## 2 Related Work  
### Existing Techniques for Predicting the Housing Market  
Multiple AI-based methods have been implemented in the real estate industry to predict housing market trends in prior studies. The paper by [10] investigated the capabilities of neural networks in the domain of house price prediction, focusing on back propagation neural networks (BPNNs) and convolutional neural networks (CNNs) to identify complex relationships between property characteristics and sale prices. The authors examined these models over three different time periods (three, five, and six months) within the Taiwanese housing market, incorporating macroeconomic factors such as the housing investment demand-to-supply ratio, the housing price-to-income ratio, and two types of housing: land and buildings, with or without park access. The CNN model demonstrated superior performance compared to the BPNN algorithm over the five-month span, achieving a remarkable R\({}^{2}\) value above 0.945. Additionally, it yielded the lowest values for mean absolute error (MAE), RMSE, mean absolute percentage error (MAPE), and root mean squared logarithmic error (RMSLE). Nevertheless, macroeconomic factors had minimal impact on housing price evaluations. This study highlights the importance of using time-series data for real estate price estimation with CNNs. Although CNNs are typically associated with image processing tasks, the results showcase their versatility and effectiveness in property price prediction. This underscores the potential for novel applications of DL techniques in predictive work, and suggests the integration of advanced methods, such as PNNs, which could further improve prediction accuracy in the real estate market. Subsequently, [PERSON] et al. [11] employed various ML techniques, including BPNNs, classification and regression tree (CART), general regression neural networks (GRNNs), and least squares support vector regression (LSSVR), to predict housing prices in Taichung, Taiwan. This prediction was conducted without considering time series or macroeconomic data. Instead, the focus was on comparing real transaction prices with estimated ones to aid stakeholders, such as property owners and agents, in making informed judgments on the property valuations. The empirical data clearly indicated that LSSVR achieved higher predictive accuracy than the other methods, obtaining the lowest MAPE of 0.228. These results presented the efficacy of ML models, indicating that the regression model was more effective in forecasting actual and estimated prices, outperforming neural networks in this regard. The authors also recommend incorporating additional components, such as photographs of residences and economic data, as inputs for model training to improve performance. This research serves as an important reference for constructing a methodological framework by applying LR to estimate rental market trends, considering the relationship between housing features and rental prices in the real estate market. Further insights were provided by [12], who utilised RF, extreme gradient boost (XGBoost), and LightGBM algorithms to forecast house values based on market preferences in the Chengdu market, China. The authors incorporated 12 significant property characteristics, including dwelling space, location, rent collection method, and transportation, to underscore the significance of comprehend local market preferences and behaviours. XGBoost, demonstrated remarkable performance, with an impressive accuracy score of 0.85 and a low mean square error (MSE) of 0.04, highlighting its potential for future investigation in similar scenarios. These chosen variables significantly influence rental cost in Chengdu, underscoring the advantages of utilising XGBoost for housing predictions and the need to select relevant features when evaluating ML models. This study provides a solid foundation for choosing suitable algorithms and testing models with appropriate feature sets.