Final consumption expenditure of households

Data - OECD

Author

François Geerolf


Info

source dataset Title .html .rData
oecd SNA_TABLE5 Final consumption expenditure of households 2025-09-29 2023-10-19
oecd SNA_TABLE1 Gross domestic product (GDP) 2025-09-29 2025-05-24

Data on housing

source dataset Title .html .rData
bdf RPP Prix de l'immobilier 2025-08-28 2025-10-25
bis LONG_PP Residential property prices - detailed series 2025-10-11 2024-05-10
bis SELECTED_PP Property prices, selected series 2025-10-11 2025-10-11
ecb RPP Residential Property Price Index Statistics 2025-11-15 2025-08-29
eurostat ei_hppi_q House price index (2015 = 100) - quarterly data 2025-11-16 2025-11-15
eurostat hbs_str_t223 Mean consumption expenditure by income quintile 2025-10-11 2025-11-15
eurostat prc_hicp_midx HICP (2015 = 100) - monthly data (index) 2025-11-16 2025-11-15
eurostat prc_hpi_q House price index (2015 = 100) - quarterly data 2025-11-16 2025-11-13
fred housing House Prices 2025-11-15 2025-11-15
insee IPLA-IPLNA-2015 Indices des prix des logements neufs et Indices Notaires-Insee des prix des logements anciens 2025-11-16 2025-11-15
oecd SNA_TABLE5 Final consumption expenditure of households 2025-09-29 2023-10-19
oecd housing NA NA NA

LAST_COMPILE

LAST_COMPILE
2025-11-17

Last

obsTime Nobs
2022 8179
2021 10750

Layout - By country

  • OECD Website. html

United States

France

Nobs - TRANSACT, MEASURE

Code 
SNA_TABLE5 %>%
  left_join(SNA_TABLE5_var$TRANSACT, by = "TRANSACT") %>%
  group_by(TRANSACT, Transact, MEASURE) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()

TRANSACT

Code 
SNA_TABLE5 %>%
  left_join(SNA_TABLE5_var$TRANSACT, by = "TRANSACT") %>%
  group_by(TRANSACT, Transact) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()

MEASURE

Code 
SNA_TABLE5 %>%
  left_join(SNA_TABLE5_var$MEASURE, by = "MEASURE") %>%
  group_by(MEASURE, Measure) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()
MEASURE Measure Nobs
C Current prices 81801
V Constant prices, national base year 77900
VOB Constant prices, OECD base year 75339
DOB Deflator 72609
VP Constant prices, previous year prices 60375
VPVOB Constant prices, constant PPPs, OECD base year 28

obsTime

Code 
SNA_TABLE5 %>%
  group_by(obsTime) %>%
  summarise(Nobs = n()) %>%
  arrange(desc(obsTime)) %>%
  print_table_conditional()

France, Germany, United Kingdom, United States

% of GDP

Code 
SNA_TABLE5 %>%
  filter(LOCATION %in% c("FRA", "DEU", "USA", "GBR"),
         obsTime == "2017",
         MEASURE == "C") %>%
  left_join(SNA_TABLE5_var$TRANSACT, by = "TRANSACT") %>%
  left_join(SNA_TABLE5_var$LOCATION, by = "LOCATION") %>%
  select(Location, TRANSACT, Transact, obsValue) %>%
  mutate(Location = gsub(" ", "-", str_to_lower(gsub(" ", "-", Location))),
         Location = paste0('<img src="../../icon/flag/vsmall/', Location, '.png" alt="Flag">')) %>%
  spread(Location, obsValue) %>%
  mutate_at(vars(-1, -2), funs(round(100*./.[TRANSACT == "B1_GE"], 1))) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

