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Record W2553302343 · doi:10.5539/ibr.v9n12p103

A Study on Housing Price in Klang Valley, Malaysia

2016· article· en· W2553302343 on OpenAlexvenueno aff
Paul Anthony Mariadas, Mahiswaran Selvanathan, Tan Kok Hong

Bibliographic record

VenueInternational Business Research · 2016
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing Market and Economics
Canadian institutionsnot available
Fundersnot available
KeywordsSpeculationInflation (cosmology)Price indexOrder (exchange)Index (typography)Government (linguistics)PopulationInflation rateEconomicsHouse priceSample (material)BusinessAgricultural economicsFinanceMonetary economicsInterest rateEconometricsDemography

Abstract

fetched live from OpenAlex

The main aim of this study is to measure the factors influencing housing price in Klang Valley, Malaysia. This paper examines empirically whether the increasing trend in the Malaysian housing price is associated to changes in the population, construction cost, housing speculation, and inflation rate. The paper is exploratory in nature. The data is collected via questionnaire survey form distributed to youngest respondents in the sample area which is Klang Valley region. Each single elements are calculated its average index respect to few level of influence under respondents opinion. The index will then treated as influencing level of the factors. The paper delivers empirical outcomes that the population, construction cost and housing speculation are the main factors of housing prices. However, fluctuations in housing prices could not necessarily be influenced by the inflation rate. The overall result of this paper strongly recommends that housing price in the Malaysian residential property market is increasing continuously. Therefore, efforts to control the hike in housing price is needed by government and policy controllers in order to maintain the affordable to own a house in Malaysia.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.024
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.124
GPT teacher head0.341
Teacher spread0.217 · 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 source (direct Gemma or distilled Codex), 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

Citations23
Published2016
Admission routes1
Has abstractyes

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