The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence) House Hunters: Machine Learning at Redfin with Akshat Kaul - #530
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Oct 26, 2021 
In this engaging discussion, Akshat Kaul, Head of Data Science and Machine Learning at Redfin, shares his expertise in leveraging machine learning to transform the real estate industry. He delves into the evolution of Redfin’s ML capabilities, emphasizing innovative applications like price estimation and property recommendations. Akshat also introduces Redeye, their internal platform for modeling, and discusses how cloud services are reshaping their ML deployment. Additionally, he reflects on how the pandemic has influenced buyer behavior and future ML strategies.
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ML at Redfin
- Redfin, a real-estate brokerage, leverages AI for various applications like the Redfin Estimate and recommendations.
- These tools have significantly impacted user growth and revenue, especially with the introduction of Redfin Now, their instant buying service.
Data Points for Redfin Estimate
- Redfin Estimate uses 500+ data points, including MLS data, public records, amenities, geographical data, and user engagement.
- User interaction data helps understand market dynamics, though Redfin has mechanisms to prevent bot manipulation.
Model Types
- Redfin uses a combination of classical models and deep learning for different applications like predicting off-market and on-market home prices.
- The models range in complexity and are tailored to specific use cases.