Structure of consumption expenditure by COICOP consumption purpose

Data - Eurostat

Info

source	dataset	Title	.html	.rData
eurostat	hbs_str_t211	Structure of consumption expenditure by COICOP consumption purpose	2026-02-12	2026-02-12

Data on housing

source	dataset	Title	.html	.rData
bdf	RPP	Prix de l'immobilier	2026-02-12	2026-02-12
bis	LONG_PP	Residential property prices - detailed series	2026-01-11	2024-05-10
bis	SELECTED_PP	Property prices, selected series	2026-02-12	2026-02-12
ecb	RPP	Residential Property Price Index Statistics	2026-02-12	2026-02-12
eurostat	ei_hppi_q	House price index (2015 = 100) - quarterly data	2026-02-11	2026-02-12
eurostat	hbs_str_t223	Mean consumption expenditure by income quintile	2025-10-11	2026-02-12
eurostat	prc_hicp_midx	HICP (2015 = 100) - monthly data (index)	2026-02-12	2026-02-12
eurostat	prc_hpi_q	House price index (2015 = 100) - quarterly data	2026-02-12	2026-02-12
fred	housing	House Prices	2026-02-12	2026-02-12
insee	IPLA-IPLNA-2015	Indices des prix des logements neufs et Indices Notaires-Insee des prix des logements anciens	2026-02-12	2026-02-12
oecd	SNA_TABLE5	Final consumption expenditure of households	2026-02-10	2023-10-19
oecd	housing	NA	NA	NA

LAST_COMPILE

LAST_COMPILE
2026-02-14

Last

hbs_str_t211 %>%
  group_by(time) %>%
  summarise(Nobs = n()) %>%
  arrange(desc(time)) %>%
  head(2) %>%
  print_table_conditional()

time	Nobs
2020	4763
2015	5901

coicop

All

hbs_str_t211 %>%
  left_join(coicop, by = "coicop") %>%
  group_by(coicop, Coicop) %>%
  summarise(Nobs = n()) %>%
  print_table_conditional()

2-digit

hbs_str_t211 %>%
  filter(nchar(coicop) == 4) %>%
  left_join(coicop, by = "coicop") %>%
  group_by(coicop, Coicop) %>%
  summarise(Nobs = n()) %>%
  print_table_conditional()

coicop	Coicop	Nobs
CP01	Food and non-alcoholic beverages	188
CP02	Alcoholic beverages, tobacco and narcotics	188
CP03	Clothing and footwear	188
CP04	Housing, water, electricity, gas and other fuels	188
CP05	Furnishings, household equipment and routine household maintenance	188
CP06	Health	188
CP07	Transport	188
CP08	Communications	188
CP09	Recreation and culture	188
CP10	Education	187
CP11	Restaurants and hotels	188
CP12	Miscellaneous goods and services	188

3-digit

hbs_str_t211 %>%
  filter(nchar(coicop) == 5) %>%
  left_join(coicop, by = "coicop") %>%
  group_by(coicop, Coicop) %>%
  summarise(Nobs = n()) %>%
  print_table_conditional()

4-digit

hbs_str_t211 %>%
  filter(nchar(coicop) == 6) %>%
  left_join(coicop, by = "coicop") %>%
  group_by(coicop, Coicop) %>%
  summarise(Nobs = n()) %>%
  print_table_conditional()

geo

hbs_str_t211 %>%
  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

hbs_str_t211 %>%
  group_by(time) %>%
  summarise(Nobs = n()) %>%
  print_table_conditional()

time	Nobs
1988	1307
1994	2201
1999	2256
2005	7075
2010	5922
2015	5901
2020	4763

France

Table

hbs_str_t211 %>%
  left_join(coicop, by = "coicop") %>%
  filter(geo == "FR",
         substr(coicop, 1, 2) == "CP",
         coicop != "CP00",
         time %in% c("2015", "2020")) %>%
  select(-unit) %>%
  select(-geo) %>%
  spread(time, values) %>%
  print_table_conditional()

2-digit

hbs_str_t211 %>%
  left_join(coicop, by = "coicop") %>%
  filter(geo == "FR",
         substr(coicop, 1, 2) == "CP",
         nchar(coicop) == 4,
         coicop != "CP00",
         time %in% c("2015", "2020")) %>%
  select(-unit) %>%
  select(-geo) %>%
  spread(time, values) %>%
  print_table_conditional()

freq	coicop	Coicop	2015	2020
A	CP01	Food and non-alcoholic beverages	143	143
A	CP02	Alcoholic beverages, tobacco and narcotics	25	25
A	CP03	Clothing and footwear	40	40
A	CP04	Housing, water, electricity, gas and other fuels	289	289
A	CP05	Furnishings, household equipment and routine household maintenance	48	48
A	CP06	Health	16	16
A	CP07	Transport	132	132
A	CP08	Communications	24	24
A	CP09	Recreation and culture	77	77
A	CP10	Education	6	6
A	CP11	Restaurants and hotels	55	55
A	CP12	Miscellaneous goods and services	147	147

3-digit

hbs_str_t211 %>%
  left_join(coicop, by = "coicop") %>%
  filter(geo == "FR",
         substr(coicop, 1, 2) == "CP",
         nchar(coicop) == 5,
         coicop != "CP00",
         time %in% c("2015", "2020")) %>%
  select(-unit) %>%
  select(-geo) %>%
  spread(time, values) %>%
  print_table_conditional()

4-digit

hbs_str_t211 %>%
  left_join(coicop, by = "coicop") %>%
  filter(geo == "FR",
         substr(coicop, 1, 2) == "CP",
         nchar(coicop) == 6,
         coicop != "CP00",
         time %in% c("2015", "2020")) %>%
  select(-unit) %>%
  select(-geo) %>%
  spread(time, values) %>%
  print_table_conditional()

Table

2015

hbs_str_t211 %>%
  filter(time == "2015",
         coicop %in% c("CP04", "CP041", "CP042")) %>%
  left_join(geo, by = "geo") %>%
  select_if(~ n_distinct(.) > 1) %>%
  spread(coicop, values) %>%
  arrange(-CP04) %>%
  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 .}

2020

hbs_str_t211 %>%
  filter(time == "2020",
         coicop %in% c("CP04", "CP041", "CP042")) %>%
  left_join(geo, by = "geo") %>%
  select_if(~ n_distinct(.) > 1) %>%
  spread(coicop, values) %>%
  arrange(-CP04) %>%
  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 .}