House price index (2015 = 100) - quarterly data

Data - Eurostat

Author

François Geerolf

Info

source dataset Title .html .rData
eurostat prc_hpi_q House price index (2015 = 100) - quarterly data 2026-01-10 2026-01-07

Data on housing

source dataset Title .html .rData
eurostat prc_hpi_q House price index (2015 = 100) - quarterly data 2026-01-10 2026-01-07
bdf RPP Prix de l'immobilier 2026-01-11 2026-01-10
bis LONG_PP Residential property prices - detailed series 2026-01-11 2024-05-10
bis SELECTED_PP Property prices, selected series 2026-01-11 2026-01-07
ecb RPP Residential Property Price Index Statistics 2026-01-11 2026-01-11
eurostat ei_hppi_q House price index (2015 = 100) - quarterly data 2026-01-11 2026-01-09
eurostat hbs_str_t223 Mean consumption expenditure by income quintile 2025-10-11 2026-01-09
eurostat prc_hicp_midx HICP (2015 = 100) - monthly data (index) 2025-12-22 2026-01-09
fred housing House Prices 2026-01-11 2026-01-07
insee IPLA-IPLNA-2015 Indices des prix des logements neufs et Indices Notaires-Insee des prix des logements anciens 2026-01-11 2026-01-10
oecd SNA_TABLE5 Final consumption expenditure of households 2026-01-11 2023-10-19
oecd housing NA NA NA

LAST_COMPILE

LAST_COMPILE
2026-01-16

Last

time Nobs
2025Q3 405

Info

  • Second quarter of 2023 compared with second quarter of 2022. pdf

purchase

Code 
prc_hpi_q %>%
  left_join(purchase, by = "purchase") %>%
  group_by(purchase, Purchase) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

unit

Code 
prc_hpi_q %>%
  left_join(unit, by = "unit") %>%
  group_by(unit, Unit) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) print_table(.) else .}
unit Unit Nobs
I15_Q Quarterly index, 2015=100 7993
RCH_Q Quarterly rate of change 7957
I10_Q Quarterly index, 2010=100 7954
RCH_A Annual rate of change 7624

geo

Code 
prc_hpi_q %>%
  left_join(geo, by = "geo") %>%
  group_by(geo, Geo) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(Geo)),
         Flag = paste0('<img src="../../bib/flags/vsmall/', Flag, '.png" alt="Flag">')) %>%
  select(Flag, everything()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

time

Code 
prc_hpi_q %>%
  group_by(time) %>%
  summarise(Nobs = n()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

Greece, Europe, France, Spain, Italy, Germany

Purchase Total

All

Code 
prc_hpi_q %>%
  filter(purchase == "TOTAL",
         geo %in% c("EL", "FR", "ES", "IT", "DE"),
         unit == "I15_Q") %>%
  quarter_to_date %>%
  group_by(geo) %>%
  arrange(date) %>%
  mutate(values = 100*values/values[1]) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA", "Europe", Geo),
         Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = Geo)) + 
  theme_minimal() + xlab("") + ylab("100 = Janv. 2006") +
  scale_x_date(breaks = seq(1960, 2028, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_color_manual(values = c("#ED2939", "#000000", "#009246", "#FFC400")) +
  add_4flags +
  scale_y_log10(breaks = seq(0, 200, 5)) +
  theme(legend.position = "none",
        legend.title = element_blank())

2009-2016

Code 
prc_hpi_q %>%
  filter(purchase == "TOTAL",
         geo %in% c("EL", "FR", "ES", "IT", "DE"),
         unit == "I15_Q") %>%
  quarter_to_date %>%
  group_by(geo) %>%
  mutate(values = 100*values/values[date == as.Date("2011-01-01")]) %>%
  filter(date >= as.Date("2009-01-01"),
         date <= as.Date("2016-01-01")) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA", "Europe", Geo),
         Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = Geo)) + 
  theme_minimal() + xlab("") + ylab("100 = Janv. 2011") +
  scale_x_date(breaks = seq(1960, 2028, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_color_manual(values = c("#ED2939", "#000000", "#009246", "#FFC400")) +
  add_4flags +
  scale_y_log10(breaks = seq(0, 200, 5)) +
  theme(legend.position = "none",
        legend.title = element_blank())

