Detailed Analysis of Texasäó»s Manufactured Housing Growth and Competition
Bibliographic record
Abstract
Manufactured homes provide a cost-effective alternative for satisfying the growing housing needs. Despite the industry commitment for improvement, manufactured home constitute small share of satisfying the housing demand. Supporting the future growth of this industry through public policy advocacy cannot be achieved without evaluating its historical production and demand trends. Available data from professional and governmental sources lack the ability to provide a granular picture of the characteristics of the industryäó»s manufacturers, customers, and product. Accordingly, this paper attempts to fill this gap by analyzing the available license record data for the manufactured homes in the state of Texas that cover the years from 1982 to 2015. The raw data included around 913,663 records of homes ownership and manufacturing. The data analysis included three main tasks: 1) data processing to integrate this large amount of data and eliminate outliers; 2) analyzing the competition characteristics of Texasäó»s manufactured housing market using descriptive entry and exit metrics; and 3) analyzing demand characteristics of manufactured homes in terms of their physical requirements the relations between their demand volume and inventory times. The conclusions of this paper would provide a more detailed understanding of the manufactured housing industry to support its growth as a viable cost-effective housing option.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".