Investing in Residential Real Estate: Understanding Homebuilder Exchange-Traded Fund Performance
Bibliographic record
Abstract
Homebuilder ETFs provide investors with a diversified portfolio of residential construction and sales companies which reduces risks associated with individual stock selection in the sector. This study examines the net monthly returns of homebuilder exchange-traded funds (ETFs) through various performance evaluation models and market situations. The results reveal that these ETFs outperformed benchmark indices in absolute returns. Despite homebuilding being part of the real estate sector, the correlation between monthly returns of homebuilder ETFs and the Dow Jones US Real Estate Index, though positive, is not very high. The performance of ETFs varied across market conditions, demonstrating both outperformance and underperformance compared to U.S. stocks. During the COVID-19 pandemic, homebuilder ETFs displayed a decline, trailing behind U.S. equities in both absolute returns and risk-adjusted performance. This result emphasizes their vulnerability during economic crises. Utilizing a modified version of the Carhart factor model, significant exposure of real estate ETFs to the stock market was observed. Moreover, an assessment of ETF portfolio managers’ skills indicated proficiency in security selection but limited capabilities in market timing. Homebuilder ETFs pose higher downside risks than other indices, evident in their elevated Value at Risk (VaR) and Conditional Value at Risk (CVaR) values.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".