
| 31 |
south dakota |
SD |
$43,680.00 |
$304,833.50 |
6.98 |
| 32 |
alaska |
AK |
$56,140.00 |
$399,150.00 |
7.11 |
| 33 |
tennessee |
TN |
$43,820.00 |
$316,300.00 |
7.22 |
| 34 |
delaware |
DE |
$49,280.00 |
$388,150.00 |
7.88 |
| 35 |
wyoming |
WY |
$47,250.00 |
$374,800.00 |
7.93 |
| 36 |
washington |
WA |
$59,920.00 |
$478,458.00 |
7.98 |
| 37 |
florida |
FL |
$45,070.00 |
$365,000.00 |
8.10 |
| 38 |
arizona |
AZ |
$47,680.00 |
$386,499.50 |
8.11 |
| 39 |
vermont |
VT |
$49,630.00 |
$418,900.00 |
8.44 |
| 40 |
new hampshire |
NH |
$49,980.00 |
$426,800.00 |
8.54 |
| 41 |
rhode island |
RI |
$50,970.00 |
$471,450.00 |
9.25 |
| 42 |
nevada |
NV |
$44,810.00 |
$420,333.50 |
9.38 |
| 43 |
oregon |
OR |
$50,010.00 |
$472,000.00 |
9.44 |
| 44 |
california |
CA |
$54,030.00 |
$514,325.00 |
9.52 |
| 45 |
massachusetts |
MA |
$60,690.00 |
$581,116.50 |
9.58 |
| 46 |
idaho |
ID |
$44,240.00 |
$433,366.50 |
9.80 |
| 47 |
colorado |
CO |
$54,050.00 |
$591,000.00 |
10.93 |
| 48 |
utah |
UT |
$47,020.00 |
$516,465.00 |
10.98 |
| 49 |
montana |
MT |
$45,690.00 |
$544,983.00 |
11.93 |
| 50 |
hawaii |
HI |
$50,510.00 |
$1,027,833.00 |
20.35 |
create_affordability_gt_table(ranked_data, 2024)
| Housing Affordability Rankings by State |
| Year: 2024 — Lower Ratio Means More Affordable |
| Rank |
State (Full) |
State (Abbrev) |
Median Income |
Median House Price |
Affordability Ratio |
| 1 |
illinois |
IL |
$50,000.00 |
$159,575.00 |
3.19 |
| 2 |
new york |
NY |
$58,560.00 |
$227,316.50 |
3.88 |
| 3 |
kansas |
KS |
$46,850.00 |
$197,067.00 |
4.21 |
| 4 |
ohio |
OH |
$48,060.00 |
$212,617.00 |
4.42 |
| 5 |
west virginia |
WV |
$43,320.00 |
$193,058.50 |
4.46 |
| 6 |
pennsylvania |
PA |
$48,550.00 |
$217,950.00 |
4.49 |
| 7 |
iowa |
IA |
$47,670.00 |
$215,083.50 |
4.51 |
| 8 |
missouri |
MO |
$46,390.00 |
$222,683.00 |
4.80 |
| 9 |
indiana |
IN |
$46,930.00 |
$228,116.50 |
4.86 |
| 10 |
oklahoma |
OK |
$43,950.00 |
$214,583.50 |
4.88 |
| 11 |
michigan |
MI |
$48,300.00 |
$241,783.50 |
5.01 |
| 12 |
louisiana |
LA |
$43,770.00 |
$226,975.00 |
5.19 |
| 13 |
mississippi |
MS |
$39,070.00 |
$205,558.50 |
5.26 |
| 14 |
arkansas |
AR |
$41,020.00 |
$220,658.50 |
5.38 |
| 15 |
nebraska |
NE |
$47,990.00 |
$262,154.00 |
5.46 |
| 16 |
maryland |
MD |
$58,050.00 |
$326,633.00 |
5.63 |
| 17 |
minnesota |
MN |
$53,810.00 |
$303,933.00 |
5.65 |
| 18 |
kentucky |
KY |
$45,740.00 |
$262,791.50 |
5.75 |
| 19 |
texas |
TX |
$47,500.00 |
$273,033.50 |
5.75 |
| 20 |
georgia |
GA |
$47,020.00 |
$270,650.00 |
5.76 |
| 21 |
alabama |
AL |
$43,830.00 |
$255,283.50 |
5.82 |
| 22 |
wisconsin |
WI |
$48,930.00 |
$287,908.50 |
5.88 |
| 23 |
north dakota |
ND |
$50,320.00 |
$308,133.50 |
6.12 |
| 24 |
new mexico |
NM |
$45,870.00 |
$282,500.00 |
6.16 |
| 25 |
virginia |
VA |
$53,020.00 |
$336,224.00 |
6.34 |
| 26 |
connecticut |
CT |
$58,400.00 |
