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Record W4381280241 · doi:10.1177/23998083231180610

Estimating household demand for transit-oriented development: A two-stage hedonic analysis in Kitchener-Waterloo, Canada

2023· article· en· W4381280241 on OpenAlexafffundabout
Yu Huang, Dawn C. Parker, Paul M. Anglin

Bibliographic record

VenueEnvironment and Planning B Urban Analytics and City Science · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing Market and Economics
Canadian institutionsUniversity of GuelphUniversity of Waterloo
FundersSocial Sciences and Humanities Research Council of CanadaChina Scholarship Council
KeywordsTransit-oriented developmentDemographicsSubdivisionInvestment (military)Smart growthTransit (satellite)BusinessAffordable housingPublic transportEconomicsUrban planningTransport engineeringEconomic growthGeographyEngineeringCivil engineeringPolitical science

Abstract

fetched live from OpenAlex

Interest in mass transit investment and transit-oriented development (TOD) is growing as a way to promote smart growth. These investments and policy changes may imply new housing demands, which are not well understood. Using Kitchener-Waterloo, Canada, as a case study, we address the following questions: (1) Do households in this mid-sized region show preferences for TOD neighborhoods? How do preferences for transit accessibility vary across space? (2) What household characteristics are associated with the demand for housing and neighborhood characteristics? With a combined dataset of household survey and housing transactions, we present a novel application of the two-stage hedonic model to understand the housing demand structure impacted by transit policies. This study provides evidence of demand for TOD and LRT accessibility by households with a range of socio-demographics. We thus recommend the region build complete TODs to satisfy a variety of housing needs.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.644
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.035
GPT teacher head0.209
Teacher spread0.174 · 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 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
Published2023
Admission routes3
Has abstractyes

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