04Statistical analysis
What makes a
property’s price?
Exploring 42,000+ Perth property records to understand how features and location relate to price.
- Organisation
- University of Western Australia
- My role
- Business Analytics student
- Focus
- Statistical analysis
- Year
- 2025
Records behind
the bigger picture.
Regression
Statistics
The challenge
Start with the
business question.
Explore the relationships between property characteristics, location and price across Perth, and communicate the findings alongside the limits of the analysis.
My contribution
From understanding
to delivery.
Analysed more than 42,000 property records in Excel using multiple linear regression, ANOVA, t-tests and a correlation matrix. I examined features including bathrooms, land size and distance to the CBD.
- 01
Explore the dataset
Worked with 42,000+ property records and examined the available characteristics.
- 02
Test relationships
Used descriptive statistics, correlation analysis, ANOVA and t-tests.
- 03
Build a regression model
Applied multiple linear regression to examine price-related factors.
- 04
Explain the limits
Considered the model’s R² of 0.39 when interpreting and presenting the findings.
Understanding the pattern and its limits.
The analysis explored relationships between price and property features, including bathrooms, land size and distance to the CBD. The model’s R² of 0.39 highlighted that substantial variation remained unexplained.
What I took away
A useful analysis needs context. Statistical relationships can help explain a pattern, but the model’s limits matter when communicating what the result can support.
An academic project using historical property data, completed in 2025.