Working Title - 2022.10

Number 2

Originally published on the 25th, edited comment on Boris Johnson

Headline Roundup

Central Bank re-calibrates mortgage criteria

The Statement can be found here

What has changed?

First Time Buyers

• Loan to income (LTI) increased from 3.5 times to 4 times income.
• No changes to LTV (Loan to Value) which remains at 90%.

Second & Subsequent buyers (SSB)

• LTI remains at 3.5 income.
• LTV changed from 80% to 90%.

Proportionate allowances

• 15% of FTB can take place above the limits
• 15% of SSB can take place above the limits
• 10% of BTL can take place above the limits.

Additionally - From a “fresh start” perspective, borrowers who are divorced or separated or have undergone bankruptcy or insolvency may be considered FTBs for the mortgage measures (where they no longer have an interest in the previous property). - FTBs who get a top-up loan or re-mortgage with an increase in the principal may be considered FTBs, provided the property remains their primary home.

*The median house price relative to the prior cap can be seen in the “Characteristics of Household Purchases” Section”.

---

Russians escalte the conflict with Iranian Drones

In response to the Ukrainians successes of late the Russians have been bombing civilian centers, adding to the tally of war crimes. Russia have purchased cheap 20k drones from Iran, apparently the drones they were using earlier in the war go for about $2 million. So evidently they’ve not been using them sparingly given they are so cheap, although the majority are negated by the Ukrainians anti-aircraft missles invariably some get through. Targeting power stations and utilities this means for more misery for the civilians and a higher bill to rebuild Ukraine when the time comes.

The Iranian nuclear deal has stalled, supplying drones will do nothing to improved engagement. Protests in Iran continue due to the death of a 22 year-old woman who was detained by the morality police for wearing “improper attire”. Reuters report that more than 200 people have been killed in a crackdown to counter the protests.. Russia relying on another pariah state like Iran is telling of their situation, this war has not gone to plan.

This week Russia have made accusations that the Ukrainians intend to use dirty bombs, NATO say there is no evidence of this amid speculation that the Russians are using this as an front to escalate further. - Ukraine will take the matter to the UN security council, what good that will do I don’t know.

This comes as some republicans baulk at the bill to support Ukraine. With US mid-term elections coming up some fear that this limit the support for Ukraine, but my reading of it so far is that pressure to maintain the support will be too much even if the republicans outperform.

Germans concede on energy price cap

Olaf Scholz has dropped his opposition to energy price cap, but acknowledged that there “is still a lot of concrete work to do” to implement the cap.

Meanwhile the Romanians attempt at a windfall tax of 80% to cover their price supports has backfired. The government have been convering the difference in the price cap to actual costs for consumers, this bill has increased wuicker than anticipated. The windfall tax was introduced to cover this added costs, with wholesalers increasing their costs due to margin pressure prices have increased and trading has slowed to a standstill.

Xi consoliodates power within the CCP

Economic data delayed until after the CCP congress where Xi consilidated the key offices in the state to his allies, as reported by Rueters, Bloomberg, The FT among other “western” media outlets. Interestingly footage emerged of Xis predecessor, Hu Jintao, being escorted from the conference, the aforementioned outlets commented that this was peculiar given the event is highly choreographed. There is much speculation on whether this was Xi flexing through a very public purge, or whether it was a case of a senile official being escorted out as an act of mercy.

Since Xi took over China has become less transparent. To 2016, half of all indicators published by the statistics bureaus have been (quietly) discontinued. - The FT report. There has been suspicions for years that Chinas GDP is grossly overstated, with Luis Martinez of the University of Chicago conducting a night-time light analysis from satellite imagery suggests growth to be overstated by a third over the last 20 years. How representative this is I am not sure I have not read the paper, but the acceptance that figures are overstated will hurt the Chinese in the long run.

Hong Kong and China plunge following the release of the delayed economic data and the Renimbi hits 2007 lows.

Truss resigns, Sunak confirmed as PM

In what became a one horse race, which threatened to see Boris Johnson return as Prime Minister ends with Rishi Sunak in Number 10. This was after Liz Truss & Kwasi Kwateng are effectively forced to resign for their economic policies. Kwateng was first to go, being summoned back to England from an IMF conference to be told by his close friend and PM to hand in his resignation.

Truss then brought in Jeremy Hunt to stem the pressure from the party, he promptly reverses Kwatengs policies but it is not enough to see her through. Truss lasted 44 days in office, solidifying her place in the pub quiz archives as the shortest stint as PM. - In the week leading up to her removal there was a live stream of a head of lettuce posted by te Daily Star, the purpose was to see would she outlive the lettuce. She did not.

Boris Johnson flew back from his holidays on the Caribbean with a view to put his name forward, more than 50 MPs came out to back him, publically. 100 was needed by 2pm Monday to be considered a contender. Boris later pulled out of the race, but in a characteristically Boris statement he nodded to his election win from 2019. I believe he thinks he have another opportunity to be leader after the next general election and is leaving that door open. - Some of the telling quotes:

• I have been attracted because I led our party into a massive election victory less than three years ago – and I believe I am therefore uniquely placed to avert a general election now.
• I believe I am well placed to deliver a Conservative victory in 2024 – and tonight I can confirm that I have cleared the very high hurdle of 102 nominations, including a proposer and a seconder, and I could put my nomination in tomorrow.

The list of critiques for the “mini-budget” is too long to begin, Bloomberg commentators referred to the damage that Truss has done to the economy as “the moron risk premium”. Fringe Tory party members have shifted the Tory party towards delusion since Cameron agreed to that referendum in 2016. While treasury yields have improved since Sunak was announced, the polls will likely favour Labour for some time to come.

AIB the first to bump rates

“Following the European Central Bank’s decisions to raise interest rates by 1.25% since July 2022 and in a rising interest rate environment, AIB Group is announcing a fixed rate mortgage increase of 0.5%”

This will be in effect from the 14th of November, as we will see later the proportion of Irish with variable loans have decreased particulalry of late. For a full view on AIBs statement follow the link.

Elsewhere

• Head of Frances Central Bank says UK crisis demonstrates tge risks of a “vicious loop” that governments face if they undermine monetary policies efforts to curb inflation.
• Eurozone business activity falls faster than estimates in October, its lowest for 2 years.
• Japanese continue intervention on the Yen as the currency approaches 32 year low.
• Berlusconi taped sympahising with Putin, discussing the gifts they have exchanged and condeming their support for Ukraine. - Not an ideal start for Melonis coalition. - Meloni having to call Zelenskyy to reassure him of Italys support.
• The top chip suppliers halt business in China following US export restrictions.
• The German head of cyber security, Arne Schonbohm has been sacked over reports of alleged ties to Russian intelligence.
• ECB look set to raise rates by 0.75 on Thursday.
• An Board Pleanala have delivered the report to its chairperson on allegations of wrongdoing, but have not yet published findings.

---

US & Markets

# Ensure to change CSV name to apprpriate version number / working directory
SNP <- getSymbols("^GSPC",src='yahoo',auto.assign=FALSE)
ISEQ <- getSymbols("^ISEQ",src='yahoo',auto.assign=FALSE)

SNP$Close.MA5 <- rollmean(SNP$GSPC.Close,5)
SNP$Close.MA5 <- Lag(SNP$Close.MA5,2)
SNP$Close.MA10 <- rollmean(SNP$GSPC.Close,10)
SNP$Close.MA10 <- Lag(SNP$Close.MA10,5)
ISEQ$Close.MA5 <- rollmean(ISEQ$ISEQ.Close,5)
ISEQ$Close.MA5 <- Lag(ISEQ$Close.MA5,2)
ISEQ$Close.MA10 <- rollmean(ISEQ$ISEQ.Close,10)
ISEQ$Close.MA10 <- Lag(ISEQ$Close.MA10,5)

write.csv(SNP,"SNP.N1.1.csv")
write.csv(ISEQ,"ISEQ.N1.1.csv")

SNP_Oct22 <- SNP %>%
  subset(date(index(.))>= "2022-10-01")
ISEQ_Oct22 <- ISEQ %>%
  subset(date(index(.))>= "2022-10-01")
SNP_YTD <- SNP %>%
  subset(date(index(.))>= "2022-01-01")
ISEQ_YTD <- ISEQ %>%
  subset(date(index(.))>= "2022-01-01")

Markets

SNP_end <- as.data.frame(tail(SNP_YTD,1))
ISEQ_end <- as.data.frame(tail(ISEQ_YTD,1))

Fig.SNP.YTD<-ggplot(data=SNP_YTD,aes(x=Index,y=GSPC.Close,group=1))+
  geom_line(data=SNP_YTD,aes(x=Index,y=Close.MA5,group=1),alpha=0.5,colour="#9200ff")+
  geom_line(data=SNP_YTD,aes(x=Index,y=Close.MA10,group=1),alpha=0.5,colour="#290047",linetype="dashed")+
  geom_line(colour="#D49CFE",size=1)+
  geom_text_repel(aes(label=round(GSPC.Close,digits=2)),data = SNP_YTD, size = 2.5,max.overlaps = 4,colour="#f6f7f6")+
  theme_dark()+
  ggtitle("S&P YTD")+
  xlab(NULL)+theme(axis.text.y=element_text(size=6),
        plot.title = element_text(size=10,face="bold"))+
  scale_y_continuous(sec.axis = sec_axis(~ ., breaks = SNP_end$GSPC.Close))+ theme(axis.text = element_text(size = 6))

Fig.ISEQ.YTD<-ggplot(data=ISEQ_YTD,aes(x=Index,y=ISEQ.Close,group=1))+
  geom_line(data=ISEQ_YTD,aes(x=Index,y=Close.MA5,group=1),alpha=0.5,colour="#15e600")+
  geom_line(data=ISEQ_YTD,aes(x=Index,y=Close.MA10,group=1),alpha=0.5,colour="#0f6606",linetype="dashed")+
  geom_line(colour="#A5FE9C",size=1)+
  geom_text_repel(aes(label=round(ISEQ.Close,digits=2)),data = ISEQ_YTD, size = 2.5,max.overlaps = 4,colour="#f6f7f6")+
  theme_dark()+
  ggtitle("ISEQ YTD")+
  xlab(NULL)+ theme(axis.text.y=element_text(size=6),
        plot.title = element_text(size=10,face="bold"))+
  scale_y_continuous(sec.axis = sec_axis(~ ., breaks = ISEQ_end$ISEQ.Close))+ theme(axis.text = element_text(size = 6))

Fig.SNP.YTD+Fig.ISEQ.YTD+plot_layout(ncol=2)+
  labs(caption = "Moving Averages: dashed line = 5 day, solid line = 10 day")

Fig.SNP.Oct<-ggplot(data=SNP_Oct22,aes(x=Index,y=GSPC.Close,group=1))+
  geom_line(data=SNP_Oct22,aes(x=Index,y=Close.MA5,group=1),alpha=0.5,colour="#9200ff")+
  geom_line(data=SNP_Oct22,aes(x=Index,y=Close.MA10,group=1),alpha=0.5,colour="#290047",linetype="dashed")+
  geom_line(colour="#D49CFE",size=1)+
  geom_text_repel(aes(label=round(GSPC.Close,digits=2)),data = SNP_Oct22, size = 2.5,max.overlaps = 4,colour="#f6f7f6")+
  theme_dark()+
  ggtitle("S&P Oct22")+
  xlab(NULL)+ theme(axis.text.y=element_text(size=6),
        plot.title = element_text(size=10,face="bold"))+
  scale_y_continuous(sec.axis = sec_axis(~ ., breaks = SNP_end$GSPC.Close))+ theme(axis.text = element_text(size = 6))

Fig.ISEQ.Oct<-ggplot(data=ISEQ_Oct22,aes(x=Index,y=ISEQ.Close,group=1))+
  geom_line(data=ISEQ_Oct22,aes(x=Index,y=Close.MA5,group=1),alpha=0.5,colour="#15e600")+
  geom_line(data=ISEQ_Oct22,aes(x=Index,y=Close.MA10,group=1),alpha=0.5,colour="#0f6606",linetype="dashed")+
  geom_line(colour="#A5FE9C",size=1)+
  geom_text_repel(aes(label=round(ISEQ.Close,digits=2)),data = ISEQ_Oct22, size = 2.5,max.overlaps = 4,colour="#f6f7f6")+
  theme_dark()+
  ggtitle("ISEQ Oct22")+
  theme(axis.text.y=element_text(size=6),
        plot.title = element_text(size=10,face="bold"))+
  xlab(NULL)+ scale_y_continuous(sec.axis = sec_axis(~ ., breaks = ISEQ_end$ISEQ.Close))+ theme(axis.text = element_text(size = 6))

Fig.SNP.Oct+Fig.ISEQ.Oct+plot_layout(ncol=2)+
  labs(caption = "Moving Averages: dashed line = 5 day, solid line = 10 day")

Currency

# Plot Currency
Fig.EURYTD + Fig.GBPYTD + Fig.AUDYTD + Fig.CHNYTD + plot_layout(ncol=2)

# Plot Dec Futures
Fig.YEN.FYTD + Fig.PES.FYTD + plot_layout(ncol=2)

Gold

#Plot Gold
Fig.GOLD + Fig.GOLDYTD + plot_layout(ncol=2)

Crypto

# Plot

#CRYPTO %>%
# ggplot(aes(x=date,y=value,group=Coin,colour=Coin))+geom_line()

Fig_BTC.YTD <- BTC.YTD %>%
  ggplot(aes(x=date,y=value,group=Coin))+
  geom_line(aes(y=SMA.5),linetype="dotted",colour="#4D4D4D")+
  geom_line(aes(y=SMA.10),linetype="dotted",colour="#F7931A")+
  geom_line(aes(y=SMA.50),colour="#4D4D4D",alpha=0.5)+
  geom_line(aes(y=SMA.100),colour="#F7931A",alpha=0.5)+
  geom_line(size=1,colour="#F7931A",alpha=0.7)+
  geom_text_repel(aes(label=round(value,digits=0)),max.overlaps=5,size=2.25,alpha=0.7)+
  ylab("BTC to USD")+
  theme_bw()+theme(axis.text.y=element_text(size=6),
        plot.title = element_text(size=10,face="bold"))+
  ggtitle("Bitcoin - Year to Date")+
  labs(caption="5 and 10 day moving average indicated with dotted line, 50 & 100 are the solid lines")

Fig_ETH.YTD <- ETH.YTD %>%
  ggplot(aes(x=date,y=value,group=Coin))+
  geom_line(aes(y=SMA.5),linetype="dotted",colour="#716b94")+
  geom_line(aes(y=SMA.10),linetype="dotted",colour="#48cbd9")+
  geom_line(aes(y=SMA.50),colour="#716b94",alpha=0.5)+
  geom_line(aes(y=SMA.100),colour="#48cbd9",alpha=0.5)+
  geom_line(size=1,colour="#14044d",alpha=0.7)+
  geom_text_repel(aes(label=round(value,digits=0)),max.overlaps=5,size=2.25,alpha=0.7)+
  ylab("ETH to USD")+
  theme_bw()+theme(axis.text.y=element_text(size=6),
        plot.title = element_text(size=10,face="bold"))+
  ggtitle("Ethereum - Year to Date")+
  labs(caption="5 and 10 day moving average indicated with dotted line, 50 & 100 are the solid lines")

Fig_BTC.YTD + Fig_ETH.YTD + plot_layout(nrow=2)

Commodities

Energy Prices

Note the decline in Gas prices, good news if it can continue through to winter.

