Affordable Housing Performance Indicators for Landed Houses in the Central Region of Malaysia
Bibliographic record
Abstract
Recently, governments strive to make housing affordable for residents. Affordable housing is not restricted only to the house prices, but it includes also the quality and amenities of the house. So, the main aim of this research is to develop affordable housing performance indicators (AHPI) for landed houses. It's based mainly on Mulliner and Malienes criteria for affordable housing and the concept of grow home for Friedman and Cammalleri. Taman Selasih (TS) and Taman Lukut Makmur (TLM) in Negeri Sembilan were chosen as a case study. They were constructed by Syarikat Perumahan Negara Berhad (SPNB) in the central region of Malaysia. The sample consists of 155 units in TS and 93 units in TLM. A physical survey was conducted to assess the housing affordability for TS and TLM by field observation and informal interviews with the residents. The collected data were analyzed via SPSS software. The result shows that fourteen criteria can be applied as AHPI for landed houses, namely; houses prices in relation to income, safety- incidence of crime, access to employment, access to public transport facilities, access to good quality schools, access to shopping facilities, access to health care, access to child care, access to leisure facilities, access to open green public space, quality of housing, energy efficiency, land properties and new spaces. The value of this research comes from proposing a set of criteria that could be used as affordable housing performance indicators (AHPI) to assess the performance of landed houses.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".