Modeling post‐disaster recovery: Accounting for rental and multi‐family housing
Bibliographic record
Abstract
Post‐disaster housing recovery models increase our understanding of recovery dynamics, vulnerable populations, and how people are affected by the direct losses that disasters create. Past recovery models have focused on single‐family owner‐occupied housing, while empirical evidence shows that rental units and multi‐family housing are disadvantaged in post‐disaster recovery. To fill this gap, this article presents an agent‐based housing recovery model that includes the four common type–tenure combinations of single‐ and multi‐family owner‐ and renter‐occupied housing. The proposed model accounts for the different recovery processes, emphasizing funding sources available to each type–tenure. The outputs of our model include the timing of financing and recovery at building resolution across a community. We demonstrate the model with a case study of Alameda, California, recovering from a simulated M7.0 earthquake on the Hayward fault. The processes in the model replicate higher non‐recovery of multi‐family housing than single‐family housing, as observed in past disasters, and a heavy reliance of single‐family renter‐occupied units on Small Business Administration funding, which is expected due to low earthquake insurance penetration. The simulation results indicate that multi‐family housing would have the highest portion of unmet need remaining; however, some buildings with unmet needs are anticipated to be able to obtain a large portion of their funding. The remaining portion may be filled using personal financing or may be overcome with downsizing or downgrades. Multi‐family housing would also benefit the most from Community Development Block Grants for Disaster Recovery (CDBG‐DR). This benefit is a result of modeling the financing sources, that CDBG‐DR is available, and that many multi‐family buildings do not qualify for other sources. Communities’ allocation of public funding is important for housing recovery. Our model can help inform and compare potential financing policies to allocate public funds.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".