## 2 Related Work
### Justification for Model Selection and Performance Metric
This highlights its enduring importance in different housing markets and research approaches; therefore, RF was chosen as one of the models in this study. * GBR: this is an ensemble approach that constructs models sequentially to rectify the mistakes made by the previous models. It is frequently successful for sophisticated datasets with complex structures, especially this dataset. * XGBoost, known as Extreme Gradient Boosting, improves its predictive accuracy by using several tree models [36]. This strategy mitigates overfitting problems by incorporating a regularisation term into the model framework, which penalises complicated models and minimises the sensitivity of predictions to individual data points [37]. The tree-based architecture of XGBoost demonstrates its advantage in terms of robustness against outliers and prevention of overfitting [38]. Thus, XGBoost is an exceptionally efficient method for resolving regression tasks due to its incorporation of regular terms and second-order derivatives, resulting in the enhanced learning and processing speed of the algorithm [39; 40]. The exceptional efficacy and versatility of this model have garnered notoriety in the current body of work on house price prediction employing ML techniques. The research of [12] demonstrated the effectiveness of XGBoost in analysing the Chengdu housing market, suggesting that this approach has high accuracy and a lower MSE with broader applications. In addition, XGBoost was employed by [15] as one of the models in a comparative comparison of ML methods, applying the Boston housing dataset. Despite achieving the highest R\({}^{2}\) value of 0.8776, the XGBoost algorithm proved its competitiveness by achieving comparable performance to the ANN, with an R\({}^{2}\) score of 0.8495, resulting in a difference of 0.0281 compared to the performance of the ANN with its R\({}^{2}\) score of 0.8776. XGBoost was deployed by [19] in practical use among the seven selected models. The result showed it has the most effectiveness in achieving accuracy rates of 0.9110 and 0.6465 on the flat and independent housing costs estimation. Additionally, [PERSON] et al. [17] employed a novel technique by integrating XGBoost with PSO to significantly improve the accuracy of the model on the Ames housing dataset. The PSO-XGBoost hybrid method outperformed other individual models and demonstrated the benefits of integrating XGBoost with optimisation techniques to capture nonlinear data patterns, leading to improved predictive reliability and precision at 0.9887. In a similar vein, [PERSON] et al. [18] disrupted the traditional method of using a single algorithm by inventing a novel ensemble learning methodology called D-Stacking, which is based on a diversity approach. The method involved adding XGBoost to a complicated prediction framework. When validated, the results of the model's performance in the Chinese and American housing datasets were better than standalone models. The collaborative efforts demonstrate the robust and durable performance of XGBoost in the domain of an AI-based approach for property price prediction, illustrating its potential as an independent solution when combined with other methods such as feature selection optimisations, hybrid models, and ensemble techniques. These integrations establish a solid foundation for the reference of methodology design in the experiment section, strengthening the significance of applying XGBoost in advancing the analysis of the real estate market. * An FNN, also referred to as a multilayer perceptron, is the most fundamental type of DL network, typically comprised of a layer for input, multiple hidden units, and an output layer [41]. Every neuron in the input layer obtains information, while the weighted total of data without bias is activated in the hidden layer; the preceding and following layers are connected to each other with the bias-weighted sum of the outcomes [42; 43].