MFI Interest Rate Statistics

Data - ECB

Author

François Geerolf

Info

source dataset .html .RData

ecb

MIR

 2024-06-19 2024-06-08

Info

Monetary financial institutions in the euro area are legally obliged to report data to their National Central Banks, which in turn report to the ECB.

  • Data Structure Definition (DSD). html / link

Data on interest rates

source dataset .html .RData

bdf

FM

 2024-06-19 2024-06-18

bdf

MIR

 2024-06-19 2024-06-18

bdf

MIR1

 2024-06-19 2024-06-18

bis

CBPOL_D

 2024-06-19 2024-05-10

bis

CBPOL_M

 2024-06-19 2024-04-19

ecb

FM

 2024-06-19 2024-06-15

ecb

MIR

 2024-06-19 2024-06-08

eurostat

ei_mfir_m

 2024-06-19 2024-06-08

eurostat

irt_lt_mcby_d

 2024-06-18 2024-06-08

eurostat

irt_st_m

 2024-06-18 2024-06-18

fred

r

 2024-06-18 2024-06-07

oecd

MEI

 2024-04-16 2024-04-15

oecd

MEI_FIN

 2024-06-19 2024-05-21

wdi

FR.INR.RINR

 2024-01-06 2024-04-14

Data on monetary policy

source dataset .html .RData

bdf

FM

 2024-06-19 2024-06-18

bdf

MIR

 2024-06-19 2024-06-18

bdf

MIR1

 2024-06-19 2024-06-18

bis

CBPOL

 2024-06-19 2024-06-07

ecb

BSI

 2024-06-19 2024-05-21

ecb

BSI_PUB

 2024-06-19 2024-06-15

ecb

FM

 2024-06-19 2024-06-15

ecb

ILM

 2024-06-19 2024-06-15

ecb

ILM_PUB

 2024-06-19 2024-01-25

ecb

liq_daily

 2024-06-19 2024-05-21

ecb

MIR

 2024-06-19 2024-06-08

ecb

RAI

 2024-06-07 2024-06-07

ecb

SUP

 2024-06-17 2024-06-07

ecb

YC

 2024-06-17 2024-05-21

ecb

YC_PUB

 2024-06-17 2024-06-07

eurostat

ei_mfir_m

 2024-06-19 2024-06-08

eurostat

irt_st_m

 2024-06-18 2024-06-18

fred

r

 2024-06-18 2024-06-07

oecd

MEI

 2024-04-16 2024-04-15

oecd

MEI_FIN

 2024-06-19 2024-05-21

LAST_COMPILE

LAST_COMPILE
2024-06-20

Last

Code 
MIR %>%
  group_by(TIME_PERIOD) %>%
  summarise(Nobs = n()) %>%
  arrange(desc(TIME_PERIOD)) %>%
  head(2) %>%
  print_table_conditional()
TIME_PERIOD Nobs
2024-04 7206
2024-03 7202

BS_ITEM

Javascript

Code 
MIR %>%
  left_join(BS_ITEM ,  by = "BS_ITEM") %>%
  group_by(BS_ITEM, Bs_item) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional_10()

Flat

Code 
MIR %>%
  left_join(BS_ITEM ,  by = "BS_ITEM") %>%
  group_by(BS_ITEM, Bs_item) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()
BS_ITEM Bs_item Nobs
A2A Loans other than revolving loans and overdrafts, convenience and extended credit card debt 389048
A2AC Loans other than revolving loans and overdrafts, convenience and extended credit card debt with collateral and/or guarantees 257192
L22 Deposits with agreed maturity 225015
A2D Other lending excluding revolving loans and overdrafts, convenience and extended credit card debt 95711
A20 Loans 87883
A2C Lending for house purchase excluding revolving loans and overdrafts, convenience and extended credit card debt 85727
A2B Loans for consumption excluding revolving loans and overdrafts, convenience and extended credit card debt 69210
A2CC Lending for house purchase excluding revolving loans and overdrafts, convenience and extended credit card debt with collateral and/or guarantees 39769
A22 Lending for house purchase 39458
A25 Credit for consumption and other lending 39166
A2BC Loans for consumption excluding revolving loans and overdrafts, convenience and extended credit card debt with collateral and/or guarantees 31555
L21 Overnight deposits 24832
L23 Deposits redeemable at notice 23142
A2Z Revolving loans and overdrafts, convenience and extended credit card debt 22499
L24 Repurchase agreements 21004
A2Z1 Revolving loans and overdrafts 12800
A2Z3 Extended credit 12192
A2J Loans to households for house purchase and to non-financial corporations (defined for cost of borrowing purposes, sum of A2C (households), A2A and A2Z (both related to non-financial corporations)) 9315
A2I Loans (defined for cost of borrowing purposes, sum of A2A and A2Z (both related to non-financial corporations)) 4660
A2L Loans to households for house purchase and to non-financial corporations 150

MATURITY_NOT_IRATE

Code 
MIR %>%
  left_join(MATURITY_NOT_IRATE, by = "MATURITY_NOT_IRATE") %>%
  group_by(MATURITY_NOT_IRATE, Maturity_not_irate) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()
MATURITY_NOT_IRATE Maturity_not_irate Nobs
A Total 384009
F Up to 1 year 172472
K Over 1 year 135222
I Over 1 and up to 5 years 105090
J Over 5 years 87288
P Over 10 years 70135
O Over 5 and up to 10 years 69495
D Up to 3 months 65581
H Over 2 years 58191
Y Period of initial rate fixation below 1 year and original maturity over 1 year 55227
Q Over 3 months and up to 1 year 54563
S Over 3 and up to 5 years 52158
R Over 1 and up to 3 years 51834
L Up to two years 45446
G Over 1 and up to 2 years 28322
AM Total calculated by weighting the volumes with a moving average (defined for cost of borrowing purposes) 9316
E Over 3 months 7879
KF Original maturity over 1 year and remaining maturity up to 1 year 7278
HL Original maturity over 2 years and remaining maturity up to 2 years 7277
HHL Original maturity over 2 years, remaining maturity over 2 years and interest rate reset within the next 24 months 7115
KKF Original maturity over 1 year, remaining maturity over 1 year and interest rate reset within the next 12 months 7115
KM Over 1 year calculated by weighting the volumes with a moving average (defined for cost of borrowing purposes) 4659
FM Up to 1 year calculated by weighting the volumes with a moving average (defined for cost of borrowing purposes) 4656

