We implemented an innovative property listing feature using AI algorithms and third-party APIs to streamline the process for users. The feature enabled owners to swiftly list their properties, autofill smart form descriptions, expedite the data entry process, identify key features in images, generate property descriptions, and analyze market trends for price estimation.
The web application used machine learning models to analyze historical property data, market trends, and economic indicators to help users estimate property rates. We have utilized predictive analytics to generate accurate and real-time insights into the dynamic nature of the real estate market.
We enhanced value proportions by integrating advanced features like a visit scheduling model for owners and potential buyers. There are supporting features like open house schedule, walk-in visit, push notifications for reminders, automatic requests, allowing multiple visitors at the same time, visit cancellations, etc. It is also integrated with the Multiple Listing System (MLS) which functions as a centralized database system that allows real estate agents to list properties for buy or sell.
Our team followed a multi-faceted approach to make the web application more dynamic. We developed features that allowed users to make the hoardings, banners and other media using marketing tools to reach a broader audience through the platform. They can find suitable individuals for buying or selling property through integrated social media platforms within the portal.
active property listings
successful property transactions
increase in user engagement
801, City Centre 2,
Science City Road,
Sola-380060
Ahmedabad, Gujarat, India
9341 Ellis Way,
Strongsville, Ohio 44136
USA
