## 2 Background  
Studies investigating house price prediction and accuracy applying various approaches have progressed apace since the 1990s, which [PERSON] and [PERSON] [47] in a systematic review of house price modelling, classified into three branches: AI-based models, Geographic Information Systems (GIS) based models and mix-based models. The application of ML within pricing and mass appraisal studies generally witnessed seminal investigations examining Artificial Neural Networks (ANNs), with much debate and mixed findings, particularly when considering their suggested adoption into mass appraisal. The study conducted by [PERSON] et al. [19] showed the efficacy of ANNs, however highlighted that they lacked transparency, explainability, and repeatability of results. More recent research also exhibited inconclusive findings with some revealing that ANNs performed better showing increased accuracy and lower predictive error, however, various research has exhibited poorer performance relative to geostatistical methods and in relation to transparency [18; 24; 25]. Other forms of ML such as Tree classification, Boosted Regression Trees (BRT), and Random Forest (RF) methods have also been propagated within house price studies, and in the main have demonstrated model superiority and the reduction of prediction error when compared to MRAs and other approaches [25; 48]. Some however, such as [PERSON] et al. [49] who comparing various regression and AI-based methods, revealed that regression-based methods were superior with homogeneous datasets, with AI approaches superior with less homogeneous data. Appositely, a number of studies have been somewhat critical of ML techniques, indicating that despite their (varied) superiority in prediction accuracy, they are sensitive to the parameters applied which are not consistent, reliant on data quality and richness, and can suffer from repeatability and model stability [50; 51; 52; 53]. In contrast, penalized regression has become a more accepted approach for price estimation especially in the context of mass appraisal, as these types of models do not lack the transparency of ML techniques, and unlike some geostatistical methods such as GWR do not suffer from 'overfitting' due to their shrinkage based approach. The literature within this area is also emerging as to the efficacy of these regression techniques with studies [54; 55; 56] showing reliable estimates and comparable prediction accuracy to other ML approaches. In a similar vein, ESF has also emerged as a reliable and effective approach for mitigating SA due to its ability to integrate into traditional regression-based techniques to produce'mix-based' geostatistical approaches that are considered transparent and understandable [6; 57; 58]. As acknowledged by [PERSON] and [PERSON] [47], an increasing number of studies are beginning to combine various algorithms with geostatistical (spatial) models to better estimate the real estate value. Studies [37; 40; 42; 43] have all successfully adopted geostatistical approaches within ML architecture to provide improved prediction accuracy and spatial dependence relative to the existing ML specifications. Equally, studies have shown the ESF approach to perform comparably with regression and other geostatistical approaches, but also to comprise some advantages in terms of its ability to identify localized spatial patterns, spatial dependency, residual autocorrelation, and less prone to multicollinearity issues [6; 59]. Further, studies have integrated the ESF approach within multilevel modelling and the ML Random Forest and Penalized regression such as Least Absolute Shrinkage and Selection Operator (LASSO) approaches in order to capture spatial heterogeneity and unexplained spatial dependency [5; 17; 41; 60; 61; 62] with the findings showing the augmentation of ESF into ML to improve model performance. The existing literature indicates that there are both advantages and disadvantages to the varying classical regression models and geostatistical and ML techniques.