When Is Depreciation Meaningful in Valuation? Changing Valuation Weights for U.S. REITs and Non-REITs
Bibliographic record
Abstract
This article regresses the market value of equity on pre-depreciation income and on depreciation expense for capital-intensive firms, referring to the coefficients from our model as valuation weights. The valuation weight on depreciation expense versus the weight on pre-depreciation income are compared, to detect depreciation biases, over time and across sectors. Our model shows that the valuation weights on depreciation expense change over time, if the persistence of the cash flow components of net income varies over time and if the accrual for depreciation is inflexible (e.g., straight-line depreciation). For Real Estate Investment Trusts (REITs), we find the valuation weight on pre-depreciation income increases with industry upturns, while the valuation weight on depreciation expense decreases during upturns. This result is contrasted to the nearly equal valuation weights for the cash flow and depreciation components of earnings for Resource firms (e.g., mines) over time. We conjecture this is because depletion accounting flexibly allows for “depreciation” to exhibit less bias than in other sectors. In summary, actual depreciation practices influence time variation in the valuation of depreciation, a point which has been underappreciated in prior studies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".