Good read - Link
Machine learning is no silver bullet if you do not consider domain, data, changing environmental factors. A classic case of missing domain knowledge is flagged in this story.
- Zillow does Real estate - selling, buying, renting, and financing
- Zillow home value estimation models failed.
- Assumption - assumption that housing prices would continue to climb without interruption at a stable rate
- The domain experts warned of issues with the predictions.
- The business went ahead anyway. Finally, it bombed
Lessons
- Domain expert warnings considered as Go / No-go for production, not just model accuracy
- Learn / Incorporate Data Changes to understand changing trends
- Performing A/B Experiments to understand customer behaviors and leverage optimal values based on outcomes
- Better model/feature management / keep improving on features / incorporate external factors based on domain expert perspectives #machinelearning #technology #datascience #domainknowledge
Another good read Zillow, Prophet, Time Series, & Prices
i cannot get over that the zillow data scientist job posting "strongly prefers" you have experience with a python library that is designed to make it as piss easy as possible for little babies to do y_t = f(t) time trend / curve fitting forecasts. pic.twitter.com/YTUcgascCi
— Senior Data Masseuse (@ryxcommar) November 3, 2021
WHY IS INTERMEDIATING HOUSES SO DIFFICULT? EVIDENCE FROM IBUYERS
- Predict that households’ wiliness to pay for liquidity is highest in those markets
- Sophisticated algorithmic pricing
My Perspectives
- I love the housing.com approach to rank an area based on amenities, wellness, connectivity
- Plus a pricing range based on amenities and facilities provided
- Plus growth potential / Availability
- Demand vs Supply
A combination of this would suggest a recommended price that a domain expert could adjust based on other external factors. ML is a guideline, not a blind predictor
Keep Thinking!!!
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