Residential Property Price Indices (RPPIs) and related housing indicators

Data - OECD

Author

François Geerolf

Info

source dataset Title .html .rData
oecd HOUSING Residential Property Price Indices (RPPIs) and related housing indicators 2025-09-29 2020-01-18

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

SUBJECT

Code 
HOUSING %>%
  left_join(HOUSING_var$SUBJECT %>%
              setNames(c("SUBJECT", "Subject")),  by = "SUBJECT") %>%
  group_by(SUBJECT, Subject) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

MEASURE

Code 
HOUSING %>%
  left_join(HOUSING_var$MEASURE %>%
              setNames(c("MEASURE", "Measure")),  by = "MEASURE") %>%
  group_by(MEASURE, Measure) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) print_table(.) else .}
MEASURE Measure Nobs
IXOB Index 132140
GP Percentage change from previous period 88461
GY Percentage change on the same period of the previous year 85775
IXOBSA Index, sa 14691
ST Transaction 4257
AL Per thousand of the National CPI Total 3207
GPSA Percentage change from previous period, sa 820
GYSA Percentage change on the same period of the previous year, sa 786

GEO_COVERAGE

Code 
HOUSING %>%
  left_join(HOUSING_var$GEO_COVERAGE %>%
              setNames(c("GEO_COVERAGE", "Geo_coverage")),  by = "GEO_COVERAGE") %>%
  group_by(GEO_COVERAGE, Geo_coverage) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) print_table(.) else .}
GEO_COVERAGE Geo_coverage Nobs
CN Whole country 312004
CY Capital/main city 15503
UA Urban areas 2630

FREQUENCY

Code 
HOUSING %>%
  left_join(HOUSING_var$FREQUENCY %>%
              setNames(c("FREQUENCY", "Frequency")),  by = "FREQUENCY") %>%
  group_by(FREQUENCY, Frequency) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) print_table(.) else .}
FREQUENCY Frequency Nobs
M Monthly 197912
Q Quarterly 103284
A Annual 28941

LOCATION

Code 
HOUSING %>%
  left_join(HOUSING_var$LOCATION %>%
              setNames(c("LOCATION", "Location")),  by = "LOCATION") %>%
  group_by(LOCATION, Location) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

France, United States, Germany, Japan

CPI: 04.1 - Actual rentals for housing - CP040100

Code 
HOUSING %>%
  filter(LOCATION %in% c("FRA", "USA", "DEU", "JPN"),
         SUBJECT == "CP040100",
         FREQUENCY == "Q",
         MEASURE == "IXOB") %>%
  quarter_to_enddate %>%
  left_join(HOUSING_var$LOCATION %>%
              setNames(c("LOCATION", "Location")), by = "LOCATION") %>%
  ggplot(.) + geom_line() + 
  aes(x = date, y = obsValue, color = Location, linetype = Location) +
  theme_minimal() + xlab("") + ylab("CPI: 04.1 - Actual rentals") +
  scale_x_date(breaks = seq(1960, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(0, 200, 10)) +
  scale_color_manual(values = viridis(5)[1:4]) +
  theme(legend.position = c(0.8, 0.20),
        legend.title = element_blank())

CPI: 04.2 - Imputed rentals for housing - CP040200

Code 
HOUSING %>%
  filter(LOCATION %in% c("FRA", "USA", "DEU", "JPN"),
         SUBJECT == "CP040200",
         FREQUENCY == "Q",
         MEASURE == "IXOB") %>%
  quarter_to_enddate %>%
  left_join(HOUSING_var$LOCATION %>%
              setNames(c("LOCATION", "Location")), by = "LOCATION") %>%
  ggplot(.) + geom_line() + 
  aes(x = date, y = obsValue, color = Location, linetype = Location) +
  theme_minimal() + xlab("") + ylab("CPI: 04.2 - Imputed rentals") +
  scale_x_date(breaks = seq(1960, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(0, 200, 10)) +
  scale_color_manual(values = viridis(5)[1:4]) +
  theme(legend.position = c(0.8, 0.20),
        legend.title = element_blank())

CPI: Housing (04.1, 04.2, 04.3) - CPGRHO01

Code 
HOUSING %>%
  filter(LOCATION %in% c("FRA", "USA", "DEU", "JPN"),
         SUBJECT == "CPGRHO01",
         FREQUENCY == "Q",
         MEASURE == "IXOB") %>%
  quarter_to_enddate %>%
  left_join(HOUSING_var$LOCATION %>%
              setNames(c("LOCATION", "Location")), by = "LOCATION") %>%
  ggplot(.) + geom_line() + 
  aes(x = date, y = obsValue, color = Location, linetype = Location) +
  theme_minimal() + xlab("") + ylab("CPI: Housing (04.1, 04.2, 04.3)") +
  scale_x_date(breaks = seq(1960, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(0, 200, 10)) +
  scale_color_manual(values = viridis(5)[1:4]) +
  theme(legend.position = c(0.8, 0.20),
        legend.title = element_blank())

