A Newly Proposed Hybrid Model for Predicting Real Estate Stock Prices in Kurdistan Region of Iraq

Background: Accurate prediction of real estate stock prices plays a significant role in enabling investment choices, econometric forecasting, and strategic investment decisions in emerging markets. The Kurdistan Region of Iraq is among the emerging markets that are marked by non-homogeneity of data, limited representativeness, and non-availability of standardized appraisal methods. Aims: This study aims to develop and test a stable hybrid machine learning model for predicting real estate stock prices in the Kurdistan region of Iraq. The new approach highlights methodological quality, representativeness of the dataset, and replicability in a bid to fill the gaps observed in the existing literature. Methodology: The research makes use of the Kurdistan House Price Prediction (KHPP) data, which has various property features. Preprocessing of the data involved leakage-secure feature encoding, missingness checks, and deduplication verification. A combined model of Random Forest (RF) and Extreme Gradient Boosting (XGB) was used, alongside nested cross-validation for hyperparameter selection and testing. Statistical significance testing, bootstrapped confidence intervals, and feature ablation experiments were also performed for redundancy. Results: The findings further indicate that the hybrid RF+XGB model outclassed individual models according to Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). While performance differences remained minute, the hybrid model always returned stable results per fold. Evaluation also observed challenges interpreting R2 and emphasized careful reporting for the raw target scale. The study also demonstrated an externally available benchmarking plan using the Ames dataset for cross-national comparability. Conclusion: The new hybrid approach provides a systematic, replicable, and efficient framework for real estate stock price prediction within the Kurdistan Region of Iraq. By adding robust validation protocols and statistical testing, this study enhances belief in hybrid ensemble methods for under-represented markets. Future research must move beyond single-modal data and local criteria for greater generalizability and real-world applications.