Housing
Completions
The Government’s Housing for All plan targets 24,600 completions in 2022, 29,000 in 2023 and 33,450 in 2024.
setwd("/Users/charten/OneDrive - Glenveagh Properties/Research & Development/1. Analysis/")
GEO_Merge <- readxl::read_xlsx(path="C:\\Users\\charten\\OneDrive - Glenveagh Properties\\Research & Development\\1. Analysis\\HPM04_GEO.xlsx",sheet = 1)
shp <- sf::read_sf("Constituency_Boundaries_Ungeneralised___OSi_National_Electoral_Boundaries___2017.shp")
## NDQ07
NDQ07 <- cso_get_data("NDQ07")
NDQ07_long <- NDQ07 %>%
pivot_longer(!1:2, names_to = "year_qtr")
NDQ07 <- NDQ07_long
rm(NDQ07_long)
NDQ07$Year_Q <- as.yearqtr(NDQ07$year_qtr)
NDQ07$Year <- year(NDQ07$Year_Q)
NDQ07_A <- NDQ07 %>%
filter(Eircode.Output=="All")
NDQ07_B <- NDQ07 %>%
filter(Eircode.Output!="All")
NDQ07_join <- full_join(NDQ07_B, GEO_Merge, by = "Eircode.Output")
NDQ07 <- NDQ07_join
rm(NDQ07_join)
rm(NDQ07_B)NDQ07_A_tail <- tail(NDQ07_A,1)
NDQ07_A_tail_lag <-head(tail(NDQ07_A,2),1)
NDQ07_A_tail_yonylag <-head(tail(NDQ07_A,5),1)
NDQ07_A_24 <- tail(NDQ07_A,24)
ggplot(data=NDQ07_A_24, aes(x=Year_Q, y=value))+
geom_col(alpha = 0.5, colour="#373634", fill = "#1b5545")+
labs(title = "New Dwelling Completions, by Quarter - NDQ07" ,
subtitle = "24 Month Series",
y="Units Completed",
x="Year-Qtr")+
geom_text(aes(label=value),vjust= 1.5, size=2)+
theme(legend.position = "bottom")
tbl_NDQ07_A_Year <- NDQ07_A %>%
group_by(Year)%>%
summarise(Average_Value = mean(value),
Total = sum(value))
ggplot(data=tbl_NDQ07_A_Year, aes(x=Year, y=Total))+
geom_col(alpha = 0.5, colour="#373634", fill = "#02218A")+
labs(title = "New Dwelling Completions, by Year - NDQ07" ,
subtitle = "total sample",
y="Units Completed",
x="Year")+
geom_text(aes(label=Total),vjust= 1.5, size=3)+
theme(legend.position = "bottom")+
scale_x_continuous(breaks= 5)
NDQ07_Map <- NDQ07 %>%
filter(year_qtr == NDQ07_A_tail$year_qtr)
NDQ07_Map_Dub <- NDQ07_Map %>%
filter(grepl('Dublin|Laoghaire', Seat_Tag))
shpNDQ <- merge(shp,NDQ07_Map, by.x = "CON_SEAT_", by.y="Seat_Tag")
shpNDQDub <- merge(shp,NDQ07_Map_Dub, by.x = "CON_SEAT_", by.y="Seat_Tag")
Map_1 <- shpNDQ %>%
ggplot()+
geom_sf(aes(fill = value))+
scale_fill_stepsn(n.breaks = 10,
colors=c("#ECEFFB","#3966FE","#02218A"),
name = "completions")+
ggtitle('Eircode Output Aggregated to Constituency: Latest Quarter')+
theme_void()+
theme(legend.title = element_text(size = 10),
legend.text = element_text(size = 8))+
theme(legend.position = "left")+
theme(plot.title = element_text(hjust = 1))
Map_2 <- shpNDQDub %>%
ggplot()+
geom_sf(aes(fill = value))+
scale_fill_stepsn(n.breaks = 10,
colors=c("#ECEFFB","#3966FE","#02218A"),
name = "completions")+
theme_void()+
