Paper Title
A Machine Learning Framework for Predicting Liquidity Flows in Real Estate and TDR Markets Using Tokenized Property Contracts

Abstract
Predicting liquidity flows in tokenized real estate and Transferable Development Rights (TDR) markets is challenging due to their inherent volatility and complexity. While tokenization improves liquidity and transparency, it also complicates forecasting liquidity gaps and price fluctuations. Traditional financial models often fail to account for the unique dynamics of tokenized real estate, where factors like population density, urban development, and infrastructure quality are significant. This paper proposes a machine learning framework to forecast liquidity trends and pricing volatility in these markets. The framework combines regression models, neural networks, and time-series analysis to develop predictive algorithms that analyze large datasets from digital property exchanges, including market data (e.g., transaction volumes, token prices) and external factors like population growth and infrastructure development. The study utilizes regression analysis, LSTM, and GRU models to analyze liquidity flows, while ARIMA and Prophet models capture temporal dependencies. By integrating urban development indices, the model offers a more comprehensive market view for improved predictions. This scalable framework is adaptable to various urban environments, assisting developers, investors, and regulators in making data-driven decisions. Ultimately, the study aims to enhance market efficiency and stability by improving liquidity forecasting in tokenized real estate markets, advancing machine learning's role in optimizing real estate performance. Keywords - Machine Learning, Liquidity Flows, Tokenization, Real Estate, TDR Markets, Neural Networks, Time-Series Analysis.