Indices des prix des logements neufs et Indices Notaires-Insee des prix des logements anciens
Data - INSEE
François Geerolf
Info
| source | dataset | .html | .RData |
|---|---|---|---|
| insee | IPLA-IPLNA-2015 | 2024-04-18 | 2024-05-09 |
Données sur l’inflation en France
| source | dataset | .html | .RData |
|---|---|---|---|
| insee | bdf2017 | 2024-05-09 | 2023-11-21 |
| insee | ILC-ILAT-ICC | 2024-05-09 | 2024-05-09 |
| insee | INDICES_LOYERS | 2024-05-09 | 2024-05-09 |
| insee | IPC-1970-1980 | 2024-05-09 | 2024-05-09 |
| insee | IPC-1990 | 2024-05-09 | 2024-05-09 |
| insee | IPC-2015 | 2024-05-09 | 2024-04-09 |
| insee | IPC-PM-2015 | 2024-05-09 | 2024-05-09 |
| insee | IPCH-2015 | 2024-05-09 | 2024-05-09 |
| insee | IPGD-2015 | 2024-05-09 | 2024-03-20 |
| insee | IPLA-IPLNA-2015 | 2024-04-18 | 2024-05-09 |
| insee | IPPI-2015 | 2024-04-18 | 2024-03-30 |
| insee | IRL | 2024-04-18 | 2024-05-09 |
| insee | SERIES_LOYERS | 2024-04-18 | 2024-05-09 |
| insee | T_CONSO_EFF_FONCTION | 2024-04-18 | 2024-04-01 |
Données sur l’immobilier
| source | dataset | .html | .RData |
|---|---|---|---|
| acpr | as151 | 2024-04-05 | 2024-04-05 |
| bdf | BSI1 | 2024-05-08 | 2024-05-08 |
| bdf | CPP | 2024-05-08 | 2024-05-08 |
| bdf | FM | 2024-05-08 | 2024-05-08 |
| bdf | immobilier | 2024-05-08 | 2024-05-07 |
| bdf | MIR | 2024-05-08 | 2024-05-08 |
| bdf | MIR1 | 2024-05-08 | 2024-05-08 |
| bdf | RPP | 2024-05-08 | 2024-05-08 |
| insee | CONSTRUCTION-LOGEMENTS | 2024-05-09 | 2024-05-09 |
| insee | ENQ-CONJ-ART-BAT | 2024-05-09 | 2023-10-25 |
| insee | ENQ-CONJ-IND-BAT | 2024-05-09 | 2024-05-09 |
| insee | ENQ-CONJ-PROMO-IMMO | 2024-05-09 | 2024-05-09 |
| insee | ENQ-CONJ-TP | 2024-05-09 | 2024-05-09 |
| insee | ILC-ILAT-ICC | 2024-05-09 | 2024-05-09 |
| insee | INDICES_LOYERS | 2024-05-09 | 2024-05-09 |
| insee | IPLA-IPLNA-2015 | 2024-04-18 | 2024-05-09 |
| insee | IRL | 2024-04-18 | 2024-05-09 |
| insee | PARC-LOGEMENTS | 2024-04-18 | 2023-12-03 |
| insee | SERIES_LOYERS | 2024-04-18 | 2024-05-09 |
| insee | t_dpe_val | 2024-04-18 | 2024-03-04 |
| notaires | arrdt | 2024-04-08 | 2024-04-08 |
| notaires | dep | 2024-04-08 | 2024-04-08 |
Data on inflation
| source | dataset | .html | .RData |
|---|---|---|---|
| bis | CPI | 2024-04-19 | 2022-01-20 |
| ecb | CES | 2024-04-19 | 2024-01-12 |
| eurostat | nama_10_co3_p3 | 2024-04-18 | 2024-04-18 |
| eurostat | prc_hicp_cow | 2024-04-18 | 2024-04-18 |
| eurostat | prc_hicp_ctrb | 2024-04-18 | 2024-04-18 |
| eurostat | prc_hicp_inw | 2024-04-18 | 2024-04-18 |
| eurostat | prc_hicp_manr | 2024-04-18 | 2024-04-18 |
| eurostat | prc_hicp_midx | 2024-04-18 | 2024-04-18 |
| eurostat | prc_hicp_mmor | 2024-04-18 | 2024-04-18 |
| eurostat | prc_ppp_ind | 2024-04-18 | 2024-04-18 |
