Permits, Starts, and Completions: Structural Relationships Versus Real Options
Bibliographic record
Abstract
Real estate development from raw land to completed structures is a multistage process. Given the current view of development as the exercise of a real option, the question arises whether development should be modeled as a compound option. This paper tests the validity of the compound option characterization by determining whether builders start units for which they have permits and then complete units started consistent with the predictions of the real options model. To do so, I first identify a reduced form relationship between permits and starts and then between starts and completions. The parameters of this relationship indicate how well permits proxy for starts and starts for completions. Then, I determine whether controlling for this structural relationship, new information, and uncertainty in returns affect permit exercise and completion rates, as in the exercise of real options. I find that current and previous quarter permits forecast current single‐family starts, while multifamily starts require more quarterly lags of permits. More than one and two year’s worth of lagged starts numbers are needed to estimate current quarter completions for single‐ and multifamilys buildings, respectively. The principal result is that once building permits have been obtained, the development process proceeds to completion. While there is no evidence that completion is the exercise of an option embedded in a start, some aspects of permits are consistent with builders treating them as an option for starts. However, even if they do, given permits obtained, it takes large changes in market conditions to affect small changes in starts.
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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 | 0.003 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".