WOZ Summary Statistics

In [2]:

library(data.table)
library(ggplot2)

source("scripts/constants.R")

# Load the CBS housing data
housing = fread("data/processed/kwb_housing.csv")

# Filter to gemeente level with valid WOZ data
housing_mun = housing[region_type == "gemeente" & !is.na(woz), ]

# Calculate CAGR for each gemeente
cagr_df = dcast(
  housing_mun[year %in% c(START_YEAR, END_YEAR), .(region, year, woz)],
  region ~ year,
  value.var = "woz"
)
setnames(cagr_df, as.character(c(START_YEAR, END_YEAR)), c("woz_start", "woz_end"))
cagr_df = cagr_df[!is.na(woz_start) & !is.na(woz_end), ]
cagr_df[, cagr := ((woz_end / woz_start)^(1/n_years) - 1) * 100]

# Plot distribution
ggplot(cagr_df, aes(x = cagr)) +
  geom_histogram(bins = 30, fill = "#2c7bb6", color = "white", alpha = 0.7) +
  geom_vline(
    xintercept = mean(cagr_df$cagr),
    color = "#d7191c", linetype = "dashed", linewidth = 1
  ) +
  geom_vline(
    xintercept = median(cagr_df$cagr),
    color = "#fdae61", linetype = "dotdash", linewidth = 1
  ) +
  annotate(
    "text", x = mean(cagr_df$cagr) + 1, y = Inf, vjust = 2,
    label = sprintf("Mean: %.1f%%", mean(cagr_df$cagr)),
    color = "#d7191c", fontface = "bold"
  ) +
  annotate(
    "text", x = median(cagr_df$cagr) - 1, y = Inf, vjust = 4,
    label = sprintf("Median: %.1f%%", median(cagr_df$cagr)),
    color = "#fdae61", fontface = "bold"
  ) +
  labs(
    x = "CAGR (%)",
    y = "Number of Municipalities"
  ) +
  theme_minimal(base_size = 12) +
  theme(panel.grid.minor = element_blank())

Figure 1: Distribution of Compound Annual Growth Rates

In [3]:

summary_dt = data.table(
  Statistic = c("Count", "Mean", "Std Dev", "Min", "25%", "Median", "75%", "Max"),
  `CAGR (%)` = c(
    format(nrow(cagr_df), big.mark = ","),
    sprintf("%.2f", mean(cagr_df$cagr)),
    sprintf("%.2f", sd(cagr_df$cagr)),
    sprintf("%.2f", min(cagr_df$cagr)),
    sprintf("%.2f", quantile(cagr_df$cagr, 0.25)),
    sprintf("%.2f", median(cagr_df$cagr)),
    sprintf("%.2f", quantile(cagr_df$cagr, 0.75)),
    sprintf("%.2f", max(cagr_df$cagr))
  ),
  woz_end_col = c(
    format(nrow(cagr_df), big.mark = ","),
    sprintf("%.0f", mean(cagr_df$woz_end)),
    sprintf("%.0f", sd(cagr_df$woz_end)),
    sprintf("%.0f", min(cagr_df$woz_end)),
    sprintf("%.0f", quantile(cagr_df$woz_end, 0.25)),
    sprintf("%.0f", median(cagr_df$woz_end)),
    sprintf("%.0f", quantile(cagr_df$woz_end, 0.75)),
    sprintf("%.0f", max(cagr_df$woz_end))
  )
)
setnames(summary_dt, "woz_end_col", paste0("WOZ ", END_YEAR, " (€1000)"))

knitr::kable(summary_dt)

In [4]:

Table 1: Summary Statistics: WOZ Growth by Municipality

Statistic	CAGR (%)	WOZ 2024 (€1000)
Count	324	324
Mean	6.31	390
Std Dev	0.90	94
Min	4.35	199
25%	5.62	330
Median	6.24	380
75%	6.84	436
Max	9.47	900