Bibliographic record
Abstract
This thesis examines the potential benefits of diversification in real estate. By calculating a set of returns for apartment blocks in Vancouver, British Columbia, two issues of diversification are dealt with: the potential of diversifying within real estate, and the benefits of including real estate in mixed-asset portfolios. To examine the potential of diversifying within real estate, the study looks at the relative proportions of systematic and unsystematic risk of real estate. Also, the paper investigates the rate at which variations of returns for randomly selected portfolios are reduced as a function of the number of properties in a portfolio. To investigate the benefits of including real estate in mixed-asset portfolios, two types of efficient portfolios are constructed: one that hedges against inflation, and the other that is mean-variance efficient. By selecting these two types of efficient portfolios, the paper considers two major investment objectives of investors: (1) that their portfolio provides a return to combat inflation; (2) that their portfolio have minimum risk for a given expected rate of return. The findings of the study show that portfolios consisting solely of real estate(of one property type in one local market) are not well diversified. The investigation found that only 29 percent of total risk is unsystematic(diversifiable). However, a large portion of the unsystematic risk can be diversified away by holding a portfolio which contains only a few properties. The findings also illustrate that real estate is a useful addition in mixed-asset portfolios. Real estate contributes to the effectiveness of both the inflation-hedged portfolio and the mean-variance efficient portfolio. In the inflation-hedged portfolio, real estate does not contribute as strongly as expected, but the results still demonstrate that real estate should be included in portfolios that are designed to hedge inflation. In the mean-variance efficient portfolio, real estate is found to have a low or negative correlation with other assets, making the potential to diversify very high.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".