MétaCan
Menu
Back to cohort
Record W4223520315 · doi:10.1108/ijhma-02-2022-0020

Housing affordability in a resource rich economy: the case of Kuwait

2022· article· en· W4223520315 on OpenAlexaboutno aff
Abdullah Adel Alfalah, Simon Stevenson, Steffen Heinig, Éamonn D’Arcy

Bibliographic record

VenueInternational Journal of Housing Markets and Analysis · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing Market and Economics
Canadian institutionsnot available
Fundersnot available
KeywordsEconomicsIndex (typography)OriginalityPopulationBusinessValue (mathematics)Resource (disambiguation)Public economicsEconomic growthPolitical science

Abstract

fetched live from OpenAlex

Purpose This paper aims to improve the housing affordability by measuring the housing affordability in a resource-rich economy and studying the impact of implementing new policies. Design/methodology/approach This paper seeks to test the impact of new policies introduced to the Kuwaiti housing market to improve affordability. In 2008, the Kuwaiti parliament introduced two policies: a tax on empty lands and, forbidding companies to own or develop residential lands or houses. Findings By constructing the housing affordability index and the price-to-income multiplier using observations from 2004 until 2017, it has been found that affordability has worsened over time regardless of the new policies introduced in 2008. Housing in Kuwait became “severely unaffordable” (equivalent to London in the UK, San Diego in USA and Toronto in Canada). Originality/value Even with its unique condition, as a rich country, small population and availability of white land and other resources, the affordability worsened over time. Introducing new policies without solving the central issue of housing supply challenges seems not worth it. This paper is the first of its kind on the Kuwait housing market, and it provides a valuable foundation for future research on this market and similar markets in the region.

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)0.003
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 score0.699
Threshold uncertainty score0.440

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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 head0.016
GPT teacher head0.229
Teacher spread0.214 · 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
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Housing Markets and AnalysisSame topicHousing Market and EconomicsFrench-language works237,207