GVH EC - Week 2 September
Cormac Harten
House Price Index
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")
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$Month_NR), "%Y %m")
HPM04_join <- full_join(HPM04, GEO_Merge, by = "Eircode.Output")
HPM04 <- HPM04_join
rm(HPM04_join)HPM09$Year <-substr(HPM09$year_month,1,4)
HPM09$Month <- sub(".* ", "", HPM09$year_month)
HPM09$Month_NR <- as.integer(factor(HPM09$Month, levels=month.name))
HPM09$Date <- as.yearmon(paste(HPM09$Year, HPM09$Month_NR), "%Y %m")
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=Date, 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_bw()+
theme(legend.position = "bottom")+theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
HPM09_1_T12 <- tail(HPM09_1,12)
RRPI_Line_2 <- ggplot(data=HPM09_1_T12, aes(x=Date, 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_bw()+
theme(legend.position = "bottom")+theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
RRPI_Line_1+RRPI_Line_2+plot_layout(ncol = 2)
HPM09_2_T24 <- tail(HPM09_2,24)
colour <- ifelse(HPM09_2_T24$value < 0,"#CC0000","#1b5545")
RPPI_Bar_2 <- ggplot(data=HPM09_2_T24, aes(x=Date, y=value, group = Type.of.Residential.Property))+
geom_col(alpha = 0.5, colour="#373634", fill = colour)+
labs(title = "Year on Year Percentage Change for Residential Property Price Index" ,
subtitle = "24 Month Series",
y="Percentage change",
x="Month")+
geom_text(aes(label=value),vjust= 1.5, size=3)+
theme_bw()+
theme(legend.position = "bottom")+
rm(HPM09)The national house price inflation for is 13%. This represents a -1 change from which was 14%.
# Data Clean
HPM04_1A <- HPM04 %>%
filter(Statistic == "Mean Sale Price")%>%
filter(Dwelling.Status == "All Dwelling Statuses") %>%
filter(Stamp.Duty.Event == "Executions") %>%
filter(Type.of.Buyer == "All Buyer Types") %>%
filter(Eircode.Output == "All")
HPM04_1B <- HPM04 %>%
filter(Statistic == "Mean Sale Price")%>%
filter(Dwelling.Status == "All Dwelling Statuses") %>%
filter(Stamp.Duty.Event == "Executions") %>%
filter(Type.of.Buyer == "All Buyer Types") %>%
filter(Eircode.Output != "All")
HPM04_1B_Map <- na.omit(HPM04_1B)
HPM04_1B_Map<- HPM04_1B_Map %>%
group_by(Seat_Tag, Date)%>% # change to/from County / Constituency
summarise(Average_Value = mean(value))
HPM04_1B_Map$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_July2022 <- HPM04_1B_Map%>%
filter(Date == "July 2022") # change month
shpHPM <- merge(shp,HPM04_1B_Map_July2022, by.x = "CON_SEAT_", by.y="Seat_Tag")
HPM04_1B_Map_July2022_Dub <- HPM04_1B_Map_July2022 %>%
filter(grepl('Dublin|Laoghaire|Meath|Wicklow|Kildare|Louth', Seat_Tag))
# Labs_HPI <- ED %>%
# filter(EDNAME=="Trim Urban"|EDNAME=="Navan Urban"|EDNAME=="Dunboyne"|EDNAME=="Innfield")
shpHPMDub <- merge(shp,HPM04_1B_Map_July2022_Dub, by.x = "CON_SEAT_", by.y="Seat_Tag")
# Maps
Map2_DiffpcAll <- shpHPM %>%
ggplot()+
geom_sf(aes(fill = Diffpc),colour=alpha("gray",0.4))+
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),colour=alpha("gray",0.4))+
geom_sf_text(aes(label=Diffpc),color="black",alpha=0.9,size=1.75)+
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 13.85%. This was 8.62% in the Dublin constituencies.
These averages the mean value of the below:
HPM04_1B_Map_July2022%>%
kbl(caption = "Year on Year ")%>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed"), full_width = T)%>%
scroll_box(width = "700px", height = "300px") | Seat_Tag | Date | Average_Value | lagvalue_12mth | Diff | Diffpc |
|---|---|---|---|---|---|
| Carlow-Kilkenny (5) | Jul 2022 | 284872.0 | 220894.3 | 63977.67 | 28.96% |
| Cavan-Monaghan (5) | Jul 2022 | 197903.0 | 161570.4 | 36332.57 | 22.49% |
| Clare (4) | Jul 2022 | 235955.7 | 206092.7 | 29863.00 | 14.49% |
| Cork East (4) | Jul 2022 | 267323.0 | 256206.0 | 11117.00 | 4.34% |
| Cork North-Central (4) | Jul 2022 | 350839.5 | 309962.5 | 40877.00 | 13.19% |
| Cork North-West (3) | Jul 2022 | 348709.2 | 239320.7 | 109388.53 | 45.71% |
| Cork South-Central (4) | Jul 2022 | 330230.0 | 328309.0 | 1921.00 | 0.59% |
| Cork South-West (3) | Jul 2022 | 289256.5 | 244953.5 | 44303.00 | 18.09% |
| Donegal (5) | Jul 2022 | 182673.3 | 156738.0 | 25935.33 | 16.55% |
| Dublin Bay North (5) | Jul 2022 | 581319.0 | 494264.3 | 87054.67 | 17.61% |
| Dublin Bay South (4) | Jul 2022 | 798902.3 | 804848.3 | -5946.00 | -0.74% |
| Dublin Central (4) | Jul 2022 | 309083.0 | 306544.0 | 2539.00 | 0.83% |
| Dublin Fingal (5) | Jul 2022 | 466015.0 | 420827.3 | 45187.67 | 10.74% |
| Dublin Mid-West (4) | Jul 2022 | 400853.5 | 370367.5 | 30486.00 | 8.23% |
| Dublin North-West (3) | Jul 2022 | 458754.0 | 434262.0 | 24492.00 | 5.64% |
| Dublin Rathdown (3) | Jul 2022 | 731581.0 | 687356.0 | 44225.00 | 6.43% |
| Dublin South-West (5) | Jul 2022 | 572909.0 | 515215.0 | 57694.00 | 11.20% |
| Dublin South Central (4) | Jul 2022 | 415488.8 | 423227.6 | -7738.80 | -1.83% |
| Dublin West (4) | Jul 2022 | 418237.5 | 385500.0 | 32737.50 | 8.49% |
| Dun Laoghaire (4) | Jul 2022 | 821332.5 | 721735.0 | 99597.50 | 13.80% |
| Galway East (3) | Jul 2022 | 340027.0 | 228739.0 | 111288.00 | 48.65% |
| Galway West (5) | Jul 2022 | 497739.0 | 265832.0 | 231907.00 | 87.24% |
| Kerry (5) | Jul 2022 | 226333.2 | 222134.2 | 4199.00 | 1.89% |
| Kildare North (4) | Jul 2022 | 419878.0 | 366211.0 | 53667.00 | 14.65% |
| Kildare South (4) | Jul 2022 | 297297.0 | 277221.6 | 20075.40 | 7.24% |
| Laois-Offaly (5) | Jul 2022 | 225580.0 | 198517.5 | 27062.50 | 13.63% |
| Limerick City (4) | Jul 2022 | 261463.0 | 255035.0 | 6428.00 | 2.52% |
| Limerick County (3) | Jul 2022 | 208000.0 | 196158.0 | 11842.00 | 6.04% |
| Longford-Westmeath (4) | Jul 2022 | 221291.0 | 192877.0 | 28414.00 | 14.73% |
| Louth (5) | Jul 2022 | 296199.5 | 269990.5 | 26209.00 | 9.71% |
| Mayo (4) | Jul 2022 | 190733.7 | 166644.2 | 24089.50 | 14.46% |
| Meath East (3) | Jul 2022 | 346002.4 | 317400.2 | 28602.20 | 9.01% |
| Meath West (3) | Jul 2022 | 345554.0 | 308129.0 | 37425.00 | 12.15% |
| Roscommon-Galway (3) | Jul 2022 | 204672.3 | 166230.7 | 38441.67 | 23.13% |
| Sligo-Leitrim (4) | Jul 2022 | 168374.2 | 158717.5 | 9656.75 | 6.08% |
| Tipperary (5) | Jul 2022 | 222141.2 | 184001.6 | 38139.62 | 20.73% |
| Waterford (4) | Jul 2022 | 217256.7 | 243727.0 | -26470.33 | -10.86% |
| Wexford (5) | Jul 2022 | 229463.2 | 227119.5 | 2343.75 | 1.03% |
| Wicklow (5) | Jul 2022 | 447703.8 | 394752.0 | 52951.75 | 13.41% |
Housing Stock
BCMS - Commencement notices taken from gov.ie. Completions and planning permissions data taken from the CSO. Commencement notices unavailable for apartments.
