PROJECT TITLE
HavenHub - Real Estate Marketplace
My Role
In this project, I handled everything, from conducting user research and designing the UI/UX to developing the frontend and integrating a third-party API for fetching real-time data.
I implemented advanced filtering options directly in the frontend, ensuring users could easily refine their searches based on location, price range, and property details, delivering a seamless and efficient user experience.
What is HavenHub
HavenHub was designed to provide a seamless experience for users seeking to buy, rent, or sell properties.
The platform offers real-time data on houses, apartments, and commercial properties for rent and sale.
The system integrates advanced search and filtering options such as square footage, rental frequency, price ranges, purpose (rent/sale), and location, ensuring that users can find properties tailored to their exact needs.
1. The Problem
The challenges in the Real Estate Market.
a. Lack of Real-Time Information: Users often encounter outdated property listings, leading to frustration and wasted time.
b. Complex Search Processes: Finding the right property based on multiple criteria like budget, location, and purpose can be cumbersome.
c. Overwhelming Data Presentation: Many platforms bombard users with excessive information, complicating their decision-making process.
Advanced Filters
Real-Time Data Integration
Property Listing Page
Property Details Page
Frontend
Next.js was utilized to build a highly responsive and intuitive web interface, ensuring seamless navigation and an optimal user experience across devices.
Backend
Node.js with Express.js was chosen to create a robust, scalable, and efficient server-side architecture for handling API requests and real-time features.
UI Framework
Ant Design provided a modern and customizable component library, accelerating the development of a clean and professional user interface.
a. Dynamic Filtering
- Integrated API endpoints for multi-criteria filtering (price, location, size, etc.).
- Implemented range sliders for budget and square footage using the fetched data.
- Filters dynamically update results in real-time without requiring page reloads.
b. Real-Time Features
- Leveraged API integrations to fetch the latest property updates in real-time.
- Optimized location-based searches by indexing geospatial data, ensuring quick and accurate proximity calculations.
c. Property Details Management
- Retrieved property details from APIs, including high-resolution images, descriptions, and pricing.
- Optimized image loading using lazy loading techniques to enhance performance.
- Lazy Loading: Images and assets were lazy-loaded with added blur to reduce initial page load times.
- Debounced filter inputs to minimize unnecessary API calls and ensure a smoother user experience.
- API Optimization: Streamlined API calls by leveraging optimized queries and caching mechanisms where applicable.
