PROPERTY VALUE PREDICTION USING MACHINE LEARNING: A HISTOGRAM GRADIENT BOOSTING APPROACH FOR PROPERTY VALUATION IN CAMBODIA

Cambodia's real estate market has expanded rapidly in recent years, yet property pricing across the country remains largely informal. Valuations are commonly set through agent intuition, word-of-mouth, and social-media asking prices rather than through a consistent, evidence-based process. This informal approach produces inconsistent and sometimes inflated prices, widens the information gap between buyers and sellers, and slows mortgage and collateral assessments for banks and lenders. This paper presents a machine-learning-based Automated Valuation Model (AVM) for Cambodian residential property, developed as a final Machine Learning course project at Norton University. A dataset of 1,001 property listings, collected across Cambodian cities and districts, was cleaned and transformed through currency and numeric cleaning, text-based feature extraction, geographical parsing, categorical classification, and dimensionality reduction. After filtering price and land-size outliers and engineering a district-level price indicator, a final modelling dataset of 880 records and seven predictor features — bedrooms, bathrooms, land size, a unified building-area measure, district price level, city, and property type — was used to train a Histogram Gradient Boosting Regressor (HGBR). Numeric features were median-imputed and robust-scaled, categorical features were one-hot encoded, and the target price was log-transformed to reduce the influence of high-value luxury listings. The model was trained on 85% of the data and evaluated on a 15% held-out test set. On the test set, the model achieved an R² of 0.9373, a Mean Absolute Error of $34,617.38, a Mean Absolute Percentage Error of 6.01%, and produced predictions within ±20% of the true price for 96.97% of test listings. These results indicate that HGBR can capture the non-linear, location-dependent relationships that shape Cambodian property prices, with accuracy comparable to benchmarks reported for XGBoost and Random Forest models in other property markets. The resulting model offers a practical reference tool for buyers, sellers, agents, investors, and lenders, while its reliance on asking-price listings, a single-