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Record W2955066563 · doi:10.1108/ijhma-01-2019-0006

Banks’ lending to public and private sectors and house prices: does bank ownership matter?

2019· article· en· W2955066563 on OpenAlexaboutno aff
Hassan F. Gholipour, Elias Oikarinen, Reza Tajaddini

Bibliographic record

VenueInternational Journal of Housing Markets and Analysis · 2019
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicBanking stability, regulation, efficiency
Canadian institutionsnot available
Fundersnot available
KeywordsPrivate sectorLoanHouse priceQuarter (Canadian coin)Shock (circulatory)BusinessPublic sectorFinancial systemGovernment (linguistics)Value (mathematics)Monetary economicsEconomicsVector autoregressionOriginalityFinanceEconomyEconomic growth

Abstract

fetched live from OpenAlex

Purpose The purpose of this study is to examine the interaction between banks’ lending to public and private sectors and house prices using data from the Iranian banking system including, commercial government-owned banks (CGBs), specialized government-owned banks and private banks. Design/methodology/approach The authors use quarterly data from the second quarter of 2004 to the first quarter of 2016 and apply structural vector autoregression models. Findings The results show that: a positive shock to the loan supply to the private sector triggers a positive response from house prices; a positive shock to the loan supply to the public sector does not trigger a positive response from house prices; house price appreciations contribute significantly to banks’ lending to the public sector but not lending to the private sector; each loan supply by three different types of banks influences house prices positively; and CGBs’ lending to the private sector does not respond to house price shocks. Originality/value Although the relationship between banks’ lending and house prices is well-established in the literature, existing studies have not yet examined whether bank ownership matters for the link between banks’ lending and house prices.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.460

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.016
GPT teacher head0.230
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

Citations0
Published2019
Admission routes1
Has abstractyes

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