theme(legend.position = "none")
Map_View <- Map_1 + Map_2 +
plot_layout(widths = c(2,1))
Map_View
Table%>%
kbl(caption = "New Dwelling Completions - Sorted by Total")%>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed"), full_width = T)%>%
scroll_box(width = "600px", height = "300px") | Eircode.Output | Year_Q | Constituency | value |
|---|---|---|---|
| D18: Dublin 18 | 2022 Q2 | Dublin Rathdown | 528 |
| D12: Dublin 12 | 2022 Q2 | Dublin South Central | 377 |
| D09: Dublin 9 | 2022 Q2 | Dublin North-West | 340 |
| H91: Galway | 2022 Q2 | Galway West | 255 |
| T12: Cork Southside | 2022 Q2 | Cork North-Central | 234 |
| A92: Drogheda | 2022 Q2 | Louth | 225 |
| W91: Naas | 2022 Q2 | Kildare North | 220 |
| C15: Navan | 2022 Q2 | Meath West | 219 |
| D07: Dublin 7 | 2022 Q2 | Dublin West | 219 |
| W23: Celbridge | 2022 Q2 | Kildare North | 196 |
| V94: Limerick | 2022 Q2 | Limerick City | 190 |
| D22: Dublin 22 | 2022 Q2 | Dublin South Central | 189 |
| A63: Greystones | 2022 Q2 | Wicklow | 166 |
| K78: Lucan | 2022 Q2 | Dublin Mid-West | 159 |
| V95: Ennis | 2022 Q2 | Clare | 144 |
| R95: Kilkenny | 2022 Q2 | Carlow-Kilkenny | 138 |
| A91: Dundalk | 2022 Q2 | Louth | 133 |
| V92: Tralee | 2022 Q2 | Kerry | 120 |
| W12: Newbridge | 2022 Q2 | Kildare South | 115 |
| R32: Portlaoise | 2022 Q2 | Laois-Offaly | 114 |
| D16: Dublin 16 | 2022 Q2 | Dublin South-West | 111 |
| D13: Dublin 13 | 2022 Q2 | Dublin Bay North | 94 |
| V93: Killarney | 2022 Q2 | Kerry | 92 |
| P43: Carrigaline | 2022 Q2 | Cork South-Central | 91 |
| T23: Cork Northside | 2022 Q2 | Cork North-Central | 88 |
| T45: Glanmire | 2022 Q2 | Cork North-Central | 87 |
| K36: Malahide | 2022 Q2 | Dublin Fingal | 84 |
| A67: Wicklow | 2022 Q2 | Wicklow | 81 |
| D01: Dublin 1 | 2022 Q2 | Dublin Central | 79 |
| K56: Rush | 2022 Q2 | Dublin Fingal | 78 |
| N91: Mullingar | 2022 Q2 | Longford-Westmeath | 70 |
| D24: Dublin 24 | 2022 Q2 | Dublin South-West | 68 |
| Y35: Wexford | 2022 Q2 | Wexford | 68 |
| P51: Mallow | 2022 Q2 | Cork East | 67 |
| X91: Waterford | 2022 Q2 | Waterford | 63 |
| F92: Letterkenny | 2022 Q2 | Donegal | 62 |
| R35: Tullamore | 2022 Q2 | Laois-Offaly | 61 |
| K32: Balbriggan | 2022 Q2 | Dublin Fingal | 57 |
| K45: Lusk | 2022 Q2 | Dublin Fingal | 54 |
| F94: Donegal | 2022 Q2 | Donegal | 51 |
| F93: Lifford | 2022 Q2 | Donegal | 49 |
| A85: Dunshaughlin | 2022 Q2 | Meath East | 48 |
| D17: Dublin 17 | 2022 Q2 | Dublin Fingal | 48 |
| H12: Cavan | 2022 Q2 | Cavan-Monaghan | 48 |
| R93: Carlow | 2022 Q2 | Carlow-Kilkenny | 48 |
| Y25: Gorey | 2022 Q2 | Wexford | 47 |
| Y21: Enniscorthy | 2022 Q2 | Wexford | 44 |
| F91: Sligo | 2022 Q2 | Sligo-Leitrim | 41 |