| eurostat | sts_inpp_m | 2024-04-18 | 2024-04-18 |
| eurostat | sts_inppd_m | 2024-04-18 | 2024-04-18 |
| eurostat | sts_inppnd_m | 2024-04-18 | 2024-04-18 |
| fred | cpi | 2024-04-19 | 2024-04-19 |
| fred | inflation | 2024-05-07 | 2024-05-07 |
| imf | CPI | 2024-01-06 | 2020-03-13 |
| oecd | MEI_PRICES_PPI | 2024-04-16 | 2024-04-15 |
| oecd | PPP2017 | 2024-04-16 | 2023-07-25 |
| oecd | PRICES_CPI | 2024-04-16 | 2024-04-15 |
| wdi | FP.CPI.TOTL.ZG | 2023-01-15 | 2024-04-14 |
| wdi | NY.GDP.DEFL.KD.ZG | 2024-04-14 | 2024-04-14 |
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 | 2024-04-18 | 2024-04-18 |
| eurostat | hbs_str_t223 | 2024-04-18 | 2024-04-18 |
| eurostat | prc_hicp_midx | 2024-04-18 | 2024-04-18 |
| eurostat | prc_hpi_q | 2024-04-18 | 2024-04-18 |
| fred | housing | 2024-04-26 | 2024-04-26 |
| insee | IPLA-IPLNA-2015 | 2024-04-18 | 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_UPDATE
`IPLA-IPLNA-2015` %>%
group_by(LAST_UPDATE) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
print_table_conditional()| LAST_UPDATE | Nobs |
|---|---|
| 2024-02-29 | 13918 |
| 2024-03-22 | 384 |
Last
`IPLA-IPLNA-2015` %>%
group_by(TIME_PERIOD) %>%
summarise(Nobs = n()) %>%
filter(TIME_PERIOD == max(TIME_PERIOD)) %>%
print_table_conditional()| TIME_PERIOD | Nobs |
|---|---|
| 2023-Q4 | 142 |
TITLE_FR
INDICATEUR
`IPLA-IPLNA-2015` %>%
left_join(INDICATEUR, by = "INDICATEUR") %>%
group_by(INDICATEUR, Indicateur) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
print_table_conditional()| INDICATEUR | Indicateur | Nobs |
|---|---|---|
| IPLA_A | Indice de prix des appartements (logements anciens) | 5644 |
| IPLA_E | Indice de prix des logements anciens | 4384 |
| IPLA_M | Indice de prix des maisons (logements anciens) | 3890 |
| IPLN | Indice de prix des logements neufs | 192 |
| IPLNA | Indice de prix des logements neufs et anciens | 192 |
CORRECTION
`IPLA-IPLNA-2015` %>%
left_join(CORRECTION, by = "CORRECTION") %>%
group_by(CORRECTION, Correction) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
print_table_conditional()| CORRECTION | Correction | Nobs |
|---|---|---|
| BRUT | Non corrigé | 7151 |
| CVS | Corrigé des variations saisonnières | 7151 |
REF_AREA
TIME_PERIOD
Paris, France
All
`IPLA-IPLNA-2015` %>%
filter(CORRECTION == "BRUT",
INDICATEUR == "IPLA_A",
REF_AREA %in% c("D75", "FM")) %>%
quarter_to_date %>%
mutate(TITLE_FR = gsub("Indice des prix des logements anciens - ", "", TITLE_FR),
TITLE_FR = gsub(" - Appartements - Base 100 en moyenne annuelle 2015 - Série brute", "", TITLE_FR)) %>%
group_by(REF_AREA) %>%
mutate(OBS_VALUE = 100*OBS_VALUE / OBS_VALUE[date == as.Date("1998-01-01")]) %>%
ggplot + geom_line(aes(x = date, y = OBS_VALUE, color = TITLE_FR)) +
theme_minimal() +