DATA_TYPE_MIR

Code 
MIR %>%
  left_join(DATA_TYPE_MIR,  by = "DATA_TYPE_MIR") %>%
  group_by(DATA_TYPE_MIR, Data_type_mir) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()
DATA_TYPE_MIR Data_type_mir Nobs
R Annualised agreed rate (AAR) / Narrowly defined effective rate (NDER) 985030
B Business volume (outstanding amount / new business) 207616
I Interest rate component of euro area month-to-month level change (Bennet binary index on interest rates) 47150
J Weight component of euro area month-to-month level change (Bennet binary index on weights) 47150
M Index on interest rates (Bennet chain index) 47150
N Index on weights (Bennet chain index) 47150
O Accumulated changes on interest rates 47150
V Coefficient of variation 47150
C Annual percentage rate of charge (APRC) 14782

AMOUNT_CAT

Code 
MIR %>%
  left_join(AMOUNT_CAT,  by = "AMOUNT_CAT") %>%
  group_by(AMOUNT_CAT, Amount_cat) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()
AMOUNT_CAT Amount_cat Nobs
A Total 993914
1 Over EUR 1 million 167680
2 Up to and including EUR 0.25 million 136889
3 Over EUR 0.25 million and up to EUR 1 million 135462
0 Up to and including EUR 1 million 56383

BS_COUNT_SECTOR

Code 
MIR %>%
  mutate(BS_COUNT_SECTOR = paste0(BS_COUNT_SECTOR)) %>%
  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()
BS_COUNT_SECTOR Bs_count_sector Nobs
2240 Non-Financial corporations (S.11) 813003
2250 Households and non-profit institutions serving households (S.14 and S.15) 544850
2230 Non-Financial corporations and Households (S.11 and S.14 and S.15) 96739
2253 Households of which sole proprietors and unincorporated partnerships (SP/UP) 35736

CURRENCY_TRANS

Code 
MIR %>%
  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
EUR Euro 1206053
CZK Czech koruna 39009
HUF Hungarian forint 38337
RON Romanian leu 37798
SEK Swedish krona 37381
DKK Danish krone 34086
PLN Polish zloty 29143
HRK Croatian kuna 27066
BGN Bulgarian lev 21487
LVL Latvian lats 7752
LTL Lithuanian litas 4581
EEK Estonian kroon 3281
SKK Slovak koruna 2700
SIT Slovenian tolar 1404
MTL Maltese lira 250

IR_BUS_COV

Code 
MIR %>%
  left_join(IR_BUS_COV, by = "IR_BUS_COV") %>%
  group_by(IR_BUS_COV, Ir_bus_cov) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()
IR_BUS_COV Ir_bus_cov Nobs
N New business 1195590
O Outstanding amount 241113
R Renegotiation 29933
P Pure new loans 23692

COLLECTION

Code 
MIR %>%
  left_join(COLLECTION ,  by = "COLLECTION") %>%
  group_by(COLLECTION, Collection) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()
COLLECTION Collection Nobs
A Average of observations through period 1274617
S Summed through period 207616
E End of period 8095

SOURCE_AGENCY

Code 
MIR %>%
  left_join(SOURCE_AGENCY, by = "SOURCE_AGENCY") %>%
  group_by(SOURCE_AGENCY, Source_agency) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()
SOURCE_AGENCY Source_agency Nobs
4F0 European Central Bank (ECB) 370957
HR2 Croatian National Bank 59393
LV2 Bank of Latvia 49950
SI2 Bank of Slovenia 48781
SK2 National Bank of Slovakia 47966
DE2 Deutsche Bundesbank (Germany) 46004
LT2 Bank of Lithuania 45902
IT2 Banca d` Italia (Italy) 45761
LU2 Banque centrale du Luxembourg 45743
ES2 Banco de Espana (Spain) 45662
AT2 Oesterreichische Nationalbank (Austria) 45604
GR2 Bank of Greece (Greece) 45017
FR2 Banque de France (France) 44796
EE2 Bank of Estonia 44071
MT2 Central Bank of Malta 43408
FI2 Bank of Finland (Finland) 43238
NL2 De Nederlandsche Bank (Netherlands) 41020
BE2 Banque Nationale de Belgique (Belgium) 39859
PT2 Banco de Portugal (Portugal) 39182
CZ2 Czech National Bank 39009
HU2 National Bank of Hungary 38337
RO2 National Bank of Romania 38006
SE2 Sveriges Riksbank (Sweden) 37381
DK2 Danmarks Nationalbank (Denmark) 34086
CY2 Central Bank of Cyprus 30554
IE2 Central Bank of Ireland (Ireland) 29599
PL2 Bank of Poland 29347
BG2 Bulgarian National Bank 21695

REF_AREA

Code 
MIR %>%
  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
U2 Euro area (Member States and Institutions of the Euro Area) changing composition 370957
HR Croatia 59393
LV Latvia 49950
SI Slovenia 48781
SK Slovakia 47966
DE Germany 46004
LT Lithuania 45902
IT Italy 45761
LU Luxembourg 45743
ES Spain 45662
AT Austria 45604
GR Greece 45017
FR France 44796
EE Estonia 44071
MT Malta 43408
FI Finland 43238
NL Netherlands 41020
BE Belgium 39859
PT Portugal 39182
CZ Czech Republic 39009
HU Hungary 38337
RO Romania 38006
SE Sweden 37381
DK Denmark 34086
CY Cyprus 30554
IE Ireland 29599
PL Poland 29347
BG Bulgaria 21695

TIME_PERIOD

Code 
MIR %>%
  group_by(TIME_PERIOD) %>%
  summarise(Nobs = n()) %>%
  arrange(desc(TIME_PERIOD)) %>%
  print_table_conditional()

Structure

BS_ITEM, MATURITY_ORIG, AMOUNT_CAT

Code 
MIR %>%
  left_join(BS_ITEM,  by = "BS_ITEM") %>%
  left_join(MATURITY_NOT_IRATE,  by = "MATURITY_NOT_IRATE") %>%
  left_join(AMOUNT_CAT,  by = "AMOUNT_CAT") %>%
  group_by(BS_ITEM, Bs_item, MATURITY_NOT_IRATE, Maturity_not_irate, AMOUNT_CAT, Amount_cat) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()

