Questions About the Equivalence of Market Rents and User Costs for Owner Occupied Housing
Bibliographic record
Abstract
For modern nations, it is recognized as important that temporal and spatial variations in costs for comparable dwelling services are carefully measured. The costs of owner occupied as well as rental housing have important roles to play in both the consumer price index (CPI) and the System of National Accounts (SNA). The 1993 System of National Accounts (SNA93) specifies that a rental value of the housing stock should be included as part of the aggregates for personal consumption, personal income, income of proprietors and value added for the real estate industry. Yet, little attention has been devoted to an underlying commonality of practice: the implicit assumption that housing cost information for either renters or owner occupiers can be used for assessing movements over time and spatial differences in the cost of housing for both renters and owners, after allowing for differences regarding payment for taxes and certain ongoing expenses such as insurance and utilities. But in the real world, are the services that renters and owner occupiers get from their dwellings comparable? Also, for both renters and owner occupiers, are there place-related differences in the services they derive from their dwellings? And if so, what are the implications for official statistics making? These are the questions raised by the empirical results presented in this paper.
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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 | 0.007 | 0.094 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.010 |
| Scholarly communication | 0.005 | 0.016 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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".