Overview
Designed a decision-support tool for residential real estate analysis using historical property sales data.
Problem
Home sale decisions depend on price, timing, and property characteristics, but local data is often hard to interpret quickly.
What I Built
- Cleaned and explored county-level property data.
- Built predictive models for sale price and timing.
- Structured the workflow around analyst-friendly inputs and outputs.
- Wrapped the analysis in a Streamlit interface for easier use.
Key Features
- Price prediction workflow.
- Sale timing analysis.
- Property-level exploratory views.
- Structured model outputs that could support real estate decision making.
Results
Created a stronger bridge between raw housing data and practical user-facing insights.
What I Learned
This project reinforced the importance of turning technical output into workflows that non-technical stakeholders can actually use.