A framework for evaluating hail loss reduction
By Fariba Choudhury, Jodi Scarlett and Tom Herman
White paper | November 2026
Roof upgrades can be evaluated as an investment in reducing future hail damage. The challenge is to establish when a stronger laboratory performance rating translates into lower losses in occupied homes, and whether the benefit justifies the additional cost. Assessing this question requires a clear distinction between laboratory observations and evidence gathered at occupied homes.
This white paper sets out a staged framework for comparing verified impact-rated asphalt roofs with otherwise comparable standard asphalt roofs in Canada. The framework first establishes the quality of available exposure and building data, then estimates differences in claim frequency and cost, and finally tests whether those estimates remain reliable in storms and locations excluded from model development. Economic comparisons use those estimates only within their demonstrated range of applicability.
The contribution is an auditable validation framework, not a new claim of product effectiveness. Published research provides a basis for laboratory comparison, claims analysis and economic evaluation, but these forms of evidence answer different questions. No new empirical results are reported in this paper. Applying the framework to a specific portfolio requires access to claims, policy and hazard data.
A Canadian benefit-cost study has already examined impact-resistant asphalt shingles using performance-based engineering and found favourable economics under its modelled conditions [1]. This provides a reason to investigate the intervention; it does not remove the need to test how well expected benefits transfer across actual properties, installation conditions and storm histories.
The practical question is therefore specific: under what conditions are verified impact-rated roofs associated with lower hail-related losses, after accounting for differences in exposure, roof age and other observable characteristics? Answering this question clarifies the strength of the evidence for loss reduction and the populations to which the findings apply.
UL 2218 evaluates steep-slope roof coverings under specified impact conditions. Its published scope does not establish a direct relationship between steel-ball test performance and performance under every hail condition; it also excludes the effects of weathering and aging from the test [2]. A classification is therefore evidence about a defined test, rather than a measured percentage reduction in insurance losses.
Brown-Giammanco and colleagues developed a laboratory protocol informed by measured hailstone characteristics and several forms of shingle damage [3]. This strengthens the physical basis of material comparison. It still leaves a separate question about the performance of an installed roof over time.
Brown and colleagues combined claims and policies-in-force from multiple insurers after a Texas hailstorm, linking roofing characteristics and loss information with radar-estimated hail exposure [4]. Their work demonstrates why the population exposed to a storm matters: claims alone describe reported losses, but do not supply the denominator needed to estimate claim frequency. Results from one event cannot establish performance throughout Canada.
Brunet and Brimelow combined Canadian hail observations and reports with lightning information to address observational gaps [5]. Their work illustrates the need to examine reporting coverage and proxy assumptions. A mapped hazard indicator should not automatically be treated as either a complete storm record or a property-loss probability.
Table 1 Evidence and the claims it can support
| Evidence | Appropriate interpretation | Additional evidence needed |
|---|---|---|
| Impact testing | Relative response under specified laboratory conditions | Installed performance and aging |
| Field loss study | Observed outcomes in a defined population and period | Comparable exposure and independent validation |
| Economic model | Conditional costs and benefits under stated assumptions | Local costs and validated loss estimates |
Synthesis of public sources [1-5]. These evidence types complement one another; none alone establishes a universal upgrade benefit.
The framework starts with existing low-rise homes with asphalt roof coverings in one Canadian region with usable records for multiple hail events. Restricting the first evaluation to a defined building and material class makes the comparison more interpretable. Findings do not automatically extend to metal roofs, wall systems or other hazards.
The exposure of interest is a documented impact-rated roof in place before the storm. The comparator is a documented standard asphalt roof. The test standard, class, exact product and installation date are verified where records permit. Eligible classes and product groups are specified in advance; materially different systems are not pooled without justification. Unknown classifications remain unknown and are analysed separately or excluded with a recorded reason.
The primary outcome is the probability of at least one hail-related roof claim per insured dwelling exposed to an eligible storm. Secondary outcomes include claim severity among claimants and mean insured roof loss across all exposed dwellings, including those without claims. Claim severity alone can be misleading if the upgrade changes which properties experience a claim.
Physical damage, estimated repair cost and insurer payment are distinct variables. Deductibles, coverage terms, settlement practices and claim development are recorded where available. Monetary outcomes use Canadian dollars with a stated price year. Claims are compared at a common development age after each event, or adjusted to a consistent ultimate-loss basis, so recent events are not understated because claims remain unsettled.
Hernán and Robins provide a methodological basis for distinguishing causal questions from prediction and for making assumptions about confounding explicit [6]. Applied here, the framework compares roofs within similar storm exposures and accounts for pre-event roof age, property characteristics and observable factors related to both upgrade selection and loss. A causal interpretation requires justified assumptions, including adequate measurement, comparable groups and sufficient overlap.