BRENT_YTD <- BRENT %>%
  subset(date(index(.))>= "2022-01-01")
WTI_YTD <- WTI %>%
  subset(date(index(.))>= "2022-01-01")
NGAS_YTD <- NGAS %>%
  subset(date(index(.))>= "2022-01-01")

BRENT_YTD_AVG <- mean(BRENT_YTD$`BZ=F.Close`,na.rm=TRUE)
BRENT_YTD_TAIL <- tail(BRENT_YTD,1)
BRENT_YTD_CLOSE <- mean(BRENT_YTD_TAIL$`BZ=F.Close`,na.rm=TRUE)
WTI_YTD_AVG <- mean(WTI_YTD$`CL=F.Close`,na.rm=TRUE)
WTI_YTD_TAIL <- tail(WTI_YTD,1)
WTI_YTD_CLOSE <- mean(WTI_YTD_TAIL$`CL=F.Close`,na.rm=TRUE)
NGAS_YTD_AVG <- mean(NGAS_YTD$`NG=F.Close`,na.rm=TRUE)
NGAS_YTD_TAIL <- tail(NGAS_YTD,1)
NGAS_YTD_CLOSE <- mean(NGAS_YTD_TAIL$`NG=F.Close`,na.rm=TRUE)

#Oil Plots
Fig.Brent2<-ggplot(data=BRENT_YTD,aes(x=Index,y=`BZ=F.Close`,group=1))+
  geom_line()+
  geom_hline(yintercept=BRENT_YTD_CLOSE,alpha=0.5)+
  geom_hline(yintercept=BRENT_YTD_AVG,linetype='dashed')+
  theme_bw()+ theme(axis.text.y=element_text(size=6),
        plot.title = element_text(size=10,face="bold"))+
  ggtitle("Year to Date")+
  ylab(NULL)+
  xlab(NULL)+ theme(axis.text = element_text(size = 6))+
  geom_text_repel(data=BRENT_TAIL,aes(label=round(`BZ=F.Close`,digits=2),y=130),size=2.5,alpha=0.6,colour="darkred")
  
Fig.WTI2<-ggplot(data=WTI_YTD,aes(x=Index,y=`CL=F.Close`,group=1))+
  geom_line(colour="darkgrey")+
  geom_hline(yintercept=WTI_YTD_CLOSE,colour="darkgrey",alpha=0.8)+
  geom_hline(yintercept=WTI_YTD_AVG,linetype='dashed',colour="darkgrey")+
  theme_bw()+theme(axis.text.y=element_text(size=6),
        plot.title = element_text(size=10,face="bold"))+
  ggtitle("Year to Date")+
  ylab(NULL)+
  xlab(NULL)+ theme(axis.text = element_text(size = 6))+
  geom_text_repel(data=WTI_TAIL,aes(label=round(`CL=F.Close`,digits=2),y=130),size=2.5,alpha=0.6,colour="darkred")

#Gas Plots
Fig.NGAS2<-ggplot(data=NGAS_YTD,aes(x=Index,y=`NG=F.Close`,group=1))+
  geom_line(colour="darkblue")+
  geom_hline(yintercept=NGAS_YTD_CLOSE,colour="darkblue",alpha=0.5)+
  geom_hline(yintercept=NGAS_YTD_AVG,linetype='dashed',colour="darkblue")+
  theme_bw()+theme(axis.text.y=element_text(size=6),
        plot.title = element_text(size=10,face="bold"))+
  ggtitle("Year to Date")+
  ylab(NULL)+
  xlab("Dates")+
  labs(caption = "dashed line = average, solid line = closing price (closing price also in red)")+ theme(axis.text = element_text(size = 6))+
  geom_text_repel(data=NGAS_TAIL,aes(label=round(`NG=F.Close`,digits=2),y=9),size=2.5,alpha=0.6,colour="darkred")

# YTD
Fig.Energy.YTD<-Fig.Brent2 + Fig.WTI2 + Fig.NGAS2 +
  plot_layout(nrow=3)

Fig.Brent1+Fig.Brent2+Fig.WTI1+Fig.WTI2+Fig.NGAS1+Fig.NGAS2+
  plot_layout(nrow=3)

rm(ISEQ)
rm(ISEQ_July22)
rm(ISEQ_YTD)
rm(SNP)
rm(SNP_July22)
rm(SNP_YTD)

Ireland & The Euro Area

Euro Area Bonds

I’ve added time lags to the Yield Curve & the 20 year maturity. We can see the curve flattening and approaching inversion, inverting at the 20-30 year. - I may replace this with a static line & moving gif but I’ve not cracked that yet.

# Euro Area
EA.1YR <- get_data("YC.B.U2.EUR.4F.G_N_A.SV_C_YM.SR_1Y")
EA.1YR$obstime<-convert_dates(EA.1YR$obstime)

Fig_EA.1YR<-ggplot(data=EA.1YR,aes(x=obstime,y=obsvalue,group=ref_area))+
  geom_line(colour="blue")+
  geom_hline(yintercept = 0, linetype='dotted')+
  geom_text_repel(aes(label=round(obsvalue,digits=2)),data = EA.1YR, size = 3)+
  theme_bw()+
  theme(legend.position = "bottom")+
  ggtitle("1 Year Maturity")+
  ylab("yield (%)")+
  xlab(NULL)

EA.5YR <- get_data("YC.B.U2.EUR.4F.G_N_A.SV_C_YM.SR_5Y")
EA.5YR$obstime<-convert_dates(EA.5YR$obstime)

Fig_EA.5YR<-ggplot(data=EA.5YR,aes(x=obstime,y=obsvalue,group=ref_area))+
  geom_line(colour="blue")+
  geom_hline(yintercept = 0, linetype='dotted')+
  geom_text_repel(aes(label=round(obsvalue,digits=2)),data = EA.5YR, size = 3)+
  theme_bw()+
  theme(legend.position = "bottom")+
  ggtitle("5 Year Maturity")+
  ylab("yield (%)")+
  xlab(NULL)

EA.10YR <- get_data("YC.B.U2.EUR.4F.G_N_A.SV_C_YM.SR_10Y")
EA.10YR$obstime<-convert_dates(EA.10YR$obstime)

Fig_EA.10YR<-ggplot(data=EA.10YR,aes(x=obstime,y=obsvalue,group=ref_area))+
  geom_line(colour="blue")+
  geom_hline(yintercept = 0, linetype='dotted')+
  geom_text_repel(aes(label=round(obsvalue,digits=2)),data = EA.10YR, size = 3)+
  theme_bw()+
  theme(legend.position = "bottom")+
  ggtitle("10 Year Maturity")+
  ylab("yield (%)")+
  xlab(NULL)

EA.30YR <- get_data("YC.B.U2.EUR.4F.G_N_A.SV_C_YM.SR_30Y")
EA.30YR$obstime<-convert_dates(EA.30YR$obstime)

Fig_EA.30YR<-ggplot(data=EA.30YR,aes(x=obstime,y=obsvalue,group=ref_area))+
  geom_line(colour="blue")+
  geom_hline(yintercept = 0, linetype='dotted')+
  geom_text_repel(aes(label=round(obsvalue,digits=2)),data = EA.30YR, size = 3)+
  theme_bw()+
  theme(legend.position = "bottom")+
  ggtitle("30 Year Maturity")+
  ylab("yield (%)")+
  xlab(NULL)

Fig_EA.1YR+Fig_EA.5YR+Fig_EA.10YR+Fig_EA.30YR+
  plot_layout(nrow=2,ncol=2)

# Generate yield curve
EA.1YR_Print<-tail(EA.1YR,1)
EA.5YR_Print<-tail(EA.5YR,1)
EA.10YR_Print<-tail(EA.10YR,1)
EA.30YR_Print<-tail(EA.30YR,1)

## Load 20 Year
EA.20YR <- get_data("YC.B.U2.EUR.4F.G_N_A.SV_C_YM.SR_20Y")
EA.20YR$obstime<-convert_dates(EA.20YR$obstime)
EA.20YR_Print<-tail(EA.20YR,1)

## Add lags
EA.1YR_Print$t = "Live"
EA.1YR_Print.30<-head(tail(EA.1YR,30),1)
EA.1YR_Print.30$t = "30 days"
EA.1YR_Print.90<-head(tail(EA.1YR,90),1)
EA.1YR_Print.90$t = "90 days"
EA.1YR_Print.360<-head(tail(EA.1YR,360),1)
EA.1YR_Print.360$t = "360 days"

EA.YC.1Y <- rbind(EA.1YR_Print,EA.1YR_Print.30,EA.1YR_Print.90,EA.1YR_Print.360)

EA.5YR_Print$t = "Live"
EA.5YR_Print.30<-head(tail(EA.5YR,30),1)
EA.5YR_Print.30$t = "30 days"
EA.5YR_Print.90<-head(tail(EA.5YR,90),1)
EA.5YR_Print.90$t = "90 days"
EA.5YR_Print.360<-head(tail(EA.5YR,360),1)
EA.5YR_Print.360$t = "360 days"

EA.YC.5Y <- rbind(EA.5YR_Print,EA.5YR_Print.30,EA.5YR_Print.90,EA.5YR_Print.360)

EA.10YR_Print$t = "Live"
EA.10YR_Print.30<-head(tail(EA.10YR,30),1)
EA.10YR_Print.30$t = "30 days"
EA.10YR_Print.90<-head(tail(EA.10YR,90),1)
EA.10YR_Print.90$t = "90 days"
EA.10YR_Print.360<-head(tail(EA.10YR,360),1)
EA.10YR_Print.360$t = "360 days"

EA.YC.10Y <- rbind(EA.10YR_Print,EA.10YR_Print.30,EA.10YR_Print.90,EA.10YR_Print.360)

EA.20YR_Print$t = "Live"
EA.20YR_Print.30<-head(tail(EA.20YR,30),1)
EA.20YR_Print.30$t = "30 days"
EA.20YR_Print.90<-head(tail(EA.20YR,90),1)
EA.20YR_Print.90$t = "90 days"
EA.20YR_Print.360<-head(tail(EA.20YR,360),1)
EA.20YR_Print.360$t = "360 days"

EA.YC.20Y <- rbind(EA.20YR_Print,EA.20YR_Print.30,EA.20YR_Print.90,EA.20YR_Print.360)

EA.30YR_Print$t = "Live"
EA.30YR_Print.30<-head(tail(EA.30YR,30),1)
EA.30YR_Print.30$t = "30 days"
EA.30YR_Print.90<-head(tail(EA.30YR,90),1)
EA.30YR_Print.90$t = "90 days"
EA.30YR_Print.360<-head(tail(EA.30YR,360),1)
EA.30YR_Print.360$t = "360 days"

EA.YC.30Y <- rbind(EA.30YR_Print,EA.30YR_Print.30,EA.30YR_Print.90,EA.30YR_Print.360)

## Bind and Plot
EA.YC <- rbind(EA.YC.1Y,EA.YC.5Y,EA.YC.10Y,EA.YC.20Y,EA.YC.30Y)

EA.YC$year <- sub("\\Y.*","",EA.YC$data_type_fm)
EA.YC$year <- sub(".*SR_","",EA.YC$year)
EA.YC$year <- as.numeric(EA.YC$year)

EA.YC %>%
  ggplot(aes(x=year,y=obsvalue,group=t))+
  geom_line(aes(colour=t,linetype=t))+
  scale_colour_manual(values=c("#477DFF","#477DFF","#477DFF","blue"))+
  scale_linetype_manual(values = c("solid","dotted","dashed","solid"))+
  geom_hline(yintercept = 0, linetype='dotted')+
  geom_text(label=round(EA.YC$obsvalue,digits=2),vjust=-1,hjust=1,aes(colour=t),size=2.75)+
  theme_bw()+
  theme(legend.position = "bottom")+
  ggtitle("Yield Curve")+
  ylab("yield (%)")+
  xlab("maturity (years)")

---

Inflation

CPI

See below, CPI is the top panel and percentage change year on year by Commodity Group makes up the panels.

# Plot
## CPM01
CPM01_1.18_LABS <- CPM01_1.18 %>%
  filter(value > 2)

Fig_ALL.18 <- CPM01_1.18 %>%
  ggplot(aes(x=Date,y=value,group=Commodity.Group))+
  geom_hline(yintercept = 2, linetype="dashed",alpha=0.6)+
  geom_col(aes(fill=colourcol),alpha=0.7)+
  scale_fill_manual(values=c("#CC0000","#1b5545"))+
  theme_bw()+
  theme(legend.position = "none")+
  ylab("%")+xlab(NULL)+
  ggtitle("Consumer Price Index - Year on Year")+
  geom_text(data=CPM01_1.18_LABS,aes(label = value,
                              vjust= ifelse(value >=0,0.25,0.5),
                              hjust= ifelse(value >=0,1,-0.25)),size = 3,angle = 90)

# Plot
## CPM01
Fig_CG.18 <- CPM01_2.18 %>%
  ggplot(aes(x=Date,y=value,group=Commodity.Group))+
  geom_hline(yintercept = 2, linetype="dashed",alpha=0.6)+
  geom_col(aes(fill=colourcol),alpha=0.7)+
  scale_fill_manual(values=c("#CC0000","#1b5545"))+
  theme_bw()+
  theme(legend.position = "none")+
  ylab("%")+xlab(NULL)+
  geom_text_repel(aes(label=value),size=2.5,max.overlaps = 6,alpha=0.7)+
  facet_wrap(~Commodity.Group,ncol=2)

Fig_ALL.18 + Fig_CG.18 + plot_layout(nrow=2,heights = c(1,5))

Contribution to Inflation

See broad based inflation thats present, some commodity groups alone are contributing 2 percentage points to CPI. - See dotted line.

