Immobilier en France
Data - BDF
François Geerolf
Info
| source | dataset | .html | .RData |
|---|---|---|---|
| bdf | immobilier | 2024-05-08 | 2024-05-07 |
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 | 2024-05-10 | 2024-05-10 |
| bdf | FM | 2024-05-10 | 2024-05-08 |
| bdf | immobilier | 2024-05-08 | 2024-05-07 |
| bdf | MIR | 2024-05-08 | 2024-05-10 |
| bdf | MIR1 | 2024-05-08 | 2024-05-10 |
| bdf | RPP | 2024-05-08 | 2024-05-10 |
| 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 | 2024-05-09 | 2024-05-09 |
| insee | IRL | 2024-05-09 | 2024-05-09 |
| insee | PARC-LOGEMENTS | 2024-05-09 | 2023-12-03 |
| insee | SERIES_LOYERS | 2024-05-09 | 2024-05-09 |
| insee | t_dpe_val | 2024-05-09 | 2024-03-04 |
| notaires | arrdt | 2024-04-08 | 2024-04-08 |
| notaires | dep | 2024-04-08 | 2024-04-08 |
Data on housing
| source | dataset | .html | .RData |
|---|---|---|---|
| bdf | RPP | 2024-05-08 | 2024-05-10 |
| 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 | 2024-05-09 | 2024-05-09 |
| eurostat | hbs_str_t223 | 2024-05-09 | 2024-05-09 |
| eurostat | prc_hicp_midx | 2024-05-09 | 2024-05-09 |
| eurostat | prc_hpi_q | 2024-05-09 | 2024-05-09 |
| fred | housing | 2024-04-26 | 2024-04-26 |
| insee | IPLA-IPLNA-2015 | 2024-05-09 | 2024-05-09 |
| oecd | housing | 2024-04-16 | 2020-01-18 |
| oecd | SNA_TABLE5 | 2024-04-16 | 2023-10-19 |
LAST_COMPILE
| LAST_COMPILE |
|---|
| 2024-05-10 |
Last
immobilier %>%
group_by(date) %>%
summarise(Nobs = n()) %>%
arrange(desc(date)) %>%
head(2) %>%
print_table_conditional()| date | Nobs |
|---|---|
| 2024-05-05 | 1 |
| 2024-05-04 | 1 |
variable
immobilier %>%
left_join(variable, by = "variable") %>%
group_by(variable, Variable) %>%
summarise(Nobs = n(),
max_date = max(date),
min_date = min(date)) %>%
arrange(desc(max_date)) %>%
print_table_conditional()| variable | Variable | Nobs | max_date | min_date |
|---|---|---|---|---|
| FM.D.U2.EUR.4F.KR.DFR.LEV | BCE - Facilité de dépôt (données brutes) | 9258 | 2024-05-05 | 1998-12-31 |
| BSI1.M.FR.N.R.A220Z.A.1.U6.2254FR.Z01.E | Crédits à l’habitat accordés aux particuliers résidents, encours | 372 | 2024-02-29 | 1993-03-31 |
| BSI1.M.FR.N.R.A26.A.1.U6.2254FR.Z01.E | Crédits accordés aux particuliers résidents, encours | 372 | 2024-02-29 | 1993-03-31 |
| BSI1.M.FR.Y.R.A220Z.A.4.U6.2254FR.Z01.E | Crédits à l’habitat accordés aux particuliers résidents, flux mensuels, CVS | 371 | 2024-02-29 | 1993-04-30 |
| BSI1.M.FR.Y.R.A220Z.A.4.U6.2254FR.Z01.V3F | Crédits à l’habitat accordés aux particuliers résidents, variation d’encours (moyenne sur 3 mois glissants), CVS | 369 | 2024-02-29 | 1993-06-30 |
| MIR1.M.FR.B.A22.A.5.A.2254U6.EUR.N | Crédits nouveaux à l’habitat des particuliers, flux CVS | 255 | 2024-02-29 | 2002-12-31 |
| MIR1.M.FR.B.A22.A.R.A.2254U6.EUR.N | Crédits nouveaux à l’habitat des particuliers, taux d’intérêt annuel | 304 | 2024-02-29 | 1998-11-30 |
| MIR1.M.FR.B.A22HR.A.5.A.2254U6.EUR.N | Crédits nouveaux à l’habitat (hors renégociations) aux particuliers résidents, flux, CVS | 162 | 2024-02-29 | 2010-09-30 |
