Australia has experienced significant increases in living costs over recent years. Rising prices for housing, food, transport and utilities have affected household budgets across the country.
This report investigates changes in major Consumer Price Index (CPI) categories using data from the Australian Bureau of Statistics (ABS). The aim is to identify which goods and services have become less affordable for Australians and to visualise these trends using interactive data analytics techniques.
What has become unaffordable for Australians between 2024 and 2026?
Australian Bureau of Statistics (ABS) Consumer Price Index (CPI) data.
all_groups <- data_clean[, c("date", names(data_clean)[2])]
all_groups <- all_groups[!is.na(all_groups[[2]]), ]
names(all_groups)[2] <- "CPI"
all_groups$CPI <- as.numeric(all_groups$CPI)To understand whether affordability pressures have increased, it is first necessary to examine the overall CPI trend in Australia.
plot_ly(
all_groups,
x = ~date,
y = ~CPI,
type = "scatter",
mode = "lines+markers"
) %>%
layout(
title = "Overall CPI Trend in Australia (2024–2026)",
xaxis = list(title = "Date"),
yaxis = list(title = "CPI Index"),
hovermode = "x unified"
)The overall CPI trend shows that prices increased steadily between 2024 and 2026. This indicates that Australians experienced ongoing inflation throughout the period. However, the overall CPI does not reveal which specific categories contributed most to the increase. The next visualisation explores individual cost categories.
While the overall CPI provides a broad picture, it does not identify which categories contributed most to rising living costs.
housing <- as.numeric(data_clean[[grep("Housing", names(data_clean))[1]]])
food <- as.numeric(data_clean[[grep(
"Food and non-alcoholic beverages",
names(data_clean)
)[1]]])
electricity <- as.numeric(data_clean[[grep(
"Electricity",
names(data_clean)
)[1]]])
transport <- as.numeric(data_clean[[grep(
"Transport",
names(data_clean)
)[1]]])
comparison <- data.frame(
date = data_clean$date,
Housing = housing,
Food = food,
Electricity = electricity,
Transport = transport
)
comparison <- comparison[complete.cases(comparison), ]plot_ly(comparison, x = ~date) %>%
add_lines(y = ~Housing, name = "Housing") %>%
add_lines(y = ~Food, name = "Food") %>%
add_lines(y = ~Electricity, name = "Electricity") %>%
add_lines(y = ~Transport, name = "Transport") %>%
layout(
title = "Cost of Living Components in Australia (2024–2026)",
xaxis = list(title = "Date"),
yaxis = list(title = "CPI Index")
)Housing, food, electricity and transport followed different patterns over time. Electricity experienced the largest fluctuations, while food and housing showed more stable growth. To determine which category became the most expensive overall, percentage increases are compared in the next visualisation.
summary_table <- data.frame(
Category = c("Housing", "Food", "Electricity", "Transport"),
Start = c(
first(comparison$Housing),
first(comparison$Food),
first(comparison$Electricity),
first(comparison$Transport)
),
End = c(
last(comparison$Housing),
last(comparison$Food),
last(comparison$Electricity),
last(comparison$Transport)
)
)
summary_table$Increase_Percent <-
round(((summary_table$End - summary_table$Start) /
summary_table$Start) * 100, 2)The next step is to compare the overall growth of each major cost category.
plot_ly(
summary_table,
x = ~reorder(Category, Increase_Percent),
y = ~Increase_Percent,
type = "bar",
text = ~paste0(Increase_Percent, "%"),
textposition = "outside",
hovertemplate =
paste(
"Category: %{x}<br>",
"Increase: %{y}%<extra></extra>"
)
) %>%
layout(
title = "Increase in Cost of Living Categories (2024–2026)",
xaxis = list(title = "Category"),
yaxis = list(title = "Percentage Increase (%)")
)Electricity recorded the largest increase at 14.52%, making it the fastest growing cost category. Housing and food also increased substantially, while transport recorded the smallest increase. This suggests that utility costs have become a major contributor to affordability pressures.
Beyond individual increases, it is also useful to explore whether major household expenses tend to rise together.
plot_ly(
comparison,
x = ~Housing,
y = ~Food,
type = "scatter",
mode = "markers",
color = ~Electricity,
text = ~date,
hovertemplate =
paste(
"Date: %{text}<br>",
"Housing: %{x}<br>",
"Food: %{y}<br>",
"Electricity: %{marker.color}<extra></extra>"
)
) %>%
layout(
title = "Relationship Between Housing and Food Costs",
xaxis = list(title = "Housing CPI"),
yaxis = list(title = "Food CPI")
)Housing and food costs appear to move together. Periods with higher housing costs are generally associated with higher food prices. The colour scale shows that electricity prices often increased during the same periods, suggesting broader inflationary pressures across multiple household expenses.
Finally, an interactive comparison highlights how cost categories evolved throughout the study period.
library(plotly)
plot_ly(
comparison,
x = ~date
) %>%
add_lines(
y = ~Housing,
name = "Housing",
hovertemplate =
paste(
"Date: %{x}<br>",
"Housing CPI: %{y}<extra></extra>"
)
) %>%
add_lines(y = ~Food, name = "Food") %>%
add_lines(y = ~Electricity, name = "Electricity") %>%
add_lines(y = ~Transport, name = "Transport") %>%
layout(
title = "Monthly Cost Components Comparison",
xaxis = list(title = "Date"),
yaxis = list(title = "CPI Index"),
hovermode = "x unified"
)The interactive comparison highlights how each category changed throughout the study period. Electricity displayed the greatest volatility and recorded the largest overall increase, while housing and food followed a more stable upward trend. Transport remained relatively stable compared with the other categories. Together, these trends help explain why many Australians experienced increasing cost of living pressures between 2024 and 2026.
• Overall CPI increased steadily from 2024 to 2026.
• Electricity recorded the largest increase (14.52%) and showed the highest volatility.
• Housing and food costs demonstrated a strong positive relationship.
• Transport costs remained relatively stable compared with other categories.
• Rising housing and electricity costs were the main contributors to cost-of-living pressure.
All data used in this analysis was obtained from the Australian Bureau of Statistics (ABS) Consumer Price Index dataset.
This analysis investigated changes in major cost of living categories in Australia between 2024 and 2026 using ABS CPI data.
The results show that electricity experienced the largest increase, followed by housing and food costs. Although transport costs also increased, the growth was comparatively smaller.
The findings suggest that essential household expenses have become progressively less affordable for Australians, particularly in relation to utilities and housing. These trends help explain the growing financial pressures experienced by many households during the study period.
Australian Bureau of Statistics (ABS) 2026, Consumer Price Index, Australia, viewed 4 June 2026, https://www.abs.gov.au.