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Record W4313592508 · doi:10.1080/10511482.2022.2157219

Yes or Not in My Backyard (YIMBY vs. NIMBY)? The Impact of New Social Housing Construction on Single-Family House Prices in Quebec City (Canada)

2023· article· en· W4313592508 on OpenAlexafffundabout
Jean Dubé, François Des Rosiers, Nicolas Devaux

Bibliographic record

VenueHousing Policy Debate · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing Market and Economics
Canadian institutionsUniversité du Québec à RimouskiUniversité Laval
FundersVille de Québec
KeywordsNIMBYPopulationPublic housingArgument (complex analysis)EconomicsBusinessEconomic growthSociologyEngineeringDemography

Abstract

fetched live from OpenAlex

The development of new social housing faces important resistance by local population, a phenomenon knows as the “not in my backyard” movement. One argument from residents to oppose such project is the idea that new construction will negatively impact property values. This is what this paper aims to investigate. The analysis is based on a complete recension of the new social housing projects built between 2000 and 2020 and on single-family house transactions that occurred between 2004 and 2020 in Quebec City (Canada). A repeated sales model integrating a difference-in-differences estimator is developed to isolate the net price premium related to the emergence of a new social housing building while accounting for the possible heterogeneity impact related to characteristics of the building, including the number of apartments and the type of clientele hosted as well as the local characteristics, such as the spatial concentration of social housing buildings and distance to the city center. The results show a complex net price premium rent function that leads to mixed conclusions and has important implications for the development of new social housing projects.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.216
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.074
GPT teacher head0.276
Teacher spread0.201 · 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.

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

Citations9
Published2023
Admission routes3
Has abstractyes

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