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Record W4224054969 · doi:10.21837/pm.v20i20.1083

THE IMPACT OF MONETARY POLICY ON HOUSING AFFORDABILITY IN MALAYSIA

2022· article· en· W4224054969 on OpenAlexaboutno aff
Zarul Azhar Nasir, Rosylin Mohd Yusof, Ahmad Rizal Mazlan

Bibliographic record

VenuePLANNING MALAYSIA · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing Market and Economics
Canadian institutionsnot available
Fundersnot available
KeywordsInterest rateQuarter (Canadian coin)EconomicsCointegrationMonetary policyMoney supplyDistributed lagShort runMonetary economicsMacroeconomicsEconometrics

Abstract

fetched live from OpenAlex

Housing affordability is a global concern, especially among researchers and policymakers around the world in both developed and developing countries. In Malaysia, it has been a decade since the median multiple house price reaching more than a tripled median household income threshold in term of housing affordability. This indicates that housing in Malaysia is seriously unaffordable. In general, this study was conducted to examine the impact of monetary policy on housing affordability in Malaysia. This study focuses on investigating both short and long-run relationships between money supply and interest rate on housing affordability. To achieve this goal, Autoregressive Distributed Lag (ARDL) estimation techniques were employed on a quarterly data from the first quarter of 2008 until the first quarter of 2021. The findings showed the existence of long-run cointegration between all indicators except for the interest rate. In addition, money supply, interest rate, and employment were found to be significant in the short run. In the matter of policy implication, it is best for policymakers to focus on regulating money supply rather than controlling interest rate in promoting housing affordability.

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 score0.162
Threshold uncertainty score0.686

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 head0.027
GPT teacher head0.255
Teacher spread0.228 · 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

Citations7
Published2022
Admission routes1
Has abstractyes

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