Data-Driven Investment Strategies in International Real Estate Markets: A Predictive Analytics Approach

Abstract

The study investigates the application of advanced predictive analytics in formulating investment strategies for the international real estate market. Utilizing extensive datasets, including real estate transaction records, economic indicators, and market reports, covering over ten years of data from 2010 to 2020 across multiple regions, we implemented predictive models such as linear regression, decision trees, random forests, support vector machines (SVM), neural networks, and gradient boosting machines (GBM). The results indicate that AI and machine learning models significantly outperform traditional statistical methods in forecasting market trends. Specifically, the neural network model achieved an R² of 0.822, while the random forest model attained an R² of 0.804, compared to an R² of 0.751 for the traditional linear regression model. Performance varied across regions and property types; for instance, the neural network model's MAE and RMSE in North America were 17,500 and 26,800, respectively, whereas in the Asia-Pacific region, the MAE and RMSE were 20,100 and 29,800. Additionally, these models resulted in an average reduction of 12.5% in operational costs and an 18.3% improvement in customer satisfaction. This study systematically integrates and compares multiple advanced predictive models, demonstrating that data-driven investment strategies offer significant competitive advantages in the real estate sector. These findings provide robust evidence supporting the use of predictive analytics to optimize investment decisions and highlight the transformative impact of these technologies on the real estate industry.