CanHomeOwner

A comprehensive analysis tool focusing on the Canadian real estate market. It integrates financial, demographic, and regional data to provide insights into housing affordability, investment opportunities, and market trends.

Project Overview

This project aims to address critical questions in real estate investment and personal housing decisions by analyzing various economic and demographic factors. It serves as a decision-making tool for individuals exploring the dynamics of the Canadian real estate market.

Features

• Economic Factors Analysis: Correlation of home prices with interest rates, inflation, and immigration.
• Affordability Assessment: Visualization of housing prices across Canada considering personal financial constraints.
• Rent vs. Buy Analysis: Comparative study of mortgage payments versus rent.
• School Quality Impact: Exploration of how school rankings influence real estate values.
• Income-Based Real Estate Affordability: Analysis of home prices against different income levels.

Technologies Used

• Data Visualization: Seaborn, HvPlot
• Data Handling: Pandas, Python
• Web Scraping: BeautifulSoup
• Geospatial Analysis: GeoPy

Results

There is a clear trend of housing prices to start escaping income.

How to Use

Clone the repository and navigate to the project directory:

git clone https://github.com/ft-p1team05/canhomeowner.git
cd fintech-project01

Run Jupyter Notebook to access the full analysis:

jupyter notebook

Data Resources

Utilized data from CREA, Bank of Canada, Statistics Canada, CMHC, and Ontario's public data.

• CREA - The Canadian Real Estate Association
  • Seasonally Adjusted Home Price Index and Benchmark
• Bank of Canada
  • Interest Rates
  • Inflation
• Statistics Canada
  • Immigration
• CMHC - Canada Mortgage and Housing Corporation
  • Rental Market Data
  • Mortgage Data
• data.ontario.ca
  • Ontario Public School Enrollment

Challenges

Overcame challenges in data acquisition, team collaboration, and complex data visualization to provide actionable insights.

Contributions

As a collaborative project, contributions ranged from data collection, analysis, machine learning model implementation to visualization and report generation.

Project Presentation

Explore our detailed findings and visualizations here: Presentation Slides

Repository Link

GitHub Repository)

Plots and Graphs

Average Mortgage Monthly Payments (pct_change) and Gross Rent (pct_change)

The calculator estimates your living expenses.