Property Price Index
Cormac Harten
library(csodata)
library(tidyverse)
library(knitr)
library(kableExtra)
library(ggplot2)
library(lubridate)
library(ggrepel)
library(zoo)
library(sf)
library(tmap)
library(rgdal)
library(readxl)
library(maps)
library(mapproj)
library(mapview)
library(patchwork)
library(Hmisc)
library(formattable)
#Load
## HPM09
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)
### Given the aggregation of the 'Type of Residential Property Field, only 'National - all residential properties' will be kept across two Statistics: 'Residential Property Price Index' & 'Percentage Change over 12 months for Residential Property Price Index'
### More granular detail loaded through 'Market-based Household Purchases'
## HPM04
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$Month_NR), "%Y %m")
## GeoSpatial
setwd("/Users/charten/OneDrive - Glenveagh Properties/Research & Development/1. Analysis/")
#shp = readOGR(dsn=".", layer = "Constituency_Boundaries_Ungeneralised___OSi_National_Electoral_Boundaries___2017")
#shp = readOGR(dsn=".", layer = "counties")
shp <- sf::read_sf("Constituency_Boundaries_Ungeneralised___OSi_National_Electoral_Boundaries___2017.shp")
GEO_Merge <- readxl::read_xlsx(path="C:\\Users\\charten\\OneDrive - Glenveagh Properties\\Research & Development\\1. Analysis\\HPM04_GEO.xlsx",sheet = 1)
#st <- st_read("/Users/charten/OneDrive - Glenveagh Properties/Research & Development/1. Analysis/Constituency_Boundaries_Ungeneralised___OSi_National_Electoral_Boundaries___2017.shp", stringsAsFactors = FALSE)
#dfCon <- fortify(shp)
HPM04_join <- full_join(HPM04, GEO_Merge, by = "Eircode.Output")
HPM04 <- HPM04_join
rm(HPM04_join)Residential Property Price Index
HPM09$month <- months(as.Date(HPM09$Month))
HPM09_1 <- HPM09 %>%
filter(Statistic == "Residential Property Price Index")%>%
filter(Type.of.Residential.Property == "National - all residential properties")
HPM09_2 <- HPM09 %>%
filter(Statistic == "Percentage Change over 12 months for Residential Property Price Index")%>%
filter(Type.of.Residential.Property == "National - all residential properties")
RPPI_tail_2 <- tail(HPM09_2,1)
RPPI_tail_2_lag <-head(tail(HPM09_2,2),1)Headline
The national house price inflation for May is 14.4%. This represents a -0.1 change from April which was 14.5%.
Previous 6 month inflation print
HPM09_2_trim <- subset(HPM09_2, select = -c(Statistic, year_month, Month))
RPPI_tail_2 <- kable(tail(HPM09_2_trim), caption = "Percentage Change over 12 months for Residential Property Price Index")
RPPI_tail_2 %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed"), full_width = F) %>%
row_spec(6, bold = T)%>%
pack_rows("Latest Period", 6, 6, color="#1b5545")| Type.of.Residential.Property | value | Year | month |
|---|---|---|---|
| National - all residential properties | 14.2 | 2021 | December |
| National - all residential properties | 14.8 | 2022 | January |
| National - all residential properties | 15.1 | 2022 | February |
| National - all residential properties | 15.1 | 2022 | March |
| National - all residential properties | 14.5 | 2022 | April |
| Latest Period | |||