# Plot
## CPM11
CPM11_1.12M_Max <- CPM11_1.12M %>%
  group_by(COICOP.Group) %>%
  slice(which.max(Date))
CPM11_1.12M_Max$lab <- paste(CPM11_1.12M_Max$Month, ":", CPM11_1.12M_Max$value)

CPM11_1.12M %>%
  ggplot(aes(x=Date,y=value,group=COICOP.Group,colour=COICOP.Group))+
  geom_hline(yintercept = 2, linetype="dashed",alpha=0.2)+
  geom_hline(yintercept = 0, alpha=0.2)+
  geom_line()+
  theme_bw()+
  theme(legend.position = "none")+
  ylab("%")+xlab(NULL)+
  ggtitle("Contribution to Inflation - Year on Year")+
  geom_text(data=CPM11_1.12M_Max,aes(label=lab,y=-3),hjust="right",size=3,fontface="bold",alpha=0.6)+
  facet_wrap(~COICOP.Group,ncol=3,labeller = labeller(COICOP.Group = label_wrap_gen(width = 35)))

The CSO released data on the Characteristics of Residential Property Purchasers. The next two sections contains data based on this series.

Residential Movement

The below illustrations are called “Sankey” charts, they are used to illustrate the flow from one object to another. Our objects are residences, so the county a purchaser moved from is on the left column and where they moved to is on the right. The colour of the flow is based on the destination county.

Dublin to GDA

For the GDA I’ve displayed to sets of sankey charts, the latter I’ve removed Dublin as a destination county. As you can see the large number of moving within Dublin makes assessing the other counties more difficult.

#### Filter for From.Dublin & To.GDA
CRP05.F_DUB.T_GDA <- CRP05 %>%
  filter(From.Region=="Dublin"|From.Region=="GDA") %>%
  filter(To.Region=="Dublin"|To.Region=="GDA")

options(scipen=999)

library()

ggplot(CRP05.F_DUB.T_GDA,
       aes(y=value,
           axis1=Purchasers.Location.Given.During.Purchase,,axis2=County.Where.Property.Was.Purchased))+
  geom_alluvium(aes(fill=County.Where.Property.Was.Purchased),width = 1/12) +
  geom_stratum(width = 1/12, fill = "black", color = "grey") +
  geom_label(stat = "stratum", aes(label = after_stat(stratum)),size=2.5)+
  theme_bw()+
  theme(legend.position = "bottom",legend.key.size = unit(1, 'cm'),
        legend.key.height = unit(0.5, 'cm'), 
        legend.key.width = unit(0.5, 'cm'),
        legend.title = element_text(size=8),
        legend.text = element_text(size=6))+
  ggtitle("Residential Movement between Dublin & The GDA")+scale_fill_brewer(type = "qual", palette = "Spectral")+
  facet_wrap(~year,ncol=2)

##### Version 2      Filter for From.Dublin & To.Midlands
CRP05.F_DUB.T_GDA2 <- CRP05 %>%
  filter(From.Region=="Dublin"|From.Region=="GDA") %>%
  filter(To.Region=="GDA")

ggplot(CRP05.F_DUB.T_GDA2,
       aes(y=value,
           axis1=Purchasers.Location.Given.During.Purchase,,axis2=County.Where.Property.Was.Purchased))+
  geom_alluvium(aes(fill=County.Where.Property.Was.Purchased),width = 1/12) +
  geom_stratum(width = 1/12, fill = "black", color = "grey") +
  geom_label(stat = "stratum", aes(label = after_stat(stratum)),size=2.5)+
  theme_bw()+
  theme(legend.position = "bottom",legend.key.size = unit(1, 'cm'),
        legend.key.height = unit(0.5, 'cm'), 
        legend.key.width = unit(0.5, 'cm'),
        legend.title = element_text(size=8),
        legend.text = element_text(size=6))+
  ggtitle("Residential Movement between Dublin & The GDA")+
  labs(subtitle="Dublin excluded from destination county")+scale_fill_brewer(type = "qual", palette = "Spectral")+
  facet_wrap(~year,ncol=2)

Dublin to Midlands

#### Filter for From.Dublin & To.Midlands
CRP05.F_DUB.T_MID <- CRP05 %>%
  filter(From.Region=="Dublin"|From.Region=="Midlands") %>%
  filter(To.Region=="Midlands")

ggplot(CRP05.F_DUB.T_MID,
       aes(y=value,
           axis1=Purchasers.Location.Given.During.Purchase,,axis2=County.Where.Property.Was.Purchased))+
  geom_alluvium(aes(fill=County.Where.Property.Was.Purchased),width = 1/12) +
  geom_stratum(width = 1/12, fill = "black", color = "grey") +
  geom_label(stat = "stratum", aes(label = after_stat(stratum)),size=2.5)+
  theme_bw()+
  theme(legend.position = "bottom",legend.key.size = unit(1, 'cm'),
        legend.key.height = unit(0.5, 'cm'), 
        legend.key.width = unit(0.5, 'cm'),
        legend.title = element_text(size=8),
        legend.text = element_text(size=6))+
  ggtitle("Residential Movement between Dublin & The Midlands")+
  labs(subtitle="Dublin excluded from destination county")+scale_fill_brewer(type = "qual", palette = "Spectral")+
  facet_wrap(~year,ncol=2)

Dublin to North

#### Filter for From.Dublin & To.North
CRP05.F_DUB.T_NOR <- CRP05 %>%
  filter(From.Region=="Dublin"|From.Region=="North") %>%
  filter(To.Region=="North")

ggplot(CRP05.F_DUB.T_NOR,
       aes(y=value,
           axis1=Purchasers.Location.Given.During.Purchase,,axis2=County.Where.Property.Was.Purchased))+
  geom_alluvium(aes(fill=County.Where.Property.Was.Purchased),width = 1/12) +
  geom_stratum(width = 1/12, fill = "black", color = "grey") +
  geom_label(stat = "stratum", aes(label = after_stat(stratum)),size=2.5)+
  theme_bw()+
  theme(legend.position = "bottom",legend.key.size = unit(1, 'cm'),
        legend.key.height = unit(0.5, 'cm'), 
        legend.key.width = unit(0.5, 'cm'),
        legend.title = element_text(size=8),
        legend.text = element_text(size=6))+
  ggtitle("Residential Movement between Dublin & The North")+
  labs(subtitle="Dublin excluded from destination county")+scale_fill_brewer(type = "qual", palette = "Spectral")+
  facet_wrap(~year,ncol=2)

Dublin to West

#### Filter for From.Dublin & To.West
CRP05.F_DUB.T_WES <- CRP05 %>%
  filter(From.Region=="Dublin"|From.Region=="West") %>%
  filter(To.Region=="West")

ggplot(CRP05.F_DUB.T_WES,
       aes(y=value,
           axis1=Purchasers.Location.Given.During.Purchase,,axis2=County.Where.Property.Was.Purchased))+
  geom_alluvium(aes(fill=County.Where.Property.Was.Purchased),width = 1/12) +
  geom_stratum(width = 1/12, fill = "black", color = "grey") +
  geom_label(stat = "stratum", aes(label = after_stat(stratum)),size=2.5)+
  theme_bw()+
  theme(legend.position = "bottom",legend.key.size = unit(1, 'cm'),
        legend.key.height = unit(0.5, 'cm'), 
        legend.key.width = unit(0.5, 'cm'),
        legend.title = element_text(size=8),
        legend.text = element_text(size=6))+
  ggtitle("Residential Movement between Dublin & The West")+
  labs(subtitle="Dublin excluded from destination county")+
  scale_fill_brewer(type = "qual", palette = "Spectral")+
  facet_wrap(~year,ncol=2)

Dublin to South

#### Filter for From.Dublin & To.South
CRP05.F_DUB.T_SOU <- CRP05 %>%
  filter(From.Region=="Dublin"|From.Region=="South") %>%
  filter(To.Region=="South")

ggplot(CRP05.F_DUB.T_SOU,
       aes(y=value,
           axis1=Purchasers.Location.Given.During.Purchase,,axis2=County.Where.Property.Was.Purchased))+
  geom_alluvium(aes(fill=County.Where.Property.Was.Purchased),width = 1/12) +
  geom_stratum(width = 1/12, fill = "black", color = "grey") +
  geom_label(stat = "stratum", aes(label = after_stat(stratum)),size=2.5)+
  theme_bw()+
  theme(legend.position = "bottom",legend.key.size = unit(1, 'cm'),
        legend.key.height = unit(0.5, 'cm'), 
        legend.key.width = unit(0.5, 'cm'),
        legend.title = element_text(size=8),
        legend.text = element_text(size=6))+
  ggtitle("Residential Movement between Dublin & The South")+
  labs(subtitle="Dublin excluded from destination county")+
  scale_fill_brewer(type = "qual", palette = "Spectral")+
  facet_wrap(~year,ncol=2)

Characteristics of Household Purchases

Ireland

Headline Characteristics

Fig.Head.VOL <- CRP01.IRL.VOL.ALL.ALL %>%
  ggplot(aes(x=year,y=value,group=Statistic))+
  geom_line(size=1.25,alpha=0.6,colour="#F59A08")+
  geom_text_repel(aes(label=value),alpha=0.7,size=3)+
  theme_bw()+
  theme(legend.position = "bottom",
        plot.title = element_text(size=12,face="bold"))+
  ggtitle("Volume of Sales")+
  xlab(NULL)+
  ylab("number")

Fig.Head.AGE <- CRP01.IRL.AGE.ALL.ALL %>%
  ggplot(aes(x=year,y=value,group=Statistic))+
  geom_line(size=1.25,alpha=0.6,colour="#08F5BC")+
  geom_text_repel(aes(label=value),alpha=0.7,size=3)+
  theme_bw()+
  theme(legend.position = "bottom",legend.key.size = unit(1, 'cm'),
        legend.key.height = unit(0.5, 'cm'), 
        legend.key.width = unit(0.5, 'cm'),
        legend.title = element_text(size=8),
        legend.text = element_text(size=6),
        plot.title = element_text(size=12,face="bold"))+
  ggtitle("Median Age")+
  xlab(NULL)+
  ylab("number")

Fig.Head.PRC <- CRP01.IRL.PRC.ALL.ALL %>%
  ggplot(aes(x=year,y=value,group=Statistic))+
  geom_line(size=1.25,alpha=0.6,colour="#32AE04")+
  geom_text_repel(aes(label=value),alpha=0.7,size=3)+
  theme_bw()+
  theme(legend.position = "bottom",legend.key.size = unit(1, 'cm'),
        legend.key.height = unit(0.5, 'cm'), 
        legend.key.width = unit(0.5, 'cm'),
        legend.title = element_text(size=8),
        legend.text = element_text(size=6),
        plot.title = element_text(size=12,face="bold"))+
  ggtitle("Median Price")+
  xlab(NULL)+
  ylab("euro")

Fig.Head.INC <- CRP01.IRL.INC.ALL.ALL %>%
  ggplot(aes(x=year,y=value,group=Statistic))+
  geom_line(size=1.25,alpha=0.6,colour="#0416AE")+
  geom_text_repel(aes(label=value),alpha=0.7,size=3)+
  theme_bw()+
  theme(legend.position = "bottom",legend.key.size = unit(1, 'cm'),
        legend.key.height = unit(0.5, 'cm'), 
        legend.key.width = unit(0.5, 'cm'),
        legend.title = element_text(size=8),
        legend.text = element_text(size=6),
        plot.title = element_text(size=12,face="bold"))+
  ggtitle("Median Income")+
  labs(caption="CRP01: Characteristics of Residential Property Purchasers")+
  xlab(NULL)+
  ylab("euro")

Fig.Head <- Fig.Head.VOL + Fig.Head.AGE + Fig.Head.PRC + Fig.Head.INC

Fig.Head

Transaction Category

By 2021 joint transactions were 1.5 times sole transactions, on average they were three years younger with a combined income that was 1.9 times higher than sole purchasers. They bought houses at 1.4 times at a median price of € 318 k, which is 13.5% above the aggregate median price.

####### View the 4 statistical measures for Ireland for each transaction category and aggregated buyers (i.e. OTH.ALL)
##### Title: Figure.IRL.TCAT
Fig.IRL.TCAT.VOL <- CRP01.IRL.VOL.OTH.ALL %>%
  ggplot(aes(x=year,y=value,group=Transaction.Class))+
  geom_line(aes(linetype=Transaction.Class),size=1.25,alpha=0.6,colour="#F59A08")+
  geom_text_repel(aes(label=value),alpha=0.3,size=3,max.overlaps = 3)+
  theme_bw()+
 theme(legend.position = "bottom",legend.key.size = unit(1, 'cm'),
        legend.key.height = unit(0.5, 'cm'), 
        legend.key.width = unit(0.5, 'cm'),
        legend.title = element_text(size=8),
        legend.text = element_text(size=6),
        plot.title = element_text(size=12,face="bold"))+
  ggtitle("Volume of Sales")+
  xlab(NULL)+
  ylab("number")

Fig.IRL.TCAT.AGE <- CRP01.IRL.AGE.OTH.ALL %>%
  ggplot(aes(x=year,y=value,group=Transaction.Class))+
  geom_line(aes(linetype=Transaction.Class),size=1.25,alpha=0.6,colour="#08F5BC")+
  geom_text_repel(aes(label=value),alpha=0.3,size=3,max.overlaps = 3)+
  theme_bw()+
 theme(legend.position = "bottom",legend.key.size = unit(1, 'cm'),
        legend.key.height = unit(0.5, 'cm'), 
        legend.key.width = unit(0.5, 'cm'),
        legend.title = element_text(size=8),
        legend.text = element_text(size=6),
        plot.title = element_text(size=12,face="bold"))+
  ggtitle("Median Age")+
  xlab(NULL)+
  ylab("number")

Fig.IRL.TCAT.PRC <- CRP01.IRL.PRC.OTH.ALL %>%
  ggplot(aes(x=year,y=value,group=Transaction.Class))+
  geom_line(aes(linetype=Transaction.Class),size=1.25,alpha=0.6,colour="#32AE04")+
  geom_text_repel(aes(label=value),alpha=0.3,size=3,max.overlaps = 3)+
  theme_bw()+
  theme(legend.position = "bottom",legend.key.size = unit(1, 'cm'),
        legend.key.height = unit(0.5, 'cm'), 
        legend.key.width = unit(0.5, 'cm'),
        legend.title = element_text(size=8),
        legend.text = element_text(size=6),
        plot.title = element_text(size=12,face="bold"))+
  ggtitle("Median Price")+
  xlab(NULL)+
  ylab("euro")

Fig.IRL.TCAT.INC <- CRP01.IRL.INC.OTH.ALL %>%
  ggplot(aes(x=year,y=value,group=Transaction.Class))+
  geom_line(aes(linetype=Transaction.Class),size=1.25,alpha=0.6,colour="#0416AE")+
  geom_text_repel(aes(label=value),alpha=0.3,size=3,max.overlaps = 3)+
  theme_bw()+
  theme(legend.position = "bottom",legend.key.size = unit(1, 'cm'),
        legend.key.height = unit(0.5, 'cm'), 
        legend.key.width = unit(0.5, 'cm'),
        legend.title = element_text(size=8),
        legend.text = element_text(size=6),
        plot.title = element_text(size=12,face="bold"))+
  ggtitle("Median Income")+
  xlab(NULL)+
  ylab("euro")

Fig.IRL.TCAT <- Fig.IRL.TCAT.VOL + Fig.IRL.TCAT.AGE + Fig.IRL.TCAT.PRC + Fig.IRL.TCAT.INC

Fig.IRL.TCAT

Buyer Category

The Buyer Category, those with and without children, trend is largely explained by the same factors as the Transaction Category. The gap bewteen incomes and prices are narrower at 1.35 and 1.17 respectively.

