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Record W4413398958 · doi:10.1177/00420980251359191

Housing affordability and rent control: The case of elderly renters

2025· article· en· W4413398958 on OpenAlexaff
Xun Bian, Ruoyu Chen, Hanchen Jiang

Bibliographic record

VenueUrban Studies · 2025
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing Market and Economics
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsControl (management)Rental housingAffordable housingBusinessEconomicsLabour economicsEconomic growthPublic economicsRentingPolitical science

Abstract

fetched live from OpenAlex

Elderly renters face mounting housing affordability challenges due to rising rents. While many see rent control as a way to assist families in need of affordable housing, evidence of its effectiveness is sparse. We fill this gap by examining the effects of New York City’s rent stabilization policy on elderly renters. We find that elderly renters experience greater rent burdens than younger renters and, on average, benefit more from rent control. Specifically, elderly renters are more likely to live in rent-stabilized units and to receive greater rent discounts. However, this “elderly advantage” is mostly explained by elderly renters’ greater housing stability and disappears (in some cases, even reverses) after tenancy duration is accounted for. Our findings suggest that while New York City’s rent stabilization offers more support to older renters overall, it also, by favoring longer tenancy, leads to uneven allocation of policy subsidies among elderly renters. The “elderly advantage” accrues mostly to stable long-term renters, but less so to unstable ones, who are more often racial minorities, immigrants, or those with low levels of education and income.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.523
Threshold uncertainty score0.341

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0000.000
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 head0.032
GPT teacher head0.247
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 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

Citations2
Published2025
Admission routes1
Has abstractyes

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