Why no one can afford to buy in Melbourne in 2026

Looking back at Melbourne House Prices 2016

Import the Data

First, we import the dataset and the pandas library.

import pandas as pd
df = pd.read_csv("../../../../data/melb_data.csv")

Sneak-peek at the data

Here’s a glimpse at the data.

df.head(10)

	Unnamed: 0	Suburb	Address	Rooms	Type	Price	Method	SellerG	Date	Distance	...	Bathroom	Car	Landsize	BuildingArea	YearBuilt	CouncilArea	Lattitude	Longtitude	Regionname	Propertycount
0	1	Abbotsford	85 Turner St	2	h	1480000.0	S	Biggin	2016-12-03	2.5	...	1	1.0	202	NaN	NaN	Yarra	-37.7996	144.9984	Northern Metropolitan	4019
1	2	Abbotsford	25 Bloomburg St	2	h	1035000.0	S	Biggin	2016-02-04	2.5	...	1	0.0	156	79.0	1900.0	Yarra	-37.8079	144.9934	Northern Metropolitan	4019
2	3	Abbotsford	5 Charles St	3	h	1465000.0	SP	Biggin	2017-03-04	2.5	...	2	0.0	134	150.0	1900.0	Yarra	-37.8093	144.9944	Northern Metropolitan	4019
3	4	Abbotsford	40 Federation La	3	h	850000.0	PI	Biggin	2017-03-04	2.5	...	2	1.0	94	NaN	NaN	Yarra	-37.7969	144.9969	Northern Metropolitan	4019
4	5	Abbotsford	55a Park St	4	h	1600000.0	VB	Nelson	2016-06-04	2.5	...	1	2.0	120	142.0	2014.0	Yarra	-37.8072	144.9941	Northern Metropolitan	4019
5	6	Abbotsford	129 Charles St	2	h	941000.0	S	Jellis	2016-05-07	2.5	...	1	0.0	181	NaN	NaN	Yarra	-37.8041	144.9953	Northern Metropolitan	4019
6	7	Abbotsford	124 Yarra St	3	h	1876000.0	S	Nelson	2016-05-07	2.5	...	2	0.0	245	210.0	1910.0	Yarra	-37.8024	144.9993	Northern Metropolitan	4019
7	8	Abbotsford	98 Charles St	2	h	1636000.0	S	Nelson	2016-10-08	2.5	...	1	2.0	256	107.0	1890.0	Yarra	-37.8060	144.9954	Northern Metropolitan	4019
8	9	Abbotsford	6/241 Nicholson St	1	u	300000.0	S	Biggin	2016-10-08	2.5	...	1	1.0	0	NaN	NaN	Yarra	-37.8008	144.9973	Northern Metropolitan	4019
9	10	Abbotsford	10 Valiant St	2	h	1097000.0	S	Biggin	2016-10-08	2.5	...	1	2.0	220	75.0	1900.0	Yarra	-37.8010	144.9989	Northern Metropolitan	4019

10 rows × 22 columns

What was the distribution of house prices in 2016?

The purpose of this graph was to show how many houses in Melbourne were sold for different prices. But the data varied from $85,0000 to over $10 million. The data was filtered to remove the upper outliers to better display the data.

Text(0.5, 1.0, 'Distribution of Melbourne House Prices under \\$5million')

What could you get for your money in Melbourne in 2016?

This graph compares the sale price of Melbourne properties compared to the number of bedrooms.

#create a scatter graph, with a trend line, specifying range of x axis. Needed to change to relplot with default being scattor graph to add the line. 
sns.relplot(df, x = "Rooms", y = "Price", s = 10, color = "blue")
sns.lineplot(df, x = "Rooms", y = "Price", color = "red")
plt.xticks(range (11))
plt.title("House Prices vs Number of Bedrooms")
plt.ylabel("Price in Millions")
plt.xlabel("Number of Bedrooms")

Text(0.5, 9.066666666666652, 'Number of Bedrooms')