Regional planning permissions data from the CSO first became available from Q1 2018.
National
Fig1 + Fig2 + plot_layout(widths = c(1.5,1))
Commencement notices have become more noticably more volatile since 2020. Planning and completions for all units appears to be returning somewhat to trend on the left panel above. The panel on the right gives a clearer picture of the mismatch with the housing stock today.
There are too many apartment units being planned but not built, due to commercial viability they likely will never be built.
Local Authority Areas
Map1<-shpData.LA_merge %>%
ggplot()+
geom_sf(aes(fill = C_of_P,alpha=0.9))+
theme_void()+
scale_fill_distiller(palette = "Spectral", name="Ratio")+
labs(title="Completions to Planning Ratio: 2018 to Date",
caption = "CSO Datasets NDQ06 & BHQ12 used for completions and planning respectively")+
facet_wrap(~Type.of.Dwelling.x,ncol=2)
Map1
There were 172599 permissions granted & 93525 completions in the sample
The ratio above is the number of completions relative to permissions granted for houses and apartments in a given area.
I am not sure what an ideal ratio would be , but a replacement rate is naturally 1. So those running above 1 will exhaust supply quicker and lead to a tighter market (locally). The lower end of the scale suggests there is capacity for more units to be supplied, but of course this does not mean they will be.
Data.LA_bind <- Data.LA_bind %>%
select(1:4)
Data.LA_bind%>%
kbl(caption = "Completion & Planning Permission Data ")%>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed"), full_width = T)%>%
scroll_box(width = "700px", height = "400px") | Statistic | Local.Authority | Type.of.Dwelling | value |
|---|---|---|---|
| Units for which Permission Granted | Leitrim | Apartment | 32 |
| Units for which Permission Granted | Leitrim | Houses | 265 |
| Units for which Permission Granted | Sligo | Apartment | 76 |
| Units for which Permission Granted | Sligo | Houses | 810 |
| Units for which Permission Granted | Cavan | Apartment | 55 |
| Units for which Permission Granted | Cavan | Houses | 1053 |
| Units for which Permission Granted | Donegal | Apartment | 272 |
| Units for which Permission Granted | Donegal | Houses | 2790 |
| Units for which Permission Granted | Monaghan | Apartment | 72 |
| Units for which Permission Granted | Monaghan | Houses | 1320 |
| Units for which Permission Granted | Galway | Apartment | 577 |
| Units for which Permission Granted | Galway | Houses | 3964 |
| Units for which Permission Granted | Galway City | Apartment | 1672 |
| Units for which Permission Granted | Galway City | Houses | 669 |
| Units for which Permission Granted | Mayo | Apartment | 106 |
| Units for which Permission Granted | Mayo | Houses | 1518 |
| Units for which Permission Granted | Roscommon | Apartment | 77 |
| Units for which Permission Granted | Roscommon | Houses | 691 |
| Units for which Permission Granted | Clare | Apartment | 197 |
| Units for which Permission Granted | Clare | Houses | 2137 |
| Units for which Permission Granted | Limerick | Apartment | 1552 |
| Units for which Permission Granted | Limerick | Houses | 3537 |
| Units for which Permission Granted | Tipperary | Apartment | 427 |
| Units for which Permission Granted | Tipperary | Houses | 2519 |
| Units for which Permission Granted | Carlow | Apartment | 110 |
| Units for which Permission Granted | Carlow | Houses | 848 |
| Units for which Permission Granted | Kilkenny | Apartment | 636 |
| Units for which Permission Granted | Kilkenny | Houses | 2362 |
| Units for which Permission Granted | Wexford | Apartment | 1010 |
| Units for which Permission Granted | Wexford | Houses | 4880 |
| Units for which Permission Granted | Waterford | Apartment | 708 |
| Units for which Permission Granted | Waterford | Houses | 2777 |
| Units for which Permission Granted | Cork | Apartment | 2156 |
| Units for which Permission Granted | Cork | Houses | 10477 |
| Units for which Permission Granted | Cork City | Apartment | 5362 |
| Units for which Permission Granted | Cork City | Houses | 2295 |
| Units for which Permission Granted | Kerry | Apartment | 416 |
| Units for which Permission Granted | Kerry | Houses | 1785 |
| Units for which Permission Granted | Dublin City | Apartment | 31097 |
| Units for which Permission Granted | Dublin City | Houses | 1268 |
| Units for which Permission Granted | South Dublin | Apartment | 7627 |
| Units for which Permission Granted | South Dublin | Houses | 3148 |
| Units for which Permission Granted | Fingal | Apartment | 7412 |
| Units for which Permission Granted | Fingal | Houses | 3071 |
| Units for which Permission Granted | Dun Laoghaire - Rathdown | Apartment | 15582 |
| Units for which Permission Granted | Dun Laoghaire - Rathdown | Houses | 2186 |
| Units for which Permission Granted | Kildare | Apartment | 3496 |
| Units for which Permission Granted | Kildare | Houses | 7297 |
| Units for which Permission Granted | Louth | Apartment | 2131 |
| Units for which Permission Granted | Louth | Houses | 2441 |
| Units for which Permission Granted | Meath | Apartment | 2953 |
| Units for which Permission Granted | Meath | Houses | 6343 |
| Units for which Permission Granted | Wicklow | Apartment | 2034 |
| Units for which Permission Granted | Wicklow | Houses | 4048 |
| Units for which Permission Granted | Laois | Apartment | 396 |
| Units for which Permission Granted | Laois | Houses | 2573 |
| Units for which Permission Granted | Longford | Apartment | 41 |
| Units for which Permission Granted | Longford | Houses | 612 |
| Units for which Permission Granted | Offaly | Apartment | 206 |
| Units for which Permission Granted | Offaly | Houses | 1686 |
| Units for which Permission Granted | Westmeath | Apartment | 937 |
| Units for which Permission Granted | Westmeath | Houses | 1804 |
| New Dwelling Completion | Cork City | Apartment | 512 |
| New Dwelling Completion | Cork City | Houses | 3098 |
| New Dwelling Completion | Clare | Apartment | 82 |
| New Dwelling Completion | Clare | Houses | 1670 |
| New Dwelling Completion | Cavan | Apartment | 41 |
| New Dwelling Completion | Cavan | Houses | 779 |