scale_x_date(breaks = as.Date(paste0(seq(1960, 2050, 2), "-01-01")),
labels = date_format("%Y")) +
theme(legend.position = c(0.25, 0.85),
legend.title = element_blank()) +
xlab("") + ylab("Indice des prix des logements anciens") +
scale_y_log10(breaks = seq(0, 7000, 50))
1996-
`IPLA-IPLNA-2015` %>%
filter(CORRECTION == "BRUT",
INDICATEUR == "IPLA_A",
REF_AREA %in% c("D75", "FM")) %>%
quarter_to_date %>%
mutate(TITLE_FR = gsub("Indice des prix des logements anciens - ", "", TITLE_FR),
TITLE_FR = gsub(" - Appartements - Base 100 en moyenne annuelle 2015 - Série brute", "", TITLE_FR)) %>%
group_by(REF_AREA) %>%
filter(date >= as.Date("1996-01-01")) %>%
mutate(OBS_VALUE = 100*OBS_VALUE / OBS_VALUE[date == as.Date("1996-01-01")]) %>%
ggplot + geom_line(aes(x = date, y = OBS_VALUE, color = TITLE_FR)) +
theme_minimal() +
scale_x_date(breaks = as.Date(paste0(seq(1960, 2050, 2), "-01-01")),
labels = date_format("%Y")) +
theme(legend.position = c(0.25, 0.85),
legend.title = element_blank()) +
xlab("") + ylab("Indice des prix des logements anciens") +
scale_y_log10(breaks = seq(0, 7000, 50))
1998-
`IPLA-IPLNA-2015` %>%
filter(CORRECTION == "BRUT",
INDICATEUR == "IPLA_A",
REF_AREA %in% c("D75", "FM")) %>%
quarter_to_date %>%
mutate(TITLE_FR = gsub("Indice des prix des logements anciens - ", "", TITLE_FR),
TITLE_FR = gsub(" - Appartements - Base 100 en moyenne annuelle 2015 - Série brute", "", TITLE_FR)) %>%
group_by(REF_AREA) %>%
filter(date >= as.Date("1998-01-01")) %>%
mutate(OBS_VALUE = 100*OBS_VALUE / OBS_VALUE[date == as.Date("1998-01-01")]) %>%
ggplot + geom_line(aes(x = date, y = OBS_VALUE, color = TITLE_FR)) +
theme_minimal() +
scale_x_date(breaks = as.Date(paste0(seq(1960, 2050, 2), "-01-01")),
labels = date_format("%Y")) +
theme(legend.position = c(0.25, 0.85),
legend.title = element_blank()) +
xlab("") + ylab("Indice des prix des logements anciens") +
scale_y_log10(breaks = seq(0, 7000, 50))
1998-2021
`IPLA-IPLNA-2015` %>%
filter(CORRECTION == "BRUT",
INDICATEUR == "IPLA_A",
REF_AREA %in% c("D75", "FM")) %>%
quarter_to_date %>%
mutate(TITLE_FR = gsub("Indice des prix des logements anciens - ", "", TITLE_FR),
TITLE_FR = gsub(" - Appartements - Base 100 en moyenne annuelle 2015 - Série brute", "", TITLE_FR)) %>%
group_by(REF_AREA) %>%
filter(date >= as.Date("1998-01-01"),
date <= as.Date("2021-09-01")) %>%
mutate(OBS_VALUE = 100*OBS_VALUE / OBS_VALUE[date == as.Date("1998-01-01")]) %>%
ggplot + geom_line(aes(x = date, y = OBS_VALUE, color = TITLE_FR)) +
scale_color_manual(values = viridis(3)[1:2]) +
theme_minimal() +
scale_x_date(breaks = as.Date(paste0(seq(1960, 2050, 2), "-01-01")),
labels = date_format("%Y")) +
theme(legend.position = c(0.25, 0.85),
legend.title = element_blank()) +
xlab("") + ylab("Indice des prix des logements anciens") +
