97% accuracy for automated real estate valuation
Canadian real estate market. Manual appraisals, slow reports, expensive workflows. We built an AI-driven valuation model using private transaction and property description data to automate property price evaluation.
97%
Accuracy Rate
1
Dedicated Engineering Team
1 Month
From Kickoff To Completion
2
Data Sources Used
AI & Analytics
About The Company
The company had private access to real estate transaction data and property description data. The opportunity was to turn that data into an AI-driven valuation model that could support faster and more accurate property price evaluation.
The Challenge
Manual appraisals took too long
Property appraisal required too much time and effort, slowing down decisions for real estate and mortgage workflows.
Valuation was expensive to produce
Traditional appraisal reports were costly to order and difficult to scale across a larger number of properties.
Private data needed structure
Transaction and property description data had to be prepared, connected, and used correctly before it could power a reliable model.
Accuracy had to stay high
The model needed to automate valuation while keeping results accurate enough to support real business decisions.
The Approach
Data Discovery
The project started with reviewing available transaction data, property description data, and the valuation logic needed for real estate price evaluation.
- Data sources reviewed
- Valuation logic mapped
- Model requirements defined
Data Preparation
The available property and transaction data was structured into a usable foundation for training and testing the valuation model.
- Transaction data prepared
- Property description data cleaned
- Data relationships structured
Model Development
An AI-driven algorithm was built to estimate property value based on historical transaction data and real estate characteristics.
- Valuation model developed
- Property features processed
- Prediction logic trained
Accuracy Testing
The model was tested against known valuation outcomes to evaluate prediction quality and improve reliability.
- Prediction errors reviewed
- Model logic refined
- Evaluation results validated
Delivery & Handover
The final model was prepared for use by the client’s team, with the core logic and performance results documented for future development.
- Accuracy results documented
- Technical handover completed
- Next-stage improvements outlined
The Results
97%
Valuation accuracy achieved using private real estate data
1 m
From data scoping to validated model delivery
1 model
Dedicated ML team from start to finish
2
Core data sources used for valuation logic
Still relying on manual workflows where data could do the work?
Turn private data into automated decisions.
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