| FM.M.U2.EUR.4F.KR.DF.LEV | BCE - Facilité de dépôt - niveau fin de mois yc mois en cours | 290 | 2023-01-31 | 1998-12-31 |
| FM.M.U2.EUR.4F.KR.MLF.LEV | BCE - Facilité de prêt marginal - niveau fin de mois yc mois en cours | 290 | 2023-01-31 | 1998-12-31 |
| FM.M.U2.EUR.4F.KR.MRR_FR.LEV | BCE - Principales opérations de refinancement (taux fixe) | 290 | 2023-01-31 | 1998-12-31 |
Comparer taux crédits immobilier et taux banque centrale
Tous
1 année
immobilier %>%
filter(variable == "MIR1.M.FR.B.A22.A.R.A.2254U6.EUR.N" |
variable == "FM.D.U2.EUR.4F.KR.DFR.LEV") %>%
left_join(variable, by = "variable") %>%
ggplot + geom_line(aes(x = date, y = value/100, color = Variable)) +
theme_minimal() + xlab("") + ylab("Taux d'intérêt (%)") +
theme(legend.position = c(0.32, 0.1),
legend.title = element_blank()) +
scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 2), "-01-01")),
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-10, 100, 1),
labels = percent_format(accuracy = 1))
2 ans
Tous
immobilier %>%
filter(variable == "MIR1.M.FR.B.A22.A.R.A.2254U6.EUR.N" |
variable == "FM.D.U2.EUR.4F.KR.DFR.LEV") %>%
left_join(variable, by = "variable") %>%
ggplot + geom_line(aes(x = date, y = value/100, color = Variable)) +
theme_minimal() + xlab("") + ylab("Taux d'intérêt (%)") +
theme(legend.position = c(0.32, 0.1),
legend.title = element_blank()) +
scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 2), "-01-01")),
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-10, 100, 0.5),
labels = percent_format(accuracy = .1))
2020-
immobilier %>%
filter(variable == "MIR1.M.FR.B.A22.A.R.A.2254U6.EUR.N" |
variable == "FM.D.U2.EUR.4F.KR.DFR.LEV") %>%
left_join(variable, by = "variable") %>%
filter(date >= as.Date("2020-01-01")) %>%
ggplot + geom_line(aes(x = date, y = value/100, color = Variable)) +
theme_minimal() + xlab("") + ylab("Taux d'intérêt (%)") +
theme(legend.position = c(0.35, 0.9),
legend.title = element_blank(),
axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
scale_x_date(breaks = "3 months",
labels = date_format("%b %Y")) +
scale_y_continuous(breaks = 0.01*seq(-10, 100, .25),
labels = percent_format(accuracy = .01))
2021-
immobilier %>%
filter(variable == "MIR1.M.FR.B.A22.A.R.A.2254U6.EUR.N" |
variable == "FM.D.U2.EUR.4F.KR.DFR.LEV") %>%
left_join(variable, by = "variable") %>%
filter(date >= as.Date("2021-01-01")) %>%
ggplot + geom_line(aes(x = date, y = value/100, color = Variable)) +
#scale_color_manual(values = viridis(3)[1:2]) +
theme_minimal() + xlab("") + ylab("Taux d'intérêt (%)") +
theme(legend.position = c(0.35, 0.9),
legend.title = element_blank(),
axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
scale_x_date(breaks = "2 months",
labels = date_format("%b %Y")) +
scale_y_continuous(breaks = 0.01*seq(-10, 100, .25),
labels = percent_format(accuracy = .01))
2022-
immobilier %>%
filter(variable == "MIR1.M.FR.B.A22.A.R.A.2254U6.EUR.N" |