Existing Dwellings - DW_EXST

Code 
prc_hpi_q %>%
  filter(purchase == "DW_EXST",
         geo %in% c("EL", "FR", "ES", "IT", "DE"),
         unit == "I15_Q") %>%
  quarter_to_date %>%
  group_by(geo) %>%
  mutate(values = 100*values/values[date == as.Date("2011-01-01")]) %>%
  filter(date >= as.Date("2009-01-01"),
         date <= as.Date("2016-01-01")) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA", "Europe", Geo),
         Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = Geo)) + 
  theme_minimal() + xlab("") + ylab("100 = Janv. 2011") +
  scale_x_date(breaks = seq(1960, 2028, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_color_manual(values = c("#ED2939", "#000000", "#009246", "#FFC400")) +
  add_4flags +
  scale_y_log10(breaks = seq(0, 200, 5)) +
  theme(legend.position = "none",
        legend.title = element_blank())

Purchases of new dwellings - DW_NEW

Code 
prc_hpi_q %>%
  filter(purchase == "DW_NEW",
         geo %in% c("EL", "FR", "ES", "IT", "DE"),
         unit == "I15_Q") %>%
  quarter_to_date %>%
  group_by(geo) %>%
  mutate(values = 100*values/values[date == as.Date("2011-01-01")]) %>%
  filter(date >= as.Date("2009-01-01"),
         date <= as.Date("2016-01-01")) %>%
  left_join(geo, by = "geo") %>%
  mutate(Geo = ifelse(geo == "EA", "Europe", Geo),
         Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = Geo)) + 
  theme_minimal() + xlab("") + ylab("100 = Janv. 2011") +
  scale_x_date(breaks = seq(1960, 2028, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_color_manual(values = c("#ED2939", "#000000", "#009246", "#FFC400")) +
  add_4flags +
  scale_y_log10(breaks = seq(0, 200, 5)) +
  theme(legend.position = "none",
        legend.title = element_blank())

Purchase Total

France, Italy, Germany

Code 
prc_hpi_q %>%
  filter(purchase == "TOTAL",
         geo %in% c("FR", "IT", "DE"),
         unit == "I15_Q") %>%
  quarter_to_date %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot() + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() +
  scale_color_identity() +
  scale_x_date(breaks = seq(1920, 2028, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  add_4flags +
  theme(legend.position = c(0.35, 0.9),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(-100, 300, 10)) +
  ylab("House Price Index") + xlab("")

Belgium, Denmark, Finland

Code 
prc_hpi_q %>%
  filter(purchase == "TOTAL",
         geo %in% c("BE", "DK", "FI"),
         unit == "I15_Q") %>%
  quarter_to_date %>%
  left_join(geo, by = "geo") %>%
  ggplot() + geom_line() + theme_minimal() +
  aes(x = date, y = values, color = Geo, linetype = Geo) +
  scale_color_manual(values = viridis(4)[1:3]) +
  scale_x_date(breaks = seq(1920, 2028, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.35, 0.9),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(-100, 300, 10)) +
  ylab("House Price Index") + xlab("")

All Series

France

Code 
prc_hpi_q %>%
  filter(geo %in% c("FR"),
         unit == "I15_Q") %>%
  quarter_to_date %>%
  left_join(geo, by = "geo") %>%
  left_join(purchase, by = "purchase") %>%
  ggplot() + geom_line() + theme_minimal() +
  aes(x = date, y = values, color = Purchase, linetype = Purchase) +
  scale_color_manual(values = viridis(4)[1:3]) +
  scale_x_date(breaks = seq(1920, 2028, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.35, 0.9),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(-100, 300, 10)) +
  ylab("House Price Index") + xlab("")

Germany

Code 
prc_hpi_q %>%
  filter(geo %in% c("DE"),
         unit == "I15_Q") %>%
  quarter_to_date %>%
  left_join(geo, by = "geo") %>%
  left_join(purchase, by = "purchase") %>%
  ggplot() + geom_line() + theme_minimal() +
  aes(x = date, y = values, color = Purchase, linetype = Purchase) +
  scale_color_manual(values = viridis(4)[1:3]) +
  scale_x_date(breaks = seq(1920, 2028, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.35, 0.9),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(-100, 300, 10)) +
  ylab("House Price Index") + xlab("")

Italy

Code 
prc_hpi_q %>%
  filter(geo %in% c("IT"),
         unit == "I15_Q") %>%
  quarter_to_date %>%
  left_join(geo, by = "geo") %>%
  left_join(purchase, by = "purchase") %>%
  ggplot() + geom_line() + theme_minimal() +
  aes(x = date, y = values, color = Purchase, linetype = Purchase) +
  scale_color_manual(values = viridis(4)[1:3]) +
  scale_x_date(breaks = seq(1920, 2028, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.6, 0.9),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(-100, 300, 5)) +
  ylab("House Price Index") + xlab("")