Find your perfect neighborhood, not just a property.
Stop drowning in endless property listings. Start by finding the right neighborhood.
Rent Map is a decision-support engine built to answer one critical question: "Where in South Bangalore should I rent based on my budget and lifestyle priorities?"
Experience the live application here: Live Deployment
Most real estate platforms focus on units first. They throw thousands of listings at you without context. Rent Map flips the paradigm. It focuses on the spatial decision first.
By prioritizing the neighborhood selection process, we help renters align their budget with livability, commute potential, and lifestyle fit before they ever look at a single listing. This is the MVP (Minimum Viable Product), acting as a "wedge" to prove the value of transparent, explainable geographical decision support.
We don't use black-box AI to tell you where to live. We use a transparent, rule-based scoring system (out of 100) based on five visible factors:
- Budget Fit (20 pts): How well does the area match your maximum rent?
- Transit Access (20 pts): Proximity to Metro stations, major bus corridors, and transit hubs.
- Livability & Lifestyle (20 pts): Parks, family-friendliness, nightlife, and local culture.
- Commute Proxy (20 pts): Relative distance to major job hubs.
- Market Pressure (20 pts): Is the rental market here highly competitive and heated, or more stable?
When you click on a neighborhood, the UI physically expands to explain exactly why it received its score. No mysteries. Just clear, actionable data.
This MVP was intentionally designed to be lightweight, performant, and visually striking.
We chose Vite with React to ensure lightning-fast cold starts, instant HMR (Hot Module Replacement) during development, and a highly optimized production build. It provides the perfect foundation for a dynamic, state-heavy single-page application.
Instead of relying on heavy, API-dependent maps (like Google Maps) which can introduce billing complexities early on, we used Leaflet (react-leaflet).
- Tiles: We use CartoDB's Dark Matter tiles to provide a stunning, high-contrast dark aesthetic.
- Polygons: We mapped rough polygon boundaries for 5 key South Bangalore neighborhoods (Jayanagar, JP Nagar, Banashankari, Basavanagudi, Koramangala). These act as interactive hit-areas.
To achieve a "wow" factor without the bloat of massive component libraries, we built a custom design system using Vanilla CSS.
- Glassmorphism: The sidebar and popups use
backdrop-filter: blur(20px)and semi-transparent backgrounds to create a modern, frosted-glass effect that sits beautifully over the map. - Dynamic Feedback: As the user drags the budget slider, React recalculates the scores via
useMemo. The map polygons instantly change color (Green > Yellow > Red) based on the new scores, providing immediate, tactile feedback.
We recently rolled out our hyper-personalization engine:
- Weighted Priorities: Users are no longer locked into fixed assumptions. You can scale the importance of Transit, Livability, Budget, etc. from
0xto3x, and the map instantly mathematically re-renders the neighborhood rankings. - Office Geocoding (Isochrones): Enter your office address, and the app uses OpenStreetMap Nominatim to drop a pin and draw a 5km (~30 minute commute) radius circle, offering a powerful visual anchor for your spatial decision.
This MVP is just the beginning. The roadmap is split into upcoming exciting phases:
- API Integration: Swap the mock rule-based data for live API feeds pulling average rent indices, live traffic data (for commute scores), and active market pressure metrics.
- Expanded Coverage: We currently cover 9 major micro-markets (Jayanagar, Koramangala, HSR Layout, Electronic City, etc.). The goal is to cover all of Bangalore.
- Positioning Engine: Allow landlords to drop a pin and instantly see suggested rent bands, likely tenant profiles (e.g., "Student vs. Young Professional"), and positioning advice.
- Market Comparables: Show landlords what similar units in their exact micro-market are renting for.
Built with ❤️ for Bangalore.