Behavioral Housing Search Choice Set Formation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The housing search process, a topic of interest to both practitioners and researchers, starts with an alternative formation and screening practice. Due to the limitation of cognitive capacity, household members at this level evaluate potential alternatives based on many factors, such as lifestyle, preferences, and so on, to form a manageable choice set. This article attempts to provide a detailed study of this screening and filtering practice to develop a modeling framework that can replicate the choice set formation process. In order to show the potential of the method, one prospective decision criteria—the average desired commute to work distance—is considered the potential attribute that the household evaluates for feasible housing alternatives. It is postulated that alternatives will only be included in the choice set if the average work distance satisfies the household distance threshold. This article explores the viability of using proportional hazard models in the housing search process. Some of the specifications of hazard-based models that are typically used on temporal data are examined on average work distance. Several household sociodemographic attributes from eight waves of the Seattle Metropolitan Area’s Puget Sound Transportation Panel (PSTP) are utilized for model estimation, along with built environment variables, characteristics of the supply side of the market, and several other economic indicators. The approach presented in this article provides a remedy for the large choice set problem typically faced in discrete choice modeling.
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.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it