####### View the 4 statistical measures for Ireland for aggregated transaction category and each buyer (i.e. ALL.OTH)
##### Title: Figure.IRL.TBUY  || Filter >= 2016
CRP01.IRL.VOL.ALL.OTH <- CRP01.IRL.VOL.ALL.OTH %>%
  filter(year>="2016")

Fig.IRL.TBUY.VOL <- CRP01.IRL.VOL.ALL.OTH %>%
  ggplot(aes(x=year,y=value,group=Type.of.Buyer))+
  geom_line(aes(colour=Type.of.Buyer),size=1.25,alpha=0.6)+
  scale_colour_manual(values=c("#F59A08","#875501"))+
  geom_text_repel(aes(label=value),alpha=0.3,size=3,max.overlaps = 3)+
  theme_bw()+
  theme(legend.position = "bottom",legend.key.size = unit(1, 'cm'),
        legend.key.height = unit(0.5, 'cm'), 
        legend.key.width = unit(0.5, 'cm'),
        legend.title = element_text(size=8),
        legend.text = element_text(size=6),
        plot.title = element_text(size=12,face="bold"))+
  ggtitle("Volume of Sales")+
  xlab(NULL)+
  ylab("number")

CRP01.IRL.AGE.ALL.OTH <- CRP01.IRL.AGE.ALL.OTH %>%
  filter(year>="2016")

Fig.IRL.TBUY.AGE <- CRP01.IRL.AGE.ALL.OTH %>%
  ggplot(aes(x=year,y=value,group=Type.of.Buyer))+
  geom_line(aes(colour=Type.of.Buyer),size=1.25,alpha=0.6)+
  scale_colour_manual(values=c("#08F5BC","#06A07B"))+
  geom_text_repel(aes(label=value),alpha=0.3,size=3,max.overlaps = 3)+
  theme_bw()+
  theme(legend.position = "bottom",legend.key.size = unit(1, 'cm'),
        legend.key.height = unit(0.5, 'cm'), 
        legend.key.width = unit(0.5, 'cm'),
        legend.title = element_text(size=8),
        legend.text = element_text(size=6),
        plot.title = element_text(size=12,face="bold"))+
  ggtitle("Median Age")+
  xlab(NULL)+
  ylab("number")

CRP01.IRL.PRC.ALL.OTH <- CRP01.IRL.PRC.ALL.OTH %>%
  filter(year>="2016")

Fig.IRL.TBUY.PRC <- CRP01.IRL.PRC.ALL.OTH %>%
  ggplot(aes(x=year,y=value,group=Type.of.Buyer))+
  geom_line(aes(colour=Type.of.Buyer),size=1.25,alpha=0.6)+
  scale_colour_manual(values=c("#32AE04","#174D03"))+
  geom_text_repel(aes(label=value),alpha=0.3,size=3,max.overlaps = 3)+
  theme_bw()+
  theme(legend.position = "bottom",legend.key.size = unit(1, 'cm'),
        legend.key.height = unit(0.5, 'cm'), 
        legend.key.width = unit(0.5, 'cm'),
        legend.title = element_text(size=8),
        legend.text = element_text(size=6),
        plot.title = element_text(size=12,face="bold"))+
  ggtitle("Median Price")+
  xlab(NULL)+
  ylab("euro")

CRP01.IRL.INC.ALL.OTH <- CRP01.IRL.INC.ALL.OTH %>%
  filter(year>="2016")

Fig.IRL.TBUY.INC <- CRP01.IRL.INC.ALL.OTH %>%
  ggplot(aes(x=year,y=value,group=Type.of.Buyer))+
  geom_line(aes(colour=Type.of.Buyer),size=1.25,alpha=0.6)+
  scale_colour_manual(values=c("#0416AE","#030B53"))+
  geom_text_repel(aes(label=value),alpha=0.3,size=3,max.overlaps = 3)+
  theme_bw()+
  theme(legend.position = "bottom",legend.key.size = unit(1, 'cm'),
        legend.key.height = unit(0.5, 'cm'), 
        legend.key.width = unit(0.5, 'cm'),
        legend.title = element_text(size=8),
        legend.text = element_text(size=6),
        plot.title = element_text(size=12,face="bold"))+
  ggtitle("Median Income")+
  xlab(NULL)+
  ylab("euro")

Fig.IRL.TBUY <- Fig.IRL.TBUY.VOL + Fig.IRL.TBUY.AGE + Fig.IRL.TBUY.PRC + Fig.IRL.TBUY.INC

Fig.IRL.TBUY

NUTS 3 Regions

Aggregate

### NUTS 3 Region
#### Volume
CRP01.NUTS3.VOL.ALL.ALL <- CRP01.NUTS3.VOL.ALL %>%
  filter(Type.of.Buyer=="All buyer types")

####### View the 4 statistical measures for NUTS3 across the aggregated categories (i.e. ALL.ALL)

CRP01.NUTS3.VOL.ALL.ALL %>%
  ggplot(aes(x=year,y=value,group=Statistic))+
  geom_line(size=1.25,alpha=0.6,colour="#F59A08")+
  geom_text_repel(aes(label=value),alpha=0.3,size=3,max.overlaps = 3)+
  theme_bw()+
  theme(legend.position = "bottom",
        plot.title = element_text(size=12,face="bold"))+
  ggtitle("Volume of Sales")+
  xlab(NULL)+
  ylab("number")+
  facet_wrap(~NUTS.3.Region,ncol=2)

CRP01.NUTS3.AGE.ALL.ALL %>%
  ggplot(aes(x=year,y=value,group=Statistic))+
  geom_line(size=1.25,alpha=0.6,colour="#08F5BC")+
  geom_text_repel(aes(label=value),alpha=0.3,size=3,max.overlaps = 3)+
  theme_bw()+
  theme(legend.position = "bottom",
        plot.title = element_text(size=12,face="bold"))+
  ggtitle("Median Age")+
  xlab(NULL)+
  ylab("number")+
  facet_wrap(~NUTS.3.Region,ncol=2)

CRP01.NUTS3.PRC.ALL.ALL %>%
  ggplot(aes(x=year,y=value,group=Statistic))+
  geom_line(size=1.25,alpha=0.6,colour="#32AE04")+
  geom_text_repel(aes(label=value),alpha=0.3,size=3,max.overlaps = 3)+
  theme_bw()+
  theme(legend.position = "bottom",
        plot.title = element_text(size=12,face="bold"))+
  ggtitle("Median Price")+
  xlab(NULL)+
  ylab("euro")+
  facet_wrap(~NUTS.3.Region,ncol=2)

CRP01.NUTS3.INC.ALL.ALL %>%
  ggplot(aes(x=year,y=value,group=Statistic))+
  geom_line(size=1.25,alpha=0.6,colour="#0416AE")+
  geom_text_repel(aes(label=value),alpha=0.3,size=3,max.overlaps = 3)+
  theme_bw()+
  theme(legend.position = "bottom",
        plot.title = element_text(size=12,face="bold"))+
  ggtitle("Median Income")+
  xlab(NULL)+
  ylab("euro")+
  facet_wrap(~NUTS.3.Region,ncol=2)

# Fig.NUTS3 <- Fig.NUTS3.VOL + Fig.NUTS3.AGE + Fig.NUTS3.PRC + Fig.NUTS3.INC # May be unsuitable, run in Markdown and if so revert to individual

Transaction Category

####### View the 4 statistical measures for NUTS3 for each transaction category and aggregated buyers (i.e. OTH.ALL)
##### Title: Figure.NUTS3.TCAT
CRP01.NUTS3.VOL.OTH.ALL %>%
  ggplot(aes(x=year,y=value,group=Transaction.Class))+
  geom_line(aes(linetype=Transaction.Class),size=1.25,alpha=0.6,colour="#F59A08")+
  geom_text_repel(aes(label=value),alpha=0.3,size=3,max.overlaps = 3)+
  theme_bw()+
  theme(legend.position = "bottom",
        plot.title = element_text(size=12,face="bold"))+
  ggtitle("Volume of Sales")+
  xlab(NULL)+
  ylab("number")+
  facet_wrap(~NUTS.3.Region,ncol=2)

CRP01.NUTS3.AGE.OTH.ALL %>%
  ggplot(aes(x=year,y=value,group=Transaction.Class))+
  geom_line(aes(linetype=Transaction.Class),size=1.25,alpha=0.6,colour="#08F5BC")+
  geom_text_repel(aes(label=value),alpha=0.3,size=3,max.overlaps = 3)+
  theme_bw()+
  theme(legend.position = "bottom",
        plot.title = element_text(size=12,face="bold"))+
  ggtitle("Median Age")+
  xlab(NULL)+
  ylab("number")+
  facet_wrap(~NUTS.3.Region,ncol=2)

CRP01.NUTS3.PRC.OTH.ALL %>%
  ggplot(aes(x=year,y=value,group=Transaction.Class))+
  geom_line(aes(linetype=Transaction.Class),size=1.25,alpha=0.6,colour="#32AE04")+
  geom_text_repel(aes(label=value),alpha=0.3,size=3,max.overlaps = 3)+
  theme_bw()+
  theme(legend.position = "bottom",
        plot.title = element_text(size=12,face="bold"))+
  ggtitle("Median Price")+
  xlab(NULL)+
  ylab("euro")+
  facet_wrap(~NUTS.3.Region,ncol=2)

CRP01.NUTS3.INC.OTH.ALL %>%
  ggplot(aes(x=year,y=value,group=Transaction.Class))+
  geom_line(aes(linetype=Transaction.Class),size=1.25,alpha=0.6,colour="#0416AE")+
  geom_text_repel(aes(label=value),alpha=0.3,size=3,max.overlaps = 3)+
  theme_bw()+
  theme(legend.position = "bottom",
        plot.title = element_text(size=12,face="bold"))+
  ggtitle("Median Income")+
  xlab(NULL)+
  ylab("euro")+
  facet_wrap(~NUTS.3.Region,ncol=2)

Price to Income Ratio

I’ve included variables for price to income, a loan ceiling/cap which is 3.5 times income plus 10% of that figure as a deposit and finally a the latter expressed as a percentage of the median price. - This being the spread between what the median buyer can afford vs what the average price is, in the absence of savings.

Naturally Central Bank rules are not the only factor that determines prices but the below gives us an indication of how far off on average the buyers are.

The median price exceeded the loan ceiling for the first rime in 2021, having acted as a cap in 2019 & 2020.

PTI.Facet1 <- RATIO %>%
  ggplot(aes(x=year,y=PtI,group=NUTS.3.Region))+
  geom_line(aes(y=CAPtI),alpha=0.8,linetype="dotted")+
  geom_line(size=1.25,colour="#5DD7D2",alpha=0.7)+
  geom_text_repel(aes(label=PtI),alpha=0.4)+
  theme_bw()+
  theme(legend.position = "bottom",
      plot.title = element_text(size=12,face="bold"))+
  labs(caption="dotted line represents 3.5x median income plus 10% of that figure")+
  ggtitle("Price to Income Ratio")+
  ylab("price to income")

RATIO$price.lab <-  comma((RATIO$price/1000),digits=0)
RATIO$CAP.lab <-  comma((RATIO$CAP/1000),digits=0)
price.Facet2 <- RATIO %>%
  ggplot(aes(x=year,y=price,group=NUTS.3.Region))+
  geom_line(size=1.25,colour="#3C46A1",alpha=0.7)+
  geom_line(aes(y=CAP),colour="darkred",alpha=0.7,size=1.25)+
  geom_text_repel(aes(label=price.lab),colour="black",hjust="left",alpha=0.3)+
  geom_text_repel(aes(label=CAP.lab,y=CAP),colour="darkred",hjust="left",vjust=0.5,alpha=0.3)+
  theme_bw()+
  theme(legend.position = "bottom",
        plot.title = element_text(size=12,face="bold"))+
  labs(caption="red line represents 3.5x median income plus 10% of that figure")+
  ggtitle("Median Prices vs Average Loan Capacity")

spr.colour <- ifelse(RATIO$SPR > 0,"darkred","#1b5545")
SPR.Facet3 <- RATIO %>%
  ggplot(aes(x=year,y=SPR,group=NUTS.3.Region))+
  geom_hline(yintercept = 0,alpha=0.6,linetype="dotted")+
  geom_line(size=1.25,colour="#A13C56",alpha=0.7)+
  geom_text_repel(aes(label=SPR,colour=spr.colour),hjust="left",alpha=0.5)+
  scale_colour_manual(values=c("#CC0000","#1b5545"))+
  theme_bw()+
  theme(legend.position = "none",
        plot.title = element_text(size=12,face="bold"))+
  labs(caption="")+
  ggtitle("Median Prices vs Average Loan Capacity: Spread")+
  ylab("euro")

#### MAP
NUTS <- sf::read_sf("C:\\Users\\charten\\OneDrive - Glenveagh Properties\\Research & Development\\1. Analysis\\NUTS_RG_01M_2021_4326.shp")
NUTS.IE <- NUTS %>%
  filter(CNTR_CODE=="IE")%>%
  filter(LEVL_CODE=="3")
NUTS.CLEAN <- readxl::read_xlsx(path="C:\\Users\\charten\\OneDrive - Glenveagh Properties\\Research & Development\\1. Analysis\\NUTS3_CLEAN.xlsx")
  
RATIO.PRC_NUTS3 <- CRP01.NUTS3.PRC.ALL.ALL %>%
  rename(price=value)
RATIO.PRC_NUTS3 <- RATIO.PRC_NUTS3 %>%
  select(3:7)
RATIO.INC_NUTS3 <- CRP01.NUTS3.INC.ALL.ALL %>%
  rename(income=value)
RATIO.INC_NUTS3 <- RATIO.INC_NUTS3 %>%
  select(3:7)