| New Dwelling Completion | Cork | Apartment | 276 |
| New Dwelling Completion | Cork | Houses | 6032 |
| New Dwelling Completion | Carlow | Apartment | 22 |
| New Dwelling Completion | Carlow | Houses | 961 |
| New Dwelling Completion | Dublin City | Apartment | 7715 |
| New Dwelling Completion | Dublin City | Houses | 2258 |
| New Dwelling Completion | Dun Laoghaire - Rathdown | Apartment | 3778 |
| New Dwelling Completion | Dun Laoghaire - Rathdown | Houses | 2100 |
| New Dwelling Completion | Donegal | Apartment | 189 |
| New Dwelling Completion | Donegal | Houses | 2030 |
| New Dwelling Completion | Fingal | Apartment | 2007 |
| New Dwelling Completion | Fingal | Houses | 6599 |
| New Dwelling Completion | Galway | Apartment | 153 |
| New Dwelling Completion | Galway | Houses | 3178 |
| New Dwelling Completion | Galway City | Apartment | 232 |
| New Dwelling Completion | Galway City | Houses | 827 |
| New Dwelling Completion | Kildare | Apartment | 451 |
| New Dwelling Completion | Kildare | Houses | 7243 |
| New Dwelling Completion | Kilkenny | Apartment | 156 |
| New Dwelling Completion | Kilkenny | Houses | 1268 |
| New Dwelling Completion | Kerry | Apartment | 156 |
| New Dwelling Completion | Kerry | Houses | 2065 |
| New Dwelling Completion | Longford | Apartment | 25 |
| New Dwelling Completion | Longford | Houses | 451 |
| New Dwelling Completion | Louth | Apartment | 249 |
| New Dwelling Completion | Louth | Houses | 2705 |
| New Dwelling Completion | Limerick | Apartment | 195 |
| New Dwelling Completion | Limerick | Houses | 2370 |
| New Dwelling Completion | Leitrim | Apartment | 18 |
| New Dwelling Completion | Leitrim | Houses | 237 |
| New Dwelling Completion | Laois | Apartment | 71 |
| New Dwelling Completion | Laois | Houses | 1332 |
| New Dwelling Completion | Meath | Apartment | 192 |
| New Dwelling Completion | Meath | Houses | 6562 |
| New Dwelling Completion | Monaghan | Apartment | 68 |
| New Dwelling Completion | Monaghan | Houses | 917 |
| New Dwelling Completion | Mayo | Apartment | 74 |
| New Dwelling Completion | Mayo | Houses | 1682 |
| New Dwelling Completion | Offaly | Apartment | 55 |
| New Dwelling Completion | Offaly | Houses | 967 |
| New Dwelling Completion | Roscommon | Apartment | 40 |
| New Dwelling Completion | Roscommon | Houses | 726 |
| New Dwelling Completion | South Dublin | Apartment | 1092 |
| New Dwelling Completion | South Dublin | Houses | 5464 |
| New Dwelling Completion | Sligo | Apartment | 101 |
| New Dwelling Completion | Sligo | Houses | 595 |
| New Dwelling Completion | Tipperary | Apartment | 138 |
| New Dwelling Completion | Tipperary | Houses | 1191 |
| New Dwelling Completion | Waterford | Apartment | 157 |
| New Dwelling Completion | Waterford | Houses | 1956 |
| New Dwelling Completion | Westmeath | Apartment | 96 |
| New Dwelling Completion | Westmeath | Houses | 1025 |
| New Dwelling Completion | Wicklow | Apartment | 443 |
| New Dwelling Completion | Wicklow | Houses | 3659 |
| New Dwelling Completion | Wexford | Apartment | 159 |
| New Dwelling Completion | Wexford | Houses | 2635 |
Daft Price Analysis
There are 40974 observations in the Daft dataset with the first listing being on 2022-01-01 and the latest listing 2022-08-14
Meath
There are 1939 observations in Meath, 760 are 3 beds
For discussion I am trying to convey the difficulty with the 350,000 cap in Meath for the First Home Scheme. I acknowledge the below is busy, I will attempt to break up the map into each electoral area, I cannot yet figure that out. - I have been able to do so for the ASP only. (See the appendix if interested)
Locations of the properties which the points represent if listing price is above or below the cap, is more challenging.
#Create Map 2
# add Y/N for if above 350,000
Daft_Meath_sf$Limit <- ifelse(Daft_Meath_sf$Listing_Price >= 350001,"Above","Below")
# plot
Daft_Meath_sf<- full_join(Daft_Meath_sf, ED_Tags, by = "ID")
Daft_Meath_sf<- rename(Daft_Meath_sf,EDNAME=geographic)
Map2<- ggplot()+
geom_sf_text(data=Labs_EDMAPMEATH22,aes(label=EDNAME),color="black",alpha=0.9)+
geom_sf(data=DaftSumE22Meath,aes(fill = Average_List),alpha=0.1,colour=alpha("gray",0.8))+
scale_fill_steps2(midpoint = 350000,
low="#71CCFF",
mid="#FDF6F6",
high="#BF0F06",
labels = scales::comma,
name = "ASP",
breaks= c(200000,250000,300000,350000,400000,450000,500000))+
geom_sf(data=Daft_Meath_sf,alpha=0.3,size=1.5,aes(group=Limit,shape=Limit,colour=Limit))+
scale_color_manual(values=c('#C40000','#00959E'))+scale_shape_manual(values=c(16, 3))+
theme_void()+
labs(title="2022* - Average Listing Price",
subtitle = "2022 to date, scales adjusted to reflect Shared Equity (FHS) caps")
# # Table
# Table_Map2 <- Daft_Meath_sf %>%
# group_by(Area,L.Above350) %>%
# summarise(Nr= n(),
# Average_List_Price = mean(Listing_Price,na.rum=TRUE))
Map2
# Table_Map2<- Table_Map2 %>% drop_na(L.Above350)
# Table_Map2%>%
# kbl(caption = "Daft 'Area' - 2022 to date")%>%
# kable_styling(bootstrap_options = c("striped", "hover", "condensed"), full_width = T)%>%
# scroll_box(width = "800px", height = "300px")
######## 3 Bed
# Daft_Meath_sf.3bed$Limit <- ifelse(Daft_Meath_sf.3bed$Listing_Price >= 350001,"Above","Below")
#
# # plot
# # Daft_Meath_sf.3bed<- full_join(Daft_Meath_sf.3bed, ED_Tags, by = "ID")
# # Daft_Meath_sf.3bed<- rename(Daft_Meath_sf.3bed,EDNAME=geographic)
#
# Map3<-ggplot()+
# geom_sf_text(data=Labs_EDMAPMEATH22,aes(label=EDNAME),color="black",alpha=0.9)+
# geom_sf(data=DaftSumE22Meath.3bed,aes(fill = Average_List),alpha=0.1,colour=alpha("gray",0.8))+
# scale_fill_steps2(midpoint = 350000,
# low="#71CCFF",
# mid="#FDF6F6",
# high="#BF0F06",
# labels = scales::comma,
# name = "ASP",
# breaks= c(200000,250000,300000,350000,400000,450000,500000))+
# geom_sf(data=Daft_Meath_sf.3bed,alpha=0.3,size=1.5,aes(group=Limit,shape=Limit,colour=Limit))+
# scale_color_manual(values=c('#C40000','#00959E'))+scale_shape_manual(values=c(16, 3))+
# theme_void()+
# labs(title="3 Beds: 2022* - Average Listing Price")
#
# Map3CSO Data
CPI
Headline & Summary
The headline inflation for August is 8.7%. This represents a -0.4 percentage change from July which was 9.1%.