variable == "FM.D.U2.EUR.4F.KR.DFR.LEV") %>%
left_join(variable, by = "variable") %>%
filter(date >= as.Date("2022-01-01")) %>%
ggplot + geom_line(aes(x = date, y = value/100, color = Variable)) +
#scale_color_manual(values = viridis(3)[1:2]) +
theme_minimal() + xlab("") + ylab("Taux d'intérêt (%)") +
theme(legend.position = c(0.35, 0.9),
legend.title = element_blank(),
axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
scale_x_date(breaks = "1 month",
labels = date_format("%b %Y")) +
scale_y_continuous(breaks = 0.01*seq(-10, 100, .25),
labels = percent_format(accuracy = .01))
Tous les taux
immobilier %>%
filter(variable == "MIR1.M.FR.B.A22.A.R.A.2254U6.EUR.N" |
variable == "FM.M.U2.EUR.4F.KR.MRR_FR.LEV" |
variable == "FM.M.U2.EUR.4F.KR.MLF.LEV" |
variable == "FM.D.U2.EUR.4F.KR.DFR.LEV") %>%
left_join(variable, by = "variable") %>%
ggplot + geom_line(aes(x = date, y = value/100, color = Variable, linetype = Variable)) +
scale_color_manual(values = c(viridis(3)[1], viridis(3)[1], viridis(3)[1], viridis(3)[2])) +
scale_linetype_manual(values = c("dashed", "longdash", "solid", "solid")) +
theme_minimal() + xlab("") + ylab("Taux d'intérêt (%)") +
theme(legend.position = c(0.32, 0.13),
legend.title = element_blank(),
legend.spacing.x = unit(0.2, 'cm'),
legend.spacing.y = unit(0.2, 'cm')) +
scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 2), "-01-01")),
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-10, 100, 1),
labels = percent_format(accuracy = 1),
limits = c(-0.02, 0.065))
Crédits à l’habitat
Encours
Linear
plot_linear <- immobilier %>%
filter(variable == "BSI1.M.FR.N.R.A26.A.1.U6.2254FR.Z01.E" |
variable == "BSI1.M.FR.N.R.A220Z.A.1.U6.2254FR.Z01.E") %>%
left_join(variable, by = "variable") %>%
ggplot + theme_minimal() + xlab("") + ylab("") +
geom_line(aes(x = date, y = value / 1000, color = Variable)) +
scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 5), "-01-01")),
labels = date_format("%Y")) +
theme(legend.position = c(0.4, 0.9),
legend.title = element_blank(),
legend.direction = "vertical") +
scale_y_continuous(breaks = seq(-10000, 10000, 100),
labels = dollar_format(suffix = " Mds€", prefix = "", accuracy = 1))
plot_linear
Montants mensuels
All
immobilier %>%
filter(variable == "BSI1.M.FR.Y.R.A220Z.A.4.U6.2254FR.Z01.E") %>%
na.omit %>%
ggplot + geom_line(aes(x = date, y = value)) +
theme_minimal() + xlab("") + ylab("") +
scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 2), "-01-01")),
labels = date_format("%Y")) +
scale_y_continuous(breaks = seq(-10000, 10000, 1000))
2000-
immobilier %>%
filter(variable == "BSI1.M.FR.Y.R.A220Z.A.4.U6.2254FR.Z01.E",
date >= as.Date("1999-12-31")) %>%
na.omit %>%
ggplot + geom_line(aes(x = date, y = value)) +
theme_minimal() + xlab("") + ylab("") +
scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 2), "-01-01")),
labels = date_format("%Y")) +
scale_y_continuous(breaks = seq(-10000, 10000, 1000))
3 mois
All
immobilier %>%
filter(variable == "BSI1.M.FR.Y.R.A220Z.A.4.U6.2254FR.Z01.V3F") %>%