| National - all residential properties | 14.4 | 2022 | May |
rm(HPM09_2_trim)Overview
RRPI_Line_1 <- ggplot(data=HPM09_1, aes(x=Month, y=value, group = Type.of.Residential.Property))+
geom_line(linejoin="mitre",size = 1.25, linetype = 1,alpha = 0.5, colour="#1b5545")+
labs(title = "Residential Property Price Index" ,
y="2015 = 100",
x="Month")+
geom_text_repel(aes(label=value),data = HPM09_1, size = 3)+
theme(legend.position = "bottom")
RRPI_Line_1
HPM09_1_T12 <- tail(HPM09_1,12)
RRPI_Line_2 <- ggplot(data=HPM09_1_T12, aes(x=Month, y=value, group = Type.of.Residential.Property))+
geom_line(linejoin="mitre",size = 1.25, linetype = 1,alpha = 0.5, colour="#1b5545")+
labs(title = "Residential Property Price Index" ,
subtitle = "12 Month Series",
y="2015 = 100",
x="Month")+
geom_text(aes(label=value),vjust= 1.5, hjust = 0, size=3)+
theme(legend.position = "bottom")
RRPI_Line_2
HPM09_2_T24 <- tail(HPM09_2,24)
colour <- ifelse(HPM09_2_T24$value < 0,"#CC0000","#1b5545")
RPPI_Bar_2 <- ggplot(data=HPM09_2_T24, aes(x=Month, y=value, group = Type.of.Residential.Property))+
geom_col(alpha = 0.5, colour="#373634", fill = colour)+
labs(title = "Year on Year Percentage Change for Residential Property Price Index" ,
subtitle = "24 Month Series",
y="Percentage change",
x="Month")+
geom_text(aes(label=value),vjust= 1.5, size=3)+
theme(legend.position = "bottom")
rm(HPM09)
House Price Index
Subset
- Mean Sale Price, All Dwelling Statuses, Executions, All Buyer Types, All
- Mean Sale Price, All Dwelling Statuses, Executions, All Buyer Types, Each Eircode Output
HPM04_1A <- HPM04 %>%
filter(Statistic == "Mean Sale Price")%>%
filter(Dwelling.Status == "All Dwelling Statuses") %>%
filter(Stamp.Duty.Event == "Executions") %>%
filter(Type.of.Buyer == "All Buyer Types") %>%
filter(Eircode.Output == "All")
HPM04_1B <- HPM04 %>%
filter(Statistic == "Mean Sale Price")%>%
filter(Dwelling.Status == "All Dwelling Statuses") %>%
filter(Stamp.Duty.Event == "Executions") %>%
filter(Type.of.Buyer == "All Buyer Types") %>%
filter(Eircode.Output != "All")HPM04 - Market-based Household Purchases of Residential Dwellings
HPM04_1A_trim <- subset(HPM04_1A, select = -c(Statistic, Month ,year_month, Month_NR, Year, Eircode.Output.Adjusted, EirCode, Return.County, Return.NUTS3, Return.Lat, Return.Lat, Return.Long, Nearest.Match.to.ED, Constituency, GUID, GUID_TAG, Seat_Tag,ID,Longitude,Latitude,Apply))
HPM04_1A_tail <- subset(HPM04_1A, select = -c(year_month, Month, Year, Month_NR))
tail_1A <- kable(tail(HPM04_1A_trim), caption = "Market-based Household Purchases of Residential Dwellings - All")
tail_1A %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed"), full_width = F) %>%
row_spec(6, bold = T)%>%
pack_rows("Latest Period", 6, 6, color = "navy")| Dwelling.Status | Eircode.Output | Stamp.Duty.Event | Type.of.Buyer | value | Date |
|---|---|---|---|---|---|
| All Dwelling Statuses | All | Executions | All Buyer Types | 340777 | Dec 2021 |
| All Dwelling Statuses | All | Executions | All Buyer Types | 345213 | Jan 2022 |
| All Dwelling Statuses | All | Executions | All Buyer Types | 339751 | Feb 2022 |
