Predictive models in real estate research often remain more theoretical than practically applicable. Beyond the limitation of restricted access to high-quality data, most studies are further constrained by short and methodologically weak prediction horizons. A common practice is to train and test models on unseen data originating from the same time period, an approach that does not reflect real predictive scenarios involving structural spatial change and future market conditions. Moreover, spatial features are often treated as auxiliary rather than as primary predictors, which is particularly problematic for spatio-temporal domains such as real estate. As a result, many reported errors reflect test performance rather than genuine predictive accuracy. In this study, we compare four spatially focused modeling approaches—XGBoost, MLP, EnsRF, and EnsMLP—to assess their ability to predict transaction prices per square meter for newly built residential apartments in truly unseen future periods and to expose the systematic bias introduced when test errors are interpreted as predictive performance. Using verified transaction data on newly built residential apartments in Vienna and a rolling, timeexplicit evaluation scheme, we demonstrate that models grounded primarily in spatial characteristics achieve robust long-term performance, with the hybrid EnsMLP architecture showing the highest temporal stability over prediction horizons of up to nine years.