An XGBoost Property Valuation Postmortem: Leakage, Overfitting, and SHAP Surprises

I spent the last few months building a property valuation engine for Orlando. The goal was to beat a basic baseline (median price per square foot) using XGBoost. My v1 model looked good on paper until I looked under the hood. It had fatal flaws that the standard metrics could not surface.

This is a postmortem on how a high R-square (R²) fooled me, how Optuna forced me to rethink my hyperparameter space, and why hyper-local real estate data will eat you alive if you treat it like a Kaggle dataset.