~/data/bdf/

Info

source	dataset	.html	.RData
bdf	BSI1	2024-05-08	2024-05-08

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	2024-05-10	2024-05-07
bdf	MIR	2024-05-10	2024-05-10
bdf	MIR1	2024-05-10	2024-05-10
bdf	RPP	2024-05-10	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	2024-05-10	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

Derniers

Dernier. html
Crédits aux particuliers, Juin 2023. pdf
Liste Données BDF Crédit aux particuliers. html
Crédits aux particuliers, Avril 2021. html / pdf
Crédits aux particuliers, Octobre 2021. html / pdf

LAST_COMPILE

LAST_COMPILE
2024-05-10

Last

date	Nobs
2024-02-29	1214

Data Structure

BSI1_metadata %>%
  select(key, name) %>%
  unique %>%
  print_table_conditional()

key	name
FREQ	Périodicité
REF_AREA	Zone géographique - ISO2
ADJUSTMENT	Correction statistique
BS_REP_SECTOR	Secteur de référence
BS_ITEM	Poste de bilan
MATURITY_ORIG	Maturité
DATA_TYPE	Type de données
COUNT_AREA	Zone géo. de contrepartie
BS_COUNT_SECTOR	Secteur contrepartie
CURRENCY_TRANS	Devise de transaction
BS_SUFFIX	Suffixe

FREQ Frequency

BSI1 %>%
  left_join(BSI1_var, by = "variable") %>%
  left_join(FREQ, by = "FREQ") %>%
  group_by(FREQ, Freq) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()

FREQ	Freq	Nobs
M	Mensuel	532164
Q	Trimestriel	152609

REF_AREA Reference area - ISO2

BSI1 %>%
  left_join(BSI1_var, by = "variable") %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  group_by(REF_AREA, Ref_area) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()

REF_AREA	Ref_area	Nobs
FR	France	480540
U2	Zone Euro (composition évolutive)	146007
ES	Espagne	4274
IT	Italie	4274
DE	Allemagne	4260
BE	Belgique	4092
AT	Autriche	4091
PT	Portugal	4081
IE	Irlande	4044
LU	Luxembourg	4044
NL	Pays-Bas	4044
FI	Finlande	4000
GR	Grèce	3994
SI	Slovénie	2816
MT	Malte	2712
CY	Chypre	2628
SK	Slovaquie	2595
EE	Estonie	2277

ADJUSTMENT Adjustment

BSI1 %>%
  left_join(BSI1_var, by = "variable") %>%
  left_join(ADJUSTMENT, by = "ADJUSTMENT") %>%
  group_by(ADJUSTMENT, Adjustment) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) print_table(.) else .}

ADJUSTMENT	Adjustment	Nobs
N	Brut	550271
Y	CVS/CJO	133192
S	CVS	1310

BS_REP_SECTOR Reference sector breakdown

BSI1 %>%
  left_join(BSI1_var, by = "variable") %>%
  left_join(BS_REP_SECTOR, by = "BS_REP_SECTOR") %>%
  group_by(BS_REP_SECTOR, Bs_rep_sector) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()

BS_REP_SECTOR	Bs_rep_sector	Nobs
A	IFM ( hors Eurosystème)	234866
V	IFM, administrations centrales et banques postales	208627
R	Etablissements de crédit	88507
U	Institutions monétaires et financières (IFM)	38068
N	Banque Centrale Nationale	28836
C	Eurosystème	22042
7	Agrégation IF métropole	20363
G	Administrations centrales et la poste	11659
2	Banques	7992
F	OPCVM monétaires	5487
1	Banques FBF	5262
5	Banques mutualistes	5262
4	CDC	5183
3	FCC	1238
6	Agrégation IFM	608
8	IFM divers non ventilé	608
AU	Autres	55
CB	Canal bancaire	55
CC	Spécialisés crédit consommation	55

BS_ITEM Balance sheet item

BSI1 %>%
  left_join(BSI1_var, by = "variable") %>%
  left_join(BS_ITEM, by = "BS_ITEM") %>%
  group_by(BS_ITEM, Bs_item) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()