colour_set <- ifelse(CPM01_1B$value < 0,"#CC0000","#007589")
FigCPM01_1B <- ggplot(data=CPM01_1B,aes(x=Date,y=value))+
geom_col(fill= colour_set, alpha = 0.5)+
labs(title = "CPI - CSO: CPM01", subtitle = "2020 to Date")+
xlab("Year-Month")+
ylab("Percentage Change over 12 months")+
theme_bw()+
theme(panel.border = element_rect(linetype = 1, fill = NA))+
geom_text(data=CPM01_1B,aes(label = value,
vjust= ifelse(value >=0,0.25,0.5),
hjust= ifelse(value >=0,-0.25,1)),
size = 2,
angle = 90)
FigCPM01_1B
CPM01_2A_end <- CPM01_2A %>%
group_by(Statistic)%>%
slice(which.max(Date))
CPM01_2A_end$lab <- paste(as.character(CPM01_2A_end$Date), "-", comma(CPM01_2A_end$value,digits=0))
FigCPM01_2A<- ggplot(data=CPM01_2A,aes(x=Date,y=value))+
geom_line(size = 1.15, linetype=1, alpha = 0.6, colour = "#946627")+
geom_hline(aes(yintercept=100),
colour= "#404040",
linetype = 1)+
geom_text_repel(aes(label=lab),data=CPM01_2A_end,size=3)+
labs(title = "CPI - CSO: CPM01", subtitle = "1997 to Date")+
xlab("Year-Month")+
ylab("Consumer Price Index (Base Dec 2016=100)")+theme_bw()+
theme(panel.border = element_rect(linetype = 1, fill = NA))
FigCPM01_2A
Analysis / Trend
FigCPM01_2 <- ggplot(data=CPM01_2,aes(x=Date,y=value))+
geom_line(size = 1.15, linetype=1, alpha = 0.6, colour = "#946627")+
geom_hline(aes(yintercept=100),
colour= "#404040",
linetype = 1)+
theme_bw()+
labs(title = "CPI - CSO: CPM01", subtitle = "2016 to Date")+
xlab("Year-Month")+
ylab("Consumer Price Index (Base Dec 2016=100)")+
theme(panel.border = element_rect(linetype = 1, fill = NA))
FigCPM01_2
CPM03_2_end <- CPM03_2 %>%
group_by(Statistic,Selected.Sub.Indices)%>%
slice(which.max(Date))
CPM03_2_end$lab <- paste(as.character(CPM03_2_end$Date), "-", comma(CPM03_2_end$value,digits=0))
FigCPM03_2 <- ggplot(data=CPM03_2, aes(x=Date, y=value, group=Selected.Sub.Indices, colour=Selected.Sub.Indices))+
scale_colour_manual(values=c("#0389bb", "#0c122f", "#88b66c"))+
labs(title = "CPI - CSO: CPM03", subtitle = "2016 to Date, selected sub indicies")+
xlab("Year-Month")+
ylab("Consumer Price Index (Base Dec 2016=100)")+
geom_line(size = 1.05, linetype=1, alpha = 0.6)+
geom_hline(aes(yintercept=0),
colour= "#404040",
linetype = 1)+
geom_text_repel(aes(label=lab),data=CPM03_2_end,size=3)+
theme_bw()+
theme(axis.text.x = element_text(angle=0))+
theme(legend.position="bottom")+
theme(axis.text = element_text(size = rel(1)))+
theme(plot.title=(element_text(vjust =2)))+
theme(panel.border = element_rect(linetype = 1, fill = NA))
FigCPM03_2
FigCPM03_1 <- ggplot(data=CPM03_1, aes(x=Date, y=value, group=Selected.Sub.Indices, colour=Selected.Sub.Indices))+
scale_colour_manual(values=c("#0389bb", "#0c122f", "#88b66c"))+
labs(title = "CPI - CSO: CPM03", subtitle = "Historical Series, selected sub indicies")+
xlab("Year-Month")+
ylab("Percentage change year on year")+
geom_line(size = 1.05, linetype=1, alpha = 0.85)+
geom_hline(aes(yintercept=0),
colour= "#404040",
linetype = 1)+theme_bw()+
theme(axis.text.x = element_text(angle=0))+
theme(legend.position="bottom")+
theme(axis.text = element_text(size = rel(1)))+
theme(plot.title=(element_text(vjust =2)))+
theme(panel.border = element_rect(linetype = 1, fill = NA))
FigCPM03_1
Public Sector Affordability
Average public sector earnings included from the 2nd of September to generate heat maps of affordability. The colours represent what the mortgage rate for a given amount is relative to the combined earnings of public sector earnings.
