Indice pour la révision d’un loyer d’habitation

Data - INSEE

~/data/insee/

Info

source	dataset	.html	.RData
insee	IPC-2015	2024-04-18	2024-04-09
insee	IRL	2024-04-15	2024-04-18

Données sur l’inflation en France

source	dataset	.html	.RData
insee	bdf2017	2024-04-18	2023-11-21
insee	ILC-ILAT-ICC	2024-04-18	2024-04-18
insee	INDICES_LOYERS	2024-04-18	2024-04-18
insee	IPC-1970-1980	2024-04-18	2024-04-18
insee	IPC-1990	2024-04-18	2024-04-18
insee	IPC-2015	2024-04-18	2024-04-09
insee	IPC-PM-2015	2024-04-18	2024-04-18
insee	IPCH-2015	2024-04-18	2024-04-18
insee	IPGD-2015	2024-04-18	2024-03-20
insee	IPLA-IPLNA-2015	2024-04-18	2024-04-18
insee	IPPI-2015	2024-04-18	2024-03-30
insee	IRL	2024-04-15	2024-04-18
insee	SERIES_LOYERS	2024-04-15	2024-04-18
insee	T_CONSO_EFF_FONCTION	2024-04-15	2024-04-01

Données sur l’immobilier

source	dataset	.html	.RData
acpr	as151	2024-04-05	2024-04-05
bdf	BSI1	2024-04-18	2024-04-18
bdf	CPP	2024-04-18	2024-04-18
bdf	FM	2024-04-18	2024-04-18
bdf	immobilier	2024-04-18	2024-04-04
bdf	MIR	2024-04-18	2024-04-18
bdf	MIR1	2024-04-18	2024-04-18
bdf	RPP	2024-04-18	2024-04-18
insee	CONSTRUCTION-LOGEMENTS	2024-04-18	2024-04-18
insee	ENQ-CONJ-ART-BAT	2024-04-18	2023-10-25
insee	ENQ-CONJ-IND-BAT	2024-04-18	2024-04-18
insee	ENQ-CONJ-PROMO-IMMO	2024-04-18	2024-04-18
insee	ENQ-CONJ-TP	2024-04-18	2024-04-18
insee	ILC-ILAT-ICC	2024-04-18	2024-04-18
insee	INDICES_LOYERS	2024-04-18	2024-04-18
insee	IPLA-IPLNA-2015	2024-04-18	2024-04-18
insee	IRL	2024-04-15	2024-04-18
insee	PARC-LOGEMENTS	2024-04-15	2023-12-03
insee	SERIES_LOYERS	2024-04-15	2024-04-18
insee	t_dpe_val	2024-04-15	2024-03-04
notaires	arrdt	2024-04-08	2024-04-08
notaires	dep	2024-04-08	2024-04-08

LAST_UPDATE

IRL %>%
  group_by(LAST_UPDATE) %>%
  summarise(Nobs = n()) %>%
  arrange(desc(LAST_UPDATE)) %>%
  print_table_conditional()

LAST_UPDATE	Nobs
2024-04-12	240
2008-02-04	62

Last

IRL %>%
  group_by(TIME_PERIOD) %>%
  summarise(Nobs = n()) %>%
  arrange(desc(TIME_PERIOD)) %>%
  head(1) %>%
  print_table_conditional()

TIME_PERIOD	Nobs
2024-Q1	6

Liens

• INSEE - La méthodologie de l’IRL. pdf

• INSEE - méthodologie IRL base 1998. pdf

• Indice de référence des loyers (IRL). html

• Dernier indice IRL connu. html

• Tableau sur le site de l’INSEE. html

i_g("bib/insee/IR95_IRL_1T22/pour-en-savoir-plus.png")

TITLE_FR

`IRL` %>%
  group_by(IDBANK, TITLE_FR) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) print_table(.) else .}

IDBANK	TITLE_FR	Nobs
001515333	Indice de référence des loyers (IRL)	86
001515334	Indice de référence des loyers (IRL) - Variation annuelle	86
000882470	Indice de référence des loyers 2005 (IRL 2005) - Série arrêtée	31
000882473	Indice de référence des loyers 2005 (IRL 2005) - Variation annuelle - Série arrêtée	31
010760507	Indice de référence des loyers dans la collectivité de Corse	17
010760508	Indice de référence des loyers dans la collectivité de Corse - Variation annuelle	17
010760509	Indice de référence des loyers dans les collectivités régies par l’article 73 de la Constitution (régions et départements d’outre-mer)	17
010760510	Indice de référence des loyers dans les collectivités régies par l’article 73 de la Constitution (régions et départements d’outre-mer) - Variation annuelle	17

