Design an Automated Home-Price Valuation Model
Overview
This question evaluates a candidate's ability to design an end-to-end machine learning system for automated home-price valuation, covering target definition, multi-year data collection and labeling, time/geographic data splits, feature engineering, model selection and training, validation/backtesting, deployment, monitoring, and handling user feedback; it is in the Machine Learning domain. It is commonly asked to assess practical, production-oriented decision-making and trade-off reasoning for real-world ML systems, emphasizing applied system design and operational monitoring rather than low-level algorithmic implementation.
Scenario
You are building an automated house-price valuation service for a real-estate platform.
Question
Design a home-price estimation system. Walk through the following components and justify your choices:
- Target definition and evaluation metric(s)
- Data collection and labeling (multi-year sale history)
- Data splitting strategy (time-based and/or geographic split)
- Feature engineering (e.g., comparable sales, property attributes, economic indicators)
- Model selection and training (baseline linear to tree-based models)
- Validation and backtesting
- Deployment and monitoring (including suburb-level monitoring)
- Handling user complaints and communicating prediction errors
Assume you are preparing for a technical phone screen and focus on practical, production-oriented decisions.
Solution
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