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Record W3123006905 · doi:10.1111/caje.12236

Sunk costs and the measurement of commercial property depreciation

2016· article· en· W3123006905 on OpenAlexafffundvenue
W. Erwin Diewert, Kevin J. Fox

Bibliographic record

VenueCanadian Journal of Economics/Revue canadienne d économique · 2016
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
KeywordsDepreciation (economics)EconomicsProperty (philosophy)ProductivityProperty taxReal propertyInflation (cosmology)Real estateNational accountsAccounting methodWelfareMicroeconomicsMacroeconomicsFinanceRevenueCapital formationAccounting

Abstract

fetched live from OpenAlex

Abstract Developments in property markets greatly influence economic growth, monetary policy, productivity measurement, inflation measurement and hence welfare payments to the disadvantaged. Property price bubbles often lead to financial crises; those experienced during the 20th century were often triggered by commercial property price movements. Yet property poses significant challenges for national accountants in producing key economic variables used in informing policy assessment and formulation. To address these challenges, this paper formalizes a framework for measuring prices and quantities of capital inputs for a commercial property. In particular, it addresses problems associated with obtaining separate estimates for the land and structure components of a property, a decomposition of property value that is important for the national accounts, productivity measurement and taxation. A key contribution is to address the problem of estimating structure depreciation taking into account the fixity of the structure. We find that structure depreciation is determined primarily by the cash flows that the property generates rather than physical deterioration of the building. Finally, we provide a framework for the determination of the optimal length of life for a structure.

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 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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.814
Threshold uncertainty score0.915

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
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 head0.139
GPT teacher head0.167
Teacher spread0.028 · 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 teacher head, not a consensus.

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

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

Citations13
Published2016
Admission routes3
Has abstractyes

Explore more

Same venueCanadian Journal of Economics/Revue canadienne d économiqueSame topicHousing Market and EconomicsFrench-language works237,207