| D04: Dublin 4 | 2022 Q2 | Dublin Bay South | 39 |
| K67: Swords | 2022 Q2 | Dublin Fingal | 38 |
| H54: Tuam | 2022 Q2 | Galway East | 37 |
| A82: Kells | 2022 Q2 | Meath East | 35 |
| P25: Midleton | 2022 Q2 | Cork East | 35 |
| V35: Kilmallock | 2022 Q2 | Limerick County | 34 |
| D06: Dublin 6 | 2022 Q2 | Dublin Bay South | 33 |
| P31: Ballincollig | 2022 Q2 | Cork North-West | 33 |
| P12: Macroom | 2022 Q2 | Cork North-West | 32 |
| A98: Bray | 2022 Q2 | Wicklow | 31 |
| N37: Athlone | 2022 Q2 | Longford-Westmeath | 31 |
| Y14: Arklow | 2022 Q2 | Wicklow | 31 |
| D15: Dublin 15 | 2022 Q2 | Dublin West | 30 |
| F28: Westport | 2022 Q2 | Mayo | 30 |
| N39: Longford | 2022 Q2 | Longford-Westmeath | 30 |
| A81: Carrickmacross | 2022 Q2 | Cavan-Monaghan | 29 |
| F12: Claremorris | 2022 Q2 | Mayo | 29 |
| E45: Nenagh | 2022 Q2 | Tipperary | 28 |
| F26: Ballina | 2022 Q2 | Mayo | 28 |
| R56: Curragh | 2022 Q2 | Kildare South | 28 |
| H65: Athenry | 2022 Q2 | Galway East | 27 |
| P85: Clonakilty | 2022 Q2 | Cork South-West | 27 |
| A83: Enfield | 2022 Q2 | Meath West | 26 |
| D02: Dublin 2 | 2022 Q2 | Dublin Bay South | 26 |
| H18: Monaghan | 2022 Q2 | Cavan-Monaghan | 26 |
| P17: Kinsale | 2022 Q2 | Cork South-West | 25 |
| F23: Castlebar | 2022 Q2 | Mayo | 24 |
| H53: Ballinasloe | 2022 Q2 | Roscommon-Galway | 23 |
| R51: Kildare | 2022 Q2 | Kildare South | 23 |
| A96: Glenageary | 2022 Q2 | Dun Laoghaire | 22 |
| H62: Loughrea | 2022 Q2 | Galway East | 22 |
| A75: Castleblaney | 2022 Q2 | Cavan-Monaghan | 21 |
| E91: Clonmel | 2022 Q2 | Tipperary | 21 |
| P72: Bandon | 2022 Q2 | Cork South-West | 21 |
| W34: Monasterevin | 2022 Q2 | Kildare South | 20 |
| E32: Carrick-on-Suir | 2022 Q2 | Tipperary | 19 |
| E41: Thurles | 2022 Q2 | Tipperary | 19 |
| P47: Dunmanway | 2022 Q2 | Cork South-West | 19 |
| P81: Skibbereen | 2022 Q2 | Cork South-West | 19 |
| D05: Dublin 5 | 2022 Q2 | Dublin Bay North | 18 |
| H23: Clones | 2022 Q2 | Cavan-Monaghan | 18 |
| R42: Birr | 2022 Q2 | Laois-Offaly | 18 |
| V31: Listowel | 2022 Q2 | Kerry | 18 |
| D03: Dublin 3 | 2022 Q2 | Dublin Bay North | 16 |
| P14: Crookstown | 2022 Q2 | Cork North-West | 15 |
| T34: Carrignavar | 2022 Q2 | Cork East | 14 |
| A86: Dunboyne | 2022 Q2 | Meath East | 13 |
| A94: Blackrock | 2022 Q2 | Dun Laoghaire | 13 |
| E34: Tipperary | 2022 Q2 | Tipperary | 13 |
| F45: Castlerea | 2022 Q2 | Roscommon-Galway | 13 |
| P24: Cobh | 2022 Q2 | Cork East | 13 |
| Y34: New Ross | 2022 Q2 | Wexford | 13 |
| D08: Dublin 8 | 2022 Q2 | Dublin South Central | 12 |
| T56: Watergrasshill | 2022 Q2 | Cork North-Central | 12 |
| P61: Fermoy | 2022 Q2 | Cork East | 11 |
