An Extended Fama-French Multi-Factor Model in Direct Real Estate Investing
Bibliographic record
Abstract
Understanding risk-adjusted returns in real estate investment are crucial, but little is known about the risk-adjusted returns for direct real estate. This paper examines risk-adjusted total returns by developing an extended capital asset pricing model (CAPM) to investigate whether direct real estate returns compensate for their risk levels. Based on a panel dataset of the residential property transaction in 62 Territorial Authorities of New Zealand from 2002Q1 to 2018Q4, a direct real estate portfolio performance in the single-factor CAPM model is compared with the national housing markets stock markets and REITs markets in New Zealand before the pandemic. The results demonstrate that the direct real estate returns outperform the market returns with a significant positive alpha and beta smaller than one but positive. The alpha is further evaluated by the five-factor CAPM model, which includes the factors of liquidity risk, value risk, time risk, credit-rating risk, and currency risk. The assessment shows that most of the excess return (alpha) can be attributed to direct real estate market risks.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".