Brief and project description
We join hands with an emerging insurance company in the USA to develop the Renno-Estimator platform, an AI-powered application that enhances the accuracy of real estate valuations.
Using Yolo, a computer vision-based open-source model as a foundation model for this project, and integration of Google Map API helped us train the model to fetch the footprints of the building and detect the exact measurement through satellite imagery. Then it maps out details such as rooms, kitchens, baths, and storage areas.
The application then predicts the construction cost and gives the current market estimation of the prices based on the market trends around the region. For insurance purposes, the exact estimations were made with the financial modeling and key factors provided by the company, plus the power of Artificial Intelligence. A subscription model is added for the company to maintain the revenue flow and expenses for the technology. The app was used by their own agents and real estate professionals.
- AI-powered detection of dimensions from satellite imagery.
- YOLO CV model training on the residential and commercial buildings dataset.
- Automated property valuation with cost estimationRegion-based pricing trends and impacts
- Subscription model for insurance agents
- End-to-end solution: from detection to valuation
Technologies and Tools:
Figma
Python
Firebase
OCI (AI Model Hosting)