| R21: Mhuine Bheag | 2022 Q2 | Carlow-Kilkenny | 11 |
| R45: Edenderry | 2022 Q2 | Laois-Offaly | 11 |
| V14: Shannon | 2022 Q2 | Clare | 11 |
| F42: Roscommon | 2022 Q2 | Roscommon-Galway | 10 |
| V15: Kilrush | 2022 Q2 | Clare | 10 |
| H14: Belturbet | 2022 Q2 | Cavan-Monaghan | 9 |
| K34: Skerries | 2022 Q2 | Dublin Fingal | 9 |
| N41: Carrick-on-Shannon | 2022 Q2 | Sligo-Leitrim | 9 |
| E25: Cashel | 2022 Q2 | Tipperary | 8 |
| P36: Youghal | 2022 Q2 | Cork East | 8 |
| V42: Newcastle West | 2022 Q2 | Limerick County | 8 |
| X42: Kilmacthomas | 2022 Q2 | Waterford | 8 |
| X35: Dungarvan | 2022 Q2 | Waterford | 7 |
| D14: Dublin 14 | 2022 Q2 | Dublin Bay South | 6 |
| P67: Mitchelstown | 2022 Q2 | Cork East | 6 |
| P75: Bantry | 2022 Q2 | Cork South-West | 6 |
| R14: Athy | 2022 Q2 | Kildare South | 6 |
| E21: Cahir | 2022 Q2 | Tipperary | 5 |
| A42: Garristown | 2022 Q2 | Dublin Fingal | 4 |
| D11: Dublin 11 | 2022 Q2 | Dublin West | 4 |
| F31: Ballinrobe | 2022 Q2 | Mayo | 4 |
| F52: Boyle | 2022 Q2 | Sligo-Leitrim | 4 |
| H16: Cootehill | 2022 Q2 | Cavan-Monaghan | 4 |
| E53: Roscrea | 2022 Q2 | Tipperary | 3 |
| F35: Ballyhaunis | 2022 Q2 | Mayo | 3 |
| F56: Ballymote | 2022 Q2 | Sligo-Leitrim | 3 |
| P56: Charleville | 2022 Q2 | Cork North-West | 3 |
| A41: Ballyboughal | 2022 Q2 | Dublin Fingal | 2 |
| A45: Oldtown | 2022 Q2 | Dublin Fingal | 2 |
| A84: Ashbourne | 2022 Q2 | Meath East | 2 |
| D6W: Dublin 6W | 2022 Q2 | Dublin South Central | 2 |
| H71: Clifden | 2022 Q2 | Galway West | 2 |
| V23: Caherciveen | 2022 Q2 | Kerry | 2 |
| D10: Dublin 10 | 2022 Q2 | Dublin South Central | 1 |
| D20: Dublin 20 | 2022 Q2 | Dublin South Central | 0 |
| P32: Rylane | 2022 Q2 | Cork North-West | 0 |
House Price Index
HPM09 <- cso_get_data("HPM09")
HPM09_long <- HPM09 %>%
pivot_longer(!1:2, names_to = "year_month")
rm(HPM09)
HPM09 <- HPM09_long
rm(HPM09_long)
HPM09$Month <- as.Date(paste(HPM09$year_month, "01", sep = "-"), "%YM%m-%d")
HPM09$Year <- year(HPM09$Month)
HPM04 <- cso_get_data("HPM04")
HPM04_long <- HPM04 %>%
pivot_longer(!1:5, names_to = "year_month")
rm(HPM04)
HPM04 <- HPM04_long
rm(HPM04_long)
### Date transformation
HPM04$Year <-substr(HPM04$year_month,1,4)
HPM04$Month <- sub(".* ", "", HPM04$year_month)
HPM04$Month_NR <- as.integer(factor(HPM04$Month, levels=month.name))
HPM04$Date <- as.yearmon(paste(HPM04$Year, HPM04$Month_NR), "%Y %m")
HPM04_join <- full_join(HPM04, GEO_Merge, by = "Eircode.Output")
HPM04 <- HPM04_join
rm(HPM04_join)HPM09$month <- months(as.Date(HPM09$Month))
HPM09_1 <- HPM09 %>%
filter(Statistic == "Residential Property Price Index")%>%
filter(Type.of.Residential.Property == "National - all residential properties")
HPM09_2 <- HPM09 %>%