| All Dwelling Statuses | All | Executions | All Buyer Types | 339313 | Mar 2022 |
| All Dwelling Statuses | All | Executions | All Buyer Types | 337318 | Apr 2022 |
| Latest Period | |||||
| All Dwelling Statuses | All | Executions | All Buyer Types | 339798 | May 2022 |
rm(HPM04_1A_trim)
rm(HPM04_1A_tail)
rm(HPM09_1)
rm(HPM09_1_T12)
rm(HPM09_2)
rm(HPM09_2_T24)Map
Average Value by Constituency
HPM04_1B_Map <- na.omit(HPM04_1B)
HPM04_1B_Map<- HPM04_1B_Map %>%
group_by(Seat_Tag, Date)%>% # change to/from County / Constituency
summarise(Average_Value = mean(value))
HPM04_1B_Map$lagvalue <- Lag(HPM04_1B_Map$Average_Value,1)
HPM04_1B_Map$Diff <- HPM04_1B_Map$Average_Value - HPM04_1B_Map$lagvalue
HPM04_1B_Map$Diffpc <- HPM04_1B_Map$Diff / HPM04_1B_Map$lagvalue
HPM04_1B_Map$Diffpc<-percent(HPM04_1B_Map$Diffpc,2)
HPM04_1B_Map_May2022 <- HPM04_1B_Map%>%
filter(Date == "May 2022")
shpHPM <- merge(shp,HPM04_1B_Map_May2022, by.x = "CON_SEAT_", by.y="Seat_Tag")
HPM04_1B_Map_May2022_Dub <- HPM04_1B_Map_May2022 %>%
filter(grepl('Dublin|Laoghaire', Seat_Tag))
shpHPMDub <- merge(shp,HPM04_1B_Map_May2022_Dub, by.x = "CON_SEAT_", by.y="Seat_Tag")
Map_AvgValueAll <- shpHPM %>%
ggplot()+
geom_sf(aes(fill = Average_Value))+
scale_fill_gradientn(
colors=c("#FAD3DA","#80D7F5","#0C4F00"),
labels = scales::comma,
limits = c(150000,850000),
oob = scales::squish,
breaks=c(100000,200000,300000,400000,500000,600000,700000,800000,900000),
name = "Average Value")+
theme_void()+
ggtitle('Average Value by Constituency - HPM04')+
theme(legend.title = element_text(size = 10),
legend.text = element_text(size = 8))+
theme(legend.position = "left")+
theme(plot.title = element_text(hjust = 1))
Map_AvgValueDub <- shpHPMDub %>%
ggplot()+
geom_sf(aes(fill = Average_Value))+
scale_fill_gradientn(
colors=c("#FAD3DA","#80D7F5","#0C4F00"),
labels = scales::comma,
limits = c(150000,850000),
oob = scales::squish,
breaks=c(100000,200000,300000,400000,500000,600000,700000,800000,900000),
name = "Average Value")+
theme_void()+
theme(legend.position = "none")
Map_AvgValue <- Map_AvgValueAll + Map_AvgValueDub +
plot_layout(widths = c(2,1))
Map_AvgValue
The average value by constituency is 336817.3, with a standard deviation of 180080.6. This was 537903.8 and 216238.7 respectively in the Dublin constituencies.
Percentage Change by Constituency
Map_DiffpcAll <- shpHPM %>%
ggplot()+
geom_sf(aes(fill = Diffpc))+
scale_fill_stepsn(n.breaks = 10,
colors=c("#F92306","#FFFFFF","#0C4F00"),
limits = c(-1,1),
name = "% change")+
ggtitle('Month on Month Change by Constituency - HPM04')+
theme_void()+
theme(legend.title = element_text(size = 10),
legend.text = element_text(size = 8))+
theme(legend.position = "left")+
theme(plot.title = element_text(hjust = 1))
Map_DiffpcDub <- shpHPMDub %>%
ggplot()+
geom_sf(aes(fill = Diffpc))+
scale_fill_stepsn(n.breaks = 10,
colors=c("#F92306","#FFFFFF","#0C4F00"),
limits = c(-1,1),
name = "% change")+
theme_void()+
theme(legend.position = "none")
Map_Diffpc <- Map_DiffpcAll + Map_DiffpcDub +
plot_layout(widths = c(2,1))
Map_Diffpc
Average percentage change month on month is 2.07%. This was 8.73% in the Dublin constituencies.
