Regional Comparisons

Housing markets can behave differently across geographic areas. This page compares selected U.S. Census Divisions using the housing price index.

For this comparison, the data is filtered to traditional, purchase-only, monthly observations at the U.S. and Census Division level.

Housing Price Index by Region

The chart compares annual average index values across six locations represented in the filtered dataset.

Why Compare Regions?

A national or overall average can hide differences between geographic areas.

Comparing regions allows us to see whether different locations follow similar or different patterns over time.

Questions to Explore

Do the regions follow similar patterns?
Are some regions more variable than others?
Are there periods when regional differences become larger?
Does the overall pattern look similar to the national trend?

The Housing Stories page uses these ideas to tell several data-based stories.

---
title: "Regional Comparisons"
---

# Regional Comparisons

Housing markets can behave differently across geographic areas. This page compares selected U.S. Census Divisions using the housing price index.

For this comparison, the data is filtered to traditional, purchase-only, monthly observations at the U.S. and Census Division level.

```{r}
#| echo: false
#| message: false
#| warning: false

library(tidyverse)

df <- read_csv("data/hpi_master.csv", show_col_types = FALSE)

regional_df <- df |>
  filter(
    hpi_type == "traditional",
    hpi_flavor == "purchase-only",
    frequency == "monthly",
    level == "USA or Census Division"
  )

selected_locations <- regional_df |>
  count(place_name, sort = TRUE) |>
  slice_head(n = 6) |>
  pull(place_name)

annual_regional <- regional_df |>
  filter(place_name %in% selected_locations) |>
  group_by(place_name, yr) |>
  summarize(
    annual_average = mean(index_nsa, na.rm = TRUE),
    .groups = "drop"
  )
```

## Housing Price Index by Region

The chart compares annual average index values across six locations represented in the filtered dataset.

```{r}
#| echo: false
#| message: false
#| warning: false

ggplot(
  annual_regional,
  aes(
    x = yr,
    y = annual_average,
    color = place_name
  )
) +
  geom_line(linewidth = 0.8) +
  theme_minimal() +
  labs(
    title = "Annual Housing Price Index by Region",
    subtitle = "Traditional purchase-only monthly observations",
    x = "Year",
    y = "Annual Average Housing Price Index",
    color = "Region"
  ) +
  theme(
    legend.position = "bottom"
  )
```

## Why Compare Regions?

A national or overall average can hide differences between geographic areas.

Comparing regions allows us to see whether different locations follow similar or different patterns over time.

## Questions to Explore

- Do the regions follow similar patterns?
- Are some regions more variable than others?
- Are there periods when regional differences become larger?
- Does the overall pattern look similar to the national trend?

The **Housing Stories** page uses these ideas to tell several data-based stories.