filter(Statistic == "Percentage Change over 12 months for Residential Property Price Index")%>%
filter(Type.of.Residential.Property == "National - all residential properties")
RPPI_tail_2 <- tail(HPM09_2,1)
RPPI_tail_2_lag <-head(tail(HPM09_2,2),1)RRPI_Line_1 <- ggplot(data=HPM09_1, aes(x=Month, y=value, group = Type.of.Residential.Property))+
geom_line(linejoin="mitre",size = 1.25, linetype = 1,alpha = 0.5, colour="#1b5545")+
labs(title = "Residential Property Price Index" ,
y="2015 = 100",
x="Month")+
geom_text_repel(aes(label=value),data = HPM09_1, size = 3)+
theme(legend.position = "bottom")
RRPI_Line_1
HPM09_1_T12 <- tail(HPM09_1,12)
RRPI_Line_2 <- ggplot(data=HPM09_1_T12, aes(x=Month, y=value, group = Type.of.Residential.Property))+
geom_line(linejoin="mitre",size = 1.25, linetype = 1,alpha = 0.5, colour="#1b5545")+
labs(title = "Residential Property Price Index" ,
subtitle = "12 Month Series",
y="2015 = 100",
x="Month")+
geom_text(aes(label=value),vjust= 1.5, hjust = 0, size=3)+
theme(legend.position = "bottom")
RRPI_Line_2
HPM09_2_T24 <- tail(HPM09_2,24)
colour <- ifelse(HPM09_2_T24$value < 0,"#CC0000","#1b5545")
RPPI_Bar_2 <- ggplot(data=HPM09_2_T24, aes(x=Month, y=value, group = Type.of.Residential.Property))+
geom_col(alpha = 0.5, colour="#373634", fill = colour)+
labs(title = "Year on Year Percentage Change for Residential Property Price Index" ,
subtitle = "24 Month Series",
y="Percentage change",
x="Month")+
geom_text(aes(label=value),vjust= 1.5, size=3)+
theme(legend.position = "bottom")
rm(HPM09)
HPM04_1A <- HPM04 %>%
filter(Statistic == "Mean Sale Price")%>%
filter(Dwelling.Status == "All Dwelling Statuses") %>%
filter(Stamp.Duty.Event == "Executions") %>%
filter(Type.of.Buyer == "All Buyer Types") %>%
filter(Eircode.Output == "All")
HPM04_1B <- HPM04 %>%
filter(Statistic == "Mean Sale Price")%>%
filter(Dwelling.Status == "All Dwelling Statuses") %>%
filter(Stamp.Duty.Event == "Executions") %>%
filter(Type.of.Buyer == "All Buyer Types") %>%
filter(Eircode.Output != "All")
HPM04_1B_Map <- na.omit(HPM04_1B)
HPM04_1B_Map<- HPM04_1B_Map %>%
group_by(Seat_Tag, Date)%>% # change to/from County / Constituency
summarise(Average_Value = mean(value))
HPM04_1B_Map$lagvalue_12mth <- Lag(HPM04_1B_Map$Average_Value,12)
HPM04_1B_Map$Diff <- HPM04_1B_Map$Average_Value - HPM04_1B_Map$lagvalue_12mth
HPM04_1B_Map$Diffpc <- HPM04_1B_Map$Diff / HPM04_1B_Map$lagvalue_12mth
HPM04_1B_Map$Diffpc<-percent(HPM04_1B_Map$Diffpc,2)
HPM04_1B_Map_May2022 <- HPM04_1B_Map%>%
filter(Date == "May 2022")
shpHPM <- merge(shp,HPM04_1B_Map_May2022, by.x = "CON_SEAT_", by.y="Seat_Tag")
HPM04_1B_Map_May2022_Dub <- HPM04_1B_Map_May2022 %>%
