About the Client
The client is a London-based fintech company focused on digitizing property valuation. Its flagship platform delivers comprehensive financial analysis of UK-based residential properties aligned with market standards. By leveraging advanced technology, the company simplifies and accelerates valuation processes for lenders, financial institutions, and estate agents, improving accuracy, efficiency, and decision-making.
Business Situation & Requirements
The client relied on traditional, manual processes involving extensive paperwork, physical inspections, and manual data entry to assess property values. These methods resulted in slower turnaround times, increased risk of human errors, and limited scalability, making it difficult to efficiently process growing volumes of property assessments.
To address these challenges, the client aimed to modernize the valuation process by introducing an AI-based digital platform. The solution needed to leverage multiple data sources and the client’s capital market expertise to deliver faster and more accurate property valuations. Additionally, the platform required robust technical infrastructure capable of supporting automation, data analytics, and scalable operations while streamlining workflows for lenders, financial institutions, and real estate professionals.
The key requirements were:
Develop an automated property valuation solution to reduce turnaround time and improve operational efficiency.
Aggregate property data from multiple sources and apply imputation techniques to address missing information.
Design an intuitive interface that enables easy navigation and quick access for both internal teams and customers.
Build a scalable platform capable of processing large volumes of valuation requests while supporting future growth.
Implement robust security controls to safeguard sensitive property data and ensure compliance with data privacy regulations.
Integrate the automated valuation system with existing software and workflows to enable seamless operations.
The Solution
Unthinkable began with a comprehensive discovery phase to understand the client’s objectives and evaluate gaps within the existing manual property valuation processes. Through detailed stakeholder consultations and analysis of real estate market dynamics, the team identified operational inefficiencies and defined a clear direction for building a data-driven, automated valuation platform.
Building on these insights, the product design team created user stories, feature lists, process workflows, and system prototypes to shape the product roadmap. The engineering team then developed an AI-powered property valuation platform using Python, Keras, and TensorFlow, enabling automated property assessments with improved accuracy, faster processing, and greater scalability.
The automated system developed incorporates the following phases and capabilities:
Data integration and preprocessing
- Consolidated and preprocessed property data from 10+ sources, including listings, transactions, and market trends.
- Processed a dataset of approximately 70 million training instances to support model development.
- Applied imputation techniques to enrich and complete over 7 million missing data values.
- Implemented outlier detection across 3 million data points to improve model accuracy and reliability.
AI model development
- Evaluated multiple machine learning models through 100+ experiments to build an accurate valuation model.
- Simulated real-world scenarios using location, transaction history, and market conditions.
- Deployed the AI valuation system with seamless integration into existing infrastructure.
- Enabled scalable cloud architecture with automated monitoring, alerts, and continuous maintenance support.
AI-driven property valuation system
- Estate agents gain performance insights using the Market feature and Automatic Valuation Model (AVM) powered data.
- Lenders evaluate risk appetite using Property Intelligence and market trend analysis.
- Users compare property size, configuration, and value with neighboring properties.
- Users review neighborhood transactions and record sales to assess property valuation.
The Impact
Using 70M+ training datasets and 7M+ enriched records, the AI-driven valuation system achieved 93% accuracy. Insights from 100+ AI model experiments improved pricing precision and reliability across property valuation workflows.
The automated platform reduced turnaround time, streamlined operations, and delivered consistent property insights. Its scalable, secure interface enabled confident financial decisions, strengthening the client’s trust and expanding our continued collaboration.
70M+
Training Datasets
7M+
Recorded Datasets
100+
AI Model Experiments Conducted