RATIO_NUTS3 <- merge(RATIO.PRC_NUTS3,RATIO.INC_NUTS3,by=c("Transaction.Class","NUTS.3.Region","Type.of.Buyer","year"))
RATIO_NUTS3 <- RATIO_NUTS3 %>%
  filter(year>="2012")
RATIO_NUTS3$PtI <- comma((RATIO_NUTS3$price/RATIO_NUTS3$income),digits = 2)
RATIO_NUTS3$CAP <- (RATIO_NUTS3$income*3.5)+(RATIO_NUTS3$income*3.5*.1)
RATIO_NUTS3$CAP <- comma(RATIO_NUTS3$CAP,big.mark = ",",digits=0)
RATIO_NUTS3$CAPtI <- comma((RATIO_NUTS3$CAP/RATIO_NUTS3$income),digits=2)
RATIO_NUTS3$SPR <- RATIO_NUTS3$CAP - RATIO_NUTS3$price  
RATIO_NUTS3$SPR <- comma(RATIO_NUTS3$SPR,big.mark = ",",digits=0)
RATIO_NUTS3$SPRPC <- comma((RATIO_NUTS3$SPR/RATIO_NUTS3$price),big.mark = ",",digits=2)*100

RATIO_NUTS3 <- merge(RATIO_NUTS3,NUTS.CLEAN,by="NUTS.3.Region")
SHP.RATIO_NUTS3 <- merge(NUTS.IE,RATIO_NUTS3,by.x="NAME_LATN",by.y="NAME_LATN")

SHP.RATIO_NUTS3.21 <- SHP.RATIO_NUTS3 %>%
  filter(year=="2021")
  
SPR.Facet4 <- SHP.RATIO_NUTS3.21 %>%
    ggplot()+
    geom_sf(aes(fill=SPRPC),alpha=0.6)+
    theme_void()+
    scale_fill_viridis_c(option = "magma", name="%")+
    geom_sf_text(aes(label=SPRPC),color="navy",size=2.5)+
    theme(legend.title = element_text(size = 10),
          legend.text = element_text(size = 8))+
    theme(legend.position = "right")+
    theme(plot.title = element_text(size=12,face="bold"))+
    facet_wrap(~year,ncol=4)+
    theme(strip.text.x = element_text(size = 10,colour = "navy"))+
    ggtitle("Spread as Percentage of price - NUTS 3 Regions")

#PTI.Facet1 + price.Facet2 + SPR.Facet3 + SPR.Facet4 + plot_layout(widths = c(1,1))
  
PTI.Facet1 

price.Facet2 

SPR.Facet3 

SPR.Facet4

SHP.RATIO_NUTS3 %>%
  ggplot()+
  geom_sf(aes(fill=PtI),alpha=0.9)+
  theme_void()+
  scale_fill_distiller(palette = "Spectral", name="Price to Income")+
  theme(legend.title = element_text(size = 10),
        legend.text = element_text(size = 8))+
  theme(legend.position = "left")+
  theme(plot.title = element_text(size=16,face="bold"))+
  facet_wrap(~year,ncol=3)+
  theme(strip.text.x = element_text(size = 10,colour = "navy"))+
  ggtitle("Price to Income - NUTS 3 Regions")

Spread

options(scipen=999)
RATIO_NUTS3_Max <- RATIO_NUTS3 %>%
  group_by(NUTS.3.Region) %>%
  slice(which.max(year))
RATIO_NUTS3_Max$lab <- paste("Spread : ", RATIO_NUTS3_Max$SPRPC," %")
spr.max.colour <- ifelse(RATIO_NUTS3_Max$SPR > 0,"darkred","#1b5545")
# 
# # Option A
# RATIO_NUTS3 %>%
#   ggplot(aes(x=year,y=price,group=NUTS.3.Region))+
#   geom_line(size=1.25,colour="#3C46A1",alpha=0.7)+
#   geom_line(aes(y=CAP),colour="darkred",alpha=0.7,size=1.25)+
#   geom_text(data=RATIO_NUTS3_Max,aes(label=lab,y=105000,colour=spr.max.colour),hjust="right",alpha=0.6)+
#   scale_colour_manual(values=c("#CC0000","#1b5545"))+
#   theme_bw()+
#   theme(legend.position = "none",
#         plot.title = element_text(size=12,face="bold"))+
#   labs(caption="red line represents 3.5x median income plus 10% of that figure")+
#   ggtitle("Median Prices vs Average Loan Capacity")+
#   facet_wrap(~NUTS.3.Region,ncol=2)
# 
# # Option B
spr.colour <- ifelse(RATIO_NUTS3$SPR > 0,"darkred","#1b5545")

RATIO_NUTS3 %>%
  ggplot(aes(x=year,y=SPRPC,group=NUTS.3.Region))+
  geom_hline(yintercept = 0,alpha=0.4,linetype="dotted")+
  geom_line(size=1.25,colour="#A13C56",alpha=0.7)+
  geom_text_repel(aes(label=SPRPC,colour=spr.colour),alpha=0.7,size=2.75)+
  scale_colour_manual(values=c("#CC0000","#1b5545"))+
  theme_bw()+
  theme(legend.position = "none",
        plot.title = element_text(size=12,face="bold"))+
  labs(caption="")+
  ggtitle("Median Prices vs Average Loan Capacity: Spread %")+
  ylab("%")+
  facet_wrap(~NUTS.3.Region,ncol=2)

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 12.2%. This represents a -1.1 change from which was 13.3%.

Prices: 12 Month Rolling Average

# Rolling 12 Month Market-based Household Purchases of Residential Dwellings

HPM08 <- cso_get_data("HPM08")

HPM08 <- HPM08 %>%
  pivot_longer(!1:5, names_to = "year_month")
HPM08$Year <-substr(HPM08$year_month,1,4)
HPM08$Month <- sub(".* ", "", HPM08$year_month)
HPM08$Month_NR <- as.integer(factor(HPM08$Month, levels=month.name))
HPM08$Date <- as.yearmon(paste(HPM08$Year, HPM08$Month_NR), "%Y %m")

HPM08$value = as.numeric(HPM08$value)
HPM08$value <- comma(HPM08$value,big.mark = ",")
HPM08$lab <- paste(HPM08$Month,":",round(HPM08$value,digits=0))
HPM08$value_short <- round((HPM08$value/1000),digits=1)
# Mean
## Subset

HPM08_1 <- HPM08 %>%
  filter(Statistic=="Moving 12 Month Mean Sale Price")

### Take the Executions & All Buyer Types, remove All Dwelling Statuses

HPM08_1 <- HPM08_1 %>%
  filter(Type.of.Buyer=="All Buyer Types") %>%
  filter(Stamp.Duty.Event=="Executions") %>%
  filter(Dwelling.Status!="All Dwelling Statuses")

### Split by all and Eircode Output Areas

HPM08_1.ALL <- HPM08_1 %>%
  filter(Eircode.Output=="All")

HPM08_1.ALL_Max <- HPM08_1.ALL %>%
  group_by(Dwelling.Status) %>%
  slice(which.max(Date))

HPM08_1.EOA <- HPM08_1 %>%
  filter(Eircode.Output!="All")

HPM08_1.EOA <- HPM08_1.EOA %>%                            # Add lagged column
  group_by(Eircode.Output,Dwelling.Status) %>%
  dplyr::mutate(lag12 = dplyr::lag(value, n = 12, default = NA)) %>% 
  as.data.frame()

HPM08_1.EOA$Diffpc <- ((HPM08_1.EOA$value-HPM08_1.EOA$lag12)/HPM08_1.EOA$lag12)*100

GEO_Merge <- readxl::read_xlsx(path="C:\\Users\\charten\\OneDrive - Glenveagh Properties\\Research & Development\\1. Analysis\\HPM04_GEO.xlsx",sheet = 1)
HPM08_1.EOA <- full_join(HPM08_1.EOA, GEO_Merge, by = "Eircode.Output")

SUM_HPM08_1.EOA <-HPM08_1.EOA %>%
  group_by(Seat_Tag, Dwelling.Status, Date)%>% 
  summarise(value = mean(value,na.rm=TRUE))
  
SUM_HPM08_1.EOA <- SUM_HPM08_1.EOA %>%                            # Add lagged column
  group_by(Seat_Tag,Dwelling.Status) %>%
  dplyr::mutate(lag12 = dplyr::lag(value, n = 12, default = NA)) %>% 
  as.data.frame()

SUM_HPM08_1.EOA$Diffpc <- ((SUM_HPM08_1.EOA$value-SUM_HPM08_1.EOA$lag12)/SUM_HPM08_1.EOA$lag12)*100

HPM08_1.EOA_Max <- HPM08_1.EOA %>%
  group_by(Eircode.Output,Dwelling.Status) %>%
  slice(which.max(Date))

SUM_HPM08_1.EOA_Max <- SUM_HPM08_1.EOA %>%
  group_by(Seat_Tag,Dwelling.Status) %>%
  slice(which.max(Date))

# Plot
HPM08_1.ALL %>%
  ggplot(aes(x=Date,y=value,group=Dwelling.Status,colour=Dwelling.Status))+
  geom_line(size=1.25,alpha=0.7)+
  theme_bw()+
  theme(legend.position = "bottom")+
  ylab("euro")+xlab(NULL)+
  ggtitle("Market-based Household Purchases")+
  labs(subtitle="Mean Rolling 12 Month") +
  geom_text_repel(aes(label=value_short),alpha=0.6,size=2.75)+
  geom_text(data=HPM08_1.ALL_Max,aes(label=lab),colour="black",hjust="right",vjust=1,size=3,fontface="bold",alpha=0.6)

A chart for each Eircode Output Area can be seen in the Appendix below.

12 Month Variation

The latest values for these can be seen in the scroll box below. Also included is the lagged value, the rolling average for the 12 month period prior. The variation for each can be seen in the right most column.

Table <- HPM08_1.EOA_Max %>%
  select(2,3,6,7,14,15,23)
Table <- Table[,c(2,7,1,3,4,5,6)]
  
Table %>%
  kbl(caption = "Eircode Output Areas")%>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"), full_width = T)%>%
  scroll_box(width = "800px", height = "300px") 

