AI-Powered Real-Time Property Valuation and Scene Automation
CLIENT
Blockchain Realty
YEAR
2026
Overview
Blockchain Realty is a real-estate platform primarily focused on the Nigerian market that leverages blockchain technology to facilitate property transactions and services. To enhance their competitive edge, Blockchain Realty sought to integrate intelligent, context-aware insights into their platform to support users and internal operations.
The Challenge
The firm faced several operational challenges that hindered efficiency and knowledge utilization:
Manual Valuation Processes
Property pricing often relied on “manual price guessing” rather than data-driven insights, leading to potential inaccuracies.
Inefficient Workflows
Manual property listing and feature identification were time-consuming for agents, extending resale cycles and time-to-market.
Data Gaps
There was a lack of a unified, automated system capable of analyzing location, property type, and comparable sales in real-time.
The Solution
Marlocks Technologies implemented an AI/ML-powered Real-Time Property Valuation and Scene Automation System built on AWS infrastructure. Key technical components of the solution included:
Automated Valuation Engine
Leveraging Amazon SageMaker AutoML V2, Marlocks built a model to provide instant market value scores based on property details like location and size.
Scene Automation
An image-tagging model was developed to automatically identify and tag key property features (e.g., swimming pools, granite countertops) from uploaded photos.
Scalable AWS Architecture
The solution utilized a serverless pipeline including Amazon S3 for data storage, AWS Lambda for middleware logic, and Amazon API Gateway to deliver real-time predictions to the end-user.
Custom Data Strategy
To overcome local data scarcity, Marlocks used a combination of publicly available data and synthetic data specifically generated for the Nigerian market.
Results & Benefits
The Proof of Value (PoV) and subsequent implementation delivered measurable improvements to Blockchain Realty’s operational capabilities:
Enhanced Accuracy: The system achieved a valuation accuracy within a ±10–15% margin of current market prices, meeting the technical success threshold of 90% accuracy for 85% of test scenarios.
Drastic Time Savings: Property valuation requests are now processed in under 2 seconds, representing a 40–60% reduction in valuation time compared to manual processes.
Automated Insights: The scene automation model successfully classifies property features at a rate of ≥85%, significantly reducing manual upload time for agents.
Scalability: The architecture was validated to handle high-frequency demands, with a capability to process over 5,000 concurrent requests.
Lessons Learned
Synthetic Data Bridges Local Gaps: In markets with limited historical data like Nigeria, using synthetic data is a necessary strategy to train highly accurate valuation models.
Serverless Pipelines Enhance Scalability: Utilizing AWS Lambda and API Gateway allowed the system to handle over 5,000 concurrent requests while maintaining sub-2-second response times.
Image Recognition Accelerates Time-to-Market: Automating feature classification (scene automation) proved to be the most effective way to reduce agent “manual upload time” and shorten resale cycles.
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