scale_y_log10(breaks = seq(0, 7000, 50))
2000-
`IPLA-IPLNA-2015` %>%
filter(CORRECTION == "BRUT",
INDICATEUR == "IPLA_A",
REF_AREA %in% c("D75", "FM")) %>%
quarter_to_date %>%
mutate(TITLE_FR = gsub("Indice des prix des logements anciens - ", "", TITLE_FR),
TITLE_FR = gsub(" - Appartements - Base 100 en moyenne annuelle 2015 - Série brute", "", TITLE_FR)) %>%
group_by(REF_AREA) %>%
filter(date >= as.Date("2000-01-01")) %>%
mutate(OBS_VALUE = 100*OBS_VALUE / OBS_VALUE[date == as.Date("2000-01-01")]) %>%
ggplot + geom_line(aes(x = date, y = OBS_VALUE, color = TITLE_FR)) +
theme_minimal() +
scale_x_date(breaks = as.Date(paste0(seq(1960, 2050, 2), "-01-01")),
labels = date_format("%Y")) +
theme(legend.position = c(0.25, 0.85),
legend.title = element_blank()) +
xlab("") + ylab("Indice des prix des logements anciens") +
scale_y_log10(breaks = seq(0, 7000, 50))
2014-
`IPLA-IPLNA-2015` %>%
filter(CORRECTION == "BRUT",
INDICATEUR == "IPLA_A",
REF_AREA %in% c("D75", "FM")) %>%
quarter_to_date %>%
mutate(TITLE_FR = gsub("Indice des prix des logements anciens - ", "", TITLE_FR),
TITLE_FR = gsub(" - Appartements - Base 100 en moyenne annuelle 2015 - Série brute", "", TITLE_FR)) %>%
group_by(REF_AREA) %>%
filter(date >= as.Date("2014-01-01")) %>%
mutate(OBS_VALUE = 100*OBS_VALUE / OBS_VALUE[date == as.Date("2014-01-01")]) %>%
ggplot + geom_line(aes(x = date, y = OBS_VALUE, color = TITLE_FR)) +
theme_minimal() +
scale_x_date(breaks = as.Date(paste0(seq(1960,2100, 1), "-01-01")),
labels = date_format("%Y")) +
theme(legend.position = c(0.25, 0.85),
legend.title = element_blank()) +
xlab("") + ylab("Indice des prix des logements anciens") +
scale_y_log10(breaks = seq(0, 7000, 5))
Paris, Lyon, Marseille, France
All
`IPLA-IPLNA-2015` %>%
filter(CORRECTION == "BRUT",
INDICATEUR == "IPLA_A",
REF_AREA %in% c("A_69123", "A_13055", "D75", "FM")) %>%
quarter_to_date %>%
mutate(TITLE_FR = gsub("Indice des prix des logements anciens - ", "", TITLE_FR),
TITLE_FR = gsub(" - Appartements - Base 100 en moyenne annuelle 2015 - Série brute", "", TITLE_FR)) %>%
group_by(REF_AREA) %>%
mutate(OBS_VALUE = 100*OBS_VALUE / OBS_VALUE[date == as.Date("1998-01-01")]) %>%
ggplot + geom_line(aes(x = date, y = OBS_VALUE, color = TITLE_FR)) +
theme_minimal() +
scale_x_date(breaks = as.Date(paste0(seq(1960, 2050, 2), "-01-01")),
labels = date_format("%Y")) +
theme(legend.position = c(0.25, 0.85),
legend.title = element_blank()) +
xlab("") + ylab("Indice des prix des logements anciens") +
scale_y_log10(breaks = seq(0, 7000, 50))
1996-
`IPLA-IPLNA-2015` %>%
filter(CORRECTION == "BRUT",
INDICATEUR == "IPLA_A",
REF_AREA %in% c("A_69123", "A_13055", "D75", "FM")) %>%
quarter_to_date %>%
mutate(TITLE_FR = gsub("Indice des prix des logements anciens - ", "", TITLE_FR),