MATURITY_ORIG Maturity origin

BSI1 %>%
  left_join(BSI1_var, by = "variable") %>%
  left_join(MATURITY_ORIG, by = "MATURITY_ORIG") %>%
  group_by(MATURITY_ORIG, Maturity_orig) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()

MATURITY_ORIG	Maturity_orig	Nobs
A	Total	539307
L	Jusqu`à 2 ans \| 41280 \| \| X \| Non applicable \| 34019 \| \| H \| Plus de 2 ans \| 20453 \| \| D \| Jusqu`à 3 mois	13027
M	Maturité jusqu`à 2 ans et remboursables avec préavis de moins de 3 mois \| 11999 \| \| F \| Jusqu`à 1 an	10916
G	Supérieur à 1 an et inférieur à 2 ans	5893
K	Plus de 1 an	2744
E	Supérieur à 3 mois	1888
J	Supérieur à 5 ans	1610
I	Plus de 1 et jusqu`à 5 ans	1533
C	Long terme	104

DATA_TYPE Data type

BSI1 %>%
  left_join(BSI1_var, by = "variable") %>%
  left_join(DATA_TYPE, by = "DATA_TYPE") %>%
  group_by(DATA_TYPE, Data_type) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()

DATA_TYPE	Data_type	Nobs
1	Encours	312566
4	Flux	191240
I	Indices notionnels des stocks	167346
S	Flux cumulés sur 1 an	7237
N	Stocks notionels	2601
Q	Contribution au taux de croissance annuel de M3	2054
U	Provisions	1035
8	Valorisation	562
D	Variation d`encours	132

COUNT_AREA Counterpart area

BSI1 %>%
  left_join(BSI1_var, by = "variable") %>%
  left_join(COUNT_AREA, by = "COUNT_AREA") %>%
  group_by(COUNT_AREA, Count_area) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()

BS_COUNT_SECTOR Counterpart sector

BSI1 %>%
  left_join(BSI1_var, by = "variable") %>%
  left_join(BS_COUNT_SECTOR, by = "BS_COUNT_SECTOR") %>%
  group_by(BS_COUNT_SECTOR, Bs_count_sector) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()

CURRENCY_TRANS Currency of transaction

BSI1 %>%
  left_join(BSI1_var, by = "variable") %>%
  left_join(CURRENCY_TRANS, by = "CURRENCY_TRANS") %>%
  group_by(CURRENCY_TRANS, Currency_trans) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()

CURRENCY_TRANS	Currency_trans	Nobs
Z01	Toutes monnaies confondues	674615
EUR	Euro	2251
CHF	Franc Suisse	1240
JPY	Yen	1240
USD	Dollar des Etats-Unis	1240
Z04	Autres devises (hors UE)	1240
Z05	Devises hors UE et hors USD, CHF et JPY	1240
XAU	Or Monetaire	1106
Z03	Autres devises de l`UE (hors euro)	601

Patrimoine

ig_b("insee", "ip1967", "table2")

Livrets réglementés

Panorama

L’épargne réglementée en 2022. pdf

ig_b("bdf", "er-2022_rapport_web", "G1")

LEP: Livret Epargne Populaire

BSI1 %>%
  filter(grepl("BSI1.M.FR.N.A.L23FRLP.A.1.U6.2251.Z01.E", variable)) %>%
  left_join(BSI1_var, by = "variable") %>%
  ggplot + theme_minimal() + xlab("") + ylab("Livret d'Epargne Populaire") +
  geom_line(aes(x = date, y = value / 1000)) +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 2), "-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(0, 100, 5),
                labels = dollar_format(suffix = " Mds€", prefix = "",  accuracy = 1))

Livrets A et Bleus, PEL

BSI1 %>%
  filter(variable %in% c("BSI1.M.FR.N.A.L23FRLA.A.1.U6.2250.Z01.E",
                         "BSI1.M.FR.N.A.L23FRAB.A.1.U6.2250.Z01.E",
                         "BSI1.M.FR.N.A.L22FRPL.A.1.U6.2251.Z01.E")) %>%
  left_join(BSI1_var, by = "variable") %>%
  ggplot + theme_minimal() + xlab("") + ylab("Livret A , Livret Bleu") +
  geom_line(aes(x = date, y = value / 1000, color = Variable)) +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 2), "-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(0, 1000, 20),
                labels = dollar_format(suffix = " Mds€", prefix = "",  accuracy = 1))