BS_ITEM, MATURITY_ORIG

Code 
MIR %>%
  left_join(BS_ITEM,  by = "BS_ITEM") %>%
  left_join(MATURITY_NOT_IRATE,  by = "MATURITY_NOT_IRATE") %>%
  group_by(BS_ITEM, Bs_item, MATURITY_NOT_IRATE, Maturity_not_irate) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()

France

Table - All

Code 
MIR %>%
  filter(REF_AREA == "FR",
         # A2A: Loans oth. than revolving loans and overdrafts, convenience and ext. CC debt
         # A: Total
         TIME_PERIOD == "2021-04") %>%
  month_to_date %>%
  select_if(~n_distinct(.) > 1) %>%
  select(-TITLE_COMPL, -TITLE, -KEY) %>%
  mutate(BS_COUNT_SECTOR = paste0(BS_COUNT_SECTOR)) %>%
  left_join(BS_COUNT_SECTOR, by = "BS_COUNT_SECTOR") %>%
  left_join(AMOUNT_CAT, by = "AMOUNT_CAT") %>%
  left_join(IR_BUS_COV, by = "IR_BUS_COV") %>%
  left_join(MATURITY_NOT_IRATE, by = "MATURITY_NOT_IRATE") %>%
  left_join(BS_ITEM ,  by = "BS_ITEM") %>%
  arrange(OBS_VALUE) %>%
  select(OBS_VALUE, Bs_count_sector, Bs_item, Amount_cat, Ir_bus_cov, Maturity_not_irate, everything()) %>%
  print_table_conditional()

Table - MATURITY_NOT_IRATE == “A”, DATA_TYPE_MIR == “R”

Code 
MIR %>%
  filter(REF_AREA == "FR",
         # A2A: Loans oth. than revolving loans and overdrafts, convenience and ext. CC debt
         # A: Total
         MATURITY_NOT_IRATE == "A",
         # R: Rate
         DATA_TYPE_MIR == "R",
         TIME_PERIOD == "2021-04") %>%
  month_to_date %>%
  select_if(~n_distinct(.) > 1) %>%
  select(-TITLE_COMPL, -TITLE, -KEY) %>%
  mutate(BS_COUNT_SECTOR = paste0(BS_COUNT_SECTOR)) %>%
  left_join(BS_COUNT_SECTOR, by = "BS_COUNT_SECTOR") %>%
  left_join(AMOUNT_CAT, by = "AMOUNT_CAT") %>%
  left_join(IR_BUS_COV, by = "IR_BUS_COV") %>%
  left_join(BS_ITEM ,  by = "BS_ITEM") %>%
  print_table_conditional()

Loans oth. than revolving loans and overdrafts - A2A

Table

Code 
MIR %>%
  filter(REF_AREA == "FR",
         # A2A: Loans oth. than revolving loans and overdrafts, convenience and ext. CC debt
         BS_ITEM == "A2A",
         # A: Total
         MATURITY_NOT_IRATE == "A",
         # R: Rate
         DATA_TYPE_MIR == "R",
         TIME_PERIOD == "2021-04") %>%
  month_to_date %>%
  select_if(~n_distinct(.) > 1) %>%
  select(-TITLE_COMPL, -TITLE, -KEY) %>%
  mutate(BS_COUNT_SECTOR = paste0(BS_COUNT_SECTOR)) %>%
  left_join(BS_COUNT_SECTOR, by = "BS_COUNT_SECTOR") %>%
  left_join(AMOUNT_CAT, by = "AMOUNT_CAT") %>%
  left_join(IR_BUS_COV, by = "IR_BUS_COV") %>%
  print_table_conditional()
AMOUNT_CAT BS_COUNT_SECTOR IR_BUS_COV OBS_VALUE Bs_count_sector Amount_cat Ir_bus_cov
0 2240 N 1.31 Non-Financial corporations (S.11) Up to and including EUR 1 million New business
1 2240 N 1.39 Non-Financial corporations (S.11) Over EUR 1 million New business
2 2240 N 1.44 Non-Financial corporations (S.11) Up to and including EUR 0.25 million New business
3 2240 N 1.14 Non-Financial corporations (S.11) Over EUR 0.25 million and up to EUR 1 million New business
A 2240 N 1.36 Non-Financial corporations (S.11) Total New business
A 2240 P 1.35 Non-Financial corporations (S.11) Total Pure new loans
A 2240 R 1.42 Non-Financial corporations (S.11) Total Renegotiation
A 2250 P 1.63 Households and non-profit institutions serving households (S.14 and S.15) Total Pure new loans
A 2250 R 1.65 Households and non-profit institutions serving households (S.14 and S.15) Total Renegotiation

Graph

All

Code 
MIR %>%
  filter(REF_AREA == "FR",
         # A2A: Loans oth. than revolving loans and overdrafts, convenience and ext. CC debt
         BS_ITEM == "A2A",
         # A: Total
         MATURITY_NOT_IRATE == "A",
         # R: Rate
         DATA_TYPE_MIR == "R") %>%
  left_join(AMOUNT_CAT, by = "AMOUNT_CAT") %>%
  month_to_date %>%
  ggplot(.) + 
  geom_line(aes(x = date, y = OBS_VALUE / 100, color = TITLE)) + 
  theme_minimal() + xlab("") + ylab("France Interest Rates (%)") +
  scale_x_date(breaks = seq(1960, 2030, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 50, 1),
                     labels = percent_format(accuracy = 1)) +
  theme(legend.position = c(0.65, 0.8),
        legend.title = element_blank(),
        legend.text = element_text(size = 8),
        legend.key.size = unit(0.9, 'lines'))

IR_BUS_COV == “N”

Code 
MIR %>%
  filter(REF_AREA == "FR",
         # A2A: Loans oth. than revolving loans and overdrafts, convenience and ext. CC debt
         BS_ITEM == "A2A",
         IR_BUS_COV == "N",
         # A: Total
         MATURITY_NOT_IRATE == "A",
         # R: Rate
         DATA_TYPE_MIR == "R") %>%
  month_to_date %>%
  left_join(AMOUNT_CAT, by = "AMOUNT_CAT") %>%
  ggplot(.) + 
  geom_line(aes(x = date, y = OBS_VALUE / 100, color = TITLE)) + 
  theme_minimal() + xlab("") + ylab("France Interest Rates (%)") +
  scale_x_date(breaks = seq(1960, 2030, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 50, 1),
                     labels = percent_format(accuracy = 1),
        limits = c(0.005, 0.08)) +
  theme(legend.position = c(0.5, 0.9),
        legend.title = element_blank(),
        legend.text = element_text(size = 8),
        legend.key.size = unit(0.9, 'lines'))

