1\section{Historical development of the literature}2\label{sec:history}34Hedonic regressions of housing prices predate \citet{rosen1974hedonic}: the method was used for price indices in the 1960s and5applied to housing by \citet{ridker1967determinants} for air pollution and by \citet{kain1970measuring} for the components of6housing quality. The 1970s and 1980s established the canonical specification, the problems of functional form7\citep{halvorsen1981choice,cropper1988choice} and the interpretation of dummy-variable coefficients in semi-logarithmic8equations \citep{halvorsen1980interpretation,kennedy1981estimation}, and the first reviews \citep{follain1985estimating,9freeman1979hedonic}. The 1990s brought spatial econometrics into the field \citep{can1992specification,dubin1992spatial,10basu1998analysis} and the recognition that the marketing process, the data source (transactions, listings, appraisals, rents) and11the submarket structure all affect estimated implicit prices. \citet{malpezzi2003hedonic} wrote the standard applied review of12this period.1314The Sirmans programme belongs to the mid-2000s. \citet{sirmans2005composition} tabulated the characteristics used and the signs15and significance obtained in a large set of hedonic studies, and \citet{sirmans2006value} ran meta-regressions of the raw16coefficients of nine structural characteristics on location, time, data type and specification, finding that implicit prices17vary by location, that the data type matters for some characteristics, and that there was no clear change over time. The18programme was extended to time-on-market coefficients \citep{sirmans2010meta} and recently updated descriptively19\citep{khoshnoud2023composition}. Section~\ref{sec:questions} describes its limitations precisely. In parallel, attribute-specific20meta-analyses multiplied: transit proximity \citep{debrezion2007impact,mohammad2013meta,higgins2016forty}, airport and traffic21noise \citep{nelson2004meta}, open space and trees \citep{mcconnell2005value,waltert2010landscape,crompton2019impact}, water22quality and proximity \citep{guignet2022property,nicholls2018impact}, flood risk \citep{beltran2018is}, contamination and23hazards \citep{simons2006effect,braden2011meta}, energy efficiency \citep{cespedes2019meta}, school quality24\citep{nguyenhoang2011capitalization}, wind turbines \citep{parsons2022property} and crime \citep{macchiaroli2025crime}, each25pooling tens of studies with inverse-variance weights and, increasingly, publication-selection diagnostics26\citep{stanley2012meta}. The 2010s and 2020s added three developments that this synthesis must accommodate: quasi-experimental27designs for environmental and locational attributes; the systematic use of listing data from online platforms, with the28attendant shift from transaction to asking prices and from single-family houses to apartments in non-US markets; and machine29learning, first as a predictive competitor to hedonic regression \citep{wang2019machine,geerts2023machine} and then as a source of30new characteristics extracted from text and images.31