TITLE_FR = gsub(" - Appartements - Base 100 en moyenne annuelle 2015 - Série brute", "", TITLE_FR)) %>%
filter(date >= as.Date("1996-01-01")) %>%
group_by(REF_AREA) %>%
mutate(OBS_VALUE = 100*OBS_VALUE / OBS_VALUE[date == as.Date("1996-01-01")]) %>%
ggplot + geom_line(aes(x = date, y = OBS_VALUE, color = TITLE_FR)) +
theme_minimal() +
scale_x_date(breaks = as.Date(paste0(seq(1960, 2050, 2), "-01-01")),
labels = date_format("%Y")) +
theme(legend.position = c(0.25, 0.85),
legend.title = element_blank()) +
xlab("") + ylab("Indice des prix des logements anciens") +
scale_y_log10(breaks = seq(0, 7000, 50))
1998-
`IPLA-IPLNA-2015` %>%
filter(CORRECTION == "BRUT",
INDICATEUR == "IPLA_A",
REF_AREA %in% c("A_69123", "A_13055", "D75", "FM")) %>%
quarter_to_date %>%
mutate(TITLE_FR = gsub("Indice des prix des logements anciens - ", "", TITLE_FR),
TITLE_FR = gsub(" - Appartements - Base 100 en moyenne annuelle 2015 - Série brute", "", TITLE_FR)) %>%
filter(date >= as.Date("1998-01-01")) %>%
group_by(REF_AREA) %>%
mutate(OBS_VALUE = 100*OBS_VALUE / OBS_VALUE[date == as.Date("1998-01-01")]) %>%
ggplot + geom_line(aes(x = date, y = OBS_VALUE, color = TITLE_FR)) +
theme_minimal() +
scale_x_date(breaks = as.Date(paste0(seq(1960, 2050, 2), "-01-01")),
labels = date_format("%Y")) +
theme(legend.position = c(0.25, 0.85),
legend.title = element_blank()) +
xlab("") + ylab("Indice des prix des logements anciens") +
scale_y_log10(breaks = seq(0, 7000, 50))
2000-
`IPLA-IPLNA-2015` %>%
filter(CORRECTION == "BRUT",
INDICATEUR == "IPLA_A",
REF_AREA %in% c("A_69123", "A_13055", "D75", "FM")) %>%
quarter_to_date %>%
mutate(TITLE_FR = gsub("Indice des prix des logements anciens - ", "", TITLE_FR),
TITLE_FR = gsub(" - Appartements - Base 100 en moyenne annuelle 2015 - Série brute", "", TITLE_FR)) %>%
filter(date >= as.Date("2000-01-01")) %>%
group_by(REF_AREA) %>%
mutate(OBS_VALUE = 100*OBS_VALUE / OBS_VALUE[date == as.Date("2000-01-01")]) %>%
ggplot + geom_line(aes(x = date, y = OBS_VALUE, color = TITLE_FR)) +
theme_minimal() +
scale_x_date(breaks = as.Date(paste0(seq(1960, 2050, 2), "-01-01")),
labels = date_format("%Y")) +
theme(legend.position = c(0.25, 0.85),
legend.title = element_blank()) +
xlab("") + ylab("Indice des prix des logements anciens") +
scale_y_log10(breaks = seq(0, 7000, 50))
2008-
`IPLA-IPLNA-2015` %>%
filter(CORRECTION == "BRUT",
INDICATEUR == "IPLA_A",
REF_AREA %in% c("A_69123", "A_13055", "D75", "FM")) %>%
quarter_to_date %>%
mutate(TITLE_FR = gsub("Indice des prix des logements anciens - ", "", TITLE_FR),
TITLE_FR = gsub(" - Appartements - Base 100 en moyenne annuelle 2015 - Série brute", "", TITLE_FR)) %>%
filter(date >= as.Date("2008-01-01")) %>%
group_by(REF_AREA) %>%
