Over the last one decade we have seen Australia’s real estate market reaching to its peak. Price of the properties have double and there is out of stipulation that it’s a real estate bubble. The article in the conversation website, “Property bubble? That’s the theory, anyway”, supports the above theory. Data used in the article is dated from the year 1880 to 2011. Visualization below show data from 2003 to 2017, keeping the article updated.
library(plotly)
library(dplyr)
library(readr)d1 <- read_csv("~/Desktop/d1.csv")## Parsed with column specification:
## cols(
## Year = col_character(),
## Sydney = col_double(),
## Melbourne = col_double(),
## Brisbane = col_double(),
## Adelaide = col_double(),
## Perth = col_double(),
## Hobart = col_double(),
## Darwin = col_double(),
## Canberra = col_double(),
## `Weighted average of eight capital cities` = col_double()
## )AUS <- read_csv("~/Desktop/AUS.csv")## Parsed with column specification:
## cols(
## TIME = col_integer(),
## Unemployment = col_double(),
## GDP = col_double(),
## Housing_debt = col_double(),
## `Household Debt to GDP for Australia` = col_double()
## )Visualization below shows the distribution of Residential Property Price Index by each city in Australia. Boxplot summarizes the date set and measures on an interval scale, central value and its variability.
p1 <- plot_ly(y = d1$Sydney,name = 'Sydney', type = "box") %>%
add_trace(y = d1$Melbourne, name = 'Melbourne') %>%
add_trace(y = d1$Brisbane, name = 'Brisbane') %>%
add_trace(y = d1$Canberra, name = 'Canberra') %>%
add_trace(y = d1$Adelaide, name = 'Adelaide') %>%
add_trace(y = d1$Hobart, name = 'Hobart')%>%
add_trace(y = d1$Darwin, name = 'Darwin')%>%
add_trace(y = d1$Perth, name = 'Perth')%>%
add_trace(y = d1$`Weighted average of eight capital cities`, name = 'Weighted average of eight capital cities')%>%
layout(title = "Residential Property Price Index in Australia by Capital Cities ", yaxis = list (title = "Price Index"))
p1Time-series plots for Residential Property Price Index in Australia from 2003 to 2017 for eight cities, namely Sydney, Melbourne, Brisbane, Adelaide, Perth, Hobart, Darwin, Canberra & Weighted average of eight capital cities.
updatemenus <- list(
list(
active = 0,
x = -.125,
type= 'buttons',
buttons = list(
list(
label = "Sydney",
method = "update",
args = list(list(visible = c(TRUE, "legendonly", "legendonly",
"legendonly", "legendonly","legendonly",
"legendonly", "legendonly","legendonly")))),
list(
label = "Melbourne",
method = "update",
args = list(list(visible = c("legendonly", TRUE, "legendonly",
"legendonly", "legendonly", "legendonly",
"legendonly", "legendonly","legendonly")))),
list(
label = "Brisbane",
method = "update",
args = list(list(visible = c("legendonly", "legendonly", TRUE,
"legendonly", "legendonly","legendonly",
"legendonly", "legendonly","legendonly")))),
list(
label = "Canberra",
method = "update",
args = list(list(visible = c("legendonly", "legendonly","legendonly", TRUE,
"legendonly", "legendonly","legendonly",
"legendonly", "legendonly","legendonly")))),
list(
label = "Adelaide",
method = "update",
args = list(list(visible = c("legendonly", "legendonly","legendonly","legendonly", TRUE,
"legendonly", "legendonly",
"legendonly", "legendonly")))),
list(
label = "Hobart",
method = "update",
args = list(list(visible = c("legendonly", "legendonly","legendonly","legendonly",
"legendonly",TRUE, "legendonly", "legendonly",
"legendonly")))),
list(
label = "Darwin",
method = "update",
args = list(list(visible = c("legendonly", "legendonly","legendonly","legendonly",
"legendonly", "legendonly",TRUE,
"legendonly", "legendonly")))),
list(
label = "Perth",
method = "update",
args = list(list(visible = c("legendonly", "legendonly","legendonly","legendonly",
"legendonly","legendonly", "legendonly", TRUE,
"legendonly")))),
list(
label = "Weighted average",
method = "update",
args = list(list(visible = c("legendonly", "legendonly", "legendonly","legendonly","legendonly","legendonly","legendonly", "legendonly",TRUE))))
)
)
)
p1 <- plot_ly(data = d1, xlim = (start= 2003), y = d1$Sydney ,name = 'Sydney', type = "scatter", mode = 'lines') %>%
add_trace(x = d1$Year,y = d1$Melbourne, name = 'Melbourne') %>%
add_trace(x = d1$Year,y = d1$Brisbane, name = 'Brisbane') %>%
add_trace(x = d1$Year,y = d1$Canberra, name = 'Canberra') %>%
add_trace(x = d1$Year,y = d1$Adelaide, name = 'Adelaide') %>%
add_trace(x = d1$Year,y = d1$Hobart, name = 'Hobart')%>%
add_trace(x = d1$Year,y = d1$Darwin, name = 'Darwin')%>%
add_trace(x = d1$Year,y = d1$Perth, name = 'Perth')%>%
add_trace(x = d1$Year,y = d1$`Weighted average of eight capital cities`, name = 'Weighted_Average of eight capital citiess') %>%
layout(title= "Residential Property Price Index in Australia from 2003 to 2017", showlegend=TRUE,
xaxis=list(zeroline = FALSE,title="Year", rangeslider = list(type = "date")),
yaxis=list(zeroline = FALSE,title="Price Indexs"),
updatemenus=updatemenus)
p1Following visualization shows other economic factors considered to support the housing bubble theory. Economic factors parameters used are GDP of Australia, Unemployment rate, Housing debt, and Household Debt to GDP for Australia.
updatemenus <- list(
list(
active = 0,
x = -.125,
type= 'buttons',
buttons = list(
list(
label = "GDP",
method = "update",
args = list(list(visible = c(TRUE, "legendonly","legendonly", "legendonly")))),
list(
label = "Unemployment",
method = "update",
args = list(list(visible = c("legendonly",TRUE, "legendonly", "legendonly")))),
list(
label = "Housing_debt",
method = "update",
args = list(list(visible = c("legendonly","legendonly",TRUE,"legendonly")))),
list(
label = "Debt to GDP",
method = "update",
args = list(list(visible = c("legendonly","legendonly", "legendonly", TRUE))))
)
)
)
p1 <- plot_ly(x =AUS$TIME , y = AUS$GDP , name = 'GDP Rate' ,type = "bar", mode = 'lines') %>%
add_trace(y= AUS$Unemployment ,name = 'Unemployment Rate' ,type = "bar")%>%
add_trace(y= AUS$Housing_debt ,name = 'Housing_debt - % of net disposable income,' ,type = "scatter")%>%
add_trace(y= AUS$`Household Debt to GDP for Australia` ,name = 'Debt to GDP ratio' ,type = "scatter", mode = 'lines+markers')%>%
layout(title= "GDP & Unemployement rate in Australia from 2002 to 2017", showlegend=TRUE,
xaxis=list(zeroline = FALSE,title="Year", rangeslider = list(type = "date")),
yaxis=list(zeroline = FALSE,title="Percentage"),
updatemenus=updatemenus)
p1