Hybrid Unsupervised-Supervised Learning for Housing Submarket Segmentation and Price Prediction in Surabaya Urban Areas

Abstract

Surabaya’s rapid population growth, reaching 3.02 million residents, has intensified housing affordability challenges and increased structural variability in residential markets. This study proposes a hybrid machine learning framework that combines unsupervised clustering with supervised classification to identify submarket segments and predict housing price categories. A dataset of 490 properties containing structural, land, ownership, and contextual features was preprocessed and analyzed using K-Means. Cluster quality assessment through elbow inspection and a silhouette score of 0.45 indicated the presence of five meaningful market segments. These segments served as targets for a supervised classification stage that evaluated seven models, optimized via randomized hyperparameter search within a standardized preprocessing pipeline.

The RBF-SVM achieved the strongest performance, reaching 97 percent accuracy and a macro-F1 score of 0.97, representing an 8 percent improvement over non-hybrid baselines and outperforming boosted ensembles such as XGBoost. Permutation importance analysis identified number of floors, building orientation, position rank, and ownership status as dominant drivers of segment differentiation. The integration of clustering and classification enhances predictive reliability while improving interpretability, offering a transparent analytical toolkit for housing market assessment.

The proposed framework provides actionable insights for developers, appraisers, and policymakers in Surabaya, enabling data-driven identification of submarkets and supporting more equitable housing strategies aligned with SDG 11 on sustainable urban development. The approach is scalable to other Indonesian cities and establishes a foundation for future work incorporating spatial, socioeconomic, or temporal predictors.