A2AC - Loans other than revolving loans and overdrafts, convenience and extended credit card debt with collateral and/or guarantees

A20 - Loans

Table

Code 
MIR %>%
  filter(REF_AREA == "FR",
         # A2A: Loans oth. than revolving loans and overdrafts, convenience and ext. CC debt
         BS_ITEM == "A20",
         # A: Total
         MATURITY_NOT_IRATE == "A",
         # R: Rate
         DATA_TYPE_MIR == "R",
         TIME_PERIOD == "2021-04") %>%
  month_to_date %>%
  select_if(~n_distinct(.) > 1) %>%
  select(-TITLE_COMPL, -TITLE, -KEY) %>%
  mutate(BS_COUNT_SECTOR = paste0(BS_COUNT_SECTOR)) %>%
  left_join(BS_COUNT_SECTOR, by = "BS_COUNT_SECTOR") %>%
  print_table_conditional()
BS_COUNT_SECTOR OBS_VALUE Bs_count_sector
2240 1.37 Non-Financial corporations (S.11)
2250 1.89 Households and non-profit institutions serving households (S.14 and S.15)

Graph

Code 
MIR %>%
  filter(REF_AREA == "FR",
         # A20: Loans
         BS_ITEM == "A20",
         # A: Total
         MATURITY_NOT_IRATE == "A",
         # R: Rate
         DATA_TYPE_MIR == "R") %>%
  month_to_date %>%
  mutate(BS_COUNT_SECTOR = paste0(BS_COUNT_SECTOR)) %>%
  left_join(BS_COUNT_SECTOR, by = "BS_COUNT_SECTOR") %>%
  ggplot(.) + 
  geom_line(aes(x = date, y = OBS_VALUE / 100, color = Bs_count_sector)) + 
  theme_minimal() + xlab("") + ylab("France Interest Rates (%)") +
  scale_x_date(breaks = seq(1960, 2024, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 50, 0.5),
                     labels = percent_format(accuracy = .1)) +
  theme(legend.position = c(0.6, 0.9),
        legend.title = element_blank())

Europe

Loans oth. than revolving loans and overdrafts

Table

Code 
MIR %>%
  filter(REF_AREA == "U2",
         # A2A: Loans oth. than revolving loans and overdrafts, convenience and ext. CC debt
         BS_ITEM == "A2A",
         # A: Total
         MATURITY_NOT_IRATE == "A",
         # R: Rate
         DATA_TYPE_MIR == "R",
         TIME_PERIOD == "2021-04") %>%
  month_to_date %>%
  select_if(~n_distinct(.) > 1) %>%
  select(-TITLE_COMPL) %>%
  print_table_conditional()
KEY AMOUNT_CAT BS_COUNT_SECTOR IR_BUS_COV OBS_VALUE OBS_STATUS TITLE
MIR.M.U2.B.A2A.A.R.0.2240.EUR.N 0 2240 N 1.79 A Bank interest rates - loans to corporations of up to EUR 1M (new business) - euro area
MIR.M.U2.B.A2A.A.R.1.2240.EUR.N 1 2240 N 1.33 A Bank interest rates - loans to corporations of over EUR 1M (new business) - euro area
MIR.M.U2.B.A2A.A.R.2.2240.EUR.N 2 2240 N 2.00 A Bank interest rates - loans to corporations of up to EUR 0.25M (new business) - euro area
MIR.M.U2.B.A2A.A.R.3.2240.EUR.N 3 2240 N 1.48 A Bank interest rates - loans to corporations of over EUR .25M & up to EUR 1M (new business) - euro area
MIR.M.U2.B.A2A.A.R.A.2240.EUR.N A 2240 N 1.47 A Bank interest rates - loans to corporations (new business) - euro area
MIR.M.U2.B.A2A.A.R.A.2240.EUR.P A 2240 P 1.43 A Bank interest rates - loans to corporations (pure new loans)- euro area
MIR.M.U2.B.A2A.A.R.A.2240.EUR.R A 2240 R 1.64 E Bank interest rates - loans to corporations (renegotiations)- euro area
MIR.M.U2.B.A2A.A.R.A.2250.EUR.P A 2250 P 2.14 A Bank interest rates - loans to households (pure new loans)- euro area

Graph

All

Code 
MIR %>%
  filter(REF_AREA == "U2",
         # A2A: Loans oth. than revolving loans and overdrafts, convenience and ext. CC debt
         BS_ITEM == "A2A",
         # A: Total
         MATURITY_NOT_IRATE == "A",
         # R: Rate
         DATA_TYPE_MIR == "R") %>%
  mutate(TITLE = gsub("Bank interest rates - loans to", "", TITLE),
         TITLE = gsub("- Germany", "", TITLE)) %>%
  month_to_date %>%
  ggplot(.) + 
  geom_line(aes(x = date, y = OBS_VALUE / 100, color = TITLE)) + 
  theme_minimal() + xlab("") + ylab("Euro Area Interest Rates (%)") +
  scale_x_date(breaks = seq(1960, 2030, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 50, 1),
                     labels = percent_format(accuracy = 1)) +
  theme(legend.position = c(0.65, 0.8),
        legend.title = element_blank(),
        legend.text = element_text(size = 8),
        legend.key.size = unit(0.9, 'lines'))

2010-

Code 
MIR %>%
  filter(REF_AREA == "U2",
         # A2A: Loans oth. than revolving loans and overdrafts, convenience and ext. CC debt
         BS_ITEM == "A2A",
         # A: Total
         MATURITY_NOT_IRATE == "A",
         # R: Rate
         DATA_TYPE_MIR == "R") %>%
  mutate(TITLE = gsub("Bank interest rates - loans to", "", TITLE),
         TITLE = gsub("- Germany", "", TITLE)) %>%
  month_to_date %>%
  filter(date >= as.Date("2010-01-01")) %>%
  ggplot(.) + 
  geom_line(aes(x = date, y = OBS_VALUE / 100, color = TITLE)) + 
  theme_minimal() + xlab("") + ylab("Germany Interest Rates (%)") +
  scale_x_date(breaks = seq(1960, 2030, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 50, 1),
                     labels = percent_format(accuracy = 1)) +
  theme(legend.position = c(0.65, 0.8),
        legend.title = element_blank(),
        legend.text = element_text(size = 8),
        legend.key.size = unit(0.9, 'lines'))