Eircode Output Areas

Eircode.Output	Constituency	Dwelling.Status	year_month	value	lag12	Diffpc
A41: Ballyboughal	Dublin Fingal	New	2022 August	NA	517,474.00	NA
A41: Ballyboughal	Dublin Fingal	Existing	2022 August	674,357.00	545,417.00	23.64
A42: Garristown	Dublin Fingal	New	2022 August	475,000.00	387,475.00	22.59
A42: Garristown	Dublin Fingal	Existing	2022 August	428,929.00	390,560.00	9.82
A45: Oldtown	Dublin Fingal	New	2022 August	597,169.00	NA	NA
A45: Oldtown	Dublin Fingal	Existing	2022 August	405,750.00	372,000.00	9.07
A63: Greystones	Wicklow	New	2022 August	548,685.00	608,014.00	-9.76
A63: Greystones	Wicklow	Existing	2022 August	559,849.00	474,033.00	18.10
A67: Wicklow	Wicklow	New	2022 August	349,294.00	373,807.00	-6.56
A67: Wicklow	Wicklow	Existing	2022 August	397,377.00	345,539.00	15.00
A75: Castleblaney	Cavan-Monaghan	New	2022 August	97,894.00	241,211.00	-59.42
A75: Castleblaney	Cavan-Monaghan	Existing	2022 August	175,249.00	176,569.00	-0.75
A81: Carrickmacross	Cavan-Monaghan	New	2022 August	261,342.00	173,770.00	50.40
A81: Carrickmacross	Cavan-Monaghan	Existing	2022 August	227,268.00	201,792.00	12.62
A82: Kells	Meath East	New	2022 August	292,293.00	234,898.00	24.43
A82: Kells	Meath East	Existing	2022 August	226,599.00	203,110.00	11.56
A83: Enfield	Meath West	New	2022 August	329,961.00	321,129.00	2.75
A83: Enfield	Meath West	Existing	2022 August	331,874.00	281,470.00	17.91
A84: Ashbourne	Meath East	New	2022 August	506,364.00	442,808.00	14.35
A84: Ashbourne	Meath East	Existing	2022 August	313,764.00	317,275.00	-1.11
A85: Dunshaughlin	Meath East	New	2022 August	370,661.00	349,468.00	6.06
A85: Dunshaughlin	Meath East	Existing	2022 August	418,030.00	380,426.00	9.88
A86: Dunboyne	Meath East	New	2022 August	418,425.00	384,410.00	8.85
A86: Dunboyne	Meath East	Existing	2022 August	421,134.00	384,627.00	9.49
A91: Dundalk	Louth	New	2022 August	290,953.00	289,546.00	0.49
A91: Dundalk	Louth	Existing	2022 August	257,736.00	226,852.00	13.61
A92: Drogheda	Louth	New	2022 August	317,629.00	311,477.00	1.98
A92: Drogheda	Louth	Existing	2022 August	269,520.00	241,723.00	11.50
A94: Blackrock	Dun Laoghaire	New	2022 August	1,065,670.00	1,003,873.00	6.16
A94: Blackrock	Dun Laoghaire	Existing	2022 August	787,026.00	730,753.00	7.70
A96: Glenageary	Dun Laoghaire	New	2022 August	857,849.00	817,604.00	4.92
A96: Glenageary	Dun Laoghaire	Existing	2022 August	864,749.00	751,126.00	15.13
A98: Bray	Wicklow	New	2022 August	575,821.00	721,499.00	-20.19
A98: Bray	Wicklow	Existing	2022 August	501,768.00	445,319.00	12.68
C15: Navan	Meath West	New	2022 August	342,694.00	369,738.00	-7.31
C15: Navan	Meath West	Existing	2022 August	298,174.00	258,289.00	15.44
D01: Dublin 1	Dublin Central	New	2022 August	461,567.00	NA	NA
D01: Dublin 1	Dublin Central	Existing	2022 August	326,794.00	305,234.00	7.06
D02: Dublin 2	Dublin Bay South	New	2022 August	704,705.00	766,962.00	-8.12
D02: Dublin 2	Dublin Bay South	Existing	2022 August	568,741.00	444,478.00	27.96
D03: Dublin 3	Dublin Bay North	New	2022 August	757,263.00	727,723.00	4.06
D03: Dublin 3	Dublin Bay North	Existing	2022 August	546,512.00	490,565.00	11.40
D04: Dublin 4	Dublin Bay South	New	2022 August	1,424,748.00	1,349,270.00	5.59
D04: Dublin 4	Dublin Bay South	Existing	2022 August	837,563.00	774,576.00	8.13
D05: Dublin 5	Dublin Bay North	New	2022 August	615,714.00	666,569.00	-7.63
D05: Dublin 5	Dublin Bay North	Existing	2022 August	466,454.00	426,934.00	9.26
D06: Dublin 6	Dublin Bay South	New	2022 August	1,334,454.00	862,005.00	54.81
D06: Dublin 6	Dublin Bay South	Existing	2022 August	870,537.00	778,205.00	11.86
D07: Dublin 7	Dublin West	New	2022 August	NA	1,022,666.00	NA
D07: Dublin 7	Dublin West	Existing	2022 August	416,169.00	373,190.00	11.52
D08: Dublin 8	Dublin South Central	New	2022 August	806,794.00	655,701.00	23.04
D08: Dublin 8	Dublin South Central	Existing	2022 August	395,331.00	376,463.00	5.01
D09: Dublin 9	Dublin North-West	New	2022 August	770,182.00	575,819.00	33.75
D09: Dublin 9	Dublin North-West	Existing	2022 August	457,270.00	431,519.00	5.97
D10: Dublin 10	Dublin South Central	New	2022 August	NA	NA	NA
D10: Dublin 10	Dublin South Central	Existing	2022 August	272,754.00	242,535.00	12.46
D11: Dublin 11	Dublin West	New	2022 August	262,131.00	269,149.00	-2.61
D11: Dublin 11	Dublin West	Existing	2022 August	334,577.00	297,358.00	12.52
D12: Dublin 12	Dublin South Central	New	2022 August	632,387.00	523,483.00	20.80
D12: Dublin 12	Dublin South Central	Existing	2022 August	389,952.00	359,035.00	8.61
D13: Dublin 13	Dublin Bay North	New	2022 August	545,287.00	471,571.00	15.63
D13: Dublin 13	Dublin Bay North	Existing	2022 August	551,397.00	467,795.00	17.87
D14: Dublin 14	Dublin Bay South	New	2022 August	776,578.00	735,756.00	5.55
D14: Dublin 14	Dublin Bay South	Existing	2022 August	683,587.00	625,472.00	9.29
D15: Dublin 15	Dublin West	New	2022 August	436,381.00	406,288.00	7.41
D15: Dublin 15	Dublin West	Existing	2022 August	399,415.00	379,896.00	5.14
D16: Dublin 16	Dublin South-West	New	2022 August	771,962.00	611,194.00	26.30
D16: Dublin 16	Dublin South-West	Existing	2022 August	571,482.00	503,481.00	13.51
D17: Dublin 17	Dublin Fingal	New	2022 August	456,777.00	NA	NA
D17: Dublin 17	Dublin Fingal	Existing	2022 August	302,113.00	269,468.00	12.11
D18: Dublin 18	Dublin Rathdown	New	2022 August	650,309.00	606,455.00	7.23
D18: Dublin 18	Dublin Rathdown	Existing	2022 August	618,147.00	554,144.00	11.55
D20: Dublin 20	Dublin South Central	New	2022 August	605,467.00	600,000.00	0.91
D20: Dublin 20	Dublin South Central	Existing	2022 August	351,834.00	347,429.00	1.27
D22: Dublin 22	Dublin South Central	New	2022 August	376,914.00	388,114.00	-2.89
D22: Dublin 22	Dublin South Central	Existing	2022 August	311,216.00	284,755.00	9.29
D24: Dublin 24	Dublin South-West	New	2022 August	402,960.00	433,158.00	-6.97
D24: Dublin 24	Dublin South-West	Existing	2022 August	341,898.00	310,444.00	10.13
D6W: Dublin 6W	Dublin South Central	New	2022 August	776,188.00	604,856.00	28.33
D6W: Dublin 6W	Dublin South Central	Existing	2022 August	624,795.00	614,362.00	1.70
E21: Cahir	Tipperary	New	2022 August	245,000.00	119,175.00	105.58
E21: Cahir	Tipperary	Existing	2022 August	195,115.00	169,073.00	15.40
E25: Cashel	Tipperary	New	2022 August	192,950.00	234,879.00	-17.85
E25: Cashel	Tipperary	Existing	2022 August	175,957.00	163,500.00	7.62
E32: Carrick-on-Suir	Tipperary	New	2022 August	340,001.00	228,506.00	48.79
E32: Carrick-on-Suir	Tipperary	Existing	2022 August	192,744.00	163,256.00	18.06
E34: Tipperary	Tipperary	New	2022 August	102,150.00	349,353.00	-70.76
E34: Tipperary	Tipperary	Existing	2022 August	164,675.00	142,192.00	15.81
E41: Thurles	Tipperary	New	2022 August	191,815.00	197,490.00	-2.87
E41: Thurles	Tipperary	Existing	2022 August	189,277.00	166,329.00	13.80
E45: Nenagh	Tipperary	New	2022 August	249,338.00	215,532.00	15.68
E45: Nenagh	Tipperary	Existing	2022 August	227,522.00	186,517.00	21.98
E53: Roscrea	Tipperary	New	2022 August	164,000.00	193,418.00	-15.21
E53: Roscrea	Tipperary	Existing	2022 August	177,820.00	154,116.00	15.38
E91: Clonmel	Tipperary	New	2022 August	342,500.00	272,514.00	25.68
E91: Clonmel	Tipperary	Existing	2022 August	220,704.00	201,325.00	9.63
F12: Claremorris	Mayo	New	2022 August	191,843.00	146,274.00	31.15
F12: Claremorris	Mayo	Existing	2022 August	166,346.00	140,615.00	18.30
F23: Castlebar	Mayo	New	2022 August	275,594.00	237,165.00	16.20
F23: Castlebar	Mayo	Existing	2022 August	190,288.00	166,861.00	14.04
F26: Ballina	Mayo	New	2022 August	229,117.00	230,776.00	-0.72
F26: Ballina	Mayo	Existing	2022 August	168,258.00	142,312.00	18.23
F28: Westport	Mayo	New	2022 August	312,053.00	211,787.00	47.34
F28: Westport	Mayo	Existing	2022 August	262,451.00	219,781.00	19.41
F31: Ballinrobe	Mayo	New	2022 August	363,200.00	190,880.00	90.28
F31: Ballinrobe	Mayo	Existing	2022 August	172,347.00	147,845.00	16.57
F35: Ballyhaunis	Mayo	New	2022 August	154,370.00	161,454.00	-4.39
F35: Ballyhaunis	Mayo	Existing	2022 August	131,370.00	117,022.00	12.26
F42: Roscommon	Roscommon-Galway	New	2022 August	308,999.00	233,305.00	32.44
F42: Roscommon	Roscommon-Galway	Existing	2022 August	176,997.00	169,576.00	4.38
F45: Castlerea	Roscommon-Galway	New	2022 August	184,573.00	127,417.00	44.86
F45: Castlerea	Roscommon-Galway	Existing	2022 August	128,502.00	103,269.00	24.43
F52: Boyle	Sligo-Leitrim	New	2022 August	147,562.00	173,613.00	-15.01
F52: Boyle	Sligo-Leitrim	Existing	2022 August	157,059.00	139,962.00	12.22
F56: Ballymote	Sligo-Leitrim	New	2022 August	191,853.00	142,818.00	34.33
F56: Ballymote	Sligo-Leitrim	Existing	2022 August	140,619.00	112,524.00	24.97
F91: Sligo	Sligo-Leitrim	New	2022 August	252,904.00	185,159.00	36.59
F91: Sligo	Sligo-Leitrim	Existing	2022 August	207,647.00	178,255.00	16.49
F92: Letterkenny	Donegal	New	2022 August	259,078.00	236,610.00	9.50
F92: Letterkenny	Donegal	Existing	2022 August	174,936.00	162,736.00	7.50
F93: Lifford	Donegal	New	2022 August	287,993.00	175,180.00	64.40
F93: Lifford	Donegal	Existing	2022 August	146,197.00	133,515.00	9.50
F94: Donegal	Donegal	New	2022 August	226,689.00	178,678.00	26.87
F94: Donegal	Donegal	Existing	2022 August	169,122.00	144,508.00	17.03
H12: Cavan	Cavan-Monaghan	New	2022 August	225,013.00	216,331.00	4.01
H12: Cavan	Cavan-Monaghan	Existing	2022 August	179,041.00	157,962.00	13.34
H14: Belturbet	Cavan-Monaghan	New	2022 August	236,186.00	206,570.00	14.34
H14: Belturbet	Cavan-Monaghan	Existing	2022 August	143,837.00	127,309.00	12.98
H16: Cootehill	Cavan-Monaghan	New	2022 August	200,600.00	NA	NA
H16: Cootehill	Cavan-Monaghan	Existing	2022 August	158,109.00	143,482.00	10.19
H18: Monaghan	Cavan-Monaghan	New	2022 August	316,525.00	386,001.00	-18.00
H18: Monaghan	Cavan-Monaghan	Existing	2022 August	200,940.00	170,905.00	17.57
H23: Clones	Cavan-Monaghan	New	2022 August	NA	NA	NA
H23: Clones	Cavan-Monaghan	Existing	2022 August	131,406.00	92,750.00	41.68
H53: Ballinasloe	Roscommon-Galway	New	2022 August	228,447.00	222,164.00	2.83
H53: Ballinasloe	Roscommon-Galway	Existing	2022 August	190,176.00	158,897.00	19.69
H54: Tuam	Galway East	New	2022 August	214,402.00	222,370.00	-3.58
H54: Tuam	Galway East	Existing	2022 August	203,953.00	178,465.00	14.28
H62: Loughrea	Galway East	New	2022 August	290,454.00	264,826.00	9.68
H62: Loughrea	Galway East	Existing	2022 August	216,463.00	204,182.00	6.01
H65: Athenry	Galway East	New	2022 August	323,264.00	285,161.00	13.36
H65: Athenry	Galway East	Existing	2022 August	286,619.00	251,219.00	14.09
H71: Clifden	Galway West	New	2022 August	440,000.00	292,830.00	50.26
H71: Clifden	Galway West	Existing	2022 August	270,808.00	205,769.00	31.61
H91: Galway	Galway West	New	2022 August	417,121.00	382,361.00	9.09
H91: Galway	Galway West	Existing	2022 August	323,597.00	292,169.00	10.76
K32: Balbriggan	Dublin Fingal	New	2022 August	344,561.00	335,215.00	2.79
K32: Balbriggan	Dublin Fingal	Existing	2022 August	282,850.00	240,914.00	17.41
K34: Skerries	Dublin Fingal	New	2022 August	347,839.00	417,362.00	-16.66
K34: Skerries	Dublin Fingal	Existing	2022 August	504,376.00	467,727.00	7.84
K36: Malahide	Dublin Fingal	New	2022 August	652,799.00	595,288.00	9.66
K36: Malahide	Dublin Fingal	Existing	2022 August	623,505.00	560,274.00	11.29
K45: Lusk	Dublin Fingal	New	2022 August	411,087.00	358,088.00	14.80
K45: Lusk	Dublin Fingal	Existing	2022 August	353,022.00	319,987.00	10.32
K56: Rush	Dublin Fingal	New	2022 August	387,796.00	386,041.00	0.45
K56: Rush	Dublin Fingal	Existing	2022 August	349,927.00	349,669.00	0.07
K67: Swords	Dublin Fingal	New	2022 August	443,510.00	404,208.00	9.72
K67: Swords	Dublin Fingal	Existing	2022 August	362,941.00	329,651.00	10.10
K78: Lucan	Dublin Mid-West	New	2022 August	452,816.00	432,288.00	4.75
K78: Lucan	Dublin Mid-West	Existing	2022 August	376,653.00	355,877.00	5.84
N37: Athlone	Longford-Westmeath	New	2022 August	319,212.00	304,504.00	4.83
N37: Athlone	Longford-Westmeath	Existing	2022 August	227,500.00	203,397.00	11.85
N39: Longford	Longford-Westmeath	New	2022 August	208,706.00	222,379.00	-6.15
N39: Longford	Longford-Westmeath	Existing	2022 August	161,751.00	132,566.00	22.02
N41: Carrick-on-Shannon	Sligo-Leitrim	New	2022 August	208,972.00	157,606.00	32.59
N41: Carrick-on-Shannon	Sligo-Leitrim	Existing	2022 August	164,708.00	141,331.00	16.54
N91: Mullingar	Longford-Westmeath	New	2022 August	355,432.00	314,949.00	12.85
N91: Mullingar	Longford-Westmeath	Existing	2022 August	235,605.00	198,412.00	18.75
P12: Macroom	Cork North-West	New	2022 August	309,853.00	261,091.00	18.68
P12: Macroom	Cork North-West	Existing	2022 August	230,613.00	217,719.00	5.92
P14: Crookstown	Cork North-West	New	2022 August	367,030.00	424,362.00	-13.51
P14: Crookstown	Cork North-West	Existing	2022 August	281,536.00	340,042.00	-17.21
P17: Kinsale	Cork South-West	New	2022 August	388,450.00	422,798.00	-8.12
P17: Kinsale	Cork South-West	Existing	2022 August	520,559.00	393,662.00	32.24
P24: Cobh	Cork East	New	2022 August	302,376.00	282,214.00	7.14
P24: Cobh	Cork East	Existing	2022 August	284,606.00	239,510.00	18.83
P25: Midleton	Cork East	New	2022 August	362,041.00	336,455.00	7.60
P25: Midleton	Cork East	Existing	2022 August	287,286.00	259,115.00	10.87
P31: Ballincollig	Cork North-West	New	2022 August	390,991.00	390,065.00	0.24
P31: Ballincollig	Cork North-West	Existing	2022 August	370,220.00	331,136.00	11.80
P32: Rylane	Cork North-West	New	2022 August	415,750.00	380,000.00	9.41
P32: Rylane	Cork North-West	Existing	2022 August	287,583.00	205,778.00	39.75
P36: Youghal	Cork East	New	2022 August	606,240.00	337,989.00	79.37
P36: Youghal	Cork East	Existing	2022 August	236,988.00	218,654.00	8.38
P43: Carrigaline	Cork South-Central	New	2022 August	358,090.00	357,223.00	0.24
P43: Carrigaline	Cork South-Central	Existing	2022 August	375,227.00	302,733.00	23.95
P47: Dunmanway	Cork South-West	New	2022 August	407,499.00	350,325.00	16.32
P47: Dunmanway	Cork South-West	Existing	2022 August	216,283.00	177,329.00	21.97
P51: Mallow	Cork East	New	2022 August	294,915.00	273,739.00	7.74
P51: Mallow	Cork East	Existing	2022 August	203,360.00	163,530.00	24.36
P56: Charleville	Cork North-West	New	2022 August	NA	125,533.00	NA
P56: Charleville	Cork North-West	Existing	2022 August	182,117.00	148,207.00	22.88
P61: Fermoy	Cork East	New	2022 August	431,350.00	332,398.00	29.77
P61: Fermoy	Cork East	Existing	2022 August	260,532.00	200,913.00	29.67
P67: Mitchelstown	Cork East	New	2022 August	316,363.00	280,972.00	12.60
P67: Mitchelstown	Cork East	Existing	2022 August	232,338.00	190,567.00	21.92
P72: Bandon	Cork South-West	New	2022 August	318,016.00	397,138.00	-19.92
P72: Bandon	Cork South-West	Existing	2022 August	282,198.00	224,716.00	25.58
P75: Bantry	Cork South-West	New	2022 August	242,615.00	261,190.00	-7.11
P75: Bantry	Cork South-West	Existing	2022 August	227,777.00	193,530.00	17.70
P81: Skibbereen	Cork South-West	New	2022 August	329,254.00	251,718.00	30.80
P81: Skibbereen	Cork South-West	Existing	2022 August	345,496.00	270,952.00	27.51
P85: Clonakilty	Cork South-West	New	2022 August	357,131.00	353,393.00	1.06
P85: Clonakilty	Cork South-West	Existing	2022 August	277,309.00	289,442.00	-4.19
R14: Athy	Kildare South	New	2022 August	300,000.00	NA	NA
R14: Athy	Kildare South	Existing	2022 August	217,302.00	200,725.00	8.26
R21: Mhuine Bheag	Carlow-Kilkenny	New	2022 August	NA	312,125.00	NA
R21: Mhuine Bheag	Carlow-Kilkenny	Existing	2022 August	257,771.00	187,940.00	37.16
R32: Portlaoise	Laois-Offaly	New	2022 August	284,450.00	248,754.00	14.35
R32: Portlaoise	Laois-Offaly	Existing	2022 August	219,724.00	196,774.00	11.66
R35: Tullamore	Laois-Offaly	New	2022 August	286,385.00	264,707.00	8.19
R35: Tullamore	Laois-Offaly	Existing	2022 August	228,192.00	195,006.00	17.02
R42: Birr	Laois-Offaly	New	2022 August	284,898.00	255,272.00	11.61
R42: Birr	Laois-Offaly	Existing	2022 August	195,449.00	157,787.00	23.87
R45: Edenderry	Laois-Offaly	New	2022 August	251,959.00	240,681.00	4.69
R45: Edenderry	Laois-Offaly	Existing	2022 August	216,735.00	185,648.00	16.75
R51: Kildare	Kildare South	New	2022 August	295,246.00	298,854.00	-1.21
R51: Kildare	Kildare South	Existing	2022 August	299,525.00	254,422.00	17.73
R56: Curragh	Kildare South	New	2022 August	410,698.00	368,386.00	11.49
R56: Curragh	Kildare South	Existing	2022 August	340,282.00	292,988.00	16.14
R93: Carlow	Carlow-Kilkenny	New	2022 August	259,384.00	249,235.00	4.07
R93: Carlow	Carlow-Kilkenny	Existing	2022 August	223,522.00	206,521.00	8.23
R95: Kilkenny	Carlow-Kilkenny	New	2022 August	394,463.00	348,345.00	13.24
R95: Kilkenny	Carlow-Kilkenny	Existing	2022 August	274,345.00	245,474.00	11.76
T12: Cork Southside	Cork North-Central	New	2022 August	441,822.00	417,636.00	5.79
T12: Cork Southside	Cork North-Central	Existing	2022 August	353,355.00	325,933.00	8.41
T23: Cork Northside	Cork North-Central	New	2022 August	425,818.00	397,421.00	7.15
T23: Cork Northside	Cork North-Central	Existing	2022 August	260,989.00	244,546.00	6.72
T34: Carrignavar	Cork East	New	2022 August	332,258.00	NA	NA
T34: Carrignavar	Cork East	Existing	2022 August	371,542.00	285,112.00	30.31
T45: Glanmire	Cork North-Central	New	2022 August	342,767.00	337,284.00	1.63
T45: Glanmire	Cork North-Central	Existing	2022 August	366,274.00	310,939.00	17.80
T56: Watergrasshill	Cork North-Central	New	2022 August	321,977.00	325,904.00	-1.20
T56: Watergrasshill	Cork North-Central	Existing	2022 August	318,477.00	292,461.00	8.90
V14: Shannon	Clare	New	2022 August	136,200.00	234,888.00	-42.01
V14: Shannon	Clare	Existing	2022 August	192,268.00	170,269.00	12.92
V15: Kilrush	Clare	New	2022 August	194,820.00	268,506.00	-27.44
V15: Kilrush	Clare	Existing	2022 August	178,253.00	154,521.00	15.36
V23: Caherciveen	Kerry	New	2022 August	244,811.00	219,491.00	11.54
V23: Caherciveen	Kerry	Existing	2022 August	243,357.00	192,963.00	26.12
V31: Listowel	Kerry	New	2022 August	224,316.00	195,605.00	14.68
V31: Listowel	Kerry	Existing	2022 August	191,157.00	165,748.00	15.33
V35: Kilmallock	Limerick County	New	2022 August	297,384.00	313,861.00	-5.25
V35: Kilmallock	Limerick County	Existing	2022 August	186,124.00	166,509.00	11.78
V42: Newcastle West	Limerick County	New	2022 August	199,859.00	166,975.00	19.69
V42: Newcastle West	Limerick County	Existing	2022 August	193,402.00	158,432.00	22.07
V92: Tralee	Kerry	New	2022 August	281,695.00	299,137.00	-5.83
V92: Tralee	Kerry	Existing	2022 August	219,929.00	192,346.00	14.34
V93: Killarney	Kerry	New	2022 August	325,856.00	345,354.00	-5.65
V93: Killarney	Kerry	Existing	2022 August	281,515.00	227,361.00	23.82
V94: Limerick	Limerick City	New	2022 August	357,728.00	331,534.00	7.90
V94: Limerick	Limerick City	Existing	2022 August	249,276.00	234,523.00	6.29
V95: Ennis	Clare	New	2022 August	321,030.00	289,794.00	10.78
V95: Ennis	Clare	Existing	2022 August	250,897.00	205,167.00	22.29
W12: Newbridge	Kildare South	New	2022 August	399,492.00	349,483.00	14.31
W12: Newbridge	Kildare South	Existing	2022 August	308,919.00	271,374.00	13.84
W23: Celbridge	Kildare North	New	2022 August	429,622.00	410,994.00	4.53
W23: Celbridge	Kildare North	Existing	2022 August	395,407.00	368,716.00	7.24
W34: Monasterevin	Kildare South	New	2022 August	323,879.00	285,566.00	13.42
W34: Monasterevin	Kildare South	Existing	2022 August	239,767.00	213,457.00	12.33
W91: Naas	Kildare North	New	2022 August	391,839.00	358,218.00	9.39
W91: Naas	Kildare North	Existing	2022 August	358,761.00	333,652.00	7.53
X35: Dungarvan	Waterford	New	2022 August	279,929.00	287,986.00	-2.80
X35: Dungarvan	Waterford	Existing	2022 August	290,448.00	228,178.00	27.29
X42: Kilmacthomas	Waterford	New	2022 August	307,473.00	297,004.00	3.52
X42: Kilmacthomas	Waterford	Existing	2022 August	246,092.00	198,152.00	24.19
X91: Waterford	Waterford	New	2022 August	326,547.00	285,653.00	14.32
X91: Waterford	Waterford	Existing	2022 August	240,571.00	209,961.00	14.58
Y14: Arklow	Wicklow	New	2022 August	534,338.00	337,608.00	58.27
Y14: Arklow	Wicklow	Existing	2022 August	263,939.00	240,034.00	9.96
Y21: Enniscorthy	Wexford	New	2022 August	265,480.00	244,564.00	8.55
Y21: Enniscorthy	Wexford	Existing	2022 August	225,866.00	204,000.00	10.72
Y25: Gorey	Wexford	New	2022 August	298,846.00	296,110.00	0.92
Y25: Gorey	Wexford	Existing	2022 August	286,177.00	235,858.00	21.33
Y34: New Ross	Wexford	New	2022 August	182,312.00	273,434.00	-33.33
Y34: New Ross	Wexford	Existing	2022 August	203,439.00	182,018.00	11.77
Y35: Wexford	Wexford	New	2022 August	280,072.00	302,877.00	-7.53
Y35: Wexford	Wexford	Existing	2022 August	236,307.00	206,572.00	14.39

