Financing Constraints and Investment Efficiency in Canadian Real Estate and Construction Firms: A Stochastic Frontier Analysis
Bibliographic record
Abstract
This article models the behavior of 179 listed and unlisted real estate and construction firms (RECFs) in Canada to study how financial constraints impact the investment efficiency of these real estate firms during the 2004–2020 period. Investment efficiency is interpreted here as the ability and ease of a firm to convert investment opportunities into actual investments. The results show that Canadian RECFs are strongly dependent on two sources of financing: equity financing and debt financing. Equity financing helped ease financing constraints due to a cash flow increase but was unlikely to decrease the uncertainty of follow-up financing of investments of these companies. This study constructed an investment efficiency index (IEI) for all 179 RECF firms. The results showed an investment rate loss of approximately 62% of the RECF firms due to financing constraints during the above period. The IEI of RECFs in Canada has demonstrated a descending pattern, and the investment efficiency level slipped from 0.47 to 0.40 from 2004 to 2020. Furthermore, a regional analysis demonstrates that compared with the RECFs located in Ontario, the investment efficiency indices of RECFs in Quebec and British Columbia were more volatile. Small RECFs demonstrated a very steady trend in investment efficiency during the sample period, which was completely different from the patterns displayed by large and medium RECFs.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".