A gradient-boosting library for predictive modeling on structured data. Useful for building and comparing property-value, demand, and risk models against explic
Last reviewed: 2026-10-03
XGBoost implements gradient boosting, commonly using ensembles of decision trees. It provides tools for supervised predictive tasks, including regression, classification, and ranking. It is a library for building a model, not a ready-made property valuation service.
For a property-value experiment, define which facts would have been known on the valuation date. Separate training and evaluation by time and consider location-based holdouts. Compare against a simple baseline and inspect large errors before presenting an estimate to a user.
No. You supply the dataset, target, preprocessing, and evaluation design.
Including information unavailable at prediction time, or randomly mixing related records across splits, can inflate apparent accuracy. Design the split around the real deployment scenario.