2015-

Code 
MIR %>%
  filter(REF_AREA == "U2",
         # A2A: Loans oth. than revolving loans and overdrafts, convenience and ext. CC debt
         BS_ITEM == "A2A",
         # A: Total
         MATURITY_NOT_IRATE == "A",
         # R: Rate
         DATA_TYPE_MIR == "R") %>%
  mutate(TITLE = gsub("Bank interest rates - loans to", "", TITLE),
         TITLE = gsub("- Germany", "", TITLE)) %>%
  month_to_date %>%
  filter(date >= as.Date("2015-01-01")) %>%
  ggplot(.) + 
  geom_line(aes(x = date, y = OBS_VALUE / 100, color = TITLE)) + 
  theme_minimal() + xlab("") + ylab("Germany Interest Rates (%)") +
  scale_x_date(breaks = seq(1960, 2026, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 50, .5),
                     labels = percent_format(accuracy = .1)) +
  theme(legend.position = c(0.65, 0.8),
        legend.title = element_blank(),
        legend.text = element_text(size = 8),
        legend.key.size = unit(0.9, 'lines'))

Germany

Loans oth. than revolving loans and overdrafts

Table

Code 
MIR %>%
  filter(REF_AREA == "DE",
         # A2A: Loans oth. than revolving loans and overdrafts, convenience and ext. CC debt
         BS_ITEM == "A2A",
         # A: Total
         MATURITY_NOT_IRATE == "A",
         # R: Rate
         DATA_TYPE_MIR == "R",
         TIME_PERIOD == "2021-04") %>%
  month_to_date %>%
  select_if(~n_distinct(.) > 1) %>%
  select(-TITLE_COMPL) %>%
  print_table_conditional()
KEY AMOUNT_CAT BS_COUNT_SECTOR IR_BUS_COV OBS_VALUE TITLE
MIR.M.DE.B.A2A.A.R.0.2240.EUR.N 0 2240 N 1.90 Bank interest rates - loans to corporations of up to EUR 1M (new business) - Germany
MIR.M.DE.B.A2A.A.R.1.2240.EUR.N 1 2240 N 1.41 Bank interest rates - loans to corporations of over EUR 1M (new business) - Germany
MIR.M.DE.B.A2A.A.R.2.2240.EUR.N 2 2240 N 2.05 Bank interest rates - loans to corporations of up to EUR 0.25M (new business) - Germany
MIR.M.DE.B.A2A.A.R.3.2240.EUR.N 3 2240 N 1.65 Bank interest rates - loans to corporations of over EUR .25M & up to EUR 1M (new business) - Germany
MIR.M.DE.B.A2A.A.R.A.2240.EUR.N A 2240 N 1.52 Bank interest rates - loans to corporations (new business) - Germany
MIR.M.DE.B.A2A.A.R.A.2240.EUR.P A 2240 P 1.50 Bank interest rates - loans to corporations (pure new loans)- Germany
MIR.M.DE.B.A2A.A.R.A.2240.EUR.R A 2240 R 1.55 Bank interest rates - loans to corporations (renegotiations)- Germany
MIR.M.DE.B.A2A.A.R.A.2250.EUR.P A 2250 P 2.11 Bank interest rates - loans to households (pure new loans)- Germany
MIR.M.DE.B.A2A.A.R.A.2250.EUR.R A 2250 R 2.44 Bank interest rates - loans to households (renegotiations)- Germany

Graph

All

Code 
MIR %>%
  filter(REF_AREA == "DE",
         # A2A: Loans oth. than revolving loans and overdrafts, convenience and ext. CC debt
         BS_ITEM == "A2A",
         # A: Total
         MATURITY_NOT_IRATE == "A",
         # R: Rate
         DATA_TYPE_MIR == "R") %>%
  mutate(TITLE = gsub("Bank interest rates - loans to", "", TITLE),
         TITLE = gsub("- Germany", "", TITLE)) %>%
  month_to_date %>%
  ggplot(.) + 
  geom_line(aes(x = date, y = OBS_VALUE / 100, color = TITLE)) + 
  theme_minimal() + xlab("") + ylab("Germany Interest Rates (%)") +
  scale_x_date(breaks = seq(1960, 2030, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 50, 1),
                     labels = percent_format(accuracy = 1)) +
  theme(legend.position = c(0.65, 0.8),
        legend.title = element_blank(),
        legend.text = element_text(size = 8),
        legend.key.size = unit(0.9, 'lines'))

2010-

Code 
MIR %>%
  filter(REF_AREA == "DE",
         # A2A: Loans oth. than revolving loans and overdrafts, convenience and ext. CC debt
         BS_ITEM == "A2A",
         # A: Total
         MATURITY_NOT_IRATE == "A",
         # R: Rate
         DATA_TYPE_MIR == "R") %>%
  mutate(TITLE = gsub("Bank interest rates - loans to", "", TITLE),
         TITLE = gsub("- Germany", "", TITLE)) %>%
  month_to_date %>%
  filter(date >= as.Date("2010-01-01")) %>%
  ggplot(.) + 
  geom_line(aes(x = date, y = OBS_VALUE / 100, color = TITLE)) + 
  theme_minimal() + xlab("") + ylab("Germany Interest Rates (%)") +
  scale_x_date(breaks = seq(1960, 2030, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 50, 1),
                     labels = percent_format(accuracy = 1)) +
  theme(legend.position = c(0.65, 0.8),
        legend.title = element_blank(),
        legend.text = element_text(size = 8),
        legend.key.size = unit(0.9, 'lines'))

2015-

Code 
MIR %>%
  filter(REF_AREA == "DE",
         # A2A: Loans oth. than revolving loans and overdrafts, convenience and ext. CC debt
         BS_ITEM == "A2A",
         # A: Total
         MATURITY_NOT_IRATE == "A",
         # R: Rate
         DATA_TYPE_MIR == "R") %>%
  mutate(TITLE = gsub("Bank interest rates - loans to", "", TITLE),
         TITLE = gsub("- Germany", "", TITLE)) %>%
  month_to_date %>%
  filter(date >= as.Date("2015-01-01")) %>%
  ggplot(.) + 
  geom_line(aes(x = date, y = OBS_VALUE / 100, color = TITLE)) + 
  theme_minimal() + xlab("") + ylab("Germany Interest Rates (%)") +
  scale_x_date(breaks = seq(1960, 2026, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 50, .5),
                     labels = percent_format(accuracy = .1)) +
  theme(legend.position = c(0.65, 0.8),
        legend.title = element_blank(),
        legend.text = element_text(size = 8),
        legend.key.size = unit(0.9, 'lines'))

