Journal of Real Estate Finance and Economics, 3:11%140 (1990) 9 1990 Kluwer Academic Publishers The Value of Risk in Real Estate Markets
Bibliographic record
Abstract
In this article we test he urban asset pricing model of Capozza nd Sick (1988) and focus on the empirical dimen-sions of the effects of risk on urban land prices. The effects of systematic and unsystematic risk are distinguished in the model which incorporates the value of the option to convert land to urban uses into the pricing of urban real estate. We find the value of systematic r sk in our Canadian urban areas to he negative and highly statistically significant. We find that approximately 2.5 percent of the value of houses in our sample arises from systematic risk. In our sample, unsystematic r sk is a larger proportion of total risk than systematic risk. Therefore, most of the effect of total risk may be ascribed to unsystematic risk. The effect of total risk on land prices is illustrated through the irreversibility premia estimates. These premia vary greatly in size and statistical significance. Thus, the effect of unsystematic risk is highly city specific. In the two regions where the irreversibility premia re statistically significant, it accounts for 22 percent and 53 percent of the average housing price; thus, unsystematic risk can be a very important determinant ofhousing prices. These results highlight he importance of risk in determining urban land prices. The value of the option to convert land to urban uses imparts considerable value to developed land and must be considered when evaluating interurban area price differences.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.090 | 0.019 |
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".