mutate(OBS_VALUE = 100*OBS_VALUE / OBS_VALUE[date == as.Date("2008-01-01")]) %>%
ggplot + geom_line(aes(x = date, y = OBS_VALUE, color = TITLE_FR)) +
theme_minimal() +
scale_x_date(breaks = as.Date(paste0(seq(1960, 2050, 2), "-01-01")),
labels = date_format("%Y")) +
theme(legend.position = c(0.25, 0.85),
legend.title = element_blank()) +
xlab("") + ylab("Indice des prix des logements anciens") +
scale_y_log10(breaks = seq(0, 7000, 10))
2014-
`IPLA-IPLNA-2015` %>%
filter(CORRECTION == "BRUT",
INDICATEUR == "IPLA_A",
REF_AREA %in% c("A_69123", "A_13055", "D75", "FM")) %>%
quarter_to_date %>%
mutate(TITLE_FR = gsub("Indice des prix des logements anciens - ", "", TITLE_FR),
TITLE_FR = gsub(" - Appartements - Base 100 en moyenne annuelle 2015 - Série brute", "", TITLE_FR)) %>%
filter(date >= as.Date("2014-01-01")) %>%
group_by(REF_AREA) %>%
mutate(OBS_VALUE = 100*OBS_VALUE / OBS_VALUE[date == as.Date("2014-01-01")]) %>%
ggplot + geom_line(aes(x = date, y = OBS_VALUE, color = TITLE_FR)) +
theme_minimal() +
scale_x_date(breaks = as.Date(paste0(seq(1960,2100, 1), "-01-01")),
labels = date_format("%Y")) +
theme(legend.position = c(0.25, 0.85),
legend.title = element_blank()) +
xlab("") + ylab("Indice des prix des logements anciens") +
scale_y_log10(breaks = seq(0, 7000, 10))
Paris, Lyon, Marseille
All
`IPLA-IPLNA-2015` %>%
filter(CORRECTION == "BRUT",
INDICATEUR == "IPLA_A",
REF_AREA %in% c("A_69123", "A_13055", "D75")) %>%
quarter_to_date %>%
mutate(TITLE_FR = gsub("Indice des prix des logements anciens - ", "", TITLE_FR),
TITLE_FR = gsub(" - Appartements - Base 100 en moyenne annuelle 2015 - Série brute", "", TITLE_FR)) %>%
group_by(REF_AREA) %>%
mutate(OBS_VALUE = 100*OBS_VALUE / OBS_VALUE[date == as.Date("1998-01-01")]) %>%
ggplot + geom_line(aes(x = date, y = OBS_VALUE, color = TITLE_FR)) +
theme_minimal() +
scale_x_date(breaks = as.Date(paste0(seq(1960, 2050, 2), "-01-01")),
labels = date_format("%Y")) +
theme(legend.position = c(0.25, 0.85),
legend.title = element_blank()) +
xlab("") + ylab("Indice des prix des logements anciens") +
scale_y_log10(breaks = seq(0, 7000, 50))
1998-
`IPLA-IPLNA-2015` %>%
filter(CORRECTION == "BRUT",
INDICATEUR == "IPLA_A",
REF_AREA %in% c("A_69123", "A_13055", "D75")) %>%
quarter_to_date %>%
mutate(TITLE_FR = gsub("Indice des prix des logements anciens - ", "", TITLE_FR),
TITLE_FR = gsub(" - Appartements - Base 100 en moyenne annuelle 2015 - Série brute", "", TITLE_FR)) %>%
filter(date >= as.Date("1998-01-01")) %>%
group_by(REF_AREA) %>%
mutate(OBS_VALUE = 100*OBS_VALUE / OBS_VALUE[date == as.Date("1998-01-01")]) %>%
ggplot + geom_line(aes(x = date, y = OBS_VALUE, color = TITLE_FR)) +
theme_minimal() +
scale_x_date(breaks = as.Date(paste0(seq(1960, 2050, 2), "-01-01")),