HPM04_1B_Map_May2022%>%
kbl(caption = "Month on Month ")%>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed"), full_width = T)%>%
scroll_box(width = "600px", height = "300px") | Seat_Tag | Date | Average_Value | lagvalue | Diff | Diffpc |
|---|---|---|---|---|---|
| Carlow-Kilkenny (5) | May 2022 | 216527.7 | 259982.3 | -43454.667 | -16.71% |
| Cavan-Monaghan (5) | May 2022 | 177097.0 | 188724.9 | -11627.857 | -6.16% |
| Clare (4) | May 2022 | 224667.0 | 196664.7 | 28002.333 | 14.24% |
| Cork East (4) | May 2022 | 254092.0 | 269807.3 | -15715.286 | -5.82% |
| Cork North-Central (4) | May 2022 | 362033.0 | 336483.0 | 25550.000 | 7.59% |
| Cork North-West (3) | May 2022 | 295421.8 | 284945.8 | 10475.950 | 3.68% |
| Cork South-Central (4) | May 2022 | 387499.0 | 377158.0 | 10341.000 | 2.74% |
| Cork South-West (3) | May 2022 | 317929.7 | 328277.8 | -10348.167 | -3.15% |
| Donegal (5) | May 2022 | 166165.3 | 162026.0 | 4139.333 | 2.55% |
| Dublin Bay North (5) | May 2022 | 477757.7 | 484701.3 | -6943.667 | -1.43% |
| Dublin Bay South (4) | May 2022 | 1061525.3 | 614099.0 | 447426.333 | 72.86% |
| Dublin Central (4) | May 2022 | 380833.0 | 299362.0 | 81471.000 | 27.21% |
| Dublin Fingal (5) | May 2022 | 400859.1 | 435193.5 | -34334.375 | -7.89% |
| Dublin Mid-West (4) | May 2022 | 374310.0 | 372356.0 | 1954.000 | 0.52% |
| Dublin North-West (3) | May 2022 | 465500.0 | 490469.0 | -24969.000 | -5.09% |
| Dublin Rathdown (3) | May 2022 | 610032.0 | 614520.0 | -4488.000 | -0.73% |
| Dublin South-West (5) | May 2022 | 558630.0 | 538180.3 | 20449.667 | 3.80% |
| Dublin South Central (4) | May 2022 | 390266.2 | 392571.2 | -2305.000 | -0.59% |
| Dublin West (4) | May 2022 | 399194.5 | 395483.0 | 3711.500 | 0.94% |
| Dun Laoghaire (4) | May 2022 | 798034.5 | 749484.5 | 48550.000 | 6.48% |
| Galway East (3) | May 2022 | 245063.7 | 240822.3 | 4241.333 | 1.76% |
| Galway West (5) | May 2022 | 256908.0 | 336685.5 | -79777.500 | -23.69% |
| Kerry (5) | May 2022 | 243051.5 | 246171.5 | -3120.000 | -1.27% |
| Kildare North (4) | May 2022 | 387790.5 | 391812.0 | -4021.500 | -1.03% |
| Kildare South (4) | May 2022 | 249614.0 | 292926.8 | -43312.800 | -14.79% |
| Laois-Offaly (5) | May 2022 | 209237.0 | 225253.2 | -16016.250 | -7.11% |
| Limerick City (4) | May 2022 | 273538.0 | 245495.0 | 28043.000 | 11.42% |
| Limerick County (3) | May 2022 | 143393.0 | 152733.5 | -9340.500 | -6.12% |
| Longford-Westmeath (4) | May 2022 | 225328.7 | 194342.7 | 30986.000 | 15.94% |
| Louth (5) | May 2022 | 271900.0 | 277855.5 | -5955.500 | -2.14% |
| Mayo (4) | May 2022 | 178087.7 | 173881.2 | 4206.500 | 2.42% |
| Meath East (3) | May 2022 | 338315.4 | 338801.0 | -485.600 | -0.14% |
| Meath West (3) | May 2022 | 271314.0 | 327649.5 | -56335.500 | -17.19% |
| Roscommon-Galway (3) | May 2022 | 191973.7 | 146247.3 | 45726.333 | 31.27% |
