Unlocking private investment in Canadian flood resilient home retrofits: a return on investment research summary
Bibliographic record
Abstract
Canadian governments, businesses, and residents currently spend a combined average of $2.9 billion every year repairing damage to homes caused by fluvial, pluvial, and coastal flooding. The distribution of losses is particularly concentrated in homes exposed to the highest levels of flood hazard. For example, homes within the top 10% (approximately 1.5 million) experience 89.3% of all losses per year whereas the top 1% (approximately 109,000) experience 34.1% (Public Safety Canada, 2022). To date very few Canadian households have invested in flood resilient retrofits before flooding occurs, opting instead to wait for government disaster recovery programs to provide support if and when flooding occurs. This reactive approach unnecessarily exposes Canadians, particularly socially vulnerable households within high-risk zones, to dire social and economic consequences (Wright et al., 2022). Without implementing adequate voluntary and regulatory flood risk reduction measures experts project that annual costs associated with repairing home flood damages may triple by 2030 (Ziolecki et al., 2020). This paper examines the grey and peer-reviewed literature that is currently available to help drive proactive private side investment in flood resilient home retrofits, identifies where gaps exist, and points to additional research that is needed to bolster retrofit adoption. The majority of research describes home flood mitigation approaches using the PARA framework (protect, accommodate, retreat, avoid) and expresses results as a benefit cost ratio (BCR). A BCR compares the total expected benefits to society for the life of a building (75 years for new builds and 50 years for retrofits) as a result of completing mitigative actions compared to related installation and maintenance costs (Doberstein et al., 2019). The research consistently documents the power of public and private incentives to increase the BCR of flood resilient retrofits and drive preventative action. It also emphasizes the importance of building all new homes to meet flood resilient standards to maximize the long-term benefits to society (Porter & Yuan, 2020; Porter et al., 2023). BCR estimates for dry flood proofing (protect) range from 11:1 for flood resilient new home construction (Porter & Scawthorn, 2020) to 6:1 for flood resilient retrofits (Porter et al., 2019). Wet flood proofing (accommodate) BCR estimates range from 6:1 for 1’ (30cm) elevation above the 100-year flood plain to 5:1 for 5’ (150cm) elevation above the 100-year flood plain (American Flood Coalition, 2022). BCR estimates for retreat range from 6:1 over 20 years (Porter et al., 2019) to 6:1 over 30 years (Nelson & Camp, 2020). Two studies proposed a methodology for estimating BCR associated with land use planning initiatives to eliminate flood risk (avoid), but did not generate any BCR results (Mechler et al., 2014; De Risi et al., 2018). To unlock private side investment in proactive flood resilient retrofits and new builds the research identifies opportunities for all participants in the real estate value chain (builders, developers, retailers, insurers, lenders, and local governments) to work together to raise public awareness of flood risks and incentivize improved flood resilience (Porter & Yuan, 2020; Porter et al., 2023; Giannitsos, 2023; Krueger, 2022; Minano & Peddle, 2018). Support for flood resilient home retrofits in high-risk zones should be prioritized, particularly for homes that provide the country’s most affordable housing units, including basement apartments, social housing, rental homes, and apartment buildings. This is critical to ensure access to safe, reliable and attainable housing for Canada’s vulnerable populations (Wright et al., 2022). Updating building codes and eliminating perverse legislation and policies that incentivize development in high-risk flood zones is also necessary to fortify the long-term flood resilience of Canada’s housing stock (Porter & Scawthorn, 2020).
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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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".