labels = date_format("%Y")) +
theme(legend.position = c(0.25, 0.85),
legend.title = element_blank()) +
xlab("") + ylab("Indice des prix des logements anciens") +
scale_y_log10(breaks = seq(0, 7000, 50))
2000-
`IPLA-IPLNA-2015` %>%
filter(CORRECTION == "BRUT",
INDICATEUR == "IPLA_A",
REF_AREA %in% c("A_69123", "A_13055", "D75")) %>%
quarter_to_date %>%
mutate(TITLE_FR = gsub("Indice des prix des logements anciens - ", "", TITLE_FR),
TITLE_FR = gsub(" - Appartements - Base 100 en moyenne annuelle 2015 - Série brute", "", TITLE_FR)) %>%
filter(date >= as.Date("2000-01-01")) %>%
group_by(REF_AREA) %>%
mutate(OBS_VALUE = 100*OBS_VALUE / OBS_VALUE[date == as.Date("2000-01-01")]) %>%
ggplot + geom_line(aes(x = date, y = OBS_VALUE, color = TITLE_FR)) +
theme_minimal() +
scale_x_date(breaks = as.Date(paste0(seq(1960, 2050, 2), "-01-01")),
labels = date_format("%Y")) +
theme(legend.position = c(0.25, 0.85),
legend.title = element_blank()) +
xlab("") + ylab("Indice des prix des logements anciens") +
scale_y_log10(breaks = seq(0, 7000, 50))
2008-
`IPLA-IPLNA-2015` %>%
filter(CORRECTION == "BRUT",
INDICATEUR == "IPLA_A",
REF_AREA %in% c("A_69123", "A_13055", "D75")) %>%
quarter_to_date %>%
mutate(TITLE_FR = gsub("Indice des prix des logements anciens - ", "", TITLE_FR),
TITLE_FR = gsub(" - Appartements - Base 100 en moyenne annuelle 2015 - Série brute", "", TITLE_FR)) %>%
filter(date >= as.Date("2008-01-01")) %>%
group_by(REF_AREA) %>%
mutate(OBS_VALUE = 100*OBS_VALUE / OBS_VALUE[date == as.Date("2008-01-01")]) %>%
ggplot + geom_line(aes(x = date, y = OBS_VALUE, color = TITLE_FR)) +
theme_minimal() +
scale_x_date(breaks = as.Date(paste0(seq(1960,2100, 1), "-01-01")),
labels = date_format("%Y")) +
theme(legend.position = c(0.25, 0.85),
legend.title = element_blank()) +
xlab("") + ylab("Indice des prix des logements anciens") +
scale_y_log10(breaks = seq(0, 7000, 10))
2010-
`IPLA-IPLNA-2015` %>%
filter(CORRECTION == "BRUT",
INDICATEUR == "IPLA_A",
REF_AREA %in% c("A_69123", "A_13055", "D75")) %>%
quarter_to_date %>%
mutate(TITLE_FR = gsub("Indice des prix des logements anciens - ", "", TITLE_FR),
TITLE_FR = gsub(" - Appartements - Base 100 en moyenne annuelle 2015 - Série brute", "", TITLE_FR)) %>%
filter(date >= as.Date("2010-01-01")) %>%
group_by(REF_AREA) %>%
mutate(OBS_VALUE = 100*OBS_VALUE / OBS_VALUE[date == as.Date("2010-01-01")]) %>%
ggplot + geom_line(aes(x = date, y = OBS_VALUE, color = TITLE_FR)) +
theme_minimal() +
scale_x_date(breaks = as.Date(paste0(seq(1960,2100, 1), "-01-01")),
labels = date_format("%Y")) +
theme(legend.position = c(0.25, 0.85),
legend.title = element_blank()) +
xlab("") + ylab("Indice des prix des logements anciens") +
scale_y_log10(breaks = seq(0, 7000, 10))
2014-
`IPLA-IPLNA-2015` %>%
