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Record W2339261194 · doi:10.34989/sdp-2016-3

New Housing Registrations as a Leading Indicator of the BC Economy

2021· preprint· en· W2339261194 on OpenAlexaff
Calista Cheung, Dmitry Granovsky

Bibliographic record

VenueEconstor (Econstor) · 2021
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Theory and Policy
Canadian institutionsBank of Canada
Fundersnot available
KeywordsPolitical scienceHumanitiesArt

Abstract

fetched live from OpenAlex

Housing starts and building permits data are commonly used as leading indicators of economic activity. In British Columbia, all new homes must be registered with the Homeowner Protection Office, a branch of BC Housing, before the issuance of building permits and the start of construction. Data on new housing registrations (NHR) could thus potentially be used as an even earlier leading indicator of economic activity. This study assesses whether NHR data have significant predictive power for economic activity in British Columbia. The authors find that quarterly increases in new registrations for single detached homes have statistically significant predictive content for growth in real GDP over the next one to three quarters, and provide stronger signals compared to housing starts and building permits over this forecast horizon. These signals remain significant for growth in real GDP over the next two quarters even in the presence of other leading indicators in the equations. However, forecasts using quarterly NHR data with other leading indicators are not able to outperform simple benchmark forecasts in an out-of-sample forecasting exercise. Nonetheless, adding the NHR variable to an AR(1) equation does produce forecasts that are superior to a simple AR(1) and that at one quarter ahead also outperform an AR(1) augmented with building permits.

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)0.005
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 score0.212
Threshold uncertainty score0.427

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.030
GPT teacher head0.245
Teacher spread0.215 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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