na.omit %>%
ggplot + geom_line(aes(x = date, y = value)) +
theme_minimal() + xlab("") + ylab("Crédits à l'habitat accordés aux particuliers résidents\n variation d'encours (moyenne sur 3 mois glissants)") +
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 1), "-01-01")),
labels = date_format("%Y")) +
scale_y_continuous(breaks = seq(-10000, 10000, 1000),
labels = dollar_format(acc = 1, pre = ""))
2000-
immobilier %>%
filter(variable == "BSI1.M.FR.Y.R.A220Z.A.4.U6.2254FR.Z01.V3F") %>%
na.omit %>%
ggplot + geom_line(aes(x = date, y = value/1000)) +
theme_minimal() + xlab("") + ylab("Crédits à l'habitat accordés aux particuliers résidents\n variation d'encours (moyenne sur 3 mois glissants)") +
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 1), "-01-01")),
labels = date_format("%Y")) +
scale_y_continuous(breaks = seq(-10000, 10000, 1000)/1000,
labels = dollar_format(acc = 1, pre = "", su = " Mds€"))
Last year
immobilier %>%
filter(date >= Sys.Date() - months(18)) %>%
filter(variable == "BSI1.M.FR.Y.R.A220Z.A.4.U6.2254FR.Z01.V3F") %>%
na.omit %>%
ggplot + geom_line(aes(x = date, y = value)) +
theme_minimal() + xlab("") + ylab("") +
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
scale_x_date(breaks = "1 month",
labels = date_format("%b %Y")) +
scale_y_continuous(breaks = seq(-10000, 10000, 1000),
labels = dollar_format(acc = 1, pre = ""))
1 mois, 3 mois
immobilier %>%
filter(variable == "BSI1.M.FR.Y.R.A220Z.A.4.U6.2254FR.Z01.V3F" |
variable == "BSI1.M.FR.Y.R.A220Z.A.4.U6.2254FR.Z01.E") %>%
left_join(variable, by = "variable") %>%
ggplot + geom_line(aes(x = date, y = value, color = Variable)) +
theme_minimal() + xlab("") + ylab("") +
theme(legend.position = c(0.5, 0.9),
legend.title = element_blank()) +
scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 2), "-01-01")),
labels = date_format("%Y")) +
scale_y_continuous(breaks = seq(-10000, 10000, 1000))
Crédits nouveaux à l’habitat (hors renégociation)
All
immobilier %>%
filter(variable == "MIR1.M.FR.B.A22HR.A.5.A.2254U6.EUR.N" |
variable == "MIR1.M.FR.B.A22.A.5.A.2254U6.EUR.N") %>%
left_join(variable, by = "variable") %>%
ggplot + geom_line(aes(x = date, y = value, color = Variable)) +
theme_minimal() + xlab("") + ylab("") +
theme(legend.position = c(0.4, 0.9),
legend.title = element_blank()) +
scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 2), "-01-01")),
labels = date_format("%Y")) +
scale_y_continuous(breaks = seq(-100, 100, 5))
Complete series
immobilier %>%
filter(variable == "MIR1.M.FR.B.A22HR.A.5.A.2254U6.EUR.N" |
variable == "MIR1.M.FR.B.A22.A.5.A.2254U6.EUR.N") %>%
left_join(variable, by = "variable") %>%
group_by(date) %>%
filter(n() == 2) %>%
ggplot + geom_line(aes(x = date, y = value, color = Variable)) +
theme_minimal() + xlab("") + ylab("") +
theme(legend.position = c(0.4, 0.9),
legend.title = element_blank()) +
scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 1), "-01-01")),
labels = date_format("%Y")) +
scale_y_continuous(breaks = seq(-100, 100, 5))