Prix au m^2 par arrondissement - arrdt

Data - Immobilier

Info

source dataset Title .html .rData
notaires arrdt Prix au m^2 par arrondissement - arrdt 2025-03-27 2025-03-27
notaires dep Prix au m^2 par département 2025-03-27 2025-03-27

Données sur l’immobilier

source dataset Title .html .rData
acpr as151 Enquête annuelle du SGACPR sur le financement de l'habitat 2022 2025-08-24 2024-04-05
bdf BSI1 Agrégats monétaires - France 2025-08-28 2025-08-24
bdf CPP Prix immobilier commercial 2025-08-28 2024-07-01
bdf FM Marché financier, taux 2025-08-28 2025-08-28
bdf MIR Taux d'intérêt - Zone euro 2025-08-28 2025-08-04
bdf MIR1 Taux d'intérêt - France 2025-08-28 2025-08-04
bdf RPP Prix de l'immobilier 2025-08-28 2025-08-24
bdf immobilier Immobilier en France 2025-08-28 2025-08-28
cgedd nombre-vente-maison-appartement-ancien Nombre de ventes de logements anciens cumulé sur 12 mois 2024-09-26 2024-09-26
insee CONSTRUCTION-LOGEMENTS Construction de logements 2025-09-26 2025-09-26
insee ENQ-CONJ-ART-BAT Conjoncture dans l'artisanat du bâtiment 2025-09-26 2025-09-26
insee ENQ-CONJ-IND-BAT Conjoncture dans l'industrie du bâtiment - ENQ-CONJ-IND-BAT 2025-09-26 2025-09-26
insee ENQ-CONJ-PROMO-IMMO Conjoncture dans la promotion immobilière 2025-09-26 2025-09-26
insee ENQ-CONJ-TP Conjoncture dans les travaux publics 2025-09-26 2025-09-26
insee ILC-ILAT-ICC Indices pour la révision d’un bail commercial ou professionnel 2025-09-26 2025-09-26
insee INDICES_LOYERS Indices des loyers - Base 2019 2025-09-26 2025-09-26
insee IPLA-IPLNA-2015 Indices des prix des logements neufs et Indices Notaires-Insee des prix des logements anciens 2025-09-26 2025-09-26
insee IRL Indice pour la révision d’un loyer d’habitation 2025-09-26 2025-09-26
insee PARC-LOGEMENTS Estimations annuelles du parc de logements (EAPL) 2025-09-26 2023-12-03
insee SERIES_LOYERS Variation des loyers 2025-09-26 2025-09-26
insee t_dpe_val Dépenses de consommation des ménages pré-engagées 2025-09-26 2025-08-29
notaires arrdt Prix au m^2 par arrondissement - arrdt 2025-03-27 2025-03-27
notaires dep Prix au m^2 par département 2025-03-27 2025-03-27

Data on housing

source dataset Title .html .rData
bdf RPP Prix de l'immobilier 2025-08-28 2025-08-24
bis LONG_PP Residential property prices - detailed series 2025-08-28 2024-05-10
bis SELECTED_PP Property prices, selected series 2025-09-25 2025-09-25
ecb RPP Residential Property Price Index Statistics 2025-08-29 2025-08-29
eurostat ei_hppi_q House price index (2015 = 100) - quarterly data 2025-09-26 2025-09-26
eurostat hbs_str_t223 Mean consumption expenditure by income quintile 2025-09-26 2025-09-26
eurostat prc_hicp_midx HICP (2015 = 100) - monthly data (index) 2025-09-26 2025-09-26
eurostat prc_hpi_q House price index (2015 = 100) - quarterly data 2025-09-26 2025-09-26
fred housing House Prices 2025-09-27 2025-09-27
insee IPLA-IPLNA-2015 Indices des prix des logements neufs et Indices Notaires-Insee des prix des logements anciens 2025-09-26 2025-09-26
oecd SNA_TABLE5 Final consumption expenditure of households 2025-09-29 2023-10-19
oecd housing NA NA NA

LAST_COMPILE

LAST_COMPILE
2025-09-29

Last

Code
arrdt %>%
  group_by(date) %>%
  summarise(Nobs = n()) %>%
  arrange(desc(date)) %>%
  head(1) %>%
  print_table_conditional()
date Nobs
2025-01-01 21