# Load Public Sector Wages
EHQ10 <- cso_get_data("EHQ10")
EHQ10 <- EHQ10 %>%
filter(Statistic=="Average Weekly Earnings") %>%
filter(Sub.Sector != "Total Public Sector including Semi State bodies")%>%
filter(Sub.Sector != "Total Public Sector excluding Semi State bodies")
EHQ10 <- EHQ10 %>%
pivot_longer(!1:2,names_to = "year_qtr")
EHQ10$Year_Q <- as.yearqtr(EHQ10$year_qtr)
EHQ10$Year <- year(EHQ10$Year_Q)
## Filter for Q2 2022
EHQ10 <- EHQ10 %>%
filter(year_qtr == "2022Q2")
## Data transformation to pair earnings
Pair <- EHQ10 %>%
select(2,4)
Pair <- Pair %>%
pivot_wider(names_from = Sub.Sector,values_from = value)
Pair$year_qtr <- "2022Q2"
EHQ10_Pair <- merge(x=EHQ10,y=Pair,by="year_qtr")
EHQ10_Pair <- EHQ10_Pair %>%
rename(Wage1 = value)
Wages <- EHQ10_Pair %>%
pivot_longer(!1:6,names_to = "Pair")
Wages <- Wages %>%
rename(Wage2 = value)
Wages$Wages <- Wages$Wage1+Wages$Wage2
# Load Template for rates and mortgage brackets
MR <- read_excel("C:/Users/charten/OneDrive - Glenveagh Properties/Research & Development/1. Analysis/Template Rates and Mortgage.xlsx")
MR$year_qtr <- "2022Q2"
# Merge MR & Wages
Data <- merge(x=Wages,y=MR,by="year_qtr")
Data$Cost <- Data$`30_Yr`/(Data$Wages*4)
# Plot
## base plot
options(scipen=999)
Data$Rate2 <- Data$Rate*100
Data$Cap <- ((Data$Wages*4)*12)*3.5
Plot1<- ggplot(Data,aes(x=Mortgage,y=Rate2))+
geom_raster(aes(fill=Cost))+
scale_fill_stepsn(colours = c("cyan", "black", "red"),
n.breaks=9) +
geom_vline(data=Data|>
dplyr::group_by(Sub.Sector,Pair)|> dplyr::summarise(Cap=mean(Cap)),
aes(xintercept=Cap),
alpha=0.5,
colour="#F5F9F9")+
geom_vline(xintercept = 200000,linetype=1,alpha=0.2)+
geom_vline(xintercept = 400000,linetype=1,alpha=0.2)+
geom_vline(xintercept = 600000,linetype=1,alpha=0.2)+
geom_vline(xintercept = 300000,linetype=2,alpha=0.2)+
geom_vline(xintercept = 500000,linetype=2,alpha=0.2)+
geom_vline(xintercept = 700000,linetype=2,alpha=0.2)+
scale_x_continuous(limits = c(200000,750000), expand = c(0, 0)) +
scale_y_continuous(limits = c(2.5,4.75), expand = c(0, 0)) +
theme_bw()+
xlab("Mortgage Amount (€)")+
ylab("Mortgage Rate (%)")+ theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))+
theme(legend.key.height= unit(1, 'cm'))+
theme(strip.text.x = element_text(size = 7),strip.text.y = element_text(size = 7))+
ggtitle("Public Sector Affordability - 30 Year Repayments as Percentage of Average Earnings")+
labs(caption="lines drawn at 100k increments: dotted line drawn at 300k, 500k & 700k. White line indicate cap of 3.5 times income",fill="% of Earnings")+ theme(legend.title = element_text(size = 8))
Plot2<-Plot1+facet_grid(Sub.Sector~Pair,labeller = labeller(Pair = label_wrap_gen(width = 25),Sub.Sector = label_wrap_gen(width = 15)))
Plot2
Fuel Excise
## Fuel Excise Clearance
FPM01 <- cso_get_data("FPM01")
FPM01 <- FPM01 %>%
pivot_longer(!1:2,names_to="year_month")
FPM01$Month <- as.Date(paste(FPM01$year_month, "01", sep = "-"), "%YM%m-%d")
FPM01$Year <- year(FPM01$Month)
##### moving average
###### 6 month
FPM01$SMA.6 <- round(SMA(FPM01$value,n=6),digits=2)
FPM01$SMA.6[FPM01$year_month=="2000M01" | FPM01$year_month=="2000M02" | FPM01$year_month=="2000M03"|FPM01$year_month=="2000M04" | FPM01$year_month=="2000M05"] <- NA
FPM01$SMA.12 <- round(SMA(FPM01$value,n=12),digits=2)
FPM01$SMA.12[FPM01$year_month=="2000M01" | FPM01$year_month=="2000M02" | FPM01$year_month=="2000M03"|FPM01$year_month=="2000M04" | FPM01$year_month=="2000M05"| FPM01$year_month=="2000M06" | FPM01$year_month=="2000M07"|FPM01$year_month=="2000M08" | FPM01$year_month=="2000M09"|FPM01$year_month=="2000M10" | FPM01$year_month=="2000M11"] <- NA
FPM01_end <- FPM01 %>%
group_by(STATISTIC,Fuel.Type)%>%
slice(which.max(Month))
FPM01_end$lab <- paste(as.character(FPM01_end$year_month), "-", comma(FPM01_end$SMA.12,digits=0))
FPM01_end_val <- FPM01_end$SMA.12
#### plot
Fig.FPM01.1<-ggplot(FPM01, aes(Month, SMA.12, group=Fuel.Type,colour=Fuel.Type,fill=Fuel.Type))+
geom_area(alpha=0.5)+
theme_bw()+
geom_text_repel(aes(label=lab),data=FPM01_end,size=3,colour="black")+
theme(legend.position = "bottom")+
ggtitle("Fuel Excise Clearance")+
ylab("million litres")+
labs(subtitle = "12 month moving average")
Fig.FPM01.2<-ggplot(FPM01, aes(Month, SMA.12, group=Fuel.Type,colour=Fuel.Type,fill=Fuel.Type))+
geom_line(alpha=0.9)+
geom_line(data=FPM01, aes(x=Month, y=SMA.6, group=Fuel.Type,colour=Fuel.Type),alpha=0.4)+
geom_line(data=FPM01, aes(x=Month, y=value, group=Fuel.Type),alpha=0.3,colour="gray")+
geom_text_repel(aes(label=SMA.12),data=FPM01_end,size=2.25,colour="black",alpha=0.9,hjust=1)+
#scale_y_continuous(sec.axis = sec_axis(~ ., breaks = FPM01_end$SMA.12))+
theme_bw()+
theme(legend.position = "none")+
ggtitle("Fuel Excise Clearance")+
ylab("million litres")+
labs(subtitle = "12 month moving average",caption = "6 month moving average also included, monthly value shown in gray")
Fig.FPM01.1
Fig.FPM01.2 + facet_wrap(~Fuel.Type,ncol=2,nrow=2)
Vehicle Registrations
## Vehicles
### TEM12
TEM12 <- cso_get_data("TEM12")
TEM12 <- TEM12 %>%
pivot_longer(!1:3,names_to="year_month")
TEM12$Month <- as.Date(paste(TEM12$year_month, "01", sep = "-"), "%YM%m-%d")
TEM12$Year <- year(TEM12$Month)
#### All Vehicles [(.).1], New Private Cars [(.).2]
TEM12.1 <- TEM12 %>%
filter(Type.of.Vehicle.Registration=="All Vehicles")
