## 2 Related Work
### Justification for Model Selection and Performance Metric  
The use of 10 models appears to be a thoughtful choice to leverage the capabilities of various algorithms and to evaluate the effectiveness in forecasting rental prices. Following is the rationale for selecting these models:  
* LR is a widely used approach for analysing relationships between variables and the target in many regression contexts [31]. This statement suggests that alterations in the independent components result in a proportional modification in the dependent variable in a linear correlation [32]. This technique may be used for prediction and estimate assistance. The use of this algorithm in many studies highlights its significance as a core methodology in the field of housing price prediction research. The research of [14,15] employed LR as a benchmark to evaluate the effectiveness of various ML models in predicting housing prices. In addition, [PERSON] et al. & [PERSON] et al. [16,19] implemented LR as a constituent of an algorithm to approximate house values in the regional real estate industry, based on the assumption that there is a direct relationship between the predictor variables and the housing prices. These studies collectively illustrate the enduring significance of LR in the field of forecasting property value in the real estate sector. It provides a basic and easily understood mechanism, making this algorithm highly valuable for comparing baselines in the regression task. * RFR is an ML model that utilises many regression trees, each having many hierarchically organised criteria [33]. Applying ensemble learning with a single decision tree improves the predictive capability to anticipate the impact [34]. This approach demonstrates high efficacy in handling a substantial quantity of variables and offers several advantages, such as exceptional precision and efficiency, as well as efficiently addressing the issue of overfitting in high-dimensional data without requiring feature selection, regardless of whether the data are numerical or categorical [35]. Consequently, the utilisation of the RF algorithm in forecasting home values has emerged as a prevalent and significant subject in several research endeavours, all of which examine its effectiveness and adaptability. [PERSON] et al. [12] applied RF to assess the Chengdu housing market and obtained an R\({}^{2}\) score of 0.83, indicating that it performed on par with the top models chosen for their research. In addition, [PERSON] [15] successfully incorporated this algorithm in a comparison analysis employing the Boston housing dataset, resulting in an impressive R\({}^{2}\) score of 0.8329. This outcome demonstrated its exceptional capacity to appropriately evaluate pricing. In the research conducted by [14], RF was deployed for the purpose of prediction due to its ability to maintain the performance of decision trees while minimising the risk of overfitting. This strategic choice emphasises the equilibrium between complexity and abstraction in a model. By incorporating PSO feature selection approaches, [PERSON] et al. [16] successfully integrated RF and achieved notable improvements in accuracy. This may be attributed to its capability to efficiently manage many input variables. According to the study by [19], RF performed exceptionally well in predicting the values of residential properties in Bangalore when compared to other ML models and achieved a remarkable R\({}^{2}\) score of 0.8512, which was the second highest among all the models evaluated, highlighting the robust forecasting ability of RF. Additionally, [PERSON] et al. [18] used this algorithm as the main part of a stacking ensemble model, combining it with other methods to improve prediction results because it can handle data in many dimensions. All the results illustrated above show that RF has a diverse array of applications in predicting home values and can effectively contribute to both individual and integrated ensemble methods.