BASIND

`IRL` %>%
  left_join(BASIND, by = "BASIND") %>%
  group_by(BASIND, Basind) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) print_table(.) else .}

BASIND	Basind	Nobs
SO	Sans objet	151
1998-T4	4ème trimestre de 1998	120
2004-T2	2ème trimestre de 2004	31

NATURE

`IRL` %>%
  left_join(NATURE, by = "NATURE") %>%
  group_by(NATURE, Nature) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()

NATURE	Nature	Nobs
INDICE	Indice	151
VARIATIONS_A	Variations annuelles	151

TIME_PERIOD

`IRL` %>%
  group_by(TIME_PERIOD) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  arrange(desc(TIME_PERIOD)) %>%
  print_table_conditional()

Indice

Linear

IRL %>%
  filter(NATURE == "INDICE") %>%
  quarter_to_date %>%
  ggplot(.) + theme_minimal() + ylab("") + xlab("") +
  geom_line(aes(x = date, y = OBS_VALUE, color = TITLE_FR)) +
  theme(legend.title = element_blank(),
        legend.position = c(0.4, 0.92)) +
  scale_x_date(breaks = seq(1950, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = seq(0, 200, 5))

Linear - 4 quarters rolling

IRL %>%
  filter(NATURE == "INDICE") %>%
  quarter_to_date %>%
  group_by(TITLE_FR) %>%
  arrange(date) %>%
  mutate(OBS_VALUE = OBS_VALUE/lag(OBS_VALUE, 4)-1) %>%
  ggplot(.) + theme_minimal() + ylab("") + xlab("") +
  geom_line(aes(x = date, y = OBS_VALUE, color = TITLE_FR)) +
  theme(legend.title = element_blank(),
        legend.position = c(0.65, 0.95)) +
  
  scale_x_date(breaks = seq(1950, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-100, 300, 0.5),
                     labels = percent_format(accuracy = .1, prefix = ""))

Log

IRL %>%
  filter(NATURE == "INDICE") %>%
  quarter_to_date %>%
  ggplot(.) + theme_minimal() + ylab("") + xlab("") +
  geom_line(aes(x = date, y = OBS_VALUE, color = TITLE_FR)) +
  theme(legend.title = element_blank(),
        legend.position = c(0.38, 0.92)) +
  
  scale_x_date(breaks = seq(1950, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(0, 200, 5))

Variation annuelle

Tous

IRL %>%
  filter(NATURE == "VARIATIONS_A") %>%
  quarter_to_date %>%
  ggplot(.) + theme_minimal() + ylab("") + xlab("") +
  geom_line(aes(x = date, y = OBS_VALUE/100, color = TITLE_FR)) +
  theme(legend.title = element_blank(),
        legend.position = c(0.5, 0.92)) +
  
  scale_x_date(breaks = seq(1950, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-200, 200, .5),
                     labels = percent_format(acc = .1),
                     limits = 0.01*c(-0.2, 4))

2008-

table1 <- IRL %>%
  filter(NATURE == "VARIATIONS_A") %>%
  quarter_to_date %>%
  filter(date >= as.Date("2008-01-01")) %>%
  select(date, OBS_VALUE, IDBANK, TITLE_FR)

table1 %>%
  ggplot(.) + theme_minimal() + ylab("") + xlab("") +
  geom_line(aes(x = date, y = OBS_VALUE/100, color = TITLE_FR)) +
  theme(legend.title = element_blank(),
        legend.position = c(0.5, 0.92)) +
  
  scale_x_date(breaks = seq(1950, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-200, 200, .5),
                     labels = percent_format(acc = .1),
                     limits = 0.01*c(-0.2, 4))

Table

table1 %>%
  print_table_conditional()

Répliquer IRL à partir de l’Ensemble hors loyer hors tabac

• Répliquer l’IRL à partir de l’ensemble hors loyer hors tabac.

• Données IRL fournies ici: https://www.insee.fr/fr/statistiques/serie/001515334.

• Peuvent être calculées juste à partir de l’IPC hors loyers, hors tabac: https://www.insee.fr/fr/statistiques/serie/001763862.

