Toronto Real Estate Market Analysis

Asset Price Bubble Investigation

Linear Regression · Python · Stata · Economic Data Analysis

Overview

Regression-based analysis of Toronto housing prices and inflation drivers, assessing asset price bubble risk using real estate and macroeconomic data.

Key Findings

• Population growth and labor force metrics are the strongest drivers of housing prices (R² = 0.93)
• Housing costs (rent, New House Price Index) are the dominant contributor to inflation (R² = 0.99)
• Multicollinearity and statistical significance tests confirm bubble risk signals

Tools & Technologies

• Languages: Python, Stata
• Libraries: NumPy, Pandas, Statsmodels, SciPy
• Methods: Linear Regression, Multiple Regression, CPI Modeling, Multicollinearity Assessment

Files

File	Description
README.md	Project overview
toronto_housing_analysis.py	Python regression scripts
toronto_housing_analysis_report.pdf	Full written report
toronto_housing_regression.do	Stata regression scripts

Data Sources

• Statistics Canada — statcan.gc.ca