The Impact of Individual Characteristics and Branch Characteristics on Housing Agent Performance: Heckit model and Hierarchical Linear Modeling
Bibliographic record
Abstract
This study investigates the impacts of individual characteristics and branch characteristics on housing agent performance. Data were analyzed using hierarchical linear modeling (HLM) to provide estimations. The empirical results suggest that individual performance varies significantly from branch to branch and is better in branches with higher levels of compensation for individual performance. Individual characteristics including college level education, having children over the age of six, work experience, the square of work experience, and work experience outside the real estate industry have significant effects on individual performance. Individual performance is also better in branches with requirements for hours worked. The individual performance of salespeople working under team compensation schemes is not significantly better than that of salespeople working in branches without team compensation schemes. When the average housing prices for the areas in which branches operate are higher, individual performance will be higher. As the average housing price for an area increases, however, the corresponding increase in individual performance will be less and less strong. According to the empirical results, there was a degree of self-selection in the samples. The results of two-stage estimation were not significantly different from the estimation results of the original model. Hence, the results demonstrate the robustness of the estimation model used.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".