$380,800.00 |
6.52 |
| 27 |
north carolina |
NC |
$46,950.00 |
$307,291.50 |
6.55 |
| 28 |
south carolina |
SC |
$44,760.00 |
$296,099.00 |
6.62 |
| 29 |
new jersey |
NJ |
$57,230.00 |
$381,450.00 |
6.67 |
| 30 |
maine |
ME |
$49,440.00 |
$342,300.00 |
6.92 |
| 31 |
south dakota |
SD |
$45,620.00 |
$317,466.50 |
6.96 |
| 32 |
tennessee |
TN |
$46,120.00 |
$327,900.00 |
7.11 |
| 33 |
alaska |
AK |
$59,400.00 |
$426,483.50 |
7.18 |
| 34 |
wyoming |
WY |
$49,160.00 |
$386,658.50 |
7.87 |
| 35 |
florida |
FL |
$46,860.00 |
$370,333.00 |
7.90 |
| 36 |
delaware |
DE |
$51,030.00 |
$407,450.00 |
7.98 |
| 37 |
washington |
WA |
$61,590.00 |
$493,745.50 |
8.02 |
| 38 |
arizona |
AZ |
$48,810.00 |
$395,000.00 |
8.09 |
| 39 |
vermont |
VT |
$52,410.00 |
$456,567.00 |
8.71 |
| 40 |
new hampshire |
NH |
$52,610.00 |
$461,783.50 |
8.78 |
| 41 |
oregon |
OR |
$53,390.00 |
$486,225.00 |
9.11 |
| 42 |
rhode island |
RI |
$54,040.00 |
$493,250.00 |
9.13 |
| 43 |
california |
CA |
$56,940.00 |
$520,916.50 |
9.15 |
| 44 |
nevada |
NV |
$46,440.00 |
$429,192.00 |
9.24 |
| 45 |
idaho |
ID |
$46,470.00 |
$442,275.00 |
9.52 |
| 46 |
massachusetts |
MA |
$62,270.00 |
$608,966.50 |
9.78 |
| 47 |
utah |
UT |
$48,600.00 |
$505,233.50 |
10.40 |
| 48 |
colorado |
CO |
$54,050.00 |
$594,683.00 |
11.00 |
| 49 |
montana |
MT |
$47,360.00 |
$530,675.00 |
11.21 |
| 50 |
hawaii |
HI |
$53,260.00 |
$939,483.50 |
17.64 |
# @parm: Dataset used for filtering
# @parm: Year to filter by
# Bar chart visual for state ranking
plot_affordability_bar <- function(data, year) {
data %>%
filter(Year == year) %>%
arrange(Affordability_Ratio) %>%
mutate(StateName = factor(StateName, levels = StateName)) %>%
ggplot(aes(x = StateName, y = Affordability_Ratio, fill = Affordability_Ratio)) +
geom_col() +
coord_flip() +
scale_fill_gradient(low = "#00441b", high = "#ccece6") +
labs(
title = paste("Housing Affordability by State in", year),
subtitle = "Lower = More Affordable",
x = "State",
y = "Affordability Ratio"
) +
theme_minimal() +
theme(
plot.title = element_text(size = 14, face = "bold"),
axis.text.y = element_text(size = 8)
)
}
plot_affordability_bar(ranked_data, 2022)
plot_affordability_bar(ranked_data, 2023)
plot_affordability_bar(ranked_data, 2024)
Visualizing Affordability Ratio & Change In Affordability Ratio
Across State based on Year # 5
us_albers_crs <- sf::st_crs(5070)
# @parm: Dataset used for filtering
# @parm: Year to filter by
# Create function to save time & lines. Reference -> https://www.w3schools.com/r/r_functions.asp
plot_affordability_map <- function(data, year) {
data_year <- data %>% filter(Year == year)
map_data_year <- left_join(states_map, data_year, by = "region")
ggplot(map_data_year, aes(long, lat, group = group, fill = Affordability_Ratio)) +
geom_polygon(color = "white") +
coord_sf(crs = us_albers_crs, default_crs = sf::st_crs(4326)) +
