Propertunity | Devpost

Landing Page
Suggestions for Property Manager in the Dashboard
Property Manager Dashboard
Property Manager Dashboard Scrolled down
Tenant side Dashboard with Chatbot for Ticketing system

Inspiration

We noticed that property managers often struggle with task prioritization due to a lack of data-driven insights for decision-making and juggling multiple maintenance requests without clear metrics on cost or hard numbers for potential consequences of delayed repairs. Meanwhile, tenants face frustration during non-working hours due to human-dependent ticket systems, leading to delays and communication gaps. We saw an opportunity to use AI/ML to bridge this gap by creating an efficient, predictive system that benefits property managers through cost estimation and opportunity cost analysis while streamlining tenant communication through an AI-powered chatbot that automates ticket creation, categorization, and prioritization 24/7.

What it does

-Automatically routes users to appropriate dashboards (PM/tenant) based on email domains. -Uses ML to predict maintenance needs by analyzing external factors like weather patterns and natural disaster risks in the location. -Delivers quarterly opportunity cost analysis and forecasts future costs to help prioritize maintenance categories. -Provides tenants with a streamlined interface for submitting and tracking maintenance requests. -Features a smart notification system for urgent property issues and real-time ticket updates. -Utilises Twilio API enabling tenants to send messages using a mobile phone -Integrates seamlessly with CBRE's existing systems and branding

How we built it

Frontend: React with TypeScript for type safety Backend: MongoDB, Gemini AI, Next.js, Twilio API Authentication: Firebase for secure, role-based access Routing: React Router for seamless navigation Design: Followed CBRE's brand guidelines for consistency

Challenges we ran into

-We couldn't make Auth0 work for user authentication, leading us to migrate to Firebase for a more streamlined solution. -Developing accurate data visualizations for our opportunity cost analysis was tough as predicting it isn't easy without enough data -Integrating Gemini AI into the chatbot and it being able to communicate real-time using MongoDB

Accomplishments that we're proud of

-We built a scalable application that CBRE can integrate into its existing Tech Stack with some optimization -Built a microservices-based application that will result in savings for server costs and much more -We Utilized Machine Learning to predict future outcomes and visualized it in a clean UI -Achieved accurate opportunity cost predictions through multiple data points -Built a scalable MongoDB architecture handling real-time ticket updates

What we learned

-Integration of ML with practical business applications -Enterprise-grade authentication implementation -Professional UI/UX design principles -Implementing microservices architecture for better scalability -How AI can automate tasks and make the lives of property managers and tenants easier -Property management industry insights