Three Essays on Real Estate Brokerage, Listing Strategy, and Housing Transactions
Bibliographic record
Abstract
This thesis consists of three essays. The first essay (chapter 2) investigates the incentives of agents working with buyers (buying agents) under the fixed percentage commission system (FPCS) and the implications on housing market outcomes. Our model shows that the absence of a binding contract creates a risk of losing clients for buying agents, which helps mitigate the conflict of interest between buying agents and their clients. Both the buying agent’s prediction accuracy regarding their client’s reservation prices and the level of tolerance given by the buyer to the buying agent affect the binding force. Results from simulations and empirical analyses using house transactions in Canada support our model’s predictions. \nThe second essay (chapter 3) investigates the impact of complaints against agents on agents’ incentives in housing transactions. Our model shows that complaints incentivize an agent to exert greater efforts and mitigate the conflict of interest due to their concern for reputation and future career. It also shows that the reputation recovery benefits and the commission rate play important roles in determining the impact of complaints on the incentives of agents. We find that complaints against agents have positive impacts on a house’s sale price and negative impacts on other transaction outcomes such as the probability of a house sale and the listing time on markets. \nThe third essay (chapter 4) investigates the effect of recurrent list-price reductions on housing transactions. Recurrent list-price reductions of a house may signal the impatience of sellers to conclude a sell transaction more quickly, leading to more visits and a higher likelihood of being sold (positive signal), but may also signal that the listing is problematic and thus harder to sell without a list-price reduction, leading to a lower likelihood of being sold (negative signal). This essay investigates which signal prevails using a joint frailty model which accounts for the inter-dependence among recurrent list-price reductions and the association between the recurrent reductions and the sold event. The results show time-varying negative impacts of list-price reductions on the likelihood of a house sale, supporting the dominance of the negative signaling effects of recurrent list-price reductions.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".