DAREA: A Dynamic AI-Driven Architecture for Real Estate Asset Tokenization on Blockchain
DOI:
https://doi.org/10.66238/fsrma104Keywords:
Real-world asset (RWA) tokenization, Real estate valuation, Blockchain, Transformer, XGBoost, Smart contracts, Oracle, Digital assetsAbstract
The tokenization of real-world real estate assets (RWA) has emerged as a promising approach to improving market liquidity, transparency, and fractional ownership accessibility. However, existing tokenization frameworks lack dynamic and trustworthy asset valuation mechanisms capable of adapting to real-time market changes, leading to pricing distortions and limited utility of RWA tokens. To address these gaps, we propose DAREA (Dynamic AI-driven Real Estate Asset Tokenization Architecture), a hybrid AI-blockchain framework that integrates transformer-based temporal value prediction, XGBoost structural valuation, and an on-chain smart contract system enabling autonomous token generation, net-asset-value (NAV) updates, and rebase-based price stabilization. DAREA employs a dual-channel valuation model combining static property attributes with multi-frequency temporal market features, while a hybrid push-pull Oracle mechanism ensures secure and continuous on-chain price feeding. Moreover, DAREA incorporates a blockchain-to-AI feedback loop in which token trading dynamics refine the valuation model over time. Experiments on multi-city real-estate transaction datasets show that DAREA achieves substantially higher valuation precision and on-chain pricing stability compared with state-of-the-art baselines. The model reduces RMSE by over 12% relative to the strongest competitor and lowers token price volatility by nearly 29% under simulated market dynamics. These results confirm DAREA’s effectiveness in generating accurate valuations and maintaining stable tokenized representations of real-estate assets on blockchain networks.
References
[1] Wouda H P, Opdenakker R. Blockchain technology in commercial real estate transactions[J]. Journal of property investment & Finance, 2019, 37(6): 570-579.
[2] Zhang R, Xue R, Liu L. Security and privacy on blockchain[J]. ACM Computing Surveys (CSUR), 2019, 52(3): 1-34.
[3] Adhami S, Giudici G, Martinazzi S. Why do businesses go crypto? An empirical analysis of initial coin offerings[J]. Journal of economics and business, 2018, 100: 64-75.
[4] Chohan U W. Non-fungible tokens: Blockchains, scarcity, and value[M]//Non-fungible tokens. Routledge, 2024: 1-11.
[5] Lee, Luke. "Examining the legal status of digital assets as property: a comparative analysis of jurisdictional approaches." arXiv preprint arXiv:2406.15391 (2024).
[6] Hoxha, Visar. "Exploring the predictive power of ANN and traditional regression models in real estate pricing: evidence from Prishtina." Journal of Property Investment & Finance 42.2 (2024): 134-150.
[7] Obagbuwa, Ibidun Christiana, and Samantha Danster. "Housing Price Prediction Using Machine Learning Techniques." 2024 International Conference on Science, Engineering and Business for Driving Sustainable Development Goals (SEB4SDG). IEEE, 2024.
[8] Yang, Zongyan, et al. "Graph convolutional network-based model for megacity real estate valuation." IEEE Access 10 (2022): 104811-104828.
[9] Nahar S, Mondal G, Ahmad M A, et al. Forecasting Real Estate Prices and Rents Using LSTM Algorithms[J]. Swami Vivekananda University Press, 2026: 94-112.
[10] Salah K, Rehman M H U, Nizamuddin N, et al. Blockchain for AI: Review and open research challenges[J]. IEEE access, 2019, 7: 10127-10149.
[11] Santosh, Kathari, et al. "Smart Contract Optimization in Business Workflows Using Deep Reinforcement Learning." 2026 World Conference on Computational Science and Technology (WcCST). IEEE, 2026.
[12] Mougayar, William. The business blockchain: promise, practice, and application of the next Internet technology. John Wiley & Sons, 2016.
[13] Wang, Tangtang, Kaijie Zhang, and Kuangcong Liu. "A Knowledge Graph and Deep Learning-Based Semantic Recommendation Database System for Advertisement Retrieval and Personalization." arXiv preprint arXiv:2601.00833 (2025).
[14] Su, Boyi, et al. "Study on Real Estate Investment Risk Assessment and Decision Support System Driven by Fintech." Proceedings of the 2nd International Symposium on Integrated Circuit Design and Integrated Systems. 2025.
[15] Hu, Zheqi, Mengdie Hu, and Zhenghang Li. "DynSupplyNet: A Dynamic Graph Neural Network with Temporal Fusion for Supply Chain Risk Prediction and Propagation Analysis." Journal of Computer Science and Frontier Technologies 3.2 (2026): 1-13.
[16] Wang, Tangtang, and Zishan Bai. "TailRisk-Trans: A Transformer-Based Dynamic Tail-Risk Prediction Model with Extreme-Event–Aware Attention for Financial Markets." Frontiers in Business and Finance 3.1 (2026): 172-182.
[17] Zhong, Ziwen, and Yuheng Huang. "CRE-CNN-LSTM-XAI: An Explainable Deep Learning Framework for Risk Assessment of Real Estate Collateralized Financial Products." Journal of Computer Science and Frontier Technologies 3.1 (2026): 127-140.
[18] Wei, Haoyan, Yuqing Wu, and Mengtong Li. "RAGN-IIoT: A Retrieval-Augmented NL2SQL Framework with Dynamic Sensor-Selection Guardrails for Industrial IoT Time-Series Data Warehouses." Journal of Computer, Signal, and System Research 2.7 (2025): 78-88.
[19] Yao, Yilin, et al. "Deep Learning Models for Multi-class Pneumonia Detection in Chest X-rays: A Comparative Study of VGG16, MobileNet, and ResNet152." Computer Simulation in Application 3.1 (2025): 66-73.
[20] Huang, Yanxiang, et al. "Enhancing Open-Vocabulary Panoptic Segmentation with Semantic-Guided Q-Tuning." 2025 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2025.
Toor, M. J., Shabbir, S., Rauf, A., Nisar, A., Safdar, S., & Zaib-Un-Nisa. (n.d.). The role of structured-communication-technique in developing pragmatic language capability among ASD-children. https://doi.org/10.63878/cjssr.v3i2.785
Toor, M. J., Mehmood, H., Yasmeen, A., Honey, Q., Waseem, M., & Wasif, M. (n.d.). Exploring the effect of verbal encouraging phrases (VEP) on Chinese language learners’ academic achievement at university level.
Toor, M. J., Arafat, M., Ayoub, M., Ali, M., & Alessi Longa, F. E. (n.d.). From coherent self to fragmented persona: Digital identity fragmentation and academic self-concept among Generation Z, with comparative insights from Generation Alpha. https://doi.org/10.63878/cjssr.v4i2.2318
Katsitadze, N., Kharadze, N., & Pirtskhalaishvili, D. (2025). The impact of social media on students’ effective time management. Baltic Journal of Economic Studies, 11(1), 200–210. https://doi.org/10.30525/2256-0742/2025-11-1-200-210
Toor, M. J., Zafar, S., Omer, F., Sarwar, M., & Hussain, A. K. (n.d.). Teachers’ perspectives on effectiveness of translanguaging in enhancing English language learning at secondary school level.
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