NeighborhoodFit

NeighborhoodFit’s interactive map interface displaying housing data and layered neighborhood insights.
Real-time neighborhood scoring updates based on infrastructure, amenities, and environmental factors.
Advanced filters for price, bedrooms, and preferences to personalize your home search.
Click any listing to view detailed home information, pricing, and neighborhood metrics.

Project Story

About the Project

NeighborhoodFit was inspired by a simple question: why is it so hard to understand what a neighborhood actually feels like before moving there?

Most home search platforms focus heavily on price, square footage, and number of bedrooms. While those are important, they don’t capture the everyday experience of living in a community. Access to parks, lighting conditions, road density, nearby amenities, and zoning patterns all shape quality of life—but this information is rarely presented in one place.

We wanted to build a tool that evaluates neighborhoods holistically rather than just evaluating houses.

How We Built It

We combined housing data with geospatial infrastructure layers to create an interactive and data-driven experience.

Our stack includes:

Frontend

React (Vite) for the frontend interface
Tailwind CSS for styling
JavaScript (ES6+) for application logic
Mapbox GL JS for interactive maps
GeoJSON rendering for spatial visualization

Backend

Python for backend logic
FastAPI for API endpoints
SQLite for database storage
Data processing scripts for feature computation
GeoJSON spatial layers for geographic data handling

We processed raw housing data and city-level GeoJSON files into structured spatial layers. These layers include parks, roads, industrial zones, lighting data, and other points of interest.

From there, we integrated everything into an interactive map interface where users can visually explore and compare neighborhoods.

Conceptually, we thought of NeighborhoodFit as a weighted scoring system:

Neighborhood Fit Score = w_1(Parks) + w_2(Amenities) + w_3(Infrastructure) - w_4(Industrial Density)

Although our current implementation focuses on visualization, this formula represents the foundation for future personalization and scoring features.

What We Learned

Throughout this project, we learned:

How to process and clean real-world housing datasets
How to work with GeoJSON and spatial data layers
How to connect backend data pipelines to a React frontend
How deployment differs between frontend (Vercel) and backend (Render) environments
The importance of modular architecture when working under hackathon time constraints

We also gained experience balancing performance and usability when rendering large spatial datasets in the browser.

Challenges We Faced

One of our biggest challenges was managing and preprocessing spatial data. GeoJSON files can be large and complex, and rendering too much data at once can significantly impact frontend performance.

Another challenge was coordinating the backend data pipeline with frontend visualization. Ensuring consistent schemas between SQLite tables and frontend API responses required careful planning.

Deployment was also non-trivial. Separating the frontend (Vercel) and backend (Render) required proper environment configuration, API routing, and CORS handling.