Market Insights

Market Insights

This page summarizes several characteristics of the housing price index dataset.

The goal is to move beyond a single chart and examine the size, time period, and variation represented in the data.

Key Numbers

Observations

186,011

Total observations.

Locations

472

Unique locations.

First Year

1975

Earliest year.

Last Year

2026

Latest year.

Annual Average

How Much Do Values Vary?

The range measures the difference between the highest and lowest index value recorded within each year.

How Should We Interpret This?

A larger range means that the index values represented in the data were more spread out within that year.

The range does not tell us why the differences occurred. It simply provides a way to describe variation in the observations.

Why This Matters

Summary statistics can help us identify patterns that are not immediately obvious from individual observations.

Use Data Explorer next to examine the underlying rows that produce these summaries.

---
title: "Market Insights"
---

# Market Insights

This page summarizes several characteristics of the housing price index dataset.

The goal is to move beyond a single chart and examine the size, time period, and variation represented in the data.

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

library(tidyverse)

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

total_observations <- nrow(df)
number_of_locations <- n_distinct(df$place_name)
first_year <- min(df$yr, na.rm = TRUE)
last_year <- max(df$yr, na.rm = TRUE)

annual <- df |>
  group_by(yr) |>
  summarize(
    average_index = mean(index_nsa, na.rm = TRUE),
    minimum_index = min(index_nsa, na.rm = TRUE),
    maximum_index = max(index_nsa, na.rm = TRUE),
    .groups = "drop"
  ) |>
  mutate(
    index_range = maximum_index - minimum_index
  )
```

## Key Numbers

::: {.columns}

::: {.column width="25%"}
### Observations

**`r format(total_observations, big.mark = ",")`**

Total observations.
:::

::: {.column width="25%"}
### Locations

**`r number_of_locations`**

Unique locations.
:::

::: {.column width="25%"}
### First Year

**`r first_year`**

Earliest year.
:::

::: {.column width="25%"}
### Last Year

**`r last_year`**

Latest year.
:::

:::

## Annual Average

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

ggplot(annual, aes(x = yr, y = average_index)) +
  geom_line(linewidth = 1) +
  theme_minimal() +
  labs(
    title = "Annual Average Housing Price Index",
    x = "Year",
    y = "Average Index"
  )
```

## How Much Do Values Vary?

The range measures the difference between the highest and lowest index value recorded within each year.

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

ggplot(annual, aes(x = yr, y = index_range)) +
  geom_line(linewidth = 1) +
  theme_minimal() +
  labs(
    title = "Annual Range of Housing Price Index Values",
    subtitle = "Maximum minus minimum index value",
    x = "Year",
    y = "Index Range"
  )
```

## How Should We Interpret This?

A larger range means that the index values represented in the data were more spread out within that year.

The range does not tell us why the differences occurred. It simply provides a way to describe variation in the observations.

## Why This Matters

Summary statistics can help us identify patterns that are not immediately obvious from individual observations.

Use **Data Explorer** next to examine the underlying rows that produce these summaries.