filter(grepl('Dublin|Laoghaire', Seat_Tag))
shpHPMDub <- merge(shp,HPM04_1B_Map_May2022_Dub, by.x = "CON_SEAT_", by.y="Seat_Tag")
Map2_DiffpcAll <- shpHPM %>%
ggplot()+
geom_sf(aes(fill = Diffpc))+
scale_fill_stepsn(n.breaks = 10,
colors=c("#F92306","#FFFFFF","#0C4F00"),
limits = c(-1,1),
name = "% change")+
ggtitle('Year on Year Change by Constituency - HPM04')+
theme_void()+
theme(legend.title = element_text(size = 10),
legend.text = element_text(size = 8))+
theme(legend.position = "left")+
theme(plot.title = element_text(hjust = 1))
Map2_DiffpcDub <- shpHPMDub %>%
ggplot()+
geom_sf(aes(fill = Diffpc))+
scale_fill_stepsn(n.breaks = 10,
colors=c("#F92306","#FFFFFF","#0C4F00"),
limits = c(-1,1),
name = "% change")+
theme_void()+
theme(legend.position = "none")
Map2_Diffpc <- Map2_DiffpcAll + Map2_DiffpcDub +
plot_layout(widths = c(2,1))
Map2_Diffpc
Average percentage change year on year is 13.79%. This was 13.57% in the Dublin constituencies.
HPM04_1B_Map_May2022%>%
kbl(caption = "Year on Year ")%>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed"), full_width = T)%>%
scroll_box(width = "600px", height = "300px") | Seat_Tag | Date | Average_Value | lagvalue_12mth | Diff | Diffpc |
|---|---|---|---|---|---|
| Carlow-Kilkenny (5) | May 2022 | 216527.7 | 199519.3 | 17008.33 | 8.52% |
| Cavan-Monaghan (5) | May 2022 | 177097.0 | 144608.4 | 32488.57 | 22.47% |
| Clare (4) | May 2022 | 224667.0 | 172103.7 | 52563.33 | 30.54% |
| Cork East (4) | May 2022 | 254092.0 | 222825.8 | 31266.17 | 14.03% |
| Cork North-Central (4) | May 2022 | 362033.0 | 296311.0 | 65722.00 | 22.18% |
| Cork North-West (3) | May 2022 | 295421.8 | 216368.6 | 79053.15 | 36.54% |
| Cork South-Central (4) | May 2022 | 387499.0 | 355174.0 | 32325.00 | 9.10% |
| Cork South-West (3) | May 2022 | 317929.7 | 218142.0 | 99787.67 | 45.74% |
| Donegal (5) | May 2022 | 166165.3 | 137711.7 | 28453.67 | 20.66% |
| Dublin Bay North (5) | May 2022 | 477757.7 | 462236.7 | 15521.00 | 3.36% |
| Dublin Bay South (4) | May 2022 | 1061525.3 | 617102.3 | 444423.00 | 72.02% |
| Dublin Central (4) | May 2022 | 380833.0 | 283600.0 | 97233.00 | 34.29% |
| Dublin Fingal (5) | May 2022 | 400859.1 | 350144.0 | 50715.12 | 14.48% |
| Dublin Mid-West (4) | May 2022 | 374310.0 | 328365.0 | 45945.00 | 13.99% |
| Dublin North-West (3) | May 2022 | 465500.0 | 450125.0 | 15375.00 | 3.42% |
| Dublin Rathdown (3) | May 2022 | 610032.0 | 584364.0 | 25668.00 | 4.39% |
| Dublin South-West (5) | May 2022 | 558630.0 | 538289.7 | 20340.33 | 3.78% |
| Dublin South Central (4) | May 2022 | 390266.2 | 406003.2 | -15737.00 | -3.88% |
| Dublin West (4) | May 2022 | 399194.5 | 390229.0 | 8965.50 | 2.30% |