France

2015-

Code 
MIR %>%
  filter(REF_AREA %in% c("FR"),
         # A2C: Lending for house purchase excluding revolving loans and overdrafts, convenience and extended credit card debt
         BS_ITEM %in% c("A2C", "A20"),
         # A: Total
         MATURITY_NOT_IRATE == "A",
         # R: Rate
         DATA_TYPE_MIR == "R",
         BS_COUNT_SECTOR == "2250") %>%
  left_join(BS_ITEM, by = "BS_ITEM") %>%
  left_join(IR_BUS_COV, by = "IR_BUS_COV") %>%
  month_to_date %>%
  filter(date >= as.Date("2015-01-01")) %>%
  mutate(OBS_VALUE = OBS_VALUE/100) %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("Lending for house purchase excluding revolving (%)") +
  geom_line(aes(x = date, y = OBS_VALUE, color = paste0(BS_ITEM, " - ", Bs_item, "\n", Ir_bus_cov))) + 
  scale_x_date(breaks = seq(1960, 2030, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 50, .5),
                     labels = percent_format(accuracy = .1)) +
  theme(legend.position = c(0.5, 0.85),
        legend.title = element_blank())

France, Germany, Italy

A2C / A20

All

Code 
MIR %>%
  filter(REF_AREA %in% c("FR", "DE", "IT"),
         # A2C: Lending for house purchase excluding revolving loans and overdrafts, convenience and extended credit card debt
         BS_ITEM %in% c("A2C", "A20"),
         # A: Total
         MATURITY_NOT_IRATE == "A",
         # R: Rate
         DATA_TYPE_MIR == "R",
         BS_COUNT_SECTOR == "2250") %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  left_join(BS_ITEM, by = "BS_ITEM") %>%
  month_to_date %>%
  arrange(desc(date)) %>%
  left_join(colors, by = c("Ref_area" = "country")) %>%
  mutate(OBS_VALUE = OBS_VALUE/100) %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("Lending for house purchase excluding revolving (%)") +
  geom_line(aes(x = date, y = OBS_VALUE, color = color, linetype = paste0(Bs_item, IR_BUS_COV))) + 
  add_flags(6) + scale_color_identity() +
  scale_x_date(breaks = seq(1960, 2030, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 50, 1),
                     labels = percent_format(accuracy = 1)) +
  theme(legend.position = c(0.8, 0.2),
        legend.title = element_blank())

2010

Code 
MIR %>%
  filter(REF_AREA %in% c("FR", "DE", "IT"),
         # A2C: Lending for house purchase excluding revolving loans and overdrafts, convenience and extended credit card debt
         BS_ITEM %in% c("A2C", "A20"),
         # A: Total
         MATURITY_NOT_IRATE == "A",
         # R: Rate
         DATA_TYPE_MIR == "R",
         BS_COUNT_SECTOR == "2250") %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  left_join(BS_ITEM, by = "BS_ITEM") %>%
  left_join(IR_BUS_COV, by = "IR_BUS_COV") %>%
  month_to_date %>%
  filter(date >= as.Date("2010-01-01")) %>%
  left_join(colors, by = c("Ref_area" = "country")) %>%
  mutate(OBS_VALUE = OBS_VALUE/100) %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("Lending for house purchase excluding revolving (%)") +
  geom_line(aes(x = date, y = OBS_VALUE, color = color, linetype = paste0(BS_ITEM, " - ", Ir_bus_cov))) + 
  add_flags(6) + scale_color_identity() +
  scale_x_date(breaks = seq(1960, 2030, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 50, .5),
                     labels = percent_format(accuracy = .1)) +
  theme(legend.position = c(0.2, 0.2),
        legend.title = element_blank())

2015-

Code 
MIR %>%
  filter(REF_AREA %in% c("FR", "DE", "IT"),
         # A2C: Lending for house purchase excluding revolving loans and overdrafts, convenience and extended credit card debt
         BS_ITEM %in% c("A2C", "A20"),
         # A: Total
         MATURITY_NOT_IRATE == "A",
         # R: Rate
         DATA_TYPE_MIR == "R",
         BS_COUNT_SECTOR == "2250") %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  left_join(BS_ITEM, by = "BS_ITEM") %>%
  left_join(IR_BUS_COV, by = "IR_BUS_COV") %>%
  month_to_date %>%
  filter(date >= as.Date("2015-01-01")) %>%
  left_join(colors, by = c("Ref_area" = "country")) %>%
  mutate(OBS_VALUE = OBS_VALUE/100) %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("Lending for house purchase excluding revolving (%)") +
  geom_line(aes(x = date, y = OBS_VALUE, color = color, linetype = paste0(BS_ITEM, " - ", Ir_bus_cov))) + 
  add_flags(6) + scale_color_identity() +
  scale_x_date(breaks = seq(1960, 2030, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 50, .5),
                     labels = percent_format(accuracy = .1)) +
  theme(legend.position = c(0.5, 0.85),
        legend.title = element_blank())

A20 - Loans

Loans to Corporations (2240 = Corporations)

Code 
MIR %>%
  filter(REF_AREA %in% c("FR", "DE", "IT"),
         # A20 - Loans 
         BS_ITEM == "A20",
         # A: Total
         MATURITY_NOT_IRATE == "A",
         # R: Rate
         DATA_TYPE_MIR == "R",
         BS_COUNT_SECTOR == "2240") %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  month_to_date %>%
  select_if(~n_distinct(.) > 1) %>%
  left_join(colors, by = c("Ref_area" = "country")) %>%
  mutate(OBS_VALUE = OBS_VALUE/100) %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("Loans to corporations (%)") +
  geom_line(aes(x = date, y = OBS_VALUE, color = color)) + 
  add_flags(3) + scale_color_identity() +
  scale_x_date(breaks = seq(1960, 2030, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 50, 1),
                     labels = percent_format(accuracy = 1))

