Property Inspector

AI based real-estate property management system which detects the damanged parts from the real estate images and score the quality.

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Technologies Used

React
Next.js
TypeScript
TailwindCSS
Node.js
Python
TensorFlow
PyTorch
PostgreSQL
AWS
Docker

Gallery

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Introduction

Property Inspector is an AI-powered property management and inspection platform designed for the real estate industry. By analyzing property images with advanced computer vision models, it automatically detects damaged areas, structural issues, and wear-and-tear, then assigns a quality score to guide property managers, insurers, and real estate agents.

This reduces the need for manual inspections, speeds up evaluation processes, and provides a consistent, unbiased assessment of property conditions.

Key Features

  • Automated Damage Detection: AI models identify cracks, stains, mold, and other property damages directly from uploaded photos.
  • Quality Scoring System: Generates standardized property quality scores that help compare, rank, and track properties over time.
  • Image-Based Reports: Generates visual inspection reports highlighting detected issues with annotations.
  • Integration with Real Estate Platforms: Seamlessly integrates with property listing portals, CRM systems, and insurance platforms.
  • Analytics & History Tracking: Tracks property condition changes over time with historical inspection data and trends.

Technical Insights

  • Frontend: Built with React, Next.js, and TypeScript, providing a fast, interactive web dashboard. TailwindCSS ensures a modern, responsive UI optimized for displaying images and inspection reports.
  • Backend & APIs: Node.js orchestrates API requests, while Python services handle computer vision tasks. REST and GraphQL APIs expose property inspection results to other systems.
  • AI/ML Models: Implemented with TensorFlow/PyTorch, trained on labeled property datasets to detect surface damages, structural issues, and material conditions.
  • Database & Persistence: PostgreSQL stores property records, inspection histories, and quality scores.
  • Cloud Infrastructure: Hosted on AWS, leveraging S3 for storing property images, EC2 for running ML inference, and Dockerized services for scalable deployment.

Challenges and Solutions

  • Accuracy of AI Damage Detection: Variations in lighting and image quality affected detection. We solved this by training models on diverse datasets and applying preprocessing techniques like normalization and augmentation.
  • Scalability for Large Portfolios: Property managers often upload thousands of images. AWS autoscaling with Docker containers ensured fast, concurrent processing.
  • Trust & Transparency: Users needed confidence in AI-driven results. We added explainable AI visualizations, highlighting detected damages with bounding boxes and confidence scores.
  • Integration with Legacy Systems: Many clients relied on outdated property platforms. We developed REST/GraphQL adapters for smooth integration with CRMs and listing systems.
  • Consistent Quality Scoring: To avoid subjectivity, we built a rule-based scoring layer on top of AI detections, ensuring standardization across inspections.