Residential Property Price Indices (RPPIs) and related housing indicators

Data - OECD

Author

François Geerolf

Info

source dataset .html .RData

oecd

HOUSING

 2024-09-11 2020-01-18

Data on housing

source dataset .html .RData

bdf

RPP

 2024-07-26 2024-07-01

bis

LONG_PP

 2024-08-09 2024-05-10

bis

SELECTED_PP

 2024-08-09 2024-05-10

ecb

RPP

 2024-09-14 2024-09-14

eurostat

ei_hppi_q

 2024-09-14 2024-09-14

eurostat

hbs_str_t223

 2024-09-10 2024-09-14

eurostat

prc_hicp_midx

 2024-09-14 2024-09-14

eurostat

prc_hpi_q

 2024-09-14 2024-09-14

fred

housing

 2024-09-14 2024-09-14

insee

IPLA-IPLNA-2015

 2024-09-14 2024-09-14

oecd

housing

 2024-09-11 2020-01-18

oecd

SNA_TABLE5

 2024-09-11 2023-10-19

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())