TEM12.1$SMA.6 <- round(SMA(TEM12.1$value,n=6),digits=2)
TEM12.1$SMA.6[TEM12.1$year_month=="2015M01" | TEM12.1$year_month=="2015M02" | TEM12.1$year_month=="2015M03"|TEM12.1$year_month=="2015M04" | TEM12.1$year_month=="2015M05"] <- NA
TEM12.1$SMA.12 <- round(SMA(TEM12.1$value,n=12),digits=2)
TEM12.1$SMA.12[TEM12.1$year_month=="2015M01" | TEM12.1$year_month=="2015M02" | TEM12.1$year_month=="2015M03"|TEM12.1$year_month=="2015M04" | TEM12.1$year_month=="2015M05"| TEM12.1$year_month=="2015M06" | TEM12.1$year_month=="2015M07"|TEM12.1$year_month=="2015M08" |TEM12.1$year_month=="2015M09"|TEM12.1$year_month=="2015M10" | TEM12.1$year_month=="2015M11"] <- NA
TEM12.2 <- TEM12 %>%
filter(Type.of.Vehicle.Registration=="New Private Cars")
TEM12.2$SMA.6 <- round(SMA(TEM12.2$value,n=6),digits=2)
TEM12.2$SMA.6[TEM12.2$year_month=="2015M01" | TEM12.2$year_month=="2015M02" | TEM12.2$year_month=="2015M03"|TEM12.2$year_month=="2015M04" | TEM12.2$year_month=="2015M05"] <- NA
TEM12.2$SMA.12 <- round(SMA(TEM12.2$value,n=12),digits=2)
TEM12.2$SMA.12[TEM12.2$year_month=="2015M01" | TEM12.2$year_month=="2015M02" | TEM12.2$year_month=="2015M03"|TEM12.2$year_month=="2015M04" | TEM12.2$year_month=="2015M05"| TEM12.2$year_month=="2015M06" | TEM12.2$year_month=="2015M07"|TEM12.2$year_month=="2015M08" |TEM12.2$year_month=="2015M09"|TEM12.2$year_month=="2015M10" | TEM12.2$year_month=="2015M11"] <- NA
#### All fuel types [(..).1], Petrol [(..).2], Diesel [(..).3], Electric [(..).4]
TEM12.1.1 <- TEM12.1 %>%
filter(Type.of.Fuel=="All fuel types")
TEM12.1.2 <- TEM12.1 %>%
filter(Type.of.Fuel=="Petrol")
TEM12.1.3 <- TEM12.1 %>%
filter(Type.of.Fuel=="Diesel")
TEM12.1.4 <- TEM12.1 %>%
filter(Type.of.Fuel=="Electric")
TEM12.2.1 <- TEM12.2 %>%
filter(Type.of.Fuel=="All fuel types")
TEM12.2.2 <- TEM12.2 %>%
filter(Type.of.Fuel=="Petrol")
TEM12.2.3 <- TEM12.2 %>%
filter(Type.of.Fuel=="Diesel")
TEM12.2.4 <- TEM12.2 %>%
filter(Type.of.Fuel=="Electric")
##### Detailed Plots
###### Petrol
# ggplot()+
# geom_line(data=TEM12.1.2,aes(x=Month,y=SMA.12),colour="darkred",size=1.25)+
# geom_line(data=TEM12.2.2,aes(x=Month,y=SMA.12),colour="navy",size=1.25)+
# geom_line(data=TEM12.1.2,aes(x=Month,y=SMA.6),colour="darkred",alpha=0.5)+
# geom_line(data=TEM12.2.2,aes(x=Month,y=SMA.6),colour="navy",alpha=0.5)+
# theme_bw()+
# ggtitle("New Licensed Vehicles - Petrol")+
# ylab("number")+
# labs(caption="red = All Vehicles, navy = Private Cars. (6 month MA included)", subtitle = "12 month moving average")
Fig.TEM12_P1<-ggplot()+
geom_line(data=TEM12.1.2[which(TEM12.1.2$Month>="2016-01-01"),],aes(x=Month,y=SMA.12),colour="darkred",size=1.25,alpha=0.6)+
geom_line(data=TEM12.2.2[which(TEM12.2.2$Month>="2016-01-01"),],aes(x=Month,y=SMA.12),colour="navy",size=1.25,alpha=0.6)+
geom_line(data=TEM12.1.2[which(TEM12.1.2$Month>="2016-01-01"),],aes(x=Month,y=SMA.6),colour="gray",alpha=0.5)+
geom_line(data=TEM12.2.2[which(TEM12.2.2$Month>="2016-01-01"),],aes(x=Month,y=SMA.6),colour="gray",alpha=0.5)+
geom_text_repel(aes(x=Month,y=SMA.12,label=SMA.12),data=TEM12.1.2[which(TEM12.1.2$Month>="2016-01-01"),],max.overlaps = 2,size=3)+
geom_text_repel(aes(x=Month,y=SMA.12,label=SMA.12),data=TEM12.2.2[which(TEM12.2.2$Month>="2016-01-01"),],max.overlaps = 2,size=3)+
theme_bw()+
ggtitle("New Licensed Vehicles - Petrol")+
ylab("number")+
labs(caption="red = All Vehicles, navy = Private Cars. (6 month MA included)", subtitle = "12 month moving average")
Fig.TEM12_D1<-ggplot()+
geom_line(data=TEM12.1.3[which(TEM12.1.3$Month>="2016-01-01"),],aes(x=Month,y=SMA.12),colour="darkred",size=1.25,alpha=0.6)+
geom_line(data=TEM12.2.3[which(TEM12.2.3$Month>="2016-01-01"),],aes(x=Month,y=SMA.12),colour="navy",size=1.25,alpha=0.6)+
geom_line(data=TEM12.1.3[which(TEM12.1.3$Month>="2016-01-01"),],aes(x=Month,y=SMA.6),colour="gray",alpha=0.5)+
geom_line(data=TEM12.2.3[which(TEM12.2.3$Month>="2016-01-01"),],aes(x=Month,y=SMA.6),colour="gray",alpha=0.5)+
geom_text_repel(aes(x=Month,y=SMA.12,label=SMA.12),data=TEM12.1.3[which(TEM12.1.3$Month>="2016-01-01"),],max.overlaps = 4,size=3)+
geom_text_repel(aes(x=Month,y=SMA.12,label=SMA.12),data=TEM12.2.3[which(TEM12.2.3$Month>="2016-01-01"),],max.overlaps = 4,size=3)+
theme_bw()+
ggtitle("New Licensed Vehicles - Diesel")+
ylab("number")+
labs(caption="red = All Vehicles, navy = Private Cars. (6 month MA included)", subtitle = "12 month moving average")
Fig.TEM12_E1<-ggplot()+
geom_line(data=TEM12.1.4[which(TEM12.1.4$Month>="2016-01-01"),],aes(x=Month,y=SMA.12),colour="darkred",size=1.25,alpha=0.6)+
geom_line(data=TEM12.2.4[which(TEM12.2.4$Month>="2016-01-01"),],aes(x=Month,y=SMA.12),colour="navy",size=1.25,alpha=0.6)+
geom_line(data=TEM12.1.4[which(TEM12.1.4$Month>="2016-01-01"),],aes(x=Month,y=SMA.6),colour="gray",alpha=0.5)+
geom_line(data=TEM12.2.4[which(TEM12.2.4$Month>="2016-01-01"),],aes(x=Month,y=SMA.6),colour="gray",alpha=0.5)+
geom_text_repel(aes(x=Month,y=SMA.12,label=SMA.12),data=TEM12.1.4[which(TEM12.1.4$Month>="2016-01-01"),],max.overlaps = 1,size=3)+
geom_text_repel(aes(x=Month,y=SMA.12,label=SMA.12),data=TEM12.2.4[which(TEM12.2.4$Month>="2016-01-01"),],max.overlaps = 1,size=3)+
theme_bw()+
ggtitle("New Licensed Vehicles - Electric")+
ylab("number")+
labs(caption="red = All Vehicles, navy = Private Cars. (6 month MA included)", subtitle = "12 month moving average")
