Greek Housing Race: Rents, House Prices and Affordability in Europe
What is this?
The OECD publishes yearly analytical house price indicators for its members and partner countries: an index of rent prices, an index of house prices, and two affordability ratios — price-to-income and price-to-rent. The OECD gives them as indices with 2015 = 100; here every value is turned into a plain percentage change since 2015 — “+13%” means rents are 13% higher than in 2015. They show how much each market moved, not the level of rents in euros.
This vignette pulls the series straight from the OECD data API and builds the same kind of visualisations as the EU Salary Race: four ranked “bar chart races” (one per indicator) and a shareable GIF of each — with Greece highlighted in red against the rest of Europe in gray. It starts with a look at Greece on its own, since rents and house prices there have taken very different paths, followed by the same chart for every other country.
Packages
# install.packages(c("dplyr", "tidyr", "countrycode", "plotly",
# "gganimate", "gifski", "scales", "ragg"))
library(dplyr)
library(tidyr)
library(countrycode) # ISO3 codes -> country names
library(plotly) # interactive charts
library(gganimate) # animation
library(gifski) # GIF renderer
library(scales) # axis formattingThe data
The OECD serves its datasets through an SDMX API that can answer with
a plain CSV, so read.csv() is all we need — no API key, no
extra package. The dataset is Analytical house prices
indicators (DSD_AN_HOUSE_PRICES@DF_HOUSE_PRICES). We
keep the annual observations of the 29 European
countries the OECD covers (EU members, Norway, Switzerland, Iceland and
the United Kingdom).
url <- paste0("https://sdmx.oecd.org/public/rest/data/",
"OECD.ECO.MPD,DSD_AN_HOUSE_PRICES@DF_HOUSE_PRICES,1.0/all",
"?startPeriod=2000&dimensionAtObservation=AllDimensions",
"&format=csvfilewithlabels")
df_raw <- read.csv(url, fileEncoding = "UTF-8-BOM", check.names = FALSE)
df_raw$STRUCTURE_NAME<-NULL
df_raw$STRUCTURE<-NULL
df_raw$STRUCTURE_ID<-NULL
df_raw$ACTION<-NULL
df_raw$`Time period`<-NULL
df_raw$`Observation value`<-NULL
df_raw$`Base period`<-NULL
df_raw$OBS_STATUS<-NULL
df_raw$Decimals<-NULL
df_raw$DECIMALS<-NULL
df_raw$ADJUSTMENT<-NULL
df_raw$Adjustment<-NULL
df_raw$`Unit multiplier`<-NULL
df_raw$UNIT_MULT<-NULL
df_raw$OBS_STATUS<-NULL
df_raw$`Observation status`<-NULL
europe <- c("AUT", "BEL", "BGR", "HRV", "CZE", "DNK", "EST", "FIN", "FRA", "DEU",
"GRC", "HUN", "IRL", "ITA", "LVA", "LTU", "LUX", "NLD", "POL", "PRT",
"ROU", "SVK", "SVN", "ESP", "SWE", "NOR", "CHE", "ISL", "GBR")
df_housing <- df_raw %>%
filter(FREQ == "A", REF_AREA %in% europe,
MEASURE %in% c("RPI", "HPI", "HPI_YDH", "HPI_RPI")) %>%
transmute(iso = REF_AREA,
country = countrycode(REF_AREA, origin = "iso3c", destination = "country.name"),
measure = MEASURE,
year = as.integer(TIME_PERIOD),
values = round(as.numeric(OBS_VALUE), 1),
change = round(values - 100, 1)) %>% # % change since 2015
filter(!is.na(values))
head(df_housing)## iso country measure year values change
## 1 IRL Ireland HPI_YDH 2000 123.6 23.6
## 2 IRL Ireland HPI 2000 79.5 -20.5
## 3 IRL Ireland HPI_YDH 2001 122.8 22.8
## 4 IRL Ireland HPI 2001 89.3 -10.7
## 5 IRL Ireland HPI_YDH 2002 124.5 24.5
## 6 IRL Ireland HPI_YDH 2003 134.8 34.8Each row is one country, one indicator, one year:
| Column | Description |
|---|---|
country |
Country name (derived from the OECD’s ISO3 REF_AREA
code) |
measure |
RPI rent prices, HPI nominal house prices,
HPI_YDH price-to-income ratio, HPI_RPI
price-to-rent ratio |
year |
Year of observation |
values |
Index, 2015 = 100 (as published) |
change |
Percentage change since 2015 (values - 100) |
For the races we need one indicator at a time, from 2015 (where every country starts at 0%) onwards, ranked within each year, with Greece in red. A small helper does that for any of the four measures:
