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This paper synthesises the global hedonic housing literature from 1970 to 2026 with a reproducible pipeline: \nRecordsIdentifiedRaw{}
records identified from Semantic Scholar, Crossref, arXiv and OpenAlex, \nRecordsAfterDedup{} unique records screened by rules
and by independent agents following a PRISMA-style protocol, \nIncludedHedonicStageOne{} hedonic studies included at stage one,
\nFulltextRetrieved{} open-access full texts retrieved, and \nStudiesExtracted{} eligible studies from \nCountriesExtracted{}
countries extracted into \nCoefExtracted{} coefficient rows with a validated double-extraction sample. Coefficients are
harmonised into percentage premiums, semi-elasticities per common unit and elasticities, and pooled with random-effects,
three-level and cluster-robust models, with publication-selection diagnostics, Bayesian and machine-learning cross-checks. The
structural core of the hedonic model is global: the elasticity of price to living area is \estLivingAreaElasticity{}, a
bathroom adds \estBathroomsSemiElasticity{} percent, a year of age subtracts \estAgeSemiElasticity{} percent and a kilometre from
the centre \estCbdDistanceSemiElasticity{} percent, with similar means across continents and decades. Signature amenities carry
premiums between four and eleven percent and a waterfront location \estWaterfrontPctPremium{} percent. Every implicit price is
heterogeneous beyond sampling error, prediction intervals span zero for most characteristics, and observable study features,
including spatial estimators, explain little of the heterogeneity. Funnel asymmetry is widespread but not interpretable as
selection alone. Machine-learning models predict better than hedonic benchmarks in most of the forty retrievable comparisons,
while AI-derived text and image features add modest accuracy and some interpretable characteristics. The synthesis extends
Sirmans et al.\ (2005, 2006) in coverage, harmonisation, inference and scope, and is bounded by the open-access retrieval rate
that it documents.%
}