MétaCan
Menu
Back to cohort
Record W3124498132

Developing Land and Structure Price Indexes for Ottawa Condominium Apartments

2016· preprint· en· W3124498132 on OpenAlexaboutno aff
W. Erwin Diewert, Ning Huang, Kate Burnett-Isaacs

Bibliographic record

VenueRePEc: Research Papers in Economics · 2016
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing Market and Economics
Canadian institutionsnot available
Fundersnot available
KeywordsApartmentDepreciation (economics)Real estateLand priceStock (firearms)BusinessUnit (ring theory)Price indexSpace (punctuation)Agricultural economicsFinanceEconomicsGeographyCivil engineeringEconometricsEngineeringMathematicsMicroeconomicsComputer scienceProfit (economics)
DOInot available

Abstract

fetched live from OpenAlex

Measuring the service flow and the stock value of condominium apartments in Canada and decomposing these values into constant quality price and quantity components is important for many purposes. In addition, the System of National Accounts requires that these service flows and stock values for condos be decomposed into constant quality land and structure components. In Canada and most other countries, such a land and structure decomposition of condominium apartment sale prices does not currently exist. In this paper, we provide such a decomposition of condominium apartment sales in Ottawa for the period 1996-2009. Specific attention is paid to the roles of communal land and structure space, as well as building commercial space, on condominium apartment unit selling prices. Key findings include methods to allocate land and building space to a single condominium unit, identifying the characteristics that best explain condominium prices, and developing an average depreciation rate for condos for the 14 year time period.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.070
Threshold uncertainty score0.140

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0060.010
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.045
GPT teacher head0.296
Teacher spread0.250 · 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 designObservational
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

Citations5
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueRePEc: Research Papers in EconomicsSame topicHousing Market and EconomicsFrench-language works237,207