Developing an AI-based property valuation system for one of the leading fintech companies in London, UK

To address the challenges outlined by the client, Daffodil Software began by analyzing the given key requirements and objectives. Through in-depth discussions with stakeholders and a thorough analysis of the real estate market dynamics, our team gained insights into the shortcomings of manual property valuation processes.

Following the research phase, our expert product designers carefully examined the solution requirements, crafted user stories, feature lists, process flow diagrams, and prototypes for the system.

With the roadmap solidified, the Daffodil team initiated the development of an AI-based property valuation system using a deep learning API known as Keras, TensorFlow, and Python for backend functionality.

The final automated system created by Daffodil consisted of the following phases and capabilities:

Data integration and preprocessing

One of the initial steps involved consolidating and preprocessing vast amounts of data from more than 10 sources including property listings, transaction histories, lease information, and market trends. The dataset comprised approximately 70 million training instances, necessitating the use of data preprocessing techniques to ensure data quality and integrity. Imputation techniques were employed to enrich more than 7 million missing data values. Additionally, we integrated outlier detection techniques to analyze over 3 million data points, ensuring model accuracy by identifying and addressing anomalies.

AI model development

With a large dataset in hand, we turned our focus to AI model development. We explored various machine learning algorithms, ranging from traditional regression models to advanced deep learning techniques. Our team conducted over 100 model experiments to develop a highly accurate valuation model capable of predicting property values with precision. Real-world testing scenarios were simulated to assess the system’s performance based on diverse factors such as location, neighborhood area, transaction history, and local market conditions.

Upon successful validation, the AI-driven property valuation system was deployed, seamlessly integrating with the client’s existing infrastructure. Leveraging cloud-based technologies, our solution was designed for scalability, allowing it to handle large volumes of data and accommodate fluctuations in user demand. Automated monitoring and alerting mechanisms were implemented to proactively identify and address performance bottlenecks, ensuring optimal system performance at all times. Additionally, ongoing maintenance and support services were provided to the client, enabling them to leverage the full potential of the property valuation system while staying ahead of evolving market trends and requirements.

Some highlights of the AI-driven property valuation system:

Leveraging this Automatic Valuation Model (AVM), estate agents could gain independent insight into their agency’s relative performance, using the Market feature to see who is moving the most stock, the quickest, and achieving the highest prices.

Lenders could review their risk appetite using the Property Intelligence feature to analyze price, stock, and liquidity trends, and make better faster decisions.

Users could use the Neighbourhood feature to compare a property’s configuration, size, and value to its neighbors. They could analyze transaction history on all neighboring properties and area record sales to form a view on valuation before even looking at comps.