% of Consumption

Code 
SNA_TABLE5 %>%
  filter(LOCATION %in% c("FRA", "DEU", "USA", "GBR"),
         obsTime == "2017",
         MEASURE == "C") %>%
  left_join(SNA_TABLE5_var$TRANSACT, by = "TRANSACT") %>%
  left_join(SNA_TABLE5_var$LOCATION, by = "LOCATION") %>%
  select(Location, TRANSACT, Transact, obsValue) %>%
  mutate(Location = gsub(" ", "-", str_to_lower(gsub(" ", "-", Location))),
         Location = paste0('<img src="../../icon/flag/vsmall/', Location, '.png" alt="Flag">')) %>%
  spread(Location, obsValue) %>%
  mutate_at(vars(-1, -2), funs(round(100*./.[TRANSACT == "P31DC"], 1))) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

Housing

% of Consumption, % of GDP

Code 
SNA_TABLE5 %>%
  filter(TRANSACT %in% c("P31DC", "P31CP040"),
         obsTime == "2017",
         MEASURE == "C") %>%
  arrange(LOCATION, TRANSACT) %>%
  select(LOCATION, TRANSACT, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  left_join(SNA_TABLE1 %>%
              filter(TRANSACT == "B1_GE",
                     MEASURE == "C",
                     obsTime == "2017") %>%
              select(LOCATION, B1_GE = obsValue), 
            by = c("LOCATION")) %>%
  left_join(SNA_TABLE5_var$LOCATION, by = "LOCATION") %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Location))),
         Flag = paste0('<img src="../../icon/flag/vsmall/', Flag, '.png" alt="Flag">')) %>%
  transmute(Flag,
            LOCATION,
            Location,
            `Housing (% of Consumption)` = (100*P31CP040 / P31DC) %>% round(digits = 1),
            `Housing (% of GDP)` = (100*P31CP040 / B1_GE) %>% round(digits = 1)) %>%
  arrange(-`Housing (% of Consumption)`) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

% of Consumption

Code 
SNA_TABLE5 %>%
  filter(TRANSACT %in% c("P31DC", "P31CP040", "P31CP041", "P31CP042"),
         obsTime == "2018",
         MEASURE == "C") %>%
  arrange(LOCATION, TRANSACT) %>%
  select(LOCATION, TRANSACT, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  left_join(SNA_TABLE5_var$LOCATION, by = "LOCATION") %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Location))),
         Flag = paste0('<img src="../../icon/flag/vsmall/', Flag, '.png" alt="Flag">')) %>%
  transmute(Flag,
            LOCATION,
            Location,
            `Housing` = (100*P31CP040 / P31DC) %>% round(digits = 1),
            `Rents (Actual)` = (100*P31CP041 / P31DC) %>% round(digits = 1),
            `Rents (Implicit)` = (100*P31CP042 / P31DC) %>% round(digits = 1)) %>%
  arrange(-Housing) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

Food, Health, Housing, Restaurant

Code 
SNA_TABLE5 %>%
  filter(TRANSACT %in% c("P31CP010", "P31DC", "P31CP040", "P31CP060", "P31CP110"),
         obsTime == "2017",
         MEASURE == "C") %>%
  arrange(LOCATION, TRANSACT) %>%
  left_join(SNA_TABLE5_var$LOCATION, by = "LOCATION") %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Location))),
         Flag = paste0('<img src="../../icon/flag/vsmall/', Flag, '.png" alt="Flag">')) %>%
  select(Flag, Location, TRANSACT, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  group_by(Flag, Location) %>%
  summarise(`Food` = (100*P31CP010 / P31DC) %>% round(digits = 1),
            `Housing` = (100*P31CP040 / P31DC) %>% round(digits = 1),
            `Health` = (100*P31CP060 / P31DC) %>% round(digits = 1),
            `Restaurant` = (100*P31CP110 / P31DC) %>% round(digits = 1)) %>%
  na.omit %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

