MVP to a multi-platform real estate app using Decoupled Drupal

The challenges

Connecting Panama real estate agents and buyers globally posed a challenge. Our task was to develop a platform working on various devices, requiring separate apps for dealers and buyers while sharing a common backend.

The approach

To meet the needs of property buyers and sellers, we designed an MVP. We created a backlog, prioritizing valuable and cost-effective features, and consulted real users and stakeholders to understand their expectations.

The solution

MVP (Minimum viable product)

We started by building the MVP (Minimum Viable Product) for our client. In the product consulting phase, we prioritized key features such as buyer and seller verification, real-time communication, and a decision-driven search system.

Insights into buyer and seller problems were gained through surveys and stakeholder engagement, guiding our MVP development.

Component-based design

The design phase focused on creating an action-based and minimalist user experience. Our component-based design ensured elements were reusable across web and mobile platforms, enhancing usability.

The MVP aimed to streamline processes, including bulk property imports, a chat-based contact center, and a mobile app for easy listing management.

Decoupled Drupal

While building the app, we chose a distributed system for third-party tools. Using API First Drupal deployment, or Decoupled Drupal, made our real estate app faster with quick updates and smooth data access.

It lets the backend team work independently, ensuring an easy-to-use and responsive experience on both web and mobile.

Web and mobile app development

Angular powered the web application, while Ionic served as the hybrid mobile app for Android and iOS. The tech stack included Firebase for real-time chats, Elastic Search for powerful search queries, and integration with external XML feeds for property data.

The outcome

In just five months, we transformed the initial idea into an MVP, listing 18,000 properties within three months of the launch. Machine learning was implemented for house-price prediction, providing valuable insights. The decision-driven user experience enhanced user trust, offering an optimized search flow tailored for the real estate sector.