| Dun Laoghaire (4) | May 2022 | 798034.5 | 789073.5 | 8961.00 | 1.14% |
| Galway East (3) | May 2022 | 245063.7 | 232117.7 | 12946.00 | 5.58% |
| Galway West (5) | May 2022 | 256908.0 | 330317.5 | -73409.50 | -22.22% |
| Kerry (5) | May 2022 | 243051.5 | 193255.0 | 49796.50 | 25.77% |
| Kildare North (4) | May 2022 | 387790.5 | 355096.5 | 32694.00 | 9.21% |
| Kildare South (4) | May 2022 | 249614.0 | 258038.0 | -8424.00 | -3.26% |
| Laois-Offaly (5) | May 2022 | 209237.0 | 187944.2 | 21292.75 | 11.33% |
| Limerick City (4) | May 2022 | 273538.0 | 244928.0 | 28610.00 | 11.68% |
| Limerick County (3) | May 2022 | 143393.0 | 158415.5 | -15022.50 | -9.48% |
| Longford-Westmeath (4) | May 2022 | 225328.7 | 186934.0 | 38394.67 | 20.54% |
| Louth (5) | May 2022 | 271900.0 | 250552.0 | 21348.00 | 8.52% |
| Mayo (4) | May 2022 | 178087.7 | 163940.8 | 14146.83 | 8.63% |
| Meath East (3) | May 2022 | 338315.4 | 346414.4 | -8099.00 | -2.34% |
| Meath West (3) | May 2022 | 271314.0 | 288824.0 | -17510.00 | -6.06% |
| Roscommon-Galway (3) | May 2022 | 191973.7 | 150357.3 | 41616.33 | 27.68% |
| Sligo-Leitrim (4) | May 2022 | 166604.0 | 159675.0 | 6929.00 | 4.34% |
| Tipperary (5) | May 2022 | 214051.4 | 152241.0 | 61810.38 | 40.60% |
| Waterford (4) | May 2022 | 285926.0 | 212470.7 | 73455.33 | 34.57% |
| Wexford (5) | May 2022 | 230609.2 | 208450.8 | 22158.50 | 10.63% |
| Wicklow (5) | May 2022 | 434795.8 | 421322.0 | 13473.75 | 3.20% |
Inflation
EIHC06 <- cso_get_data("EIHC06")
EIHC06 <- EIHC06 %>%
pivot_longer(!1:3, names_to = "year_month")
EIHC06 <- EIHC06 %>%
filter(Statistic=="Contribution to CPI percentage change in last 12 months")
EIHC06$Decile_Nr <- extract_numeric(EIHC06$Income.Deciles)
EIHC06A<- EIHC06 %>%
filter(COICOP.Division=="All Items")%>%
filter(Income.Deciles=="All deciles")
EIHC06B<- EIHC06 %>%
filter(COICOP.Division!="All Items")%>%
filter(Income.Deciles!="All deciles")See below contribution to inflation by income decile. Next quarters will likely show the mortgage rate increase
HeatMap<-ggplot(data=EIHC06B, mapping = aes(x = Income.Deciles, y = COICOP.Division, fill = value))+
geom_tile()+
xlab(label = "Income Deciles")+
theme(axis.text.x = element_text(angle = 90, hjust = 1))+
theme(legend.position = "bottom")+
scale_fill_gradient(name = "Contribution to Inflation",
low = "#F9F9FA",
high = "#010874")
HeatMap + facet_wrap(~year_month,ncol=1)
Retail Sales
## NDQ07
RSM05 <- cso_get_data("RSM05")
RSM05 <- RSM05 %>%
pivot_longer(!1:2, names_to = "year_month")
# Date transformation
## Take adjusted index only
RSM05 <- RSM05%>%filter(Statistic=="Retail Sales Index Value Adjusted"|Statistic=="Retail Sales Index Volume Adjusted")
RSM05$Month <- as.Date(paste(RSM05$year_month, "01", sep = "-"), "%YM%m-%d")
