Redfin data of SoCal

This dataset supports the research article <i>“Predicting Residential Property Values Using XGBoost and Spatial–Temporal Encoding: Evidence from Southern California”</i> by <b>Michael Quintana (2025)</b>.The dataset contains a cleaned and anonymized subset of residential property transactions derived from Redfin’s publicly available data export (June 2025).<br>Each observation represents a single-family home, condominium, or townhouse sold in Southern California.<br>Variables include sale price, living area, lot size, year built, bedrooms, bathrooms, ZIP code, and days on market.The dataset was used to train and validate an <b>XGBoost regression model</b> designed to estimate home prices using both structural and spatial–temporal features.<br>All personally identifiable or proprietary location data have been removed or aggregated at the ZIP-code level to maintain privacy while preserving statistical utility.This dataset and accompanying R scripts allow replication of the core results presented in the study, including model training, feature importance analysis, and predictive performance evaluation.