The maps below are the aggregate values of each Eircode Area rolled up to Constituency level. The variation displayed on the maps represent the difference between the average value of this aggregation and its 12 month lag. - Not an average of the variation in the scroll box above.

# Map
setwd("/Users/charten/OneDrive - Glenveagh Properties/Research & Development/1. Analysis/")
shp <- sf::read_sf("Constituency_Boundaries_Ungeneralised___OSi_National_Electoral_Boundaries___2017.shp")

shpEOA <- merge(shp,SUM_HPM08_1.EOA_Max, by.x = "CON_SEAT_", by.y="Seat_Tag")

shpEOA.GDA <- shpEOA %>%
  filter(grepl('Dublin|Laoghaire|Meath|Louth|Kildare|Wicklow', CON_SEAT_))

shpEOA %>%
  ggplot()+
  geom_sf(aes(fill = Diffpc),colour=alpha("white",0.5))+
  scale_fill_stepsn(n.breaks = 10,
                    colors=c("#F92306","#FFFFFF","#0C4F00"),
                    limits = c(-25,25),
                    name = "% change")+
  ggtitle('National: Percentage Change - CSO: HPM08')+
  labs(subtitle="12 month rolling average: Locations aggregated to constituency",caption="scales limited to absolute value of 25%")+
  theme_void()+
  theme(legend.title = element_text(size = 10),
        legend.text = element_text(size = 8))+
  theme(legend.position = "left")+
  theme(plot.title = element_text(size=16,face="bold"))+
  facet_wrap(~Dwelling.Status,ncol=1)+
  theme(strip.text.x = element_text(size = 10,colour = "navy"))

shpEOA.GDA %>%
  ggplot()+
  geom_sf(aes(fill = Diffpc),colour=alpha("white",0.5))+
  scale_fill_stepsn(n.breaks = 10,
                    colors=c("#F92306","#FFFFFF","#0C4F00"),
                    limits = c(-25,25),
                    name = "% change")+
  geom_sf_text(aes(label=Diffpc),color="black",alpha=0.9,size=2.25)+
  ggtitle('Greater Dublin Area - CSO: HPM08')+
  labs(subtitle="12 month rolling average: Locations aggregated to constituency",caption="scales limited to absolute value of 25%")+
  theme_void()+
  theme(legend.title = element_text(size = 10),
        legend.text = element_text(size = 8))+
  theme(legend.position = "left")+
  theme(plot.title = element_text(size=16,face="bold"))+
  facet_wrap(~Dwelling.Status,ncol=1)+
  theme(strip.text.x = element_text(size = 10,colour = "navy"))

Irish Economy

Lending Data

Included below are loan volumes and growth rates, interest rates and the proportion of mortgages which are variable rate loans in Ireland vs the Euro Area average.

Loans

Facet1 <- CBM01.OUT %>%
  ggplot(aes(x=Date,y=value,group=Type.of.Loan,colour=Type.of.Loan))+
  geom_line(size=1.5,alpha=0.5)+
  geom_text_repel(aes(label=value),max.overlaps = 8,size=2.5)+
  theme_bw()+
  theme(legend.position = "bottom",
        plot.title = element_text(face="bold"))+
  ggtitle("Outstanding Amount of Loans and Securitisations")+
  labs(subtitle="CSO - CBM01: Loans, including Securitisations to Irish Residents")+
  ylab("euro million")+
  xlab(NULL)+
  geom_text(data=CBM01.OUT_Max,aes(label=lab,x=Date,y=value,group=Type.of.Loan),size=3,hjust="right",vjust="bottom",fontface="bold",colour="black",alpha=0.6)

CBM01.GRY_Max <- CBM01.GRY %>%
  group_by(Type.of.Loan) %>%
  slice(which.max(Date))
CBM01.GRY_Max$lab <- paste(CBM01.GRY_Max$Month,":",CBM01.GRY_Max$value)

CBM01.GRY <- CBM01.GRY %>%
  filter(Date >= "2004-01-01")

Facet2 <- CBM01.GRY %>%
  ggplot(aes(x=Date,y=value,group=Type.of.Loan,colour=Type.of.Loan))+
  geom_hline(yintercept = 0,alpha=0.5, linetype="dotted")+
  geom_line(size=1.25,alpha=0.5)+
  geom_text_repel(aes(label=value),max.overlaps = 10,size=2.5)+
  theme_bw()+
  theme(legend.position = "none",
        plot.title = element_text(face="bold"))+
  ggtitle("Annual Growth Rate")+
  ylab("%")+
  xlab(NULL)+
  geom_text(data=CBM01.GRY_Max,aes(label=lab,x=Date,y=value,group=Type.of.Loan),size=3,hjust="right",vjust="bottom",fontface="bold",colour="black",alpha=0.6)+
  facet_wrap(~Type.of.Loan,nrow=2,labeller = labeller(Type.of.Loan = label_wrap_gen(width = 45)))

Facet1 + Facet2 + plot_layout(ncol=2, widths = c(2,1))

Rates: CSO vs ECB Loan Data

We have been working of the ECB statistics for interest rate statistics, comparing them with other eurozone countries. Part of the CSOs obligations is to report statistics to Eurostat, the interest rate statistics that the CSO publishes from their database can be seen below. I’ve included a dotted line for the ECB statistic which is:

“Bank interest rates - loans to households for house purchase with a floating rate and an IRF period of up to one year (new business)”

CBM02 %>%
  ggplot(aes(x=Date,y=value,group=Statistic,colour=Statistic))+
  geom_line(size=1.25,alpha=0.6)+
  scale_colour_manual(values = c("#0857F5","#F59A08"))+
  geom_text_repel(aes(label=value),max.overlaps = 6,size=2.5)+
  theme_bw()+
  theme(legend.position = "bottom",
        plot.title = element_text(face="bold"))+
  ggtitle("Retail Interest Rates: Households")+
  labs(subtitle="CSO - CBM02",caption="Dotted line represents ECB statistic for Household Loans for Ireland")+
  ylab("%")+
  xlab(NULL)+
  geom_text(data=CBM02_Max,aes(label=lab,x=Date,y=value,group=Statistic),size=3,hjust="right",vjust="bottom",fontface="bold",colour="black",alpha=0.8)+
  geom_line(data=IE.BIR_H,aes(x=obstime,y=obsvalue,group=ref_area),colour="black",alpha=0.6,linetype="dotted")+
  geom_text(data=IE.BIR_H_Max,aes(label=obsvalue,x=obstime,y=obsvalue,group=ref_area),colour="black",alpha=0.6,size=2.75,hjust="left")

Variable Rate Loans

SVL %>%
  ggplot(aes(x=obstime,y=obsvalue,group=area,colour=area))+
  geom_line(size=1.05,alpha=0.5)+
  scale_colour_manual(values=c("navy","darkgreen"))+
  geom_text_repel(aes(label=lab),max.overlaps = 5,alpha=0.35,size=3)+
  geom_text(data=SVL_Max,aes(label=lab),colour="black",alpha=0.9,size=3,hjust="right",fontface="bold")+
  theme_bw()+
  theme(legend.position="bottom",
        plot.title = element_text(face="bold"))+
  xlab(NULL)+
  ylab("percent (%)")+
  ggtitle("Share of Variable Rate Loans in Total Loans")

In Ireland, the share of variable rate loans has halved year to date.