Prévisions INSEE

Estimation

i_g("bib/insee/PdC_09-05-22/table5.png")

IRL Juillet

`IPC-2015` %>%
  filter(PRIX_CONSO %in% c("4600"),
         COICOP2016 %in% c("00", "SO"),
         MENAGES_IPC == "ENSEMBLE",
         NATURE == "INDICE",
         REF_AREA == "FE",
         FREQ == "M") %>%
  select(TIME_PERIOD, OBS_VALUE) %>%
  arrange(desc(TIME_PERIOD))

# # A tibble: 314 × 2
#    TIME_PERIOD OBS_VALUE
#    <chr>           <dbl>
#  1 2024-02          119.
#  2 2024-01          118 
#  3 2023-12          118.
#  4 2023-11          118.
#  5 2023-10          118.
#  6 2023-09          118.
#  7 2023-08          119.
#  8 2023-07          118.
#  9 2023-06          118.
# 10 2023-05          117.
# # ℹ 304 more rows

Ensemble

Trimestriel

All

`IPC-2015` %>%
  filter(PRIX_CONSO %in% c("4600"),
         COICOP2016 %in% c("SO"),
         MENAGES_IPC == "ENSEMBLE",
         NATURE == "INDICE",
         REF_AREA == "FE",
         FREQ == "M") %>%
  month_to_date %>%
  arrange(date) %>%
  transmute(date,
            `Inflation Indice des loyers (IRL): Variation sur 12 mois de la moyenne sur 12 mois de l'IPC hors loyers, hors tabac` = zoo::rollmean(OBS_VALUE, 12, fill = NA, align = "right"),
            `Inflation IPC hors loyers, hors tabac: Variation sur 12 mois de l'IPC hors loyers, hors tabac` = OBS_VALUE) %>%
  filter(month(date) %in% c(12, 3, 6, 9)) %>%
  gather(variable, value, -date) %>%
  group_by(variable) %>%
  mutate(value_d12 = value/lag(value, 4) - 1) %>%
  ggplot() + ylab("Indice des loyers (simulé)") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = value_d12, color = variable)) +
  scale_color_manual(values = c("blue", "red")) +
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.5, 0.9),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-100, 300, 0.5),
                     labels = percent_format(accuracy = .1, prefix = ""))

2006-

`IPC-2015` %>%
  filter(PRIX_CONSO %in% c("4600"),
         COICOP2016 %in% c("00", "SO"),
         MENAGES_IPC == "ENSEMBLE",
         NATURE == "INDICE",
         REF_AREA == "FE",
         FREQ == "M") %>%
  month_to_date %>%
  filter(date >= as.Date("2006-01-01")) %>%
  arrange(date) %>%
  transmute(date,
            `Inflation Indice des loyers (IRL):\nVariation sur 12 mois de la moyenne sur 12 mois de l'IPC hors loyers, hors tabac` = zoo::rollmean(OBS_VALUE, 12, fill = NA, align = "right"),
            `Inflation Indice des Prix à la Consommation (IPC-) hors loyers, hors tabac: \nVariation sur 12 mois de l'IPC hors loyers, hors tabac` = OBS_VALUE) %>%
  filter(month(date) %in% c(12, 3, 6, 9)) %>%
  gather(variable, value, -date) %>%
  group_by(variable) %>%
  mutate(value_d12 = value/lag(value, 4) - 1) %>%
  na.omit %>%
  filter(date >= as.Date("2008-01-01")) %>%
  ggplot() + ylab("Indice des loyers (simulé)") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = value_d12, color = variable)) +
  scale_color_manual(values = c("blue", "red")) +
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.45, 0.9),
        legend.title = element_blank(),
        legend.key.size= unit(1.0, 'cm')) +
  scale_y_continuous(breaks = 0.01*seq(-100, 300, 0.5),
                     labels = percent_format(accuracy = .1, prefix = ""))