Exemples

2008-

Code
arrdt %>%
  filter(date %in% c(as.Date("2008-01-01"), max(date))) %>%
  spread(date, value) %>%
  #setNames(c("Arrondissement", "2021T3", "2023T2")) %>%
  mutate(`Croissance (%)` = round(100*(.[[3]]/.[[2]]-1), 1),
         `Croissance (€)` = round(.[[3]]-.[[2]])) %>%
  arrange(-`Croissance (%)`) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

2009Q2-

Code
arrdt %>%
  filter(date %in% c(as.Date("2009-04-01"), max(date))) %>%
  mutate(date = paste0("Prix m2 ", date)) %>%
  spread(date, value) %>%
  #setNames(c("Arrondissement", "2021T3", "2023T2")) %>%
  mutate(`Croissance (%)` = round(100*(.[[3]]/.[[2]]-1), 1),
         `Croissance (€)` = round(.[[3]]-.[[2]])) %>%
  arrange(-`Croissance (%)`) %>%
  rename(arrdt = Location) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

2008-2020

Code
arrdt %>%
  filter(date %in% c(as.Date("2008-01-01"), as.Date("2020-01-01"))) %>%
  spread(date, value) %>%
  #setNames(c("Arrondissement", "2021T3", "2023T2")) %>%
  mutate(`Croissance (%)` = round(100*(.[[3]]/.[[2]]-1), 1),
         `Croissance (€)` = round(.[[3]]-.[[2]])) %>%
  arrange(-`Croissance (%)`) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

2022T2-2023T2

Code
arrdt %>%
  filter(date %in% c(as.Date("2022-04-01"), as.Date("2023-04-01"))) %>%
  spread(date, value) %>%
  #setNames(c("Arrondissement", "2021T3", "2023T2")) %>%
  mutate(`Croissance (%)` = round(100*(.[[3]]/.[[2]]-1), 1),
         `Croissance (€)` = round(.[[3]]-.[[2]])) %>%
  arrange(-`Croissance (%)`) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

2021T3-2023T2

Code
arrdt %>%
  filter(date %in% c(as.Date("2021-07-01"), as.Date("2023-04-01"))) %>%
  spread(date, value) %>%
  #setNames(c("Arrondissement", "2021T3", "2023T2")) %>%
  mutate(`Croissance (%)` = round(100*(.[[3]]/.[[2]]-1), 1),
         `Croissance (€)` = round(.[[3]]-.[[2]])) %>%
  arrange(-`Croissance (%)`) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

100 = 2008

Code
arrdt %>%
  filter(Location %in% c("Centre", "6e", "10e")) %>%
  group_by(Location) %>%
  mutate(value = 100*value/value[date == as.Date("2008-01-01")]) %>%
  ggplot + geom_line(aes(x = date, y = value, color = Location)) +
  
  theme_minimal()  +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2050, 2), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.25, 0.85),
        legend.title = element_blank()) +
  xlab("") + ylab("Indice des prix au mètre carré (2008 = 100") +
  scale_y_log10(breaks = seq(0, 7000, 10))

Carte arrondissements

Code
i_g("bib/france/arrondissements-paris-2.jpg")

Prix mètre carré

13ème, 14ème, 15ème arrondissement

All

Code
arrdt %>%
  filter(Location %in% c("13e", "14e", "15e")) %>%
  group_by(Location) %>%
  ggplot + geom_line(aes(x = date, y = value, color = gsub("e", "ème arrondissement", Location))) +
  
  theme_minimal()  +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2050, 2), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.25, 0.85),
        legend.title = element_blank()) +
  xlab("") + ylab("Prix au mètre carré") +
  scale_y_log10(breaks = seq(0, 17000, 1000),
                labels = dollar_format(p = "", su = "€", a = 1))

2014-

Code
arrdt %>%
  filter(Location %in% c("13e", "14e", "15e")) %>%
  group_by(Location) %>%
  filter(date >= as.Date("2014-01-01")) %>%
  ggplot + geom_line(aes(x = date, y = value, color = gsub("e", "ème arrondissement", Location))) +
  
  theme_minimal()  +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2050, 1), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.25, 0.85),
        legend.title = element_blank()) +
  xlab("") + ylab("Prix au mètre carré") +
  scale_y_log10(breaks = seq(0, 17000, 1000),
                labels = dollar_format(p = "", su = "€", a = 1))