filter(CORRECTION == "BRUT",
INDICATEUR == "IPLA_A",
REF_AREA %in% c("A_69123", "A_13055", "D75")) %>%
quarter_to_date %>%
mutate(TITLE_FR = gsub("Indice des prix des logements anciens - ", "", TITLE_FR),
TITLE_FR = gsub(" - Appartements - Base 100 en moyenne annuelle 2015 - Série brute", "", TITLE_FR)) %>%
filter(date >= as.Date("2014-01-01")) %>%
group_by(REF_AREA) %>%
mutate(OBS_VALUE = 100*OBS_VALUE / OBS_VALUE[date == as.Date("2014-01-01")]) %>%
ggplot + geom_line(aes(x = date, y = OBS_VALUE, color = TITLE_FR)) +
theme_minimal() +
scale_x_date(breaks = as.Date(paste0(seq(1960,2100, 1), "-01-01")),
labels = date_format("%Y")) +
theme(legend.position = c(0.25, 0.85),
legend.title = element_blank()) +
xlab("") + ylab("Indice des prix des logements anciens") +
scale_y_log10(breaks = seq(0, 7000, 10))
Neuf vs. ancien
Indice = 2015
`IPLA-IPLNA-2015` %>%
filter(CORRECTION == "BRUT",
INDICATEUR %in% c("IPLN", "IPLNA", "IPLA_E"),
REF_AREA == "FM") %>%
quarter_to_date %>%
left_join(INDICATEUR, by = "INDICATEUR") %>%
group_by(INDICATEUR) %>%
#mutate(OBS_VALUE = 100*OBS_VALUE / OBS_VALUE[date == as.Date("1998-01-01")]) %>%
ggplot + geom_line(aes(x = date, y = OBS_VALUE, color = Indicateur)) +
theme_minimal() +
scale_x_date(breaks = as.Date(paste0(seq(1960, 2050, 2), "-01-01")),
labels = date_format("%Y")) +
theme(legend.position = c(0.25, 0.85),
legend.title = element_blank()) +
xlab("") + ylab("") +
scale_y_log10(breaks = seq(0, 7000, 10))
Indice = 2000
`IPLA-IPLNA-2015` %>%
filter(CORRECTION == "BRUT",
INDICATEUR %in% c("IPLN", "IPLNA", "IPLA_E"),
REF_AREA == "FM") %>%
quarter_to_date %>%
left_join(INDICATEUR, by = "INDICATEUR") %>%
group_by(INDICATEUR) %>%
mutate(OBS_VALUE = 100*OBS_VALUE / OBS_VALUE[date == as.Date("2000-01-01")]) %>%
ggplot + geom_line(aes(x = date, y = OBS_VALUE, color = Indicateur)) +
theme_minimal() +
scale_x_date(breaks = as.Date(paste0(seq(1960, 2050, 2), "-01-01")),
labels = date_format("%Y")) +
theme(legend.position = c(0.3, 0.9),
legend.title = element_blank()) +
xlab("") + ylab("") +
scale_y_log10(breaks = seq(0, 7000, 10))
Indice = 2000, post - 2000
`IPLA-IPLNA-2015` %>%
filter(CORRECTION == "BRUT",
INDICATEUR %in% c("IPLN", "IPLNA", "IPLA_E"),
REF_AREA == "FM") %>%
quarter_to_date %>%
left_join(INDICATEUR, by = "INDICATEUR") %>%
group_by(INDICATEUR) %>%
mutate(OBS_VALUE = 100*OBS_VALUE / OBS_VALUE[date == as.Date("2000-01-01")]) %>%
filter(date >= as.Date("2000-01-01")) %>%
ggplot + geom_line(aes(x = date, y = OBS_VALUE, color = Indicateur)) +
theme_minimal() +
scale_x_date(breaks = as.Date(paste0(seq(1960, 2050, 2), "-01-01")),
labels = date_format("%Y")) +
theme(legend.position = c(0.3, 0.9),
legend.title = element_blank()) +
xlab("") + ylab("") +
scale_y_log10(breaks = seq(0, 7000, 10))