Regional model of multi family housing starts : an inventory adjustment approach
Bibliographic record
Abstract
The decline in multi family housing starts in Urban Canada during the 1970's and the attendant low vacancy rates has generated comment on the efficiency of housing markets and the possibity of a housing crisis. Traditionally, existing vacant units are cited as a buffer between the demand and supply. This paper proposes that the inventory of new units offers a more direct buffer. Therefore the use of an inventory adjustment model to estimate and predict multi family housing starts is suggested. The theoretical model suggests that there is some desired inventory level which builders or investors will try to maintain depending on expected profitability, the relative yield on other investments and the cost of maintaining vacant units. Demand for new rental units will depend upon the deomographic structure of the population, unemployment, income, and the relationship between rents ownership costs and the cost of other goods and services. Starts will be set to satisfy the expected demand and adjust actual inventory levels to the desired inventory level. Quarterly data for the Vancouver CMA, from 1963 to 1982 are used to test this model. The empirical analysis consists of three parts: a structural- model based on the inventory adjustment process, a time series model to provide a measure for evaluating the success of the structural model, and an analysis of the forecasting capabilities of these two models. Data from 1963 to 1980 are used to estimate the models and 1981 to 1982 data are maintained to test the forecasts. The results suffer from inadequacies of the available data, immeasurable impacts of government policy and programs and possible idiosyncracies of the Vancouver housing market. In general, the results indicate that inventories do provide a buffer between the demand and supply of multi family housing units. As such, the vacancy rate of existing units, in isolation, is an insufficient indicator of market activity and response. Similar analysis for other urban centres in Canada is needed to determine the uniqueness of these results. Also, it may be possible to develop an improved forecasting model by incorporating the time series coefficients with the structural model.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.005 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".