2008-

table2 <- `IPC-2015` %>%
  filter(PRIX_CONSO %in% c("4600"),
         COICOP2016 %in% c("00", "SO"),
         MENAGES_IPC == "ENSEMBLE",
         NATURE == "INDICE",
         REF_AREA == "FE",
         FREQ == "M") %>%
  month_to_date %>%
  filter(date >= as.Date("2006-01-01")) %>%
  arrange(date) %>%
  transmute(date,
            `Inflation Indice des loyers (IRL):\nVariation sur 12 mois de la moyenne sur 12 mois de l'IPC hors loyers, hors tabac` = zoo::rollmean(OBS_VALUE, 12, fill = NA, align = "right"),
            `Inflation Indice des Prix à la Consommation (IPC-) hors loyers, hors tabac: \nVariation sur 12 mois de l'IPC hors loyers, hors tabac` = OBS_VALUE) %>%
  filter(month(date) %in% c(12, 3, 6, 9)) %>%
  gather(variable, value, -date) %>%
  group_by(variable) %>%
  mutate(value_d12 = value/lag(value, 4) - 1) %>%
  na.omit %>%
  filter(date >= as.Date("2008-01-01"))
  

table2 %>%
  ggplot() + ylab("Indice des loyers (simulé)") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = value_d12, color = variable)) +
  scale_color_manual(values = c("blue", "red")) +
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.45, 0.9),
        legend.title = element_blank(),
        legend.key.size= unit(1.0, 'cm')) +
  scale_y_continuous(breaks = 0.01*seq(-100, 300, 0.5),
                     labels = percent_format(accuracy = .1, prefix = ""))

Mensuel

`IPC-2015` %>%
  filter(PRIX_CONSO %in% c("4600"),
         COICOP2016 %in% c("00", "SO"),
         MENAGES_IPC == "ENSEMBLE",
         NATURE == "INDICE",
         REF_AREA == "FE",
         FREQ == "M") %>%
  month_to_date %>%
  arrange(date) %>%
  transmute(date,
            `Inflation Indice des loyers (IRL): Variation sur 12 mois de la moyenne sur 12 mois de l'IPC hors loyers, hors tabac` = zoo::rollmean(OBS_VALUE, 12, fill = NA, align = "right"),
            `Inflation IPC hors loyers, hors tabac: Variation sur 12 mois de l'IPC hors loyers, hors tabac` = OBS_VALUE) %>%
  gather(variable, value, -date) %>%
  group_by(variable) %>%
  mutate(value_d12 = value/lag(value, 12) - 1) %>%
  ggplot() + ylab("Indice des loyers (simulé)") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = value_d12, color = variable)) +
  scale_color_manual(values = c("blue", "red")) +
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.5, 0.9),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-100, 300, 0.5),
                     labels = percent_format(accuracy = .1, prefix = ""))

Glissement annuel, Trimestriel

Tous

`IPC-2015` %>%
  filter(PRIX_CONSO %in% c("4600", "SO"),
         COICOP2016 %in% c("00", "SO"),
         MENAGES_IPC == "ENSEMBLE",
         NATURE == "INDICE",
         REF_AREA == "FE",
         FREQ == "M") %>%
  select_if(~ n_distinct(.) > 1) %>%
  month_to_date %>%
  arrange(date) %>%
  left_join(PRIX_CONSO, by = "PRIX_CONSO") %>%
  left_join(COICOP2016, by = "COICOP2016") %>%
  group_by(Prix_conso) %>%
  filter(month(date) %in% c(12, 3, 6, 9)) %>%
  mutate(OBS_VALUE = OBS_VALUE/lag(OBS_VALUE, 4)-1,
         Prix_conso = ifelse(PRIX_CONSO == "SO", "Ensemble", Prix_conso)) %>%
  na.omit %>%
  ggplot() + ylab("Inflation sur un an (IPC, IPCH)") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = OBS_VALUE, color = Prix_conso)) +
  
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.35, 0.9),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-100, 300, 0.5),
                     labels = percent_format(accuracy = .1, prefix = ""))

1998-

`IPC-2015` %>%
  filter(PRIX_CONSO %in% c("4600", "SO"),
         COICOP2016 %in% c("00", "SO"),
         MENAGES_IPC == "ENSEMBLE",
         NATURE == "INDICE",
         REF_AREA == "FE",
         FREQ == "M") %>%
  select_if(~ n_distinct(.) > 1) %>%
  month_to_date %>%
  arrange(date) %>%
  left_join(PRIX_CONSO, by = "PRIX_CONSO") %>%
  group_by(Prix_conso) %>%
  filter(month(date) %in% c(12, 3, 6, 9)) %>%
  mutate(OBS_VALUE = OBS_VALUE/lag(OBS_VALUE, 4)-1,
         Prix_conso = ifelse(PRIX_CONSO == "SO", "Ensemble", Prix_conso)) %>%
  filter(date >= as.Date("1998-01-01")) %>%
  na.omit %>%
  ggplot() + ylab("Glissement annuel") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = OBS_VALUE, color = Prix_conso)) +
  
