Using machine learning to investigate the role of real estate in a mixed-asset portfolio

Investing in real estate offers significant benefits, such as diversification and potential long-term appreciation, making it an attractive option compared to stocks and bonds. However, direct investments in real estate often require substantial capital, which is a barrier for many individual investors. To overcome this, investors often use Real Estate Investment Trusts (REITs), which allow for indirect investment in real estate through shares in companies that own income-generating properties. This study examines the added value of including real estate in a diversified investment portfolio, utilising innovative methods to optimise asset allocation. Instead of relying on historical data, it employs machine learning algorithms (such as Linear Regression, Support Vector Regression, k-Nearest Neighbours, Extreme Gradient Boosting, and LSTM Neural Networks) to predict future asset prices. The study also incorporates Technical Analysis Indicators (TAIs) to further improve predictive accuracy. Furthermore, a Genetic Algorithm (GA) is used to determine optimal portfolio weightings, considering the expected returns and risks of each asset class. The study compares the performance of portfolios constructed using price predictions with those based on historical data, assessing diversification benefits and risk-adjusted returns. Overall, by integrating machine learning techniques, technical analysis, and optimisation algorithms, the study aims to demonstrate the potential advantages of including real estate investments in a diversified portfolio, enabling investors to make more informed decisions and improve their investment outcomes.