######## run facets for graphs above
Fig.TEM12_P1.A<-ggplot()+
geom_line(data=TEM12.1.2[which(TEM12.1.2$Month>="2016-01-01"),],aes(x=Month,y=SMA.12),colour="darkred",size=1.25,alpha=0.6)+
geom_line(data=TEM12.2.2[which(TEM12.2.2$Month>="2016-01-01"),],aes(x=Month,y=SMA.12),colour="navy",size=1.25,alpha=0.6)+
geom_line(data=TEM12.1.2[which(TEM12.1.2$Month>="2016-01-01"),],aes(x=Month,y=SMA.6),colour="gray",alpha=0.5)+
geom_line(data=TEM12.2.2[which(TEM12.2.2$Month>="2016-01-01"),],aes(x=Month,y=SMA.6),colour="gray",alpha=0.5)+
geom_text_repel(aes(x=Month,y=SMA.12,label=SMA.12),data=TEM12.1.2[which(TEM12.1.2$Month>="2016-01-01"),],max.overlaps = 8,size=2.5)+
geom_text_repel(aes(x=Month,y=SMA.12,label=SMA.12),data=TEM12.2.2[which(TEM12.2.2$Month>="2016-01-01"),],max.overlaps = 8,size=2.5)+
theme_bw()+
ggtitle("New Licensed Vehicles - 12 month moving average")+
ylab("number")+
xlab(NULL)+
labs(subtitle = "Petrol")
Fig.TEM12_D1.A<-ggplot()+
geom_line(data=TEM12.1.3[which(TEM12.1.3$Month>="2016-01-01"),],aes(x=Month,y=SMA.12),colour="darkred",size=1.25,alpha=0.6)+
geom_line(data=TEM12.2.3[which(TEM12.2.3$Month>="2016-01-01"),],aes(x=Month,y=SMA.12),colour="navy",size=1.25,alpha=0.6)+
geom_line(data=TEM12.1.3[which(TEM12.1.3$Month>="2016-01-01"),],aes(x=Month,y=SMA.6),colour="gray",alpha=0.5)+
geom_line(data=TEM12.2.3[which(TEM12.2.3$Month>="2016-01-01"),],aes(x=Month,y=SMA.6),colour="gray",alpha=0.5)+
geom_text_repel(aes(x=Month,y=SMA.12,label=SMA.12),data=TEM12.1.3[which(TEM12.1.3$Month>="2016-01-01"),],max.overlaps = 6,size=2.5)+
geom_text_repel(aes(x=Month,y=SMA.12,label=SMA.12),data=TEM12.2.3[which(TEM12.2.3$Month>="2016-01-01"),],max.overlaps = 6,size=2.5)+
theme_bw()+
ylab("number")+xlab(NULL)+
labs(subtitle = "Diesel")
Fig.TEM12_E1.A<-ggplot()+
geom_line(data=TEM12.1.4[which(TEM12.1.4$Month>="2016-01-01"),],aes(x=Month,y=SMA.12),colour="darkred",size=1.25,alpha=0.6)+
geom_line(data=TEM12.2.4[which(TEM12.2.4$Month>="2016-01-01"),],aes(x=Month,y=SMA.12),colour="navy",size=1.25,alpha=0.6)+
geom_line(data=TEM12.1.4[which(TEM12.1.4$Month>="2016-01-01"),],aes(x=Month,y=SMA.6),colour="gray",alpha=0.5)+
geom_line(data=TEM12.2.4[which(TEM12.2.4$Month>="2016-01-01"),],aes(x=Month,y=SMA.6),colour="gray",alpha=0.5)+
geom_text_repel(aes(x=Month,y=SMA.12,label=SMA.12),data=TEM12.1.4[which(TEM12.1.4$Month>="2016-01-01"),],max.overlaps = 6,size=2.5)+
geom_text_repel(aes(x=Month,y=SMA.12,label=SMA.12),data=TEM12.2.4[which(TEM12.2.4$Month>="2016-01-01"),],max.overlaps = 6,size=2.5)+
theme_bw()+
ylab("number")+
labs(caption="red = All Vehicles, navy = Private Cars. (6 month MA included)", subtitle = "Electric")
Fig.TEM12_A <- Fig.TEM12_P1.A + Fig.TEM12_D1.A + Fig.TEM12_E1.A + plot_layout(nrow=2,ncol = 2)
Fig.TEM12_A
### TEM02
TEM02 <- cso_get_data("TEM02")
TEM02 <- TEM02 %>%
pivot_longer(!1:2,names_to="year_month")
TEM02$value <- as.integer(TEM02$value)
TEM02$year_month[TEM02$year_month=="2021m02"] <- "2021M02"
TEM02$Month <- as.Date(paste(TEM02$year_month, "01", sep = "-"), "%YM%m-%d")
TEM02$Year <- year(TEM02$Month)
#### Secondhand Private Cars [(.).1], New Private Cars [(.).2]
TEM02.1 <- TEM02 %>%
filter(Type.of.Vehicle.Registration=="Secondhand Private Cars")
TEM02.1$SMA.6 <- round(SMA(TEM02.1$value,n=6),digits=2)
TEM02.1$SMA.6[TEM02.1$year_month=="1965M01" | TEM02.1$year_month=="1965M02" | TEM02.1$year_month=="1965M03"|TEM02.1$year_month=="1965M04" | TEM02.1$year_month=="1965M05"] <- NA
TEM02.1$SMA.12 <- round(SMA(TEM02.1$value,n=12),digits=2)
TEM02.1$SMA.12[TEM02.1$year_month=="1965M01" | TEM02.1$year_month=="1965M02" | TEM02.1$year_month=="1965M03"|TEM02.1$year_month=="1965M04" | TEM02.1$year_month=="1965M05"| TEM02.1$year_month=="1965M06" | TEM02.1$year_month=="1965M07"|TEM02.1$year_month=="1965M08" |TEM02.1$year_month=="1965M09"|TEM02.1$year_month=="1965M10" | TEM02.1$year_month=="1965M11"] <- NA
TEM02.2 <- TEM02 %>%
filter(Type.of.Vehicle.Registration=="New Private Cars")
TEM02.2$SMA.6 <- round(SMA(TEM02.2$value,n=6),digits=2)
TEM02.2$SMA.6[TEM02.2$year_month=="1965M01" | TEM02.2$year_month=="1965M02" | TEM02.2$year_month=="1965M03"|TEM02.2$year_month=="1965M04" | TEM02.2$year_month=="1965M05"] <- NA
TEM02.2$SMA.12 <- round(SMA(TEM02.2$value,n=12),digits=2)
TEM02.2$SMA.12[TEM02.2$year_month=="1965M01" | TEM02.2$year_month=="1965M02" | TEM02.2$year_month=="1965M03"|TEM02.2$year_month=="1965M04" | TEM02.2$year_month=="1965M05"| TEM02.2$year_month=="1965M06" | TEM02.2$year_month=="1965M07"|TEM02.2$year_month=="1965M08" |TEM02.2$year_month=="1965M09"|TEM02.2$year_month=="1965M10" | TEM02.2$year_month=="1965M11"] <- NA
TEM02.A <- rbind(TEM02.1,TEM02.2)
TEM02.A_end <- TEM02.A %>%
group_by(STATISTIC,Type.of.Vehicle.Registration)%>%
slice(which.max(Month))
Fig1.TEM02<- ggplot(TEM02.A,aes(x=Month,y=SMA.12,group=Type.of.Vehicle.Registration,colour=Type.of.Vehicle.Registration))+
geom_line(size=1.25,alpha=0.6)+
scale_colour_manual(values = c("navy","purple"))+
geom_text_repel(aes(x=Month,y=SMA.12,label=SMA.12),data=TEM02.A,max.overlaps = 12,size=2.75,colour="black")+
theme_bw()+
theme(legend.position = "bottom")+
ggtitle("Vehicles Licensed for the First Time")+
ylab("number")+
labs(subtitle = "TEM02: 12 month moving average")
