Precision in Real Estate: Hybrid Multi-Stage Predictions for House Prices

Abstract

In the dynamic world of real estate, proper house price forecasting is extremely important for buyers, sellers, and policymakers. This work presents a hybrid multi-stage machine learning system that integrates diverse data modalities—numerical, textual, and visual—to achieve high-precision property price forecasting. The model is organized in three stages. Firstly, a Neighbourhood Scoring Module exploits geo-spatial and socio-economic features extracted from images with CLIP (Contrastive Language–Image Pre-training), giving contextual knowledge of the surroundings of the property. Secondly, the House Attribute Module exploits structured information—e.g., area, number of rooms, and amenities—coupled with text features such as property descriptions represented using SBERT (Sentence-BERT). The third and last stage, the Fusion Module, integrates predictions from the earlier stages through late fusion methods and XGBoost to provide the final estimate of the house price. The datasets are preprocessed meticulously to eliminate duplicates, manage missing values, and preserve image-text alignment. For training, the system is trained independently on every modality and then combines the predictions for accuracy improvement. Metrics such as MAE, RMSE, R²-score, and classification metrics (in the case of price range prediction) show that the modular strategy outperforms single-modality models quite dramatically. This paper demonstrates the power of integrating deep learning with structured modeling to address challenging real-world problems. The proposed solution is encoded in a modular, scalable, and reproducible manner with Python, rendering it deployable in online real estate websites. Finally, the suggested approach advances the state of accuracy in real estate valuation through multi-modal data fusion.