California Housing Dashboard

An interactive dashboard for exploring the geographic and socioeconomic drivers of housing prices in California (1990).

Live dashboards

• Stable: https://019c91e4-9ae2-9f80-d307-5b31052f2781.share.connect.posit.cloud/
• Preview: https://019c91ed-c71c-d6a8-76fc-1417d4c15a89.share.connect.posit.cloud/

Why this dashboard exists (for users)

Housing prices vary dramatically across California due to geography, income levels, housing characteristics, and proximity to amenities such as the ocean. This dashboard enables user-driven exploration of these relationships using California housing data from 1990.

It is designed to help users:

• visually investigate spatial patterns in house prices
• understand how socioeconomic and structural factors relate to price variation
• identify clusters of high- and low-value regions without writing code

The project also serves as a proof of concept for building extensible housing dashboards that could be adapted to more recent or broader datasets.

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What you can do with it

• Explore an interactive map of median house values by location
• Investigate relationships between house price and:
  • median household income
  • housing age
  • proximity to the ocean
• Compare housing characteristics using scatter plots and bar charts

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Run locally (for contributors)

Requirements

• Conda
• Python (managed via environment.yml)
• Posit Shiny for Python

Clone the repository

Using HTTPS:

git clone https://github.com/UBC-MDS/DSCI-532_2026_5_california_housing.git

Or using SSH:

git clone git@github.com:UBC-MDS/DSCI-532_2026_5_california_housing.git

Navigate to the project root:

cd DSCI-532_2026_5_california_housing

Create the environment

conda env create -f environment.yml
conda activate dsci-532-dashboard

Launch the dashboard

shiny run --reload src/app.py

Open http://127.0.0.1:8000 in your browser.

Testing

Run all tests (unit + E2E) with:

conda activate dsci-532-dashboard  
playwright install                 # one-time: download browser binaries
pytest --base-url http://127.0.0.1:8765

• Unit tests (tests/test_utils.py): pytest tests for filtering and aggregation logic.
• E2E tests (tests/test_e2e.py): Playwright tests; they start the Shiny app automatically. The AI Chatbot tab requires GITHUB_TOKEN in .env for the app to start; if missing, E2E tests may be skipped.

See TESTING.md for test coverage and reflection.

Contributing

Contributions, issues, and suggestions are welcome.

Please read CONTRIBUTING.md before opening an issue or submitting a pull request.

Authors

• Ali Boloor Foroosh
• Fu Hung (Teem) Kwong
• Nicole Link
• Shrabanti Bala Joya

Attribution

Generative AI tools (Google Gemini, OpenAI ChatGPT, and GitHub Copilot) were used to assist with code generation and documentation drafting. All generated content was reviewed and edited by the authors to ensure accuracy and quality.