You are given historical home sales with features such as lot area, year built, number of rooms, neighborhood, and sale date. Design an end‑to‑end approach to predict the sale price for new listings. Cover: problem framing and target transformation (e.g., log‑price); data cleaning, missing‑value imputation, outlier handling, and leakage risks; feature engineering for numeric/categorical/time/location features and useful interactions; baseline and model choices (linear models, regularized regressions, tree‑based methods, ensembles); evaluation protocol and metrics (RMSE, MAPE, time‑based cross‑validation); hyperparameter tuning and error analysis; interpretability and fairness considerations; handling non‑stationarity and market shifts with monitoring and retraining; and how you would deploy a lightweight model under latency and memory constraints.