race_data <- function(df, which, highlight = "Greece", from = 2015) {
d <- df[df$measure == which & df$year >= from, ]
d$rank <- ave(-d$change, d$year, FUN = function(v) rank(v, ties.method = "first"))
d$color <- ifelse(d$country %in% highlight, "#E74C3C", "gray70")
d
}
df_rent <- race_data(df_housing, "RPI") # rent prices
df_house_price <- race_data(df_housing, "HPI") # house prices
df_income <- race_data(df_housing, "HPI_YDH") # house price to income
df_price_rent <- race_data(df_housing, "HPI_RPI") # house price to rent
title_rent <- "How much more expensive rents got since 2015 (%)"
title_house_price <- "How much more expensive houses got since 2015 (%)"
title_price_rent <- "How much faster house prices grew than rents since 2015 (%)"
# "+13%" / "-3%"; rounding first (+ 0 turns -0 into 0) so -0.4 reads "+0%", never "-0%"
pct <- function(v) sprintf("%+.0f%%", round(v) + 0)
title_income <- "How much faster house prices grew than incomes since 2015 (%)"
# data source as a footnote inside each plotly chart (it stays on the chart
# when it is downloaded as a PNG from the plotly toolbar): `b` is the bottom
# margin that makes room for it; the note sits at the bottom of that margin,
# in pixels, so it lands in the same place whatever the chart's height
oecd_url <- paste0("https://data-explorer.oecd.org/vis?df[ds]=dsDisseminateFinalDMZ",
"&df[id]=DSD_AN_HOUSE_PRICES%40DF_HOUSE_PRICES&df[ag]=OECD.ECO.MPD")
add_source <- function(p, what, b) {
plotly::layout(p,
margin = list(b = b),
annotations = list(list(
text = paste0("Source: <a href='", oecd_url, "'>OECD, Analytical house prices indicators</a>",
" (", what, "), annual; 2015 = 100 index shown as % change since 2015"),
xref = "paper", yref = "paper", x = 1, y = 0, yshift = -(b - 22),
xanchor = "right", yanchor = "top", showarrow = FALSE,
font = list(size = 11, color = "gray50"))))
}Greece at a glance
Before racing Greece against Europe, it helps to see its four indicators side by side, as a change against 2015 (the dotted zero line). House prices and rents both fell hard in the years of the crisis, but they recovered at very different speeds: the price-to-rent line shows how much faster house prices have climbed than rents, and — because incomes recovered more slowly still — the price-to-income line shows how much less affordable buying a home has become.
labels_measure <- c(RPI = "Rent prices", HPI = "House prices",
HPI_YDH = "Price to income", HPI_RPI = "Price to rent")
df_greece <- df_housing %>%
filter(country == "Greece", measure %in% names(labels_measure)) %>%
mutate(indicator = labels_measure[measure])
df_greece_temp <- df_greece[order(df_greece$measure, df_greece$year), c("year", "indicator", "change")]
plot_ly(df_greece_temp, height = 800, width = 900,
x = ~year,
y = ~change,
color = ~indicator,
colors = c("#0082c8", "#E74C3C", "#3cb44b", "#f39c12"),
type = "scatter",
mode = "lines+markers",
text = ~paste0("Year: ", year, "<br>", indicator, ": ", sprintf("%+.1f%%", change)),
hoverinfo = "text",
marker = list(size = 10)) %>%
plotly::layout(title = list(text = "Greece: change in house pricing since 2015 (%)",
x = 0.02, xanchor = "left",
y = 0.97, yanchor = "top"),
margin = list(t = 80),
xaxis = list(title = "Year", tickangle = -90),
yaxis = list(title = "% vs 2015", ticksuffix = "%"),
legend = list(orientation = "h", y = -0.2),
font = list(size = 16, color = "gray25"),
shapes = list(list(type = "line",
x0 = min(df_greece_temp$year),
x1 = max(df_greece_temp$year),
y0 = 0, y1 = 0,
line = list(dash = "dot", color = "gray")))) %>%
add_source("rent prices RPI, house prices HPI, price-to-income HPI_YDH, price-to-rent HPI_RPI", b = 190)The code below generates a plot for each country, showing the change in rents, house prices, price-to-income and price-to-rent since 2015. Greece is plotted first, then every other country alphabetically.