Loans to Households (2250)

Code 
MIR %>%
  filter(REF_AREA %in% c("FR", "DE", "IT"),
         # A2C: Lending for house purchase excluding revolving loans and overdrafts, convenience and extended credit card debt
         BS_ITEM == "A20",
         # A: Total
         MATURITY_NOT_IRATE == "A",
         # R: Rate
         DATA_TYPE_MIR == "R",
         BS_COUNT_SECTOR == "2250") %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  month_to_date %>%
  select_if(~n_distinct(.) > 1) %>%
  left_join(colors, by = c("Ref_area" = "country")) %>%
  mutate(OBS_VALUE = OBS_VALUE/100) %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("Loans to households (%)") +
  geom_line(aes(x = date, y = OBS_VALUE, color = color)) + 
  add_flags(3) + scale_color_identity() +
  scale_x_date(breaks = seq(1960, 2030, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 50, 1),
                     labels = percent_format(accuracy = 1))

A2C - Lending for house purchase excluding revolving

All

Code 
MIR %>%
  filter(REF_AREA %in% c("FR", "DE", "IT"),
         # A2C: Lending for house purchase excluding revolving loans and overdrafts, convenience and extended credit card debt
         BS_ITEM == "A2C",
         # A: Total
         MATURITY_NOT_IRATE == "A",
         # R: Rate
         DATA_TYPE_MIR == "R",
         # N: New business
         IR_BUS_COV == "N") %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  month_to_date %>%
  arrange(date) %>%
  left_join(colors, by = c("Ref_area" = "country")) %>%
  mutate(OBS_VALUE = OBS_VALUE/100) %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("Lending for house purchase excluding revolving (%)") +
  geom_line(aes(x = date, y = OBS_VALUE, color = color)) + 
  add_flags(3) + scale_color_identity() +
  scale_x_date(breaks = seq(1960, 2030, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 50, 1),
                     labels = percent_format(accuracy = 1))

2010

Code 
MIR %>%
  filter(REF_AREA %in% c("FR", "DE", "IT"),
         # A2A: Loans oth. than revolving loans and overdrafts, convenience and ext. CC debt
         BS_ITEM == "A2C",
         # A: Total
         MATURITY_NOT_IRATE == "A",
         # R: Rate
         DATA_TYPE_MIR == "R",
         # N: New business
         IR_BUS_COV == "N") %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  month_to_date %>%
  filter(date >= as.Date("2010-01-01")) %>%
  left_join(colors, by = c("Ref_area" = "country")) %>%
  mutate(OBS_VALUE = OBS_VALUE/100) %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("Lending for house purchase excluding revolving (%)") +
  geom_line(aes(x = date, y = OBS_VALUE, color = color)) + 
  add_flags(3) + scale_color_identity() +
  scale_x_date(breaks = seq(1960, 2030, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 50, .5),
                     labels = percent_format(accuracy = .1))

A22 - Lending for house purchase

Table

Code 
MIR %>%
  filter(BS_ITEM == "A22",
         # A: Total
         MATURITY_NOT_IRATE == "A",
         # R: Rate
         DATA_TYPE_MIR == "R") %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  group_by(REF_AREA, Ref_area) %>%
  filter(TIME_PERIOD == max(TIME_PERIOD) | TIME_PERIOD == "2022-01") %>%
  select(REF_AREA, Ref_area, TIME_PERIOD, OBS_VALUE) %>%
  spread(TIME_PERIOD, OBS_VALUE) %>%
  ungroup %>%
  mutate(change = .[[4]] - .[[3]]) %>%
  arrange(-change) %>%
  print_table_conditional
REF_AREA Ref_area 2022-01 2024-04 change
LT Lithuania 1.94 5.90 3.96
EE Estonia 2.01 5.97 3.96
PL Poland 3.74 7.62 3.88
PT Portugal 0.82 4.66 3.84
LV Latvia 2.30 6.13 3.83
FI Finland 0.79 4.08 3.29
SE Sweden 1.47 4.66 3.19
ES Spain 1.10 3.69 2.59
CY Cyprus 2.03 4.60 2.57
DK Denmark 1.64 3.75 2.11
AT Austria 1.37 3.24 1.87
SI Slovenia 2.02 3.57 1.55
IT Italy 1.59 3.12 1.53
LU Luxembourg 1.56 2.96 1.40
IE Ireland 2.47 3.70 1.23
HU Hungary 4.63 5.80 1.17
SK Slovakia 1.30 2.22 0.92
U2 Euro area (Member States and Institutions of the Euro Area) changing composition 1.61 2.45 0.84
BE Belgium 1.73 2.15 0.42
FR France 1.43 1.70 0.27
NL Netherlands 2.32 2.59 0.27
DE Germany 1.73 1.98 0.25
HR Croatia 3.09 2.96 -0.13
MT Malta 2.80 2.66 -0.14
BG Bulgaria 2.98 2.80 -0.18
CZ Czech Republic NA NA NA
GR Greece NA NA NA
RO Romania 4.66 NA NA

Estonia, Lithuania, Portugal, Latvia

Code 
MIR %>%
  filter(REF_AREA %in% c("EE", "LT", "LV", "PT"),
         # A22: Lending for house purchase
         BS_ITEM == "A22",
         # A: Total
         MATURITY_NOT_IRATE == "A",
         # R: Rate
         DATA_TYPE_MIR == "R") %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  month_to_date %>%
  filter(date >= as.Date("2018-01-01")) %>%
  arrange(date) %>%
  left_join(colors, by = c("Ref_area" = "country")) %>%
  mutate(OBS_VALUE = OBS_VALUE/100,
         color = ifelse(REF_AREA == "FR", color2, color)) %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("Lending for house purchase, Old loans (%)") +
  geom_line(aes(x = date, y = OBS_VALUE, color = color)) + 
  add_flags(4) + scale_color_identity() +
  scale_x_date(breaks = seq(1960, 2030, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 50, .5),
                     labels = percent_format(accuracy = .1))

