AI-Powered Real-Time Property Valuation and Scene Automation
Client
Blockchain Realty
Partner
Marlocks Technologies
Market
Nigeria
Overview
Blockchain Realty is a real-estate platform 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 — replacing manual valuation guesswork with data-driven, real-time property pricing and automated listing workflows.
Marlocks Technologies implemented an AI/ML-powered Real-Time Property Valuation and Scene Automation System built on AWS infrastructure, purpose-built for the Nigerian real estate market.
Key Challenges
Manual Valuation Processes
Property pricing relied on 'manual price guessing' rather than data-driven insights, leading to inaccuracies that eroded buyer and seller trust in the platform.
Inefficient Agent Workflows
Manual property listing and feature identification were time-consuming for agents, extending resale cycles and slowing time-to-market for new listings.
Data Gaps in the Nigerian Market
A lack of unified historical data on location, property type, and comparable sales made it difficult to build a reliable automated valuation model for the local market.
The Solution
Marlocks implemented an AI/ML-powered system across four key components, tackling valuation accuracy, listing efficiency, scalability, and data availability simultaneously.
Automated Valuation Engine — Amazon SageMaker AutoML V2 built a model providing instant market value scores based on property details like location and size.
Scene Automation — An image-tagging model automatically identifies and tags key property features (swimming pools, granite countertops) from uploaded photos.
Scalable AWS Architecture — A serverless pipeline including Amazon S3 for data storage, AWS Lambda for middleware logic, and Amazon API Gateway for real-time predictions.
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.
Key Results
| Metric | Outcome |
|---|---|
| Valuation Accuracy | Within ±10–15% of market price (90% of test scenarios) |
| Processing Speed | Under 2 seconds per request |
| Valuation Time Reduction | 40–60% vs. manual processes |
| Scene Classification Rate | ≥85% feature accuracy |
| Concurrent Request Capacity | 5,000+ simultaneous requests |
Key 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:
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.