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This paper asks whether new forecasting architectures improve residential price forecasts over strong conventional methods, and under which conditions. Using the audited Quebec land-registry sample of UQO Working Paper 22 (\NSales{} sales, 2021--2026) and its real-time quality-adjusted index vintages, we define three tasks in a strict pseudo-real-time design: the price of a property sold one to twelve months after a forecast origin (Task~A), the one- to twelve-month change of municipal, regional and provincial quality-adjusted indices (Task~B), and calibrated predictive distributions for both (Task~C). We implement five candidate architectures (a hierarchical spatiotemporal hedonic network, a dynamic spatial graph forecasting network, a regime-adaptive mixture of experts, a Bayesian hierarchical probabilistic forecaster and a quality-adjusted neural state-space model) and an integrated stacked architecture, against \NBenchB{} benchmarks ranging from no-change and momentum rules to gradient boosting, recurrent, convolutional and attention networks. Evaluation uses Diebold--Mariano and Giacomini--White tests, model confidence sets and block bootstraps, with Monte Carlo experiments on synthetic markets whose latent dynamics range from unforecastable random walks to regime switching and regional spillovers. For the price of a future sale, gradient boosting on hedonic and real-time local market features is the most accurate model at every horizon and the hierarchical network does not improve on it; for index changes, one- to six-month movements are forecastable through the cell's own momentum and seasonality (MASE 0.75 at three months for a pooled tree model), spatial, regime and hierarchical components add nothing that survives ablation, and at twelve months nothing beats an autoregression; the state-space forecaster is the only model with calibrated intervals at every horizon. Every component of the architectures exists in the literature; the contribution is the measured answer, on transaction-level data, to the question of when complexity pays.%
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