RSM05$Year <- year(RSM05$Month)
RSM05$Lag <- Lag(RSM05$value,1)
RSM05$Diff <- RSM05$value-RSM05$Lag
RSM05_A <- RSM05%>%
filter(NACE.Group=="All retail businesses")
RSM05_B <- RSM05%>%
filter(NACE.Group=="Motor trades (45)"|NACE.Group=="Retail sale in non-specialised stores with food, beverages or tobacco predominating (4711)"|NACE.Group=="Department stores (4719)"|NACE.Group=="Retail sale of automotive fuel (4730)"|NACE.Group=="Retail sale of hardware, paints and glass (4752)"|NACE.Group=="Retail sale of furniture and lighting (4759)"|NACE.Group=="Bars (5630)")
RSM05_A1 <- RSM05_A%>%filter(Statistic=="Retail Sales Index Value Adjusted")
RSM05_A2 <- RSM05_A%>%filter(Statistic=="Retail Sales Index Volume Adjusted")
RSM05_B1 <- RSM05_B%>%filter(Statistic=="Retail Sales Index Value Adjusted")
RSM05_B2 <- RSM05_B%>%filter(Statistic=="Retail Sales Index Volume Adjusted")
ggplot(data=RSM05_A1,aes(x=Month,y=value))+
geom_line(size = 1.15, linetype=1, alpha = 0.6, colour = "#4aa98a")+
geom_hline(aes(yintercept=100),
colour= "#404040",
linetype = 1)+
labs(title = "RSI Value (Adjusted) - CSO: RSM05", subtitle = "2015 to Date")+
xlab("Year-Month")+
ylab("Retail Sales Index (Base Dec 2015=100)")+
theme(panel.border = element_rect(linetype = 1, fill = NA))
Wholesale Construction
Wholesale Price Index for Building and Construction Materials has 40 Types of Materials. Black line (100) indicates 2015 as baseline.
WPM28 <- cso_get_data("WPM28")
WPM28 <- WPM28 %>%
pivot_longer(!1:2, names_to = "year_month")
# Date transformation
WPM28$Month <- as.Date(paste(WPM28$year_month, "01", sep = "-"), "%YM%m-%d")
WPM28$Year <- year(WPM28$Month)
WPM28$Lag <- Lag(WPM28$value,1)
WPM28$Diff <- WPM28$value-WPM28$Lag
# Take year greater than or equal to 2019
WPM28 <- WPM28 %>%
filter(Year >= "2021")
WPM28_A <- WPM28%>%
filter(Statistic=="Wholesale Price Index (Excl VAT) for Building and Construction Materials")
Fig2<-ggplot(WPM28_A, aes(x=Month, y=value, group=Type.of.Material, colour=Type.of.Material))+
geom_line(aes(group=Type.of.Material),size = 1.05, linetype=1, alpha = 0.65)+
labs(title = "Wholesale Price Index (Excl VAT) for Building and Construction Materials")+
xlab("Year-Month")+
ylab("2015 = 100")+
geom_hline(aes(yintercept=100),
colour= "#404040",
linetype = 1)+
scale_x_date(date_labels="%b-%Y",date_breaks ="3 month")+
theme(axis.text.x = element_text(angle=90))+
theme(axis.text.x=element_text(size=10))+
theme(legend.position="none")+
theme(axis.text = element_text(size = rel(1)))+
theme(plot.title=(element_text(vjust =2)))+
theme(panel.border = element_rect(linetype = 1, fill = NA))
Fig2 + facet_wrap(~Type.of.Material, ncol = 2)