A Random Forest Meta-Learning Approach for Optimal AI Algorithm Selection in Real Estate Market Prediction

The increasing complexities of real estate market forecasting, in combination with the accelerated evolution of machine learning (ML) algorithms, necessitates the optimisation of algorithm selection to reduce computational demands and enhance model accuracy. While numerous studies have examined the performance of individual algorithms, a significant research gap remains concerning the impact of dataset characteristics on algorithmic performance within this specific domain. The present study aims to address this research gap by undertaking a systematic meta-learning analysis of 54 real estate forecasting studies conducted between 2001 and 2024. The study explores the relationship between dataset characteristics and algorithm performance, focusing on factors such as dataset size, dimensionality, and variable categories. Two models, a decision tree and a random forest model, were utilised to assess the impact of these characteristics on the accuracy of various algorithm categories, including artificial neural networks (ANNs), ensemble methods, and support vector machines (SVMs).The study's findings suggest that the random forest algorithm, when applied to dataset characteristics, serves as a reliable tool for predicting the best-performing algorithm for a given real estate market forecasting dataset. The model attained an average area under the curve (AUC) of 0.98 and an overall accuracy of 88%, underscoring the practical relevance of meta-learning approaches in econometrics and highlighting the potential for further enhancing algorithm selection methodologies in this research domain.This research contributes to the expanding field of automated machine meta-learning by providing a framework for more efficient and accurate real estate market forecasting.