Info

source	dataset	.html	.RData
eurostat	prc_hpi_q	2024-04-18	2024-05-09

Data on housing

source	dataset	.html	.RData
bdf	RPP	2024-05-08	2024-05-08
bis	LONG_PP	2024-04-19	2024-04-19
bis	SELECTED_PP	2024-04-19	2024-04-19
ecb	RPP	2024-04-19	2024-04-19
eurostat	ei_hppi_q	2024-05-09	2024-05-09
eurostat	hbs_str_t223	2024-05-09	2024-05-09
eurostat	prc_hicp_midx	2024-05-09	2024-05-09
eurostat	prc_hpi_q	2024-04-18	2024-05-09
fred	housing	2024-04-26	2024-04-26
insee	IPLA-IPLNA-2015	2024-05-09	2024-05-09
oecd	housing	2024-04-16	2020-01-18
oecd	SNA_TABLE5	2024-04-16	2023-10-19

LAST_COMPILE

LAST_COMPILE
2024-05-09

Last

time	Nobs
2023Q4	405

Info

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

purchase

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

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	7084
I10_Q	Quarterly index, 2010=100	7052
RCH_Q	Quarterly rate of change	7044
RCH_A	Annual rate of change	6720

geo

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

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

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

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

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

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

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

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

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

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

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("")

House price index (2015 = 100) - quarterly data

Data - Eurostat

François Geerolf

Info

Data on housing

LAST_COMPILE

Last

Info

purchase

unit

geo

time

Greece, Europe, France, Spain, Italy, Germany

Purchase Total

All

2009-2016

Existing Dwellings - DW_EXST

Purchases of new dwellings - DW_NEW

Purchase Total

France, Italy, Germany

Belgium, Denmark, Finland

All Series

France

Germany

Italy