| Sligo-Leitrim (4) | May 2022 | 166604.0 | 189258.0 | -22654.000 | -11.97% |
| Tipperary (5) | May 2022 | 214051.4 | 182901.2 | 31150.125 | 17.03% |
| Waterford (4) | May 2022 | 285926.0 | 255514.7 | 30411.333 | 11.90% |
| Wexford (5) | May 2022 | 230609.2 | 247395.2 | -16786.000 | -6.79% |
| Wicklow (5) | May 2022 | 434795.8 | 504783.0 | -69987.250 | -13.86% |
HPM04_1B_Map <- na.omit(HPM04_1B)
HPM04_1B_Map<- HPM04_1B_Map %>%
group_by(Seat_Tag, Date)%>% # change to/from County / Constituency
summarise(Average_Value = mean(value))
HPM04_1B_Map$lagvalue_12mth <- Lag(HPM04_1B_Map$Average_Value,12)
HPM04_1B_Map$Diff <- HPM04_1B_Map$Average_Value - HPM04_1B_Map$lagvalue_12mth
HPM04_1B_Map$Diffpc <- HPM04_1B_Map$Diff / HPM04_1B_Map$lagvalue_12mth
HPM04_1B_Map$Diffpc<-percent(HPM04_1B_Map$Diffpc,2)
HPM04_1B_Map_May2022 <- HPM04_1B_Map%>%
filter(Date == "May 2022")
shpHPM <- merge(shp,HPM04_1B_Map_May2022, by.x = "CON_SEAT_", by.y="Seat_Tag")
HPM04_1B_Map_May2022_Dub <- HPM04_1B_Map_May2022 %>%
filter(grepl('Dublin|Laoghaire', Seat_Tag))
shpHPMDub <- merge(shp,HPM04_1B_Map_May2022_Dub, by.x = "CON_SEAT_", by.y="Seat_Tag")
Map2_DiffpcAll <- shpHPM %>%
ggplot()+
geom_sf(aes(fill = Diffpc))+
scale_fill_stepsn(n.breaks = 10,
colors=c("#F92306","#FFFFFF","#0C4F00"),
limits = c(-1,1),
name = "% change")+
ggtitle('Year on Year Change by Constituency - HPM04')+
theme_void()+
theme(legend.title = element_text(size = 10),
legend.text = element_text(size = 8))+
theme(legend.position = "left")+
theme(plot.title = element_text(hjust = 1))
Map2_DiffpcDub <- shpHPMDub %>%
ggplot()+
geom_sf(aes(fill = Diffpc))+
scale_fill_stepsn(n.breaks = 10,
colors=c("#F92306","#FFFFFF","#0C4F00"),
limits = c(-1,1),
name = "% change")+
theme_void()+
theme(legend.position = "none")
Map2_Diffpc <- Map2_DiffpcAll + Map2_DiffpcDub +
plot_layout(widths = c(2,1))
Map2_Diffpc
Average percentage change year on year is 13.79%. This was 13.57% in the Dublin constituencies.
HPM04_1B_Map_May2022%>%
kbl(caption = "Year on Year ")%>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed"), full_width = T)%>%
scroll_box(width = "600px", height = "300px") | Seat_Tag | Date | Average_Value | lagvalue_12mth | Diff | Diffpc |
|---|---|---|---|---|---|
| Carlow-Kilkenny (5) | May 2022 | 216527.7 | 199519.3 | 17008.33 | 8.52% |
| Cavan-Monaghan (5) | May 2022 | 177097.0 | 144608.4 | 32488.57 | 22.47% |
| Clare (4) | May 2022 | 224667.0 | 172103.7 | 52563.33 | 30.54% |
| Cork East (4) | May 2022 | 254092.0 | 222825.8 | 31266.17 | 14.03% |
| Cork North-Central (4) | May 2022 | 362033.0 | 296311.0 | 65722.00 | 22.18% |
| Cork North-West (3) | May 2022 | 295421.8 | 216368.6 | 79053.15 | 36.54% |
| Cork South-Central (4) | May 2022 | 387499.0 | 355174.0 | 32325.00 | 9.10% |
| Cork South-West (3) | May 2022 | 317929.7 | 218142.0 | 99787.67 | 45.74% |