Implicit / real Rents

Table 1

Code 
library(gt)
library(gtExtras)
#devtools::install_github("rstudio/gt")
countrypops_new <- countrypops %>%
  select(LOCATION = country_code_3, LOCATION_2digit = country_code_2) %>%
  unique
table1 <- SNA_TABLE5 %>%
  filter(TRANSACT %in% c("P31CP042", "P31DC", "P31CP041"),
         MEASURE == "C",
         obsTime == "2019") %>%
  left_join(SNA_TABLE5_var$LOCATION, by = "LOCATION") %>%
  left_join(SNA_TABLE5_var$TRANSACT, by = "TRANSACT") %>%
  group_by(LOCATION) %>%
  mutate(values = 100*obsValue /obsValue[TRANSACT == "P31DC"]) %>%
  filter(TRANSACT != "P31DC") %>%
  mutate(Location = case_when(LOCATION == "EA19" ~ "Euro Area (19)",
                              LOCATION == "EA27" ~ "Europe (27)",
                              T ~ Location)) %>%
  left_join(countrypops_new, by = "LOCATION") %>%
  mutate(LOCATION_2digit = case_when(LOCATION == "EA19" ~ "EU",
                                     LOCATION == "EA27" ~ "EU",
                              T ~ LOCATION_2digit)) %>%
  ungroup %>%
  select(Location, LOCATION_2digit, Transact, values) %>%
  spread(Transact, values) %>%
  transmute(LOCATION_2digit, Location,
            `Actual rents` = `Actual rentals for housing`,
            `Imputed rents` = `Imputed rentals for housing`,
            `All rents` = `Actual rentals for housing` + `Imputed rentals for housing`) %>%
  gt() %>%
  fmt_number(columns = 3:5 , locale = "fr", decimals = 1, pattern = "{x}%") |>
  cols_align(align = "center", columns = 3:5) |> 
  fmt_flag(columns = LOCATION_2digit, height = "1.5em") %>%
  cols_width(3:5 ~ px(50)) |> 
  gt_theme_538()


gtsave(table1, filename = "SNA_TABLE5_files/figure-html/table1.png")
i_g("data/oecd/SNA_TABLE5_files/figure-html/table1.png")

France, Germany, US

Code 
SNA_TABLE5 %>%
  filter(TRANSACT %in% c("P31CP042", "P31DC", "P31CP041"),
         MEASURE == "C",
         LOCATION %in% c("FRA", "DEU", "USA")) %>%
  left_join(SNA_TABLE5_var$LOCATION, by = "LOCATION") %>%
  left_join(SNA_TABLE5_var$TRANSACT, by = "TRANSACT") %>%
  year_to_date %>%
  group_by(date, LOCATION) %>%
  mutate(obsValue = obsValue /obsValue[TRANSACT == "P31DC"]) %>%
  filter(TRANSACT != "P31DC") %>%
  left_join(colors, by = c("Location" = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color, linetype = Transact)) + 
  scale_color_identity() + add_6flags + theme_minimal() +
  scale_x_date(breaks = seq(1920, 2025, 10) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-7, 26, 1),
                     labels = scales::percent_format(accuracy = 1)) +
  theme(legend.position = c(0.2, 0.9),
        legend.title = element_blank()) +
  ylab("Rentals (% of Consumption)") + xlab("")

Italy, Spain, Netherlands

Code 
SNA_TABLE5 %>%
  filter(TRANSACT %in% c("P31CP042", "P31DC", "P31CP041"),
         MEASURE == "C",
         LOCATION %in% c("ITA", "ESP", "NLD")) %>%
  left_join(SNA_TABLE5_var$LOCATION, by = "LOCATION") %>%
  left_join(SNA_TABLE5_var$TRANSACT, by = "TRANSACT") %>%
  year_to_date %>%
  group_by(date, LOCATION) %>%
  mutate(obsValue = obsValue /obsValue[TRANSACT == "P31DC"]) %>%
  filter(TRANSACT != "P31DC") %>%
  left_join(colors, by = c("Location" = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color, linetype = Transact)) + 
  scale_color_identity() + add_6flags + theme_minimal() +
  scale_x_date(breaks = seq(1920, 2025, 10) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-7, 26, 1),
                     labels = scales::percent_format(accuracy = 1)) +
  theme(legend.position = c(0.2, 0.9),
        legend.title = element_blank()) +
  ylab("Rentals (% of Consumption)") + xlab("")