labels_measure <- c(RPI = "Rent prices", HPI = "House prices",
HPI_YDH = "Price to income", HPI_RPI = "Price to rent")
country_plot <- function(cname) {
d <- df_housing %>%
filter(country == cname, measure %in% names(labels_measure)) %>%
mutate(indicator = labels_measure[measure]) %>%
arrange(measure, year) %>%
select(year, indicator, change)
plot_ly(d, height = 800, width =900,
x = ~year,
y = ~change,
color = ~indicator,
colors = c("#0082c8", "#E74C3C", "#3cb44b", "#f39c12"),
type = "scatter",
mode = "lines+markers",
text = ~paste0("Year: ", year, "<br>", indicator, ": ", sprintf("%+.1f%%", change)),
hoverinfo = "text",
marker = list(size = 8)) %>%
plotly::layout(title = list(text = paste0(cname, ": change in house pricing since 2015 (%)"),
x = 0.02, xanchor = "left",
y = 0.97, yanchor = "top"),
margin = list(t = 80),
xaxis = list(title = "", tickangle = -90),
yaxis = list(title = "% vs 2015", ticksuffix = "%"),
legend = list(orientation = "h", y = -0.2),
font = list(size = 10, color = "gray25"),
shapes = list(list(type = "line",
x0 = min(d$year), x1 = max(d$year),
y0 = 0, y1 = 0,
line = list(dash = "dot", color = "gray")))) %>%
add_source("rent prices RPI, house prices HPI, price-to-income HPI_YDH, price-to-rent HPI_RPI", b = 190)
}
# Greece first, then every other country alphabetically
countries <- c("Greece", sort(setdiff(unique(df_housing$country), "Greece")))
htmltools::tagList(lapply(countries, country_plot))Building the visualisations
Step 1 — bar chart race, ranked
The frame argument of plot_ly turns a bar
chart into a play/pause animation stepping through each year, and
plotting by rank instead of country name makes the bars
physically reorder every year. Greece is layered on top in red so it is
always visible against the gray field. The same function draws four
races: rents, house prices, and house prices relative to incomes and to
rents.
bar_race <- function(d, title, what) {
plot_ly(height = 800, width =1000) %>%
add_bars(data = d[d$color == "gray70", ],
x = ~rank, y = ~change, frame = ~year, ids = ~rank,
marker = list(color = "gray70"),
text = ~paste0(country, " ", sprintf("%+.0f%%", change)), hoverinfo = "text",
textposition = "outside", textangle = 90, showlegend = FALSE) %>%
add_bars(data = d[d$color %in% "#E74C3C", ],
x = ~rank, y = ~change, frame = ~year, ids = ~rank,
marker = list(color = "#E74C3C"),
text = ~paste0(country, " ", sprintf("%+.0f%%", change)), hoverinfo = "text",
textposition = "outside", textangle = 90, showlegend = FALSE) %>%
layout(xaxis = list(title = "Rank", tickmode = "array"),
yaxis = list(title = "% vs 2015", ticksuffix = "%"),
title = title,
margin = list(l = 50, r = 50, b = 200, t = 100),
font = list(size = 20, color = "gray25", weight = "bold")) %>%
animation_opts(frame = 1000, transition = 0, easing = "linear", redraw = TRUE) %>%
add_source(what, b = 330)
}
bar_race(df_rent, title_rent, "rent prices, RPI")Step 2 — dark themed GIF with gganimate
A rendered GIF is easier to share than an interactive chart.
ggplot2 + gganimate recreates each of the four
ranked races as a horizontal bar chart with a dark background, country
labels and a large year watermark.