| Donegal (5) | May 2022 | 166165.3 | 137711.7 | 28453.67 | 20.66% |
| Dublin Bay North (5) | May 2022 | 477757.7 | 462236.7 | 15521.00 | 3.36% |
| Dublin Bay South (4) | May 2022 | 1061525.3 | 617102.3 | 444423.00 | 72.02% |
| Dublin Central (4) | May 2022 | 380833.0 | 283600.0 | 97233.00 | 34.29% |
| Dublin Fingal (5) | May 2022 | 400859.1 | 350144.0 | 50715.12 | 14.48% |
| Dublin Mid-West (4) | May 2022 | 374310.0 | 328365.0 | 45945.00 | 13.99% |
| Dublin North-West (3) | May 2022 | 465500.0 | 450125.0 | 15375.00 | 3.42% |
| Dublin Rathdown (3) | May 2022 | 610032.0 | 584364.0 | 25668.00 | 4.39% |
| Dublin South-West (5) | May 2022 | 558630.0 | 538289.7 | 20340.33 | 3.78% |
| Dublin South Central (4) | May 2022 | 390266.2 | 406003.2 | -15737.00 | -3.88% |
| Dublin West (4) | May 2022 | 399194.5 | 390229.0 | 8965.50 | 2.30% |
| Dun Laoghaire (4) | May 2022 | 798034.5 | 789073.5 | 8961.00 | 1.14% |
| Galway East (3) | May 2022 | 245063.7 | 232117.7 | 12946.00 | 5.58% |
| Galway West (5) | May 2022 | 256908.0 | 330317.5 | -73409.50 | -22.22% |
| Kerry (5) | May 2022 | 243051.5 | 193255.0 | 49796.50 | 25.77% |
| Kildare North (4) | May 2022 | 387790.5 | 355096.5 | 32694.00 | 9.21% |
| Kildare South (4) | May 2022 | 249614.0 | 258038.0 | -8424.00 | -3.26% |
| Laois-Offaly (5) | May 2022 | 209237.0 | 187944.2 | 21292.75 | 11.33% |
| Limerick City (4) | May 2022 | 273538.0 | 244928.0 | 28610.00 | 11.68% |
| Limerick County (3) | May 2022 | 143393.0 | 158415.5 | -15022.50 | -9.48% |
| Longford-Westmeath (4) | May 2022 | 225328.7 | 186934.0 | 38394.67 | 20.54% |
| Louth (5) | May 2022 | 271900.0 | 250552.0 | 21348.00 | 8.52% |
| Mayo (4) | May 2022 | 178087.7 | 163940.8 | 14146.83 | 8.63% |
| Meath East (3) | May 2022 | 338315.4 | 346414.4 | -8099.00 | -2.34% |
| Meath West (3) | May 2022 | 271314.0 | 288824.0 | -17510.00 | -6.06% |
| Roscommon-Galway (3) | May 2022 | 191973.7 | 150357.3 | 41616.33 | 27.68% |
| Sligo-Leitrim (4) | May 2022 | 166604.0 | 159675.0 | 6929.00 | 4.34% |
| Tipperary (5) | May 2022 | 214051.4 | 152241.0 | 61810.38 | 40.60% |
| Waterford (4) | May 2022 | 285926.0 | 212470.7 | 73455.33 | 34.57% |
| Wexford (5) | May 2022 | 230609.2 | 208450.8 | 22158.50 | 10.63% |
| Wicklow (5) | May 2022 | 434795.8 | 421322.0 | 13473.75 | 3.20% |
Describe Categorical Variables
distinct(data.frame(HPM04$Statistic))## HPM04.Statistic
## 1 Volume of Sales
## 2 Value of Sales
## 3 Mean Sale Price
## 4 Median Pricedistinct(data.frame(HPM04$Dwelling.Status))## HPM04.Dwelling.Status
## 1 All Dwelling Statuses
## 2 New
## 3 Existingdistinct(data.frame(HPM04$Stamp.Duty.Event))## HPM04.Stamp.Duty.Event
## 1 Filings
## 2 Executionsdistinct(data.frame(HPM04$Type.of.Buyer))## HPM04.Type.of.Buyer
## 1 All Buyer Types
## 2 Household Buyer - First-Time Buyer Owner-Occupier
## 3 Household Buyer - Former Owner-Occupier
## 4 Household Buyer - Non-Occupier