Livret A et PEL

Tous

BSI1 %>%
  filter(variable %in% c("BSI1.M.FR.N.A.L23FRAB.A.1.U6.2250.Z01.E",
                         "BSI1.M.FR.N.A.L22FRPL.A.1.U6.2251.Z01.E")) %>%
  left_join(BSI1_var, by = "variable") %>%
  ggplot + theme_minimal() + xlab("") + ylab("Livret A , Livret Bleu") +
  geom_line(aes(x = date, y = value / 1000, color = Variable)) +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 2), "-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(0, 1000, 20),
                labels = dollar_format(suffix = " Mds€", prefix = "",  accuracy = 1))

2012-

BSI1 %>%
  filter(variable %in% c("BSI1.M.FR.N.A.L23FRAB.A.1.U6.2250.Z01.E",
                         "BSI1.M.FR.N.A.L22FRPL.A.1.U6.2251.Z01.E")) %>%
  left_join(BSI1_var, by = "variable") %>%
  filter(date >= as.Date("2011-12-31")) %>%
  ggplot + theme_minimal() + xlab("") + ylab("Livret A , Livret Bleu") +
  geom_line(aes(x = date, y = value / 1000, color = Variable)) +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 1), "-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(0, 1000, 20),
                labels = dollar_format(suffix = " Mds€", prefix = "",  accuracy = 1))

PEL, tous

BSI1 %>%
  left_join(BSI1_var, by = "variable") %>%
  filter(BS_ITEM %in% c("L22FRPL")) %>%
  ggplot + theme_minimal() + xlab("") + ylab("Livret A , Livret Bleu") +
  geom_line(aes(x = date, y = value / 1000, color = Variable)) +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 2), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.5, 0.6),
        legend.title = element_blank(),
        legend.direction = "vertical") +
  scale_y_continuous(breaks = seq(0, 1000, 20),
                labels = dollar_format(suffix = " Mds€", prefix = "",  accuracy = 1))

Dépôts à vue (L21)

BSI1 %>%
  left_join(BSI1_var, by = "variable") %>%
  filter(BS_ITEM %in% c("L21"),
         variable == "BSI1.M.FR.N.A.L21.A.1.U6.2300.Z01.E") %>%
  ggplot + theme_minimal() + xlab("") + ylab("Dépôts à vue") +
  geom_line(aes(x = date, y = value / 1000, color = Variable)) +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 2), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.5, 0.6),
        legend.title = element_blank(),
        legend.direction = "vertical") +
  scale_y_log10(breaks = seq(100, 3000, 100),
                labels = dollar_format(suffix = " Mds€", prefix = "",  accuracy = 1))

Dépôts (L20)

Tous

BSI1 %>%
  left_join(BSI1_var, by = "variable") %>%
  filter(variable %in% c("BSI1.M.FR.N.A.L20.A.1.U6.2250.Z01.E",
                         "BSI1.M.FR.N.A.L20.A.1.U6.2254FR.Z01.E",
                         "BSI1.M.FR.N.A.L21.A.1.U6.2300.Z01.E")) %>%
  ggplot + theme_minimal() + xlab("") + ylab("Dépôts à vue") +
  geom_line(aes(x = date, y = value / 1000, color = Variable)) +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 2), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.5, 0.6),
        legend.title = element_blank(),
        legend.direction = "vertical") +
  scale_y_log10(breaks = seq(100, 3000, 100),
                labels = dollar_format(suffix = " Mds€", prefix = "",  accuracy = 1))

2012-

BSI1 %>%
  left_join(BSI1_var, by = "variable") %>%
  filter(variable %in% c("BSI1.M.FR.N.A.L20.A.1.U6.2250.Z01.E",
                         "BSI1.M.FR.N.A.L20.A.1.U6.2254FR.Z01.E",
                         "BSI1.M.FR.N.A.L21.A.1.U6.2300.Z01.E"),
         date >= as.Date("2011-12-31")) %>%
  ggplot + theme_minimal() + xlab("") + ylab("Dépôts, Dépôts à vue") +
  geom_line(aes(x = date, y = value / 1000, color = Variable)) +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 2), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.4, 0.9),
        legend.title = element_blank(),
        legend.direction = "vertical") +
  scale_y_log10(breaks = seq(100, 3000, 100),
                labels = dollar_format(suffix = " Mds€", prefix = "",  accuracy = 1))