race_gif <- function(d, title, file) {
n_countries <- max(d$rank)
hi <- max(d$change)
lo <- min(0, min(d$change)) # a few markets were below 2015 for a while
pad <- (hi - lo) * 0.02
p <- ggplot(d, aes(x = rank, y = change, fill = color, group = country)) +
geom_col(width = 0.85, show.legend = FALSE) +
# country names left of the panel, value at the end of each bar
geom_text(aes(y = lo - pad, label = country, color = color),
hjust = 1, size = 3.8, fontface = "bold", show.legend = FALSE) +
geom_text(aes(y = pmax(change, 0) + pad, label = pct(change)),
hjust = 0, color = "gray80", size = 3.2) +
geom_hline(yintercept = 0, color = "#546E7A", linewidth = 0.4) +
scale_fill_identity() +
scale_color_identity() +
scale_x_reverse(breaks = NULL) +
scale_y_continuous(labels = function(v) paste0(v, "%"),
expand = expansion(mult = c(0.02, 0.1))) +
geom_text(
data = d %>% dplyr::distinct(year),
aes(x = n_countries, y = hi, label = year),
inherit.aes = FALSE,
hjust = 1, vjust = 0,
size = 20, color = "white", alpha = 0.15,
fontface = "bold"
) +
coord_flip(clip = "off") +
labs(title = title,
x = NULL,
y = "% vs 2015",
caption = "Source: OECD Analytical house prices indicators | Greece highlighted in red") +
theme_minimal(base_size = 10) +
theme(
plot.background = element_rect(fill = "#0F1923", color = NA),
panel.background = element_rect(fill = "#0F1923", color = NA),
panel.grid.major.x = element_line(color = "#1E2D3D", linewidth = 0.4),
panel.grid.major.y = element_blank(),
panel.grid.minor = element_blank(),
axis.text.x = element_text(color = "#90A4AE", size = 10),
axis.text.y = element_blank(),
axis.title.x = element_text(color = "#90A4AE", size = 10),
plot.title = element_text(color = "white", face = "bold", size = 12,
margin = margin(12, 0, 8, 0)),
plot.caption = element_text(color = "#546E7A", size = 8, hjust = 1,
margin = margin(8, 0, 8, 0)),
plot.margin = margin(10, 40, 10, 130)) +
transition_states(year, transition_length = 0, state_length = 5) +
ease_aes("cubic-in-out")
animate(p,
nframes = 100,
fps = 10,
duration = 20,
width = 900,
height = 900,
units = "px",
res = 100, # set explicitly: when knitting, gganimate otherwise uses the chunk dpi
renderer = gifski_renderer(file))
}
gifs <- c(rent = "greek_housing_race_rent.gif",
house_price = "greek_housing_race_house_price.gif",
income = "greek_housing_race_income.gif",
price_rent = "greek_housing_race_price_rent.gif")
# each GIF is drawn in its own chunk below: knitr shows only one animate()
# result per chunk
What the charts show
- In Greece, rents kept falling after 2015 and bottomed out in 2018 (-8% against 2015). By 2025 they were +13%, while house prices were +79%: since 2015, buying has become far more expensive than renting.
- House prices also outran incomes: the price-to-income ratio was +19% against 2015, which puts Greece 8th of 28 European countries in how much less affordable homes have become.
- In the house price race Greece was 16th of 29, and in the price-to-rent race 9th of 27 (+58%): since 2015, house prices in Greece have outgrown rents by that much.
- In the rent race Greece was 25th of 27 in 2025: rents rose less there than in most of Europe. The race ranks how much each market moved since 2015, not which country has the most expensive rents.
- A caveat for Greece: the OECD rent index comes from the rents in consumer price statistics, which follow all tenancies, most of them on older contracts. Rents asked for new leases, the ones a person moving house actually faces, have risen much faster than this index shows — listing-based indices (e.g. Spitogatos or the Bank of Greece’s analyses) are the place to look for those.
Customising it
Start the race from another year
Further reading
- OECD Analytical house prices indicators — the dataset in the OECD Data Explorer
- OECD Affordable Housing Database — housing costs as a share of income, tenure and social housing indicators
- OECD SDMX API — how to build data queries
- gganimate documentation — all transition types and rendering options
Rendered with R 4.6.1 · plotly · ggplot2 · gganimate · OECD SDMX API