scale_fill_gradient(low = "#ccece6", high = "#00441b", na.value = "grey90") +
labs(
title = paste("Affordability Ratio -", year),
fill = "Affordability",
caption = "Lower Ratio = More Affordable"
) +
theme_minimal() +
theme(plot.title = element_text(size = 12))
}
# Create the three yearly maps
map_2022 <- plot_affordability_map(affordability_data_clean, 2022)
map_2023 <- plot_affordability_map(affordability_data_clean, 2023)
map_2024 <- plot_affordability_map(affordability_data_clean, 2024)
# Display plots
plot(map_2022)
plot(map_2023)
plot(map_2024)
# Determine change between 2022-2024
affordability_wide <- affordability_data_clean %>%
select(StateName, region, Year, Affordability_Ratio) %>%
pivot_wider(
names_from = Year,
values_from = Affordability_Ratio,
names_prefix = "Y"
) %>%
filter(!is.na(Y2022) & !is.na(Y2023) & !is.na(Y2024))
# Calculate year-by-year changes and total change
affordability_change <- affordability_wide %>%
mutate(
Change_2022_2023 = Y2023 - Y2022,
Change_2023_2024 = Y2024 - Y2023,
Total_Change = Change_2022_2023 + Change_2023_2024
)
# Join with spatial data
affordability_map_data <- left_join(states_map, affordability_change, by = "region")
# Plot the Total_Change map
change_map <- ggplot(affordability_map_data, aes(long, lat, group = group, fill = Total_Change)) +
geom_polygon(color = "white") +
coord_sf(crs = us_albers_crs, default_crs = sf::st_crs(4326)) +
scale_fill_gradient2(
low = "#1a9850", # Green = improved affordability
mid = "white", # No change
high = "#d73027", # Red = worsened affordability
midpoint = 0,
name = "Change"
) +
labs(
title = "Affordability Ratio Change",
caption = "Positive = Less Affordable; Negative = More Affordable"
) +
theme_minimal() +
theme(plot.title = element_text(size = 12))
# Combine all 4 maps into a single figure
combined_maps <- map_2022 + map_2023 + map_2024 + change_map +
plot_layout(ncol = 2) + # Arrange the maps into two columns
plot_annotation(
title = "Housing Affordability by State (2022–2024)",
subtitle = "Including Change from 2022 to 2024"
)
# Display the combined figure
combined_maps
# Bar chart visual of median change across the years
# Median Affordability Ratio across each year
median_ratio_by_year <- affordability_data_clean %>%
group_by(Year) %>%
summarize(Median_Affordability = median(Affordability_Ratio, na.rm = TRUE))
# Bar chart visualizing the national median Affordability Ratio trend
ggplot(median_ratio_by_year, aes(x = factor(Year), y = Median_Affordability)) +
geom_col(fill = "#3182bd") +
geom_text(
aes(label = round(Median_Affordability, 2)),
vjust = -0.5,
size = 4
) +
labs(
title = "Median Housing Affordability Ratio by Year",
subtitle = "Higher Ratio = Less Affordable Housing",
x = "Year",
y = "Median Affordability Ratio",
caption = "Affordability = Median House Price ÷ Median Income"
) +
theme_minimal()