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.2, 0.9),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-100, 300, 0.5),
                     labels = percent_format(accuracy = .1, prefix = ""))

2018-

`IPC-2015` %>%
  filter(PRIX_CONSO %in% c("4600", "SO"),
         COICOP2016 %in% c("00", "SO"),
         MENAGES_IPC == "ENSEMBLE",
         NATURE == "INDICE",
         REF_AREA == "FE",
         FREQ == "M") %>%
  select_if(~ n_distinct(.) > 1) %>%
  month_to_date %>%
  arrange(date) %>%
  left_join(PRIX_CONSO, by = "PRIX_CONSO") %>%
  group_by(Prix_conso) %>%
  filter(month(date) %in% c(12, 3, 6, 9)) %>%
  mutate(OBS_VALUE = OBS_VALUE/lag(OBS_VALUE, 4)-1,
         Prix_conso = ifelse(PRIX_CONSO == "SO", "Ensemble", Prix_conso)) %>%
  filter(date >= as.Date("2018-01-01")) %>%
  na.omit %>%
  ggplot() + ylab("Glissement annuel") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = OBS_VALUE, color = Prix_conso)) +
  
  scale_x_date(breaks = seq(1920, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.35, 0.9),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-100, 300, 0.5),
                     labels = percent_format(accuracy = .1, prefix = ""))

Evolution entre deux périodes de la moyenne sur 12 mois de l’indice des prix à la consommation

Il se déduit de l’indice du même trimestre de l’année précédente en lui appliquant l’évolution entre ces deux périodes de la moyenne sur douze mois consécutifs de l’indice des prix à la consommation hors tabac et hors loyers (IPC).

All

`IPC-2015` %>%
  filter(PRIX_CONSO %in% c("4600", "SO"),
         COICOP2016 %in% c("00", "SO"),
         MENAGES_IPC == "ENSEMBLE",
         NATURE == "INDICE",
         REF_AREA == "FE",
         FREQ == "M") %>%
  select_if(~ n_distinct(.) > 1) %>%
  month_to_date %>%
  arrange(date) %>%
  left_join(PRIX_CONSO, by = "PRIX_CONSO") %>%
  group_by(Prix_conso) %>%
  filter(date >= as.Date("2000-01-01")) %>%
  mutate(OBS_VALUE_mean = zoo::rollmean(OBS_VALUE, 12, fill = NA, align = "right"),
         Prix_conso = ifelse(PRIX_CONSO == "SO", "Ensemble", Prix_conso)) %>%
  filter(month(date) %in% c(12, 3, 6, 9)) %>%
  mutate(OBS_VALUE_mean_d1 = OBS_VALUE_mean/lag(OBS_VALUE_mean, 4)-1,
         OBS_VALUE_d1 = OBS_VALUE/lag(OBS_VALUE, 4)-1) %>%
  na.omit %>%
  ggplot() + ylab("Indice des loyers (simulé)") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = OBS_VALUE_d1, color = Prix_conso)) +
  
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.8, 0.9),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-100, 300, 0.5),
                     labels = percent_format(accuracy = .1, prefix = ""))

2017-

`IPC-2015` %>%
  filter(PRIX_CONSO %in% c("4600", "SO"),
         COICOP2016 %in% c("00", "SO"),
         MENAGES_IPC == "ENSEMBLE",
         NATURE == "INDICE",
         REF_AREA == "FE",
         FREQ == "M") %>%
  select_if(~ n_distinct(.) > 1) %>%
  month_to_date %>%
  arrange(date) %>%
  left_join(PRIX_CONSO, by = "PRIX_CONSO") %>%
  group_by(Prix_conso) %>%
  filter(date >= as.Date("2000-01-01")) %>%
  mutate(OBS_VALUE_mean = zoo::rollmean(OBS_VALUE, 12, fill = NA, align = "right"),
         Prix_conso = ifelse(PRIX_CONSO == "SO", "Ensemble", Prix_conso)) %>%
  filter(month(date) %in% c(12, 3, 6, 9)) %>%
  mutate(OBS_VALUE_mean_d1 = OBS_VALUE_mean/lag(OBS_VALUE_mean, 4)-1,
         OBS_VALUE_d1 = OBS_VALUE/lag(OBS_VALUE, 4)-1) %>%
  na.omit %>%
  filter(date >= as.Date("2017-01-01")) %>%
  ggplot() + ylab("Indice des loyers (simulé)") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = OBS_VALUE_d1, color = Prix_conso)) +
  
  scale_x_date(breaks = seq(1920, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.3, 0.9),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-100, 300, 0.5),
                     labels = percent_format(accuracy = .1, prefix = ""))

