Abstract: This case study showcases the collaboration between OctoShrew and Winleads, a Belgian real estate company, to create an automated house value estimator. By leveraging web scraping techniques to collect recent house sales data and relevant attributes, OctoShrew developed a comprehensive solution that predicts house values based on various factors. Gaussian processes were initially employed to predict square meter prices based on location, while a gradient boosting model incorporated all house variables for precise estimations. The implementation of this automated estimator on the Winleads website resulted in a significant 30% increase in conversions, enhancing user engagement and satisfaction.

Introduction

Winleads, a prominent Belgian real estate company, aimed to overcome challenges in accurately estimating house values. To address this, OctoShrew was engaged to develop an automated house value estimator. The objective was to leverage machine learning techniques to predict house values based on scraped data from real estate websites, including variables such as location, number of rooms, square meters, year built, and renovation state. The implementation of this estimator aimed to improve user engagement and increase conversions on the Winleads website.

Problem Statement

Winleads faced the challenge of accurately estimating house values, a crucial factor in engaging potential customers. The absence of a reliable automated estimator led to inefficiencies and inconsistencies in property valuation. This motivated Winleads to collaborate with OctoShrew to develop a solution that would automate the estimation process and provide accurate results to website visitors.

Data Collection and Preparation

OctoShrew employed web scraping techniques to collect recent house sales data and associated attributes from major real estate websites. The gathered data included variables such as number of rooms, square meters, year built, renovation state, and location. Extensive data preprocessing and cleaning were performed to ensure accuracy and consistency.

Square Meter Price Prediction using Gaussian Processes

OctoShrew utilized Gaussian processes to predict square meter prices based on house location. By mapping out various locations and creating a comprehensive square meter price model, the team could accurately estimate property values. Rigorous evaluation and validation processes were conducted to assess the model's performance and ensure its reliability.

House Value Estimation using Gradient Boosting

OctoShrew implemented a gradient boosting model, a powerful machine learning technique, to estimate house values. This model incorporated all relevant variables such as number of rooms, square meters, year built, renovation state, and location. By considering predictions from individual trees within the gradient boosters, the model provided users with a confidence range for the estimated house value.

Implementation and Integration

The house value estimator developed by OctoShrew was seamlessly integrated into the Winleads website. The user interface was designed to allow visitors to input their house details, enabling the estimation process. OctoShrew and Winleads worked closely to ensure a user-friendly experience and a smooth integration process.

Results and Impact

The implementation of the automated house value estimator had a significant impact on Winleads' website conversions. A remarkable increase of 30% in conversions was observed, indicating enhanced user engagement and satisfaction. Users appreciated the automated estimation process and the confidence range provided, which added transparency and credibility to the estimations.

Conclusion

The collaboration between OctoShrew and Winleads resulted in the development and successful implementation of an automated house value estimator. By leveraging machine learning techniques, including Gaussian processes and gradient boosting, the estimator accurately predicted house values based on various attributes. The significant increase in conversions on the Winleads website demonstrated the effectiveness and value of the automated estimation tool. This case study highlights the development process, implementation, and the positive impact on user engagement and conversions, paving the way for future enhancements in property valuation techniques.