MétaCan
Menu
Back to cohort

A Conceptual Framework for Commercial Property Price Indexes

2015· preprint· en· W1529432891 on OpenAlexaff
Erwin Diewert, Chihiro Shimizu

Bibliographic record

VenueJournal of Statistical Science and Application · 2015
Typepreprint
Languageen
FieldSocial Sciences
TopicEconomic and Technological Developments in Russia
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsDepreciation (economics)CapitalizationNational accountsEconomicsProperty (philosophy)Price indexBalance sheetConsumption of fixed capitalEconometricsFinancial economicsMicroeconomicsMacroeconomicsFinance

Abstract

fetched live from OpenAlex

The paper studies the problems associated with the construction of price indexes for commercial properties that could be used in the System of National Accounts. Property price indexes are required for the stocks of commercial properties in the Balance Sheets of the country and related price indexes for the land and structure components of a commercial property are required in the Income Accounts of the country if the Multifactor Productivity of the Commercial Property Industry is calculated as part of the System of National accounts. The paper suggests a variant of the capitalization of the Net Operating Income approach to the construction of property price indexes and uses the one hoss shay or light bulb model of depreciation as a model of depreciation for the structure component of a commercial property.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.027
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.016
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.027
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0090.013
Science and technology studies0.0030.010
Scholarly communication0.0160.025
Open science0.0040.004
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0140.003

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 head0.066
GPT teacher head0.377
Teacher spread0.311 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations11
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Statistical Science and ApplicationSame topicEconomic and Technological Developments in RussiaFrench-language works237,207