Ensemble

All

`IPC-2015` %>%
  filter(PRIX_CONSO %in% c("4600", "SO"),
         COICOP2016 %in% c("00", "SO"),
         MENAGES_IPC == "ENSEMBLE",
         NATURE == "INDICE",
         REF_AREA == "FE",
         FREQ == "M") %>%
  select_if(~ n_distinct(.) > 1) %>%
  month_to_date %>%
  arrange(date) %>%
  left_join(PRIX_CONSO, by = "PRIX_CONSO") %>%
  group_by(Prix_conso) %>%
  filter(date >= as.Date("2000-01-01")) %>%
  mutate(OBS_VALUE_mean = zoo::rollmean(OBS_VALUE, 12, fill = NA, align = "right"),
         Prix_conso = ifelse(PRIX_CONSO == "SO", "Ensemble", Prix_conso)) %>%
  filter(month(date) %in% c(12, 3, 6, 9)) %>%
  transmute(date, 
            OBS_VALUE_mean_d1 = OBS_VALUE_mean/lag(OBS_VALUE_mean, 4)-1,
            OBS_VALUE_d1 = OBS_VALUE/lag(OBS_VALUE, 4)-1,
            Prix_conso) %>%
  na.omit %>%
  gather(variable, value, -date, -Prix_conso) %>%
  mutate(variable = case_when(variable == "OBS_VALUE_mean_d1" ~ "Inflation de l'IRL (Indice de Référence des Loyers) - Moyenne 12 mois",
                              variable == "OBS_VALUE_d1" ~ "Inflation de l'IPC (Indice des Prix à la Consommation) - Instantanée")) %>%
  ggplot() + ylab("Indice des loyers (simulé)") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = value, color = Prix_conso, linetype = variable)) +
  
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.4, 0.8),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-100, 300, 0.5),
                     labels = percent_format(accuracy = .1, prefix = ""))

2017-

`IPC-2015` %>%
  filter(PRIX_CONSO %in% c("4600", "SO"),
         COICOP2016 %in% c("00", "SO"),
         MENAGES_IPC == "ENSEMBLE",
         NATURE == "INDICE",
         REF_AREA == "FE",
         FREQ == "M") %>%
  select_if(~ n_distinct(.) > 1) %>%
  month_to_date %>%
  arrange(date) %>%
  left_join(PRIX_CONSO, by = "PRIX_CONSO") %>%
  group_by(Prix_conso) %>%
  filter(date >= as.Date("2000-01-01")) %>%
  mutate(OBS_VALUE_mean = zoo::rollmean(OBS_VALUE, 12, fill = NA, align = "right"),
         Prix_conso = ifelse(PRIX_CONSO == "SO", "Ensemble", Prix_conso)) %>%
  filter(month(date) %in% c(12, 3, 6, 9)) %>%
  transmute(date, 
            OBS_VALUE_mean_d1 = OBS_VALUE_mean/lag(OBS_VALUE_mean, 4)-1,
            OBS_VALUE_d1 = OBS_VALUE/lag(OBS_VALUE, 4)-1,
            Prix_conso) %>%
  na.omit %>%
  filter(date >= as.Date("2017-01-01")) %>%
  gather(variable, value, -date, -Prix_conso) %>%
  mutate(variable = case_when(variable == "OBS_VALUE_mean_d1" ~ "Inflation de l'IRL (Indice de Référence des Loyers) - Moyenne 12 mois",
                              variable == "OBS_VALUE_d1" ~ "Inflation de l'IPC (Indice des Prix à la Consommation) - Instantanée")) %>%
  ggplot() + ylab("Indice des loyers (simulé)") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = value, color = Prix_conso, linetype = variable)) +
  
  scale_x_date(breaks = seq(1920, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.4, 0.8),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-100, 300, 0.5),
                     labels = percent_format(accuracy = .1, prefix = ""))