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Record W2153541381 · doi:10.1111/joes.12117

COMMERCIAL PROPERTY PRICE INDEXES AND THE SYSTEM OF NATIONAL ACCOUNTS

2015· article· en· W2153541381 on OpenAlex
W. Erwin Diewert, Kevin J. Fox, Chihiro Shimizu

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Economic Surveys · 2015
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing Market and Economics
Canadian institutionsUniversity of British Columbia
FundersAustralian Research CouncilSocial Sciences and Humanities Research Council of Canada
KeywordsPrice indexEconomicsDepreciation (economics)Property (philosophy)National accountsIndex (typography)EconometricsIndustrial production indexProductivityService (business)Production (economics)MicroeconomicsMacroeconomicsEconomyComputer science

Abstract

fetched live from OpenAlex

Abstract This paper studies the problems associated with the construction of price indexes for commercial properties that could be used in the System of National Accounts (SNA). Property price indexes are required for the stocks of commercial properties in the Balance Sheets of the country. Related service price indexes for the land and structure input components of a commercial property are required in the Production Accounts of the country if the Multifactor Productivity of the Commercial Property Industry is calculated as part of the SNA. The paper reviews existing methods for constructing an overall Commercial Property Price Index (CPPI) and concludes that most methods are biased (due to their neglect of depreciation) and more importantly, not able to provide separate land and structure subindexes. A class of hedonic regression models that is not subject to these problems is discussed.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.013
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.130
Threshold uncertainty score0.455

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.072
GPT teacher head0.241
Teacher spread0.168 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it