Is Renting Better? A Review of the Home Ownership Decision amid Increasing Risk
Bibliographic record
Abstract
There has always been an avid debate on the merits of owning versus renting a residence. There is a commonly accepted sentiment that owning a home is a wise investment. However, this sentiment is often unproven or supported with non-substantial evidence. The scholarly literature on the buy versus rent decision has conflicting results. Further, recent events in the US residential real estate market suggest increased asset riskiness which may have a dramatic effect on home ownership. Our research uses a capital budgeting model, similar to the lease versus buy analysis, with the output being the present value of buying instead of renting. The present value model includes the difference in cash flows between buying and renting for two standardized holding periods. A key contribution of the paper is a more accurate estimate of required return on equity, the discount rate in our present value model. As real estate values have recently demonstrated greater risk and the capital structure of homeowners may be highly leveraged, the cost of equity is higher than often suggested. The benchmark model uses point estimates for each variable with subsequent models including scenario analysis for key variables. The results suggest buying is better, in the benchmark model as well as scenarios allowing rents, home appreciation, mortgage rates and required return to vary. However, most scenarios show negative present values are possible, which contrasts the historic view that home ownership always has a positive return.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".