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Record W2037800201 · doi:10.5539/mas.v6n3p58

The Impact of Urban Rail Transit on Surrounding Residential Prices--Line 1 of Chengdu Metro as an Example

2012· article· en· W2037800201 on OpenAlexvenueno aff
Xin Wei, Wei Kang Zhang, Wang Cheng, Guang Jun Xu

Bibliographic record

VenueModern Applied Science · 2012
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing Market and Economics
Canadian institutionsnot available
Fundersnot available
KeywordsUrban rail transitDowntownHedonic pricingAgricultural economicsTransit (satellite)Line (geometry)Value (mathematics)Residential propertyTransport engineeringGeographyBusinessEconomicsMathematicsEconomic geographyEconometricsPublic transportStatisticsEngineeringArchaeology

Abstract

fetched live from OpenAlex

This article aims to build a hedonic price model of the research region by making a summary of relevant research in this field, and taking the running Line 1 of Chengdu Metro as an example. To be specific, the research discusses the influence of Line 1 of Chengdu Metro on surrounding residential prices taking samples of the estates within 2 km of Line 1 within a spatiotemporal perspective. The results shows that: after the operation of Line 1 (from July in 2010 to June in 2011), the growth rate of the residential prices has been increased by 5.89%, 9.44%, 12.45% and 11.03% from 1st ring to the third ring respectively, reaching at an average rising rate of 9.51%. Besides, the residential prices far away from the downtown area were more likely to be found ahead than those around the city center and represented a far more sensitivity to the metro operation. By quantitative calculating, it finds that: regions that are closer to subway stations per meter from 1st ring to the third, the residential prices increased 0.91 Yuan/m2, 1.16 Yuan/m2 and 1.21 Yuan/m2, respectively. At the same time, Line 1 of Chengdu Metro has been increased a total number of 7.814 billion Yuan up to now of surrounding residential value.

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)0.004
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.484
Threshold uncertainty score0.518

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.065
GPT teacher head0.270
Teacher spread0.204 · 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

Citations4
Published2012
Admission routes1
Has abstractyes

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