Fig2.TEM02<-ggplot(TEM02.A,aes(x=Month,y=SMA.6,group=Type.of.Vehicle.Registration,colour=Type.of.Vehicle.Registration))+
geom_line(size=1.25,alpha=0.6)+
scale_colour_manual(values = c("navy","purple"))+
geom_text_repel(aes(x=Month,y=SMA.6,label=SMA.6),data=TEM02.A,max.overlaps = 9,size=2.75,colour="black")+
theme_bw()+
theme(legend.position = "bottom")+
ggtitle("Vehicles Licensed for the First Time")+
ylab("number")+
labs(subtitle = "TEM02: 6 month moving average")
Fig1.TEM02 + scale_y_continuous(sec.axis = sec_axis(~ ., breaks = TEM02.A_end$SMA.12))
#facet_wrap(~Type.of.Vehicle.Registration,nrow=2)Building & Construction
## Indices of Total Production in Building and Construction Sector (Base 2015=100)
BEQ04 <- cso_get_data("BEQ04")
BEQ04 <- BEQ04 %>%
pivot_longer(!1:2,names_to="year_qtr")
BEQ04$Year_Q <- as.yearqtr(BEQ04$year_qtr)
BEQ04$Year <- year(BEQ04$Year_Q)
##### generate lags and percentage change
BEQ04$Lag <- Lag(BEQ04$value,1)
BEQ04$Lag[BEQ04$year_qtr=="2000Q1"]<- NA
BEQ04$Diff<- BEQ04$value-BEQ04$Lag
BEQ04$Diffpc<- ((BEQ04$value-BEQ04$Lag)/BEQ04$Lag)*100
BEQ04$Diffpc<- round(BEQ04$Diffpc,digits=2)
### subset value [(.).A] & volume [(.).B]
BEQ04.A <- BEQ04 %>%
filter(Statistic=="Value of Production Index in Building and Construction (Seasonally Adjusted)")
BEQ04.B <- BEQ04 %>%
filter(Statistic=="Volume of Production Index in Building and Construction (Seasonally Adjusted)")
##### generate lags and percentage change
# BEQ04.A$Lag <- Lag(BEQ04.A$value,1)
# BEQ04.A$Lag[BEQ04.A$year_qtr=="2000Q1"]<- NA
# BEQ04.A$Diff<- BEQ04.A$value-BEQ04.A$Lag
# BEQ04.A$Diffpc<- ((BEQ04.A$value-BEQ04.A$Lag)/BEQ04.A$Lag)*100
# BEQ04.A$Diffpc<- round(BEQ04.A$Diffpc,digits=2)
#
# BEQ04.B$Lag <- Lag(BEQ04.B$value,1)
# BEQ04.B$Lag[BEQ04.B$year_qtr=="2000Q1"]<- NA
# BEQ04.B$Diff<- BEQ04.B$value-BEQ04.B$Lag
# BEQ04.B$Diffpc<- ((BEQ04.B$value-BEQ04.B$Lag)/BEQ04.B$Lag)*100
# BEQ04.B$Diffpc<- round(BEQ04.B$Diffpc,digits=2)
### seasonally adjusted [(.)_S]
BEQ04_S <- BEQ04 %>%
filter(Statistic=="Value of Production Index in Building and Construction (Seasonally Adjusted)"|Statistic=="Volume of Production Index in Building and Construction (Seasonally Adjusted)")
Fig1.BEQ04<- ggplot(BEQ04_S,aes(x=Year_Q,y=value,group=Statistic,colour=Statistic))+
geom_line()+
theme_bw()+
geom_hline(yintercept = 100,colour="black")+
theme(legend.position = "bottom")+
ggtitle("Indices of Total Production in Building and Construction Sector")+
ylab("(2015 = 100)")
#Fig1.BEQ04 + facet_wrap(~Type.of.Building.and.Construction,nrow=5)
### subset from 2015
BEQ04_S.15 <- BEQ04_S %>%
filter(Year_Q >= "2015 Q1")
Fig2.BEQ04<-ggplot(BEQ04_S.15,aes(x=Year_Q,y=value,group=Statistic,colour=Statistic))+
geom_line(size=1.25,alpha=0.6)+
theme_bw()+
geom_hline(yintercept = 100,colour="black",alpha=0.4,linetype=2)+
geom_text_repel(aes(x=Year_Q,y=value,label=round(value,digits = 1)),data=BEQ04_S.15,max.overlaps = 6,size=2.75,colour="black")+
theme(legend.position = "none")+
ggtitle("Indices of Total Production in Building and Construction Sector")+
ylab("(2015 = 100)")+
labs(subtitle = "Orange = Value Index, Blue = Volume Index")
Fig2.BEQ04 + facet_wrap(~Type.of.Building.and.Construction,nrow=3,ncol=2)
#### subset for Residential
BEQ04_S.15.Res<- BEQ04_S.15%>%
filter(Type.of.Building.and.Construction =="Residential building")
Fig3.BEQ04 <- ggplot(BEQ04_S.15.Res,aes(x=Year_Q,y=Diffpc,group=Statistic,colour=Statistic,fill=Statistic))+
geom_col(size=1.25,alpha=0.6)+
geom_text_repel(aes(x=Year_Q,y=Diffpc,label=Diffpc),data=BEQ04_S.15.Res,colour="black",max.overlaps = 5,size=3)+
theme_bw()+
theme(legend.position = "bottom")+
ylab("% change, q on q")
Fig3.BEQ04 + facet_wrap(~Statistic,ncol=2)
Wholesale Price Index
Year on year percentage change
colour <-ifelse(WPM28_B$value < 0,"#CC0000","#1b5545")
Fig1<-ggplot(WPM28_B, aes(x=Date, y=value, group=Type.of.Material))+
geom_col(aes(group=Type.of.Material), alpha = 0.65, colour="#373634", fill = colour)+
labs(title = "Percentage Change over 12 month in Wholesale Price Index")+
xlab("Year-Month")+
ylab("Percentage change")+
# scale_x_date(date_labels="%b-%Y",date_breaks ="3 month")+
geom_text_repel(aes(label=value),data = WPM28_B, size = 3)+
theme_bw()+
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))
Fig1 + facet_wrap(~Type.of.Material, ncol = 3)
Wholesale Price Index
Black line (100) indicates 2015 as baseline
Fig2<-ggplot(WPM28_A, aes(x=Date, 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)+geom_text_repel(aes(label=value),data = WPM28_A, size = 3)+
labs(title = "Percentage Change over 12 month in Wholesale Price Index")+
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_bw()+
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 = 3)
Appendix
Meath ASP by ED
#Map3 <-
DaftSumE22Meath %>%
ggplot()+
#geom_sf_text(aes(label=Average_List),color="black",alpha=0.9)+
geom_sf(aes(fill = Average_List),alpha=0.9,colour=alpha("gray",0.8))+
scale_fill_steps2(midpoint = 350000,
low="#71CCFF",
mid="#FDF6F6",
high="#BF0F06",
labels = scales::comma,
name = "ASP",
breaks= c(200000,250000,300000,350000,400000,450000,500000))+
theme_void()+
labs(title="2022* - Average Listing Price",
subtitle = "2022 to date, scales adjusted to reflect Shared Equity (FHS) caps")+ facet_wrap(~EDNAME,ncol=3)
#Map3 <- + facet_wrap(~EDNAME,ncol=4)