AI-Based LSTM-X Model for Risk Assessment in Fractional Commercial Real Estate Investments

Abstract

  This paper introduces a novel approach powered by artificial intelligence for risk evaluation in fractional commercial real estate transactions. Though it involves special risks owing to market volatility, property performance uncertainty, and economic considerations, fractional investing has become well-known as it permits smaller capital involvement in high-value assets. We present a Long Short-Term Memory (LSTM-X) model integrating external factors to efficiently analyze market dynamics and forecast dangers. Comparing our method to conventional statistical and artificial intelligence models shows better accuracy in predicting changes in property value, rental revenue variations, and related investment hazards. Python in Google Colab with the Redfin dataset was used in experiments to find improved risk prediction accuracy. The results support the increasing uses of artificial intelligence in real estate by offering a strong instrument for fractional investors to control risks and make decisions.