A Quantile Decision-Support Model for Predicting Court Judgment Amounts in Multi-Family Housing Defect Litigation

This study aims to develop a pre-litigation decision-support model that estimates the expected court judgment amount in multi-family housing defect litigation using only information available before a suit is filed. To this end, 106 final-instance South Korean court judgments (2014–2025) were compiled and decomposed into 33,997 item-level records, on which a three-layer hybrid model was built. The first layer is an ordinary least squares (OLS) regression of the area-normalized award on three pre-filing variables—elapsed months, log exclusive area, and log claimed amount (R2 = 0.626). The second layer is a multinomial logistic classifier predicting the liability limitation band, with a ±1-band accuracy of 94.4%. The third layer is a probabilistic quantile layer that combines the two to output five quantiles. Under leakage-free five-fold cross-validation, the model achieved a Q50 mean absolute percentage error (MAPE) of 28.4% with well-calibrated interval coverage (50% interval—49.1%; 80% interval—79.2%). A preliminary external validation on 22 independent cases gave a MAPE of 21.4% (bootstrap 95% CI [15.0%, 28.6%]). The interquartile band (Q25–Q75) provides both parties an objective non-litigation settlement range, offering a shared basis to move from adversarial litigation toward earlier, information-based settlement.