Encours crédit entreprises

2240: crédits entrerpises

Linear

data_ombeline <- BSI1 %>%
  group_by(variable) %>%
  summarise(max_date = max(date),
            min_date = min(date)) %>%
  left_join(variable, by = "variable") %>%
  filter(BS_COUNT_SECTOR == "2240") %>%
  arrange(desc(max_date))

save(data_ombeline, file = "~/data_ombeline.RData")

Encours crédit à l’habitat

Linear

BSI1 %>%
  filter(grepl("BSI1.M.FR.N.R.A220Z.A.1.U6.2250.Z01.E", variable) |
           grepl("BSI1.M.FR.N.R.A220Z.A.1.U6.2254FR.Z01.E", variable) |
           grepl("BSI1.M.FR.N.2.A22.A.1.U6.2251.Z01.E", variable)) %>%
  left_join(BSI1_var, 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, 2), "-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))

Log

BSI1 %>%
  filter(grepl("BSI1.M.FR.N.R.A220Z.A.1.U6.2250.Z01.E", variable) |
           grepl("BSI1.M.FR.N.R.A220Z.A.1.U6.2254FR.Z01.E", variable) |
           grepl("BSI1.M.FR.N.2.A22.A.1.U6.2251.Z01.E", variable)) %>%
  left_join(BSI1_var, 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, 2), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.4, 0.9),
        legend.title = element_blank(),
        legend.direction = "vertical") +
  scale_y_log10(breaks = seq(-10000, 10000, 100),
                labels = dollar_format(suffix = " Mds€", prefix = "",  accuracy = 1))

Encours crédit à la consommation

Linear

BSI1 %>%
  filter(grepl("BSI1.M.FR.N.R.A210Z.A.1.U6.2254FR.Z01.E", variable) |
           grepl("BSI1.M.FR.N.R.A210Z.A.1.U6.2250.Z01.E", variable)) %>%
  left_join(BSI1_var, 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, 2), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.5, 0.9),
        legend.title = element_blank(),
        legend.direction = "vertical") +
  scale_y_continuous(breaks = seq(-10000, 10000, 10),
                labels = dollar_format(suffix = " Mds€", prefix = "",  accuracy = 1))

Log

BSI1 %>%
  filter(grepl("BSI1.M.FR.N.R.A210Z.A.1.U6.2254FR.Z01.E", variable) |
           grepl("BSI1.M.FR.N.R.A210Z.A.1.U6.2250.Z01.E", variable)) %>%
  left_join(BSI1_var, 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, 2), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.5, 0.9),
        legend.title = element_blank(),
        legend.direction = "vertical") +
  scale_y_log10(breaks = seq(-10000, 10000, 10),
                labels = dollar_format(suffix = " Mds€", prefix = "",  accuracy = 1))

Agrégats monétaires - France

Data - BDF

François Geerolf

Info

Données sur l’immobilier

Data on housing

Derniers

LAST_COMPILE

Last

Data Structure

FREQ Frequency

REF_AREA Reference area - ISO2

ADJUSTMENT Adjustment

BS_REP_SECTOR Reference sector breakdown

BS_ITEM Balance sheet item

MATURITY_ORIG Maturity origin

DATA_TYPE Data type

COUNT_AREA Counterpart area

BS_COUNT_SECTOR Counterpart sector

CURRENCY_TRANS Currency of transaction

Patrimoine

Livrets réglementés

Panorama

LEP: Livret Epargne Populaire

Livrets A et Bleus, PEL

Livret A et PEL

Tous

2012-

PEL, tous

Dépôts à vue (L21)

Dépôts (L20)

Tous

2012-

Encours crédit entreprises

Linear

Encours crédit à l’habitat

Linear

Log

Encours crédit à la consommation

Linear

Log