Abstract

In this paper, we propose a machine learning-based model to predict the prices of tokenized real-estate assets, combining blockchain data, real-estate data, and sentiment data. Tokenization refers to partitioning a physical asset into fractional tokens on a blockchain, where each token represents a fraction of that asset. These include token liquidity, trading volume, platform activity, and the on-chain activity of investors. These new variables require data-based analytics that are capable of accounting for more complex relationships than customary real estate valuation approaches. To address this, we propose a multimodal dataset that incorporates on-chain data (token supply, number of wallet-holders, transaction frequency), property fundamentals (property value, property value-yield, property rent, property size), and sentiment indicators including social media analytics. We trained and evaluated four supervised machine learning models including a Neural Network, a Linear Support Vector Machine (LSVM), an XGBoost and a Linear Regression model to determine the best-performing model for predicting short-term token price movements. The Neural Network model led to the best result. The R2 equals 0.9859; the correlation coefficients for train and test datasets equal 0.982/0.982; the relative minimum error equals 0.035. The LSVM and the XGBoost based models result in similar values in terms of RMSE (0.9719 and 0.9727, consecutively). The Regression model returns the lowest value, with R2 equal to 0.628 and an error of 0.930. The findings depict the importance of using non-linear learning architectures to capture the high dimensional interdependencies between blockchain activity, property fundamentals, and investor sentiment. The study provides an understanding into the drivers of value for tokenized real estate. Feature analysis revealed that real estate value, annual yield of the asset, and wallet holders were the key predictors, with sentiment and trading volume being a secondary determinant of behavior in the tokenized real estate market. The main contribution of the research is the strong, repeatable and interpretable model that informs decision makers from the perspective of the investor, developer and regulator. This work will ultimately contribute to the transparency and efficiency of this emerging ecosystem of tokenized real estate with a thorough and extensible foundation of heterogeneous data modalities and ML-based algorithms that support scalable hybrid financial and blockchain analysis and processing of this novel real estate market segment.

Publication Date

12-2025

Document Type

Thesis

Student Type

Graduate

Degree Name

Professional Studies (MS)

Department, Program, or Center

Graduate Programs & Research

Advisor

Sanjay Modak

Advisor/Committee Member

Hammou Messatfa

Campus

RIT Dubai

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