France, Europe, Belgium, Germany, Netherlands

Code 
MIR %>%
  filter(REF_AREA %in% c("FR", "BE", "DE", "NL"),
         # A22: Lending for house purchase
         BS_ITEM == "A22",
         # A: Total
         MATURITY_NOT_IRATE == "A",
         # R: Rate
         DATA_TYPE_MIR == "R") %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  mutate(Ref_area = ifelse(REF_AREA == "U2", "Europe", Ref_area)) %>%
  month_to_date %>%
  filter(date >= as.Date("2018-01-01")) %>%
  arrange(date) %>%
  left_join(colors, by = c("Ref_area" = "country")) %>%
  mutate(OBS_VALUE = OBS_VALUE/100) %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("Lending for house purchase, Old loans (%)") +
  geom_line(aes(x = date, y = OBS_VALUE, color = color)) + 
  add_flags(4) + scale_color_identity() +
  scale_x_date(breaks = seq(1960, 2030, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 50, .2),
                     labels = percent_format(accuracy = .1))

France, Germany, Italy, Spain, Netherlands

All

Code 
MIR %>%
  filter(REF_AREA %in% c("FR", "DE", "IT", "ES", "NL"),
         # A22: Lending for house purchase
         BS_ITEM == "A22",
         # A: Total
         MATURITY_NOT_IRATE == "A",
         # R: Rate
         DATA_TYPE_MIR == "R") %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  month_to_date %>%
  arrange(date) %>%
  left_join(colors, by = c("Ref_area" = "country")) %>%
  mutate(OBS_VALUE = OBS_VALUE/100,
         color = ifelse(REF_AREA == "FR", color2, color)) %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("Lending for house purchase, Old loans (%)") +
  geom_line(aes(x = date, y = OBS_VALUE, color = color)) + 
  add_flags(5) + scale_color_identity() +
  scale_x_date(breaks = seq(1960, 2030, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 50, 1),
                     labels = percent_format(accuracy = 1))

2018-

Code 
MIR %>%
  filter(REF_AREA %in% c("FR", "DE", "IT", "ES", "NL"),
         # A22: Lending for house purchase
         BS_ITEM == "A22",
         # A: Total
         MATURITY_NOT_IRATE == "A",
         # R: Rate
         DATA_TYPE_MIR == "R") %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  month_to_date %>%
  filter(date >= as.Date("2018-01-01")) %>%
  arrange(date) %>%
  left_join(colors, by = c("Ref_area" = "country")) %>%
  mutate(OBS_VALUE = OBS_VALUE/100,
         color = ifelse(REF_AREA == "FR", color2, color)) %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("Lending for house purchase, Old loans (%)") +
  geom_line(aes(x = date, y = OBS_VALUE, color = color)) + 
  add_flags(5) + scale_color_identity() +
  scale_x_date(breaks = seq(1960, 2030, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 50, .5),
                     labels = percent_format(accuracy = .1))

Estonia, Lithuania, Portugal, Finland, Spain

Code 
MIR %>%
  filter(REF_AREA %in% c("EE", "LT", "LV", "PT", "FI", "ES"),
         # A22: Lending for house purchase
         BS_ITEM == "A22",
         # A: Total
         MATURITY_NOT_IRATE == "A",
         # R: Rate
         DATA_TYPE_MIR == "R") %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  month_to_date %>%
  filter(date >= as.Date("2018-01-01")) %>%
  arrange(date) %>%
  left_join(colors, by = c("Ref_area" = "country")) %>%
  mutate(OBS_VALUE = OBS_VALUE/100,
         color = ifelse(REF_AREA == "FR", color2, color)) %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("Lending for house purchase, Old loans (%)") +
  geom_line(aes(x = date, y = OBS_VALUE, color = color)) + 
  add_flags(6) + scale_color_identity() +
  scale_x_date(breaks = seq(1960, 2030, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 50, .5),
                     labels = percent_format(accuracy = .1))

France, Europe, Belgium, Germany, Netherlands

Code 
MIR %>%
  filter(REF_AREA %in% c("FR", "U2", "BE", "DE", "NL"),
         # A22: Lending for house purchase
         BS_ITEM == "A22",
         # A: Total
         MATURITY_NOT_IRATE == "A",
         # R: Rate
         DATA_TYPE_MIR == "R") %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  mutate(Ref_area = ifelse(REF_AREA == "U2", "Europe", Ref_area)) %>%
  month_to_date %>%
  filter(date >= as.Date("2018-01-01")) %>%
  arrange(date) %>%
  left_join(colors, by = c("Ref_area" = "country")) %>%
  mutate(OBS_VALUE = OBS_VALUE/100) %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("Lending for house purchase, Old loans (%)") +
  geom_line(aes(x = date, y = OBS_VALUE, color = color)) + 
  add_flags(5) + scale_color_identity() +
  scale_x_date(breaks = seq(1960, 2030, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 50, .2),
                     labels = percent_format(accuracy = .1))

Credit for consumption and other lending - A25

All

Code 
MIR %>%
  filter(REF_AREA %in% c("FR", "DE", "IT"),
         # A2A: Loans oth. than revolving loans and overdrafts, convenience and ext. CC debt
         BS_ITEM == "A25",
         # A: Total
         MATURITY_NOT_IRATE == "A",
         # R: Rate
         DATA_TYPE_MIR == "R") %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  month_to_date %>%
  arrange(date) %>%
  left_join(colors, by = c("Ref_area" = "country")) %>%
  mutate(OBS_VALUE = OBS_VALUE/100) %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("Credit for consumption and other lending (%)") +
  geom_line(aes(x = date, y = OBS_VALUE, color = color)) + 
  add_flags(3) + scale_color_identity() +
  scale_x_date(breaks = seq(1960, 2030, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 50, 1),
                     labels = percent_format(accuracy = 1))

2017-

Code 
MIR %>%
  filter(REF_AREA %in% c("FR", "DE", "IT"),
         # A2A: Loans oth. than revolving loans and overdrafts, convenience and ext. CC debt
         BS_ITEM == "A25",
         # A: Total
         MATURITY_NOT_IRATE == "A",
         # R: Rate
         DATA_TYPE_MIR == "R") %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  month_to_date %>%
  arrange(date) %>%
  left_join(colors, by = c("Ref_area" = "country")) %>%
  mutate(OBS_VALUE = OBS_VALUE/100) %>%
  filter(date >= as.Date("2017-01-01")) %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("Credit for consumption and other lending (%)") +
  geom_line(aes(x = date, y = OBS_VALUE, color = color)) + 
  add_flags(3) + scale_color_identity() +
  scale_x_date(breaks = seq(1960, 2030, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 50, 1),
                     labels = percent_format(accuracy = 1))