2018-

Code
arrdt %>%
  filter(Location %in% c("13e", "14e", "15e")) %>%
  group_by(Location) %>%
  filter(date >= as.Date("2018-01-01")) %>%
  ggplot + geom_line(aes(x = date, y = value, color = gsub("e", "ème arrondissement", Location))) +
  
  theme_minimal()  +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2050, 1), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.15, 0.85),
        legend.title = element_blank()) +
  xlab("") + ylab("Prix au mètre carré") +
  scale_y_log10(breaks = seq(0, 17000, 200),
                labels = dollar_format(p = "", su = "€", a = 1))

16ème, 6ème, 7ème arrondissement

Value

Code
arrdt %>%
  filter(Location %in% c("16e", "6e", "7e")) %>%
  group_by(Location) %>%
  ggplot + geom_line(aes(x = date, y = value, color = gsub("e", "ème arrondissement", Location))) +
  
  theme_minimal()  +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2050, 2), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.25, 0.85),
        legend.title = element_blank()) +
  xlab("") + ylab("Prix au mètre carré") +
  scale_y_log10(breaks = seq(0, 17000, 1000),
                labels = dollar_format(p = "", su = "€", a = 1))

100 = 2008

Code
arrdt %>%
  filter(Location %in% c("16e", "6e", "7e")) %>%
  group_by(Location) %>%
  mutate(value = 100*value/value[date == as.Date("2008-01-01")]) %>%
  ggplot + geom_line(aes(x = date, y = value, color = gsub("e", "ème arrondissement", Location))) +
  
  theme_minimal()  +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2050, 2), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.25, 0.85),
        legend.title = element_blank()) +
  xlab("") + ylab("Indice des prix au mètre carré (2008 = 100)") +
  scale_y_log10(breaks = seq(0, 7000, 10))

6ème, 5ème, 14ème arrondissement

Value

Code
arrdt %>%
  filter(Location %in% c("6e", "5e", "14e")) %>%
  group_by(Location) %>%
  ggplot + geom_line(aes(x = date, y = value, color = gsub("e", "ème arrondissement", Location))) +
  
  theme_minimal()  +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2050, 2), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.25, 0.85),
        legend.title = element_blank()) +
  xlab("") + ylab("Prix au mètre carré") +
  scale_y_log10(breaks = seq(0, 17000, 1000),
                labels = dollar_format(p = "", su = "€", a = 1))

100 = 2008

Code
arrdt %>%
  filter(Location %in% c("6e", "5e", "14e")) %>%
  group_by(Location) %>%
  mutate(value = 100*value/value[date == as.Date("2008-01-01")]) %>%
  ggplot + geom_line(aes(x = date, y = value, color = gsub("e", "ème arrondissement", Location))) +
  
  theme_minimal()  +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2050, 2), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.25, 0.85),
        legend.title = element_blank()) +
  xlab("") + ylab("Indice des prix au mètre carré (2008 = 100)") +
  scale_y_log10(breaks = seq(0, 7000, 10))

1er, 2ème, 3ème arrondissement

Value

Code
arrdt %>%
  filter(Location %in% c("1er", "2e", "3e")) %>%
  mutate(Location = case_when(Location == "1er" ~ "1er arrondissement",
                              Location == "2e" ~ "2ème arrondissement",
                              Location == "3e" ~ "3ème arrondissement")) %>%
  group_by(Location) %>%
  ggplot + geom_line(aes(x = date, y = value, color = Location)) +
  
  theme_minimal()  +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2050, 2), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.25, 0.85),
        legend.title = element_blank()) +
  xlab("") + ylab("Prix au mètre carré") +
  scale_y_log10(breaks = seq(0, 17000, 1000),
                labels = dollar_format(p = "", su = "€", a = 1))

100 = 2008

Code
arrdt %>%
  filter(Location %in% c("1er", "2e", "3e")) %>%
  mutate(Location = case_when(Location == "1er" ~ "1er arrondissement",
                              Location == "2e" ~ "2ème arrondissement",
                              Location == "3e" ~ "3ème arrondissement")) %>%
  group_by(Location) %>%
  mutate(value = 100*value/value[date == as.Date("2008-01-01")]) %>%
  ggplot + geom_line(aes(x = date, y = value, color = Location)) +
  
  theme_minimal()  +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2050, 2), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.25, 0.85),
        legend.title = element_blank()) +
  xlab("") + ylab("Indice des prix au mètre carré (2008 = 100)") +
  scale_y_log10(breaks = seq(0, 7000, 10))