LeaseLens

LeaseLens is a prototype rental intelligence app for two audiences:

• Renters comparing properties using local rent and home-value history.
• Landlords checking an explainable forecast score for whether a property is likely to gain value.

The current build uses a local sample dataset shaped after Zillow Research rental and home-value time series. The Zillow Research data portal referenced for production ingestion is:

• https://www.zillow.com/research/data/

Stack

• Frontend: React + Vite + CSS
• Backend: FastAPI
• Data model: local JSON fixture that mirrors Zillow-style monthly history

Project structure

.
├── backend
│   ├── data/properties.json
│   ├── main.py
│   └── requirements.txt
├── src
│   ├── App.jsx
│   ├── main.jsx
│   └── styles.css
├── index.html
├── package.json
└── vite.config.js

Run locally

Backend

cd /Users/mytruong/staging-planner
python3 -m venv .venv
source .venv/bin/activate
pip install -r backend/requirements.txt
uvicorn backend.main:app --reload

Frontend

In a second terminal:

cd /Users/mytruong/staging-planner
npm install
npm run dev

Open the Vite URL that prints in the terminal. The frontend expects the backend at http://127.0.0.1:8000.

Notes on Zillow integration

This repo does not pull Zillow data directly yet. The next backend step is to replace backend/data/properties.json with an ingestion pipeline that:

1. Downloads the selected Zillow CSVs.
2. Normalizes city/ZIP/metro identifiers.
3. Joins the market history to landlord-owned properties.
4. Recomputes the forecast features from the real data.

Important product note

The current landlord recommendation is not actual AI or financial advice. It is an explainable heuristic based on recent rent growth, home-value growth, occupancy, and neighborhood accessibility.