## 1 Introduction  
There has been increasing emphasis placed on the accuracy of house price and mass appraisal estimation and its role in informing urban, housing and taxation policy. In light of this importance, the accuracy, stability and defenceability of house price models has been a key cornerstone for improving mass appraisal valuation models [1, 2]. Typically, the hedonic model has been applied within real estate economics to estimate house prices, which observes house prices to be a function of the property characteristics [3]. However, it is well known that house prices tend to be spatially dependent due to similar physical characteristics shared by neighboring houses and commonalities attributable to their neighbourhood environment such as access to public facilities and socioeconomic status [4; 5]. This spatial autocorrelation (SA), or heterogeneity, has been shown to violate the independent observations assumption required for standard hedonic modelling due to error-bias when not recognizing the spatial heterogeneity of pricing effects [5; 6]. This results in the inflation of Type \(I\) errors which can overestimate the degrees of freedom, reduce confidence intervals and produce bias and inconsistent parameter estimates, leading to inappropriate conclusions [7; 8; 9]. This heightened awareness has generated considerable attention for accounting for spatial non-stationarity within house price studies and for mass appraisal estimation. Indeed, insights into the 'confounding effects of space' has been well documented and accounted for in more recent statistical specifications [8; 10; 11; 12], largely due to advances in spatial information, data sources and both parametric and non-parametric statistical methods. These well-known (geo)statistical methodologies incorporating spatial structural instability into hedonic price modelling have evolved over time which all make use of the spatial characteristics of variables to improve results through reduced error terms and spatial independence [1; 13]. Despite these appealing methodological improvements, there remains criticisms as to the stability and superiority of some of these methods, all of which have been subject to scrutiny [6; 14; 15; 16; 17]. In a similar vein, there has been a research into machine learning (ML) which has gained traction within mass appraisal studies, with a suite of ML algorithms honed and developed since the early 1990s, particularly with respect to its role in Automated Valuation Modelling (AVM) for mass appraisal systems [18; 19]. However, these early forms of ML generated some debate with respect to their predictive capacity [20; 21; 22] and wider adoption as a consequence of their initial \"black box\" data-driven nature [10; 23] culminating in reduced transparency and opaqueness, both of which are fundamental for defensibility and explainability, particularly within mass appraisal [24; 25]. More recent ML approaches have become more prominent due to the increasing availability of open source software packages, codes, digitization and the ability to unearth new pattern recognition which have shown better out-of-sample predictions and valuation accuracy [26; 27; 28; 29; 30; 31; 32; 33]. Equally, the \"black box\" aspect of ML has become less opaque with the augmentation and visibility of (normalized) importance weightings which provide a basis for understanding value significant effects [5]. Yet despite these sizeable improvements, applications of ML have seldom been adopted for mass appraisal modelling or considered SA within their frameworks due to their complexity and unsuitability for public scrutiny [34; 35; 36; 37]. Notably, for mass appraisal accuracy and the alleviation of horizontal and vertical inequity, as [PERSON] et al. [38] contend, if the presence of SA that is not appropriately accounted for or detected, this will affect the training set which will inherently impact upon the test (out-of-sample) data robustness, reliability and prediction accuracy.