This is some comfort given that rates are increasing, this limits the contribution of mortgage interest to inflation over the coming months.

SVL_20 <- SVL %>%
  filter(obstime>="2020-01-01")
dates_vline <- as.Date("2022-07-01")
dates_vline <- which(SVL_20$obstime %in% dates_vline)

SVL_20 %>%
  ggplot(aes(x=obstime,y=obsvalue,group=area,colour=area))+
  geom_line(size=1.05,alpha=0.5)+
  scale_colour_manual(values=c("navy","darkgreen"))+
  geom_text_repel(aes(label=lab),max.overlaps = 5,alpha=0.35,size=3)+
  geom_text(data=SVL_Max,aes(label=lab),colour="black",alpha=0.9,size=3,hjust="right",fontface="bold")+
  theme_bw()+
  theme(legend.position="bottom",
        plot.title = element_text(face="bold"))+
  xlab(NULL)+
  ylab("percent (%)")+
  ggtitle("Signs of Switching?")+
  labs(subtitle="Share of Variable Rate Loans in Total Loans",caption="Red line: first ECB rate hike of the cycle")+
  geom_vline(xintercept = as.numeric(SVL_20$obstime[dates_vline]),colour="darkred",linetype="dotted")

Construction Price Index

Year on year percentage change

colour <-ifelse(WPM28_B$value < 0,"#CC0000","#1b5545")

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))+
  facet_wrap(~Type.of.Material, ncol = 3)

Government Finance

The below is useful to get an idea of the proportion of government debt and expenditure relative to income.

Debt to GDP

This statistic is a notoriously contentious one, it was heavily debated in the austerity years with the IMF arguing a level of 90% based on research by Reinhart and Rogoff, this was included in their historical analysis of financial crisis. - This Time is Different. Later, it was found that this level was insignificant but they reached the conclusion due to an excel spreadsheet error.

Rogoff at one stage was approached to be special advisor to the UK government, according to Howard Davies in his latest book The Chancellors: Steering the British Economy in Crisis Times. He had to refuse for family/health reasons, regardless his findings were influential into austerity.

I read the book, there are still many useful historical case studies in it.

####################################################### General Government Net Worth, Gross and Net Debt ESA 2010
# Load & Clean
GFQ12 <- cso_get_data("GFQ12")

GFQ12 <- GFQ12 %>%
  pivot_longer(!1:2,names_to="year_qtr")
GFQ12$Year_Q <- as.yearqtr(GFQ12$year_qtr)
GFQ12$Year <- year(GFQ12$Year_Q)

GFQ12_Max <- GFQ12 %>%
  slice(which.max(Year_Q))
GFQ12_Max$lab <- paste(GFQ12_Max$Year_Q,": ",GFQ12_Max$value,"%")

#Plot
GFQ12 %>%
  ggplot(aes(x=Year_Q,y=value))+
  geom_line(size=1.25,colour="darkgreen",alpha=0.6)+
  theme_bw()+
  theme(legend.position = "none",
        plot.title = element_text(face="bold"))+
  ggtitle("Gross Government Debt to GDP")+
  ylab("%")+
  xlab(NULL)+
  labs(subtitle="GFQ12: Debt ESA2010 as a Percentage of the Annualised Gross Domestic Product")+
  geom_text_repel(data=GFQ12_Max,aes(label=lab),colour="darkgreen",fontface="bold")

Financing

# Plot
GFQ01.SUR %>%
  ggplot(aes(x=Year_Q,y=value,group=Item))+
  geom_hline(yintercept = 0,colour="darkred",alpha=0.5)+
  geom_line(aes(colour=Item,linetype=Item),size=1,alpha=0.7)+
  scale_colour_manual(values=c("#6C7272","#09716D"))+
  geom_line(aes(y=SMA.12,linetype=Item),colour="#027100",alpha=0.2)+
  theme_bw()+
  theme(legend.position = "bottom",
        plot.title = element_text(face="bold"),
        legend.key.size = unit(1, 'cm'),
        legend.key.height = unit(0.5, 'cm'), 
        legend.key.width = unit(0.5, 'cm'),
        legend.title = element_text(size=8),
        legend.text = element_text(size=8))+
  ggtitle("General Government Surplus & Deficit")+
  ylab("euro million")+
  xlab(NULL)+
  labs(caption="thin green line represents the 12 month moving average")

Revenue

GFQ01.REV %>%
  ggplot(aes(x=Year_Q,y=value,group=Item))+
  geom_line(aes(colour=Item),alpha=0.4)+
  geom_line(aes(y=SMA.12,colour=Item),alpha=0.8)+
  theme_bw()+
  theme(legend.position = "none",
        plot.title = element_text(face="bold"))+
  ggtitle("General Government Transactions: Revenue")+
  ylab("euro million")+
  xlab(NULL)+
  labs(subtitle="12 month moving average")+
  facet_wrap(~Item,ncol=2,labeller = labeller(Item = label_wrap_gen(width = 55)))+
  geom_text(data=GFQ01.REV_Max,aes(x=Year_Q,y=10000,label=lab,colour=Item),hjust="right")

Expenditure

GFQ01.EXP %>%
  ggplot(aes(x=Year_Q,y=value,group=Item))+
  geom_line(aes(colour=Item),alpha=0.4)+
  geom_line(aes(y=SMA.12,colour=Item),alpha=0.8)+
  scale_colour_brewer(palette = "Set3")+scale_colour_hue(l = 45)+
  theme_bw()+
  theme(legend.position = "none",
        plot.title = element_text(face="bold"))+
  ggtitle("General Government Transactions: Expenditure")+
  ylab("euro million")+
  xlab(NULL)+
  labs(subtitle="12 month moving average")+
  facet_wrap(~Item,ncol=2,labeller = labeller(Item = label_wrap_gen(width = 55)))+
  geom_text(data=GFQ01.EXP_Max,aes(x=Year_Q,y=11000,label=lab,colour=Item),hjust="right")

Trade

We seen this in more detail last issue, I will keep the overall moving average going once new data is out (data released in the past week). I there is anything interesting in the more granular data I will include that as and when I see it.

# Step 1. Load Commodity Groups (local) and Value of Merchandise Trade from the CSO
CG <- readxl::read_xlsx(path="C:\\Users\\charten\\OneDrive - Glenveagh Properties\\Research & Development\\1. Analysis\\Commodity Groups.xlsx",sheet = 1)

TSM10 <- cso_get_data("TSM10")
TSM10 <- TSM10 %>%
  pivot_longer(!1:3,names_to="year_month")

TSM10$Year <-substr(TSM10$year_month,1,4)
TSM10$Month <- sub(".* ", "", TSM10$year_month)
TSM10$Month_NR <- as.integer(factor(TSM10$Month, levels=month.name))
TSM10$Date <- as.yearmon(paste(TSM10$Year, TSM10$Month_NR), "%Y %m")

# Step 2. Create Subsets
## 2.1 Total merchandise trade (0 - 9)
TSM10.1 <- TSM10 %>%
  filter(Commodity.group=="Total merchandise trade (0 - 9)")

### Total
TSM10.1.T <- TSM10.1 %>%
  group_by(Statistic,Commodity.group,year_month,Year,Date) %>%
  summarise(value = sum(value,na.rm = TRUE))
TSM10.1.T <- TSM10.1.T[order(TSM10.1.T$Statistic,TSM10.1.T$Date),]

TSM10.1.T$SMA.6 <- round(SMA(TSM10.1.T$value,n=6),digits=2)
TSM10.1.T$SMA.6[TSM10.1.T$Date=="Jan 2015" | TSM10.1.T$Date=="Feb 2015" | TSM10.1.T$Date=="Mar 2015"|TSM10.1.T$Date=="Apr 2015" | TSM10.1.T$Date=="May 2015"] <- NA

## 2.2 Exclude Total Merchandise Trade & Unclassified estimates
TSM10.2 <- TSM10 %>%
  filter(Commodity.group!="Total merchandise trade (0 - 9)") %>%
  filter(Commodity.group!="Unclassified estimates")

TSM10.2.T <- TSM10.2 %>%
  group_by(Statistic,Commodity.group,year_month,Year,Date) %>%
  summarise(value = sum(value,na.rm = TRUE))
TSM10.2.T <- TSM10.2.T[order(TSM10.2.T$Statistic,TSM10.2.T$Commodity.group,TSM10.2.T$Date),]

TSM10.2.T$SMA.6 <- round(SMA(TSM10.2.T$value,n=6),digits=2)
TSM10.2.T$SMA.6[TSM10.2.T$Date=="Jan 2015" | TSM10.2.T$Date=="Feb 2015" | TSM10.2.T$Date=="Mar 2015"|TSM10.2.T$Date=="Apr 2015" | TSM10.2.T$Date=="May 2015"] <- NA

TSM10.2.T <- merge(x=TSM10.2.T,y=CG,by.x="Commodity.group",by.y="Commodity.Group")
TSM10.2.T <- TSM10.2.T[order(TSM10.2.T$Statistic,TSM10.2.T$Commodity.group,TSM10.2.T$Date),]

## Subset by Group
TSM10.2.T.0 <- TSM10.2.T %>%
  filter(Group=="0")
TSM10.2.T.1 <- TSM10.2.T %>%
  filter(Group=="1")
TSM10.2.T.2 <- TSM10.2.T %>%
  filter(Group=="2")
TSM10.2.T.3 <- TSM10.2.T %>%
  filter(Group=="3")
TSM10.2.T.4 <- TSM10.2.T %>%
  filter(Group=="4")
TSM10.2.T.5 <- TSM10.2.T %>%
  filter(Group=="5")
TSM10.2.T.6 <- TSM10.2.T %>%
  filter(Group=="6")
TSM10.2.T.7 <- TSM10.2.T %>%
  filter(Group=="7")
TSM10.2.T.8 <- TSM10.2.T %>%
  filter(Group=="8")
TSM10.2.T.9 <- TSM10.2.T %>%
  filter(Group=="9")

# Step 3. Plot
options(scipen = 999)
TSM10.1.T_max <- TSM10.1.T %>%
  group_by(Statistic)%>%
  slice(which.max(Date))
TSM10.1.T_max$lab_SMA.6 <- paste(as.character(TSM10.1.T_max$Date), "-", comma(TSM10.1.T_max$SMA.6,digits=0))
TSM10.1.T_max$lab_value <- comma(TSM10.1.T_max$value,digits=0)

TSM10.1.T %>%
  ggplot(aes(x=Date,y=SMA.6,group=Statistic,colour=Statistic))+
  geom_text(data=TSM10.1.T_max,aes(label=lab_SMA.6),size=2.75,hjust=1.15,vjust=1,colour="black")+
  geom_text(data=TSM10.1.T_max,aes(y=value,label=lab_value),size=2.5,colour="#616161")+
  geom_line(aes(x=Date,y=value,group=Statistic,colour=Statistic),alpha=0.25)+
  geom_line()+
  theme_bw()+
  theme(legend.position = "bottom")+
  ggtitle("Value of Merchandise Trade")+
  labs(subtitle="CSO: TSM10 - Total merchandise",caption="latest month actual value printed in grey at the end of the monthly series")+
  ylab("6 month moving average - (Euro Thousand)")+
  scale_y_continuous(labels = scales::number_format(accuracy = 1,decimal.mark = ','))

Appendix

House Prices

HPM08_1.EOA.DUB <- HPM08_1.EOA %>%
  filter(Return.County == "Dublin")
HPM08_1.EOA.DUB_Max <- HPM08_1.EOA.DUB %>% 
  group_by(Eircode.Output,Dwelling.Status)%>%
  slice(which.max(Date))

HPM08_1.EOA.DUB %>%
  ggplot(aes(x=Date,y=value,group=Dwelling.Status,colour=Dwelling.Status))+
  geom_line(size=1.25,alpha=0.7)+
  theme_bw()+
  theme(legend.position = "bottom")+
  ylab("euro")+xlab(NULL)+
  ggtitle("Market-based Household Purchases")+
  labs(subtitle="Mean Rolling 12 Month") +
  geom_text_repel(aes(label=value_short),alpha=0.6,size=2.75)+
  geom_text(data=HPM08_1.EOA.DUB_Max,aes(label=lab),colour="black",hjust="right",vjust=1,size=2.75,fontface="bold",alpha=0.6) +
  facet_wrap(~Eircode.Output,ncol=2)

options(scipen = 999)

HPM08_1.EOA.OTH <- HPM08_1.EOA %>%
  filter(Return.County != "Dublin")
HPM08_1.EOA.OTH_Max <- HPM08_1.EOA.OTH %>%
  group_by(Eircode.Output,Dwelling.Status)%>%
  slice(which.max(Date))

HPM08_1.EOA.OTH %>%
  ggplot(aes(x=Date,y=value,group=Dwelling.Status,colour=Dwelling.Status))+
  geom_line(size=1.25,alpha=0.7)+
  theme_bw()+
  theme(legend.position = "bottom")+
  ylab("euro")+xlab(NULL)+
  ggtitle("Market-based Household Purchases")+
  labs(subtitle="Mean Rolling 12 Month") +
  geom_text_repel(aes(label=value_short),alpha=0.6,size=2.75)+
  geom_text(data=HPM08_1.EOA.OTH_Max,aes(label=lab),colour="black",hjust="right",vjust=1,size=2.75,fontface="bold",alpha=0.6) +
  facet_wrap(~Eircode.Output,ncol=2)

Wholesale Price Index

Black line (100) indicates 2015 as baseline

WPM28_A_Max <- WPM28_A %>%
  group_by(Type.of.Material) %>%
  slice(which.max(Date))
WPM28_A_Max$lab <- paste(WPM28_A_Max$Month,":",WPM28_A_Max$value)

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))+
  geom_text_repel(data=WPM28_A_Max,aes(label=lab,y=160),colour="black",alpha=0.2,size=3)+
  facet_wrap(~Type.of.Material, ncol = 3,labeller = labeller(Type.of.Material = label_wrap_gen(width = 40)))