Working Title - 2022.07
Cormac Harten
7/30/2022
Headline Roundup
ECB raise 50 points and give up on forward guidance.
Draghi out as right wing gains ground in Italy. While strong tourism leads to GDP growth of 1%, beating estimates.
Germany GDP unchanged as Russia cuts gas delivery, again.
TikTok refuse to Chinese propoganda account, officially. HSBC align themselves with the CCP further, creating an internal Communist Part committee.
IMF increase inflation expectations for the 7th forecast in a row.
Eurozone consumer sentiment at the lowest on record.
US Markets
Technically speaking, not in a technical recession
SNP <- getSymbols("^GSPC",src='yahoo',auto.assign=FALSE)
ISEQ <- getSymbols("^ISEQ",src='yahoo',auto.assign=FALSE)
SNP_July22 <- SNP %>%
subset(date(index(.))>= "2022-07-01")
ISEQ_July22 <- ISEQ %>%
subset(date(index(.))>= "2022-07-01")
SNP_YTD <- SNP %>%
subset(date(index(.))>= "2022-01-01")
ISEQ_YTD <- ISEQ %>%
subset(date(index(.))>= "2022-01-01")The S&P 500 was up 9.1% in July, its best return since November 2020. - In my view another suckers rally. US GDP shrank for the second successive quarter, this was the traditional yardstick of a recession. The National Bureau of Economic Research (NBER) have a more stringent definition of a recession which, by my understanding, depends on:
- Employment
- Industrial production
- Real personal Income
- Sales of the manufacturing and Trade Volumes
For now at least, not all of these are in decline, so by the book the US is not in recession. However, my reading of the data is that the US has never had two periods of decline without a recession following. So smart money is on a recession.
Confusing but materially but for the US its more political than academic. Republicans say recession, Democrats and the Fed say no. With the Fed raising another 75 basis points in interest rates the markets, in my view, seem to have misread the situation and rallied. Equally it could be that stocks being picked for their fundamentals lead to the rally, but I don’t have a grasp on the data to tell the difference.
July
chartSeries(SNP_July22,name="S&P 500",theme='black', type = "line",up.col="#D49CFE", TA = NULL)
chartSeries(ISEQ_July22,name="ISEQ",theme='black', type = "line",up.col="#A5FE9C",TA = NULL)
The market is not the economy, and with inflation still a problem and consumer confidence vanishing I believe the index is due for another decline. I’ve left the ISEQ (Irish Stock Exchange) in for both periods for comparison, note the strong correlation.
YTD
chartSeries(SNP_YTD,name="S&P 500",theme='black', type = "line",up.col="#D49CFE", TA = NULL)
chartSeries(ISEQ_YTD,name="ISEQ",theme='black', type = "line",up.col="#A5FE9C",TA = NULL)
Rates
Hikes tend to precede recessions, increase in mortgage rates observed in the US is a trend that we are likely to observe in Europe.
Fed Funds - 2000 to date
US 30 year fixed - 2000 to date
rm(ISEQ)
rm(ISEQ_July22)
rm(ISEQ_YTD)
rm(SNP)
rm(SNP_July22)
rm(SNP_YTD)Ireland
Latest numbers from the CSO
Housing
The Government’s Housing for All plan targets 24,600 completions in 2022, 29,000 in 2023 and 33,450 in 2024.
setwd("/Users/harte/OneDrive/Experimental/")
GEO_Merge <- readxl::read_xlsx(path="C:\\Users\\harte\\OneDrive\\Experimental\\HPM04_GEO.xlsx",sheet = 1)
shp <- sf::read_sf("/Users/harte/OneDrive/Experimental//Constituency_Boundaries_Ungeneralised___OSi_National_Electoral_Boundaries___2017.shp", stringsAsFactors=FALSE)
## 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)Completions surprised (me at least), to the upside.
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)
China
Will she? Won’t she?
US Speaker Nancy Pelosi going to Taiwan is sure to aggravate China, considering that they consider Taiwan China and any visit undermining that. Threatening intervention, Xi Jing Ping spoke to Biden on the phone. Drawing comparisons to playing with fire no solution was reached and it is unclear whether Pelosi will go or not, but a climb down would be difficult for her.
Chinas problems in housing continue with Country Garden, the countries largest developer no another problem child. It don’t see how China gets out of this without a huge price correction. - The CCP and local governments are averse to this given the revenue that it brings to their budgets. Housing in China makes up a fifth of the economy.
It is no wonder there are protests, below from the FT shows the scale of the bubble in China. Where the rest of the world is stuck for housing supply the Chinese are demolishing units.
Many Chinese home-buyers pay for homes before completion, but I am not sure to what extent. This is not the only issue the CCP is contending with, Pakistan and Sri Lanka who are heavily indebted to China through the Belt and Road initiative look set for default. An increasingly gloomy global outlook will exacerbate this, paying for many of the vanity projects which the initiative funded (although in principle being funds for trade) such as the Lotus Tower skyscraper in Sri Lanka, may be a decreasing priority for countries under duress. Inflation in Pakistan is currently at 21.3%.
The CCP have been debt collectors of late. More restructuring likely.