CPI: Housing excluding imputed rentals for housing (04.1, 04.3) - CPGRHO02

Code 
HOUSING %>%
  filter(LOCATION %in% c("FRA", "USA", "DEU", "JPN"),
         SUBJECT == "CPGRHO02",
         FREQUENCY == "Q",
         MEASURE == "IXOB") %>%
  quarter_to_enddate %>%
  left_join(HOUSING_var$LOCATION %>%
              setNames(c("LOCATION", "Location")), by = "LOCATION") %>%
  ggplot(.) + geom_line() + 
  aes(x = date, y = obsValue, color = Location, linetype = Location) +
  theme_minimal() + xlab("") + ylab("CPI: Housing (04.1, 04.2, 04.3)") +
  scale_x_date(breaks = seq(1960, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(0, 200, 10)) +
  scale_color_manual(values = viridis(5)[1:4]) +
  theme(legend.position = c(0.8, 0.20),
        legend.title = element_blank())

Canada, United Kingdom, Norway, Denmark

CPI: 04.1 - Actual rentals for housing - CP040100

Code 
HOUSING %>%
  filter(LOCATION %in% c("CAN", "GBR", "DNK", "NOR"),
         SUBJECT == "CP040100",
         FREQUENCY == "Q",
         MEASURE == "IXOB") %>%
  quarter_to_enddate %>%
  left_join(HOUSING_var$LOCATION %>%
              setNames(c("LOCATION", "Location")), by = "LOCATION") %>%
  ggplot(.) + geom_line() + 
  aes(x = date, y = obsValue, color = Location, linetype = Location) +
  theme_minimal() + xlab("") + ylab("CPI: 04.1 - Actual rentals") +
  scale_x_date(breaks = seq(1960, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(0, 200, 10)) +
  scale_color_manual(values = viridis(5)[1:4]) +
  theme(legend.position = c(0.8, 0.20),
        legend.title = element_blank())

CPI: 04.2 - Imputed rentals for housing - CP040200

Code 
HOUSING %>%
  filter(LOCATION %in% c("CAN", "GBR", "DNK", "NOR"),
         SUBJECT == "CP040200",
         FREQUENCY == "Q",
         MEASURE == "IXOB") %>%
  quarter_to_enddate %>%
  left_join(HOUSING_var$LOCATION %>%
              setNames(c("LOCATION", "Location")), by = "LOCATION") %>%
  ggplot(.) + geom_line() + 
  aes(x = date, y = obsValue, color = Location, linetype = Location) +
  theme_minimal() + xlab("") + ylab("CPI: 04.2 - Imputed rentals") +
  scale_x_date(breaks = seq(1960, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(0, 200, 10)) +
  scale_color_manual(values = viridis(5)[1:4]) +
  theme(legend.position = c(0.8, 0.20),
        legend.title = element_blank())

CPI: Housing (04.1, 04.2, 04.3) - CPGRHO01

Code 
HOUSING %>%
  filter(LOCATION %in% c("CAN", "GBR", "DNK", "NOR"),
         SUBJECT == "CPGRHO01",
         FREQUENCY == "Q",
         MEASURE == "IXOB") %>%
  quarter_to_enddate %>%
  left_join(HOUSING_var$LOCATION %>%
              setNames(c("LOCATION", "Location")), by = "LOCATION") %>%
  ggplot(.) + geom_line() + 
  aes(x = date, y = obsValue, color = Location, linetype = Location) +
  theme_minimal() + xlab("") + ylab("CPI: Housing (04.1, 04.2, 04.3)") +
  scale_x_date(breaks = seq(1960, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(0, 200, 10)) +
  scale_color_manual(values = viridis(5)[1:4]) +
  theme(legend.position = c(0.8, 0.20),
        legend.title = element_blank())

CPI: Housing excluding imputed rentals for housing (04.1, 04.3) - CPGRHO02

Code 
HOUSING %>%
  filter(LOCATION %in% c("CAN", "GBR", "DNK", "NOR"),
         SUBJECT == "CPGRHO02",
         FREQUENCY == "Q",
         MEASURE == "IXOB") %>%
  quarter_to_enddate %>%
  left_join(HOUSING_var$LOCATION %>%
              setNames(c("LOCATION", "Location")), by = "LOCATION") %>%
  ggplot(.) + geom_line() + 
  aes(x = date, y = obsValue, color = Location, linetype = Location) +
  theme_minimal() + xlab("") + ylab("CPI: Housing (04.1, 04.2, 04.3)") +
  scale_x_date(breaks = seq(1960, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(0, 200, 10)) +
  scale_color_manual(values = viridis(5)[1:4]) +
  theme(legend.position = c(0.8, 0.20),
        legend.title = element_blank())