Intelligent Rental Valuation: A Full-Stack Approach to Multi-Modal Property Price Prediction Using Machine Learning and Deep Learning

Abstract

The rapid expansion of digital property rental platforms has intensified the challenge of determining fair and competitive rental prices in highly dynamic markets. Property rental prices are influenced by a combination of structural attributes, geographical location, rental duration, and latent market demand, making manual pricing strategies unreliable and inconsistent. This research presents an intelligent, full-stack rental valuation system that integrates Machine Learning (ML) and Deep Learning (DL) models into a real-world property rental platform to deliver accurate, transparent, and scalable price predictions.

Introduction

The rapid growth of digital rental platforms has increased the need for accurate and transparent rental price estimation, as pricing now depends on multiple factors such as property characteristics, location, rental duration, and market conditions. Traditional manual pricing methods often lead to overpricing or underpricing, reducing market efficiency and user trust. While machine learning (ML) and deep learning (DL) have shown promising results for rental price prediction, most existing studies focus only on offline model evaluation and rarely address real-world deployment, geospatial interaction, or scalability. This research proposes a full-stack rental price prediction platform that integrates predictive models into a live web application, supporting manual input, interactive map-based location selection, and batch CSV predictions.

The literature review highlights the evolution of rental price prediction from traditional hedonic regression models to advanced ML techniques such as Random Forest, Gradient Boosting, XGBoost, and Deep Neural Networks (DNNs). Ensemble learning methods consistently outperform linear models by capturing nonlinear relationships among property, location, and amenity features, while DNNs provide strong predictive capability for large datasets. Recent studies also emphasize the importance of geospatial information; however, most treat location as static numerical data and lack interactive mapping features. This research addresses these limitations by combining ensemble ML, deep learning, interactive geospatial inputs, and scalable deployment within a unified framework.

The proposed methodology models rental price estimation as a supervised regression problem and employs a complete data preprocessing pipeline, including data cleaning, feature encoding, scaling, and dataset splitting. The system provides three prediction modes: manual feature-based prediction using ensemble ML models, map-based prediction utilizing Google Maps and Haversine distance features, and CSV-based batch prediction for large-scale valuation. Models such as Linear Regression, Random Forest, XGBoost, and DNN are trained and optimized using cross-validation, dropout, early stopping, and grid search. The full-stack architecture consists of a React-based frontend, RESTful backend APIs, independent ML inference services using Flask/FastAPI, and secure data services with Google Maps integration. Experimental results demonstrate that XGBoost achieves the highest prediction accuracy, followed by Random Forest and DNN, while Linear Regression serves as an interpretable baseline. Feature importance analysis confirms that geospatial attributes and property type are the strongest predictors of rental price. Overall, the proposed system delivers an accurate, scalable, and practical solution for real-time rental price prediction, with future enhancements including image-based valuation, temporal forecasting, explainable AI, fairness-aware learning, and online model updates.

Conclusion

This research presented an intelligent, full-stack rental price prediction framework that integrates machine learning, deep learning, and geospatial intelligence within a real-world property rental platform. The proposed system supports three complementary prediction modes—manual feature-based input, interactive map-based location selection, and scalable CSV-driven batch processing—addressing both usability and deployment challenges often overlooked in existing studies. Experimental evaluation confirms that ensemble-based machine learning models, particularly XGBoost, achieve superior predictive accuracy and generalization across multiple rental durations. Deep neural networks further enhance performance in data-rich scenarios, especially when spatial features are incorporated, highlighting the critical role of location-aware modeling in rental valuation. Beyond predictive performance, the study demonstrates the feasibility of deploying ML and DL models as real-time services within a production-grade full-stack architecture. The modular design ensures scalability, low-latency inference, and seamless user interaction, effectively bridging the gap between academic research and practical implementation. Overall, this work contributes a deployable, multi-modal pricing intelligence framework that promotes transparent, data-driven rental valuation and supports fair pricing and informed decision-making in digital rental markets.

References

[1] Cao, S., & Huang, J. (2024). Research on Renting Price Prediction Based on Machine Learning. Proceedings of the Management Science Informatization and Economic Innovation Development Conference. [2] Lektorov, A., Abdelfattah, E., & Joshi, S. (2023). Airbnb Rental Price Prediction Using Machine Learning Models. IEEE Annual Computing and Communication Workshop and Conference. [3] Zhang, J. (2024). Predicting Rental Prices in India: A Machine Learning Approach with Hyperparameter Optimization. Advances in Economics, Management and Political Sciences, 137, 160–166. [4] Tan, H., Su, T., Wu, X., Cheng, P., & Zheng, T. (2024). A Sustainable Rental Price Prediction Model Based on Multimodal Input and Deep Learning. Sustainability, 16(15). [5] Jiang, L., Li, Y., Luo, N., Wang, J., & Ning, Q. (2023). A Multi-Source Information Learning Framework for Airbnb Price Prediction. arXiv preprint. [6] Yoshida, T., & Seya, H. (2021). Spatial Prediction of Apartment Rent Using Regression-Based and Machine Learning-Based Approaches. arXiv preprint. [7] Tang, S. (2025). Investigation of Rental Market Pricing Models Based on Machine Learning. Highlights in Business, Economics and Management. [8] Gupta, Y., Sharma, R., & Mehta, P. (2024). House Rent Prediction Using Machine Learning Techniques. International Journal for Research in Applied Science and Engineering Technology, 12(4), 55–63. [9] Kumar, N., Mehta, M., & Arora, A. (2023). Real Estate Price Prediction Using Machine Learning. International Journal for Research in Applied Science and Engineering Technology. [10] Çetin, A. I. (2024). Evaluating Machine Learning Models for House Price Prediction with Different Sampling Techniques. International Journal of Computational and Experimental Science and Engineering. [11] Li, H. (2023). House Price Prediction and Analysis Based on Random Forest and XGBoost Models. Highlights in Business, Economics and Management, 21. [12] Zhang, L. (2023). Housing Price Prediction Using Machine Learning Algorithms. Journal of World Economy. [13] Sharma, R., & Patel, K. (2023). House Price Prediction Using Machine Learning. International Journal for Research in Applied Science and Engineering Technology. [14] Li, C. (2024). House Price Prediction Using Machine Learning Techniques. Applied and Computational Engineering. [15] Wang, S., Li, J., & Xu, Y. (2019). Airbnb Price Prediction Using Machine Learning and Sentiment Analysis. arXiv preprint. [16] Chen, T., & Si, S. (2024). Predicting Rental Prices with Machine Learning and Large Language Models. arXiv preprint. [17] Singh, P., & Verma, A. (2024). House Rent Prediction in India: A Comparative Study of Machine Learning Algorithms. International Journal of Intelligent Systems and Applications in Engineering. [18] Rahman, M. (2022). House Price Prediction Using Random Forest Machine Learning Technique. Procedia Computer Science. [19] Zulkifley, N. H., Salleh, R., & Aziz, N. (2020). A Survey of Machine Learning Techniques for House Price Prediction. International Journal of Modern Education and Computer Science. [20] Chen, Y., Zhang, X., & Liu, H. (2022). Airbnb Rental Price Modeling Using Gradient Boosting Techniques. Machine Learning with Applications

Copyright

Copyright © 2026 Abhishek Singh, Alok Kumar Yadav, Ashutosh Yadav, Mr. Shashank Kumar Srivastava. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.