Housing price dynamics within an integrated urban model: testing the spatial and temporal transferability
Bibliographic record
Abstract
This study evaluates spatio-temporal transferability of housing price models within an integrated urban modelling framework. Multi-year housing sales data for the city of Vancouver is used to develop models in pre-, during-, and post-pandemic periods. The models adopt a random parameter autoregressive method to accommodate autocorrelation and random parameters to capture unobserved heterogeneity. Results confirm the influence of temporal, dwelling, land use, neighbourhood and accessibility attributes on housing prices. For instance, in the case of land use attributes, prices are more likely to be higher in diverse land mix areas pre-pandemic. Results also confirm several differences among three phases of the pandemic and variations in effects across observations. For example, prices are less likely to be higher in diverse land mix areas during and post-pandemic for a certain number of observations. Results further reveal that housing prices tend to be influenced by nearby dwelling prices. For spatial transferability, the Vancouver model, developed using pre-pandemic data, was applied to another city within the same province – Kelowna, which features different geographic and population characteristics. The performance measures reveal that the model can be adapted to the Kelowna context with careful calibration and minimal adjustments, despite differences in parameter estimates. Additionally, the temporal transferability of the Vancouver model is assessed by applying the pre-pandemic model to both pandemic and postpandemic contexts. Results suggest that while the pre-pandemic model is not effectively transferable to the pandemic period, it demonstrates reasonable transferability to the post-pandemic context with minor adjustments. Overall, the findings highlight the importance of local and temporal dynamics in the housing market, particularly when adapting models across different regions or during periods of socio-economic change. This study will assist in enhancing the ability of the model to predict housing market behaviours over the long-term in response to disruptive socio-economic conditions across different regions.HighlightsTests the spatial and temporal transferability of housing price models.Utilizes multi-year housing sales data for Vancouver City to develop models.Adopts random parameter autoregressive modelling method.Develops pre-, during, and post-pandemic models to assess temporal transferability.Vancouver model was transferred to Kelowna city to evaluate spatial transferability.Developed models will be implemented in an integrated urban model.
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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 | 0.002 | 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 | 0.001 |
| Open science | 0.001 | 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".