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a. Lack of Real-Time Information: Users often encounter outdated property listings, leading to frustration and wasted time.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.

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Advanced Filters

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    Real-Time Data Integration

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      Property Listing Page

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        Property Details Page

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          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

          1. Integrated API endpoints for multi-criteria filtering (price, location, size, etc.).
          2. Implemented range sliders for budget and square footage using the fetched data.
          3. Filters dynamically update results in real-time without requiring page reloads.

          b. Real-Time Features

          1. Leveraged API integrations to fetch the latest property updates in real-time.
          2. Optimized location-based searches by indexing geospatial data, ensuring quick and accurate proximity calculations.

          c. Property Details Management

          1. Retrieved property details from APIs, including high-resolution images, descriptions, and pricing.
          2. Optimized image loading using lazy loading techniques to enhance performance.
          To ensure a fast and responsive platform:
          1. Lazy Loading: Images and assets were lazy-loaded with added blur to reduce initial page load times.
          2. Debounced filter inputs to minimize unnecessary API calls and ensure a smoother user experience.
          3. API Optimization: Streamlined API calls by leveraging optimized queries and caching mechanisms where applicable.
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