Financial conditions, local competition, and local market leaders: The case of real estate developers
Bibliographic record
Abstract
Abstract This paper studies whether (and how) corporate decisions are affected by internal factors (e.g., the financial conditions of own company) and external factors (e.g., the actions of local competitors) in an imperfectly competitive environment. We study the listed real estate developers in Beijing as a case study. Our hand‐collected dataset includes transaction‐level information booked indicators (e.g., profitability, liability, and liquidity) and unbooked financial indicators (political connections). Our multi‐step empirical model shows that both the firm's financial conditions and its competitors' counterparts are essential but play different roles in the output design, pricing, and the time‐on‐the‐market (TOM). Internal versus external factors' relative importance relates nonlinearly to the degrees of market concentration. Market leaders' existence alters the small firms' strategy and leads to higher selling prices and slower selling pace in the local market. Our comprehensive financial indicators (booked and unbooked) better predict corporate behaviors than traditional measures.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".