The practical analysis begins with descriptive checks, then uses a prespecified adjustment strategy, such as matching or weighting, if the data support it. If comparable groups cannot be formed, the limitation is reported rather than extrapolated. Where installation quality or other relevant factors remain unmeasured, findings are described as adjusted associations, with sensitivity analyses for residual confounding.
The framework separates three questions: whether the calculations have been implemented correctly, whether the estimates predict observed outcomes, and whether a difference between roof groups can reasonably be attributed to the upgrade. A successful software test answers the first question; it cannot substitute for the other two.
Entire storms are reserved for evaluation where the number of events permits, with further temporal or geographic checks when feasible. Properties from the same storm share exposure conditions; mixing them across development and evaluation sets can make performance appear more transferable than it is. Repeated observations of the same property are also tracked to prevent leakage.
Predicted claim probabilities are compared with observed frequencies, including calibration by relevant groups and an overall probability error measure. Predicted loss costs are assessed against observed aggregate costs and the largest errors. Results include uncertainty intervals and event-level variation, not only one average accuracy score. Event-based resampling can be considered when enough independent storms are available.
Table 2 Validation record
| Check | Evidence to retain |
|---|---|
| Data quality | Missingness, linkage errors, exclusions and classification checks |
| Group comparability | Exposure balance, roof-age balance and overlap diagnostics |
| Generalisation | Results on held-out storms and any external evaluation |
| Uncertainty | Interval estimates and sensitivity to consequential assumptions |
| Reproducibility | Prespecified protocol, data dictionary and versioned analysis record |
Acceptable error and interval width are chosen in relation to the research question before evaluation. Findings from a selected study population need further evidence before supporting conclusions about other populations. Sample-size planning considers the number of independent storms, the availability of each roof category and the precision required for the primary outcome. No universal minimum sample size is assumed here.
Null or inconclusive findings are reported as useful results. A narrow estimate of little benefit, or evidence that the available data cannot distinguish roof groups fairly, can guide investment and future data collection as meaningfully as a favourable estimate.
An economic comparison identifies who pays for the upgrade and who receives the benefits. Avoided repair costs, avoided insurer payments and household premium changes are different outcomes; they should not be combined as if they were independent savings. An insurer payment is also a transfer between parties, rather than an additional avoided physical repair cost.
At a scheduled roof replacement, the relevant initial cost is the difference between the upgrade and the standard replacement. An early replacement requires a different comparison because remaining roof life and earlier expenditure matter. Each analysis states the comparison, time horizon, discount rate, price year and maintenance assumptions. Published benefit-cost work provides a precedent for examining these choices [1], but its numerical conclusions should not be copied into a new population without evaluating their applicability.
Loss estimates conditional on a storm cannot be treated as annual expected savings. That conversion requires a separately justified local storm-frequency and severity model, including uncertainty. Without it, conclusions are limited to event-conditional losses.
Experimental work by Meisenzahl and colleagues examined repeated smaller-hail impacts, weathering and subsequent larger-hail testing across six asphalt shingle products [7]. It supports investigating cumulative exposure and roof condition rather than assuming that new-product performance remains constant throughout service life. The experiment does not establish a universal service life or a Canadian claims-reduction factor.
Canada’s Changing Climate Report 2026 finds insufficient evidence to establish historical changes in severe hail for Canada as a whole. Its assessment of possible future increases in severe-hail frequency in parts of western Canada carries very low confidence [8]. A regional climate projection should therefore not be presented as a precise forecast for an individual address.
Historical field findings and future climate assessments answer different questions. A study of losses in past storms does not, by itself, establish how the same population will perform under future conditions. Conclusions should retain this distinction and identify the time period and geographic scope of the evidence.
The first step is a data audit, not a completed predictive model. It establishes whether roof classifications can be verified, whether non-claiming exposed properties can be identified, and whether enough independent storms and comparable roof groups exist. If essential information is absent, the next deliverable is a structured collection protocol rather than an unsupported estimate of effectiveness.
Access to insurer records, inspection records or restricted hazard datasets requires appropriate agreements and institutional review where applicable. Exact property locations are treated as potentially identifying. Data minimisation, restricted access and aggregate reporting are built into the design. The availability of one public dataset is not taken as permission to use all linked records.
A first application focuses on the relationship between verified roof characteristics and observed hail losses within a selected region. This scope allows one important question to be answered rigorously without claiming findings beyond the available evidence.
The outputs are an evidence review; a documented study population and data dictionary; an analysis with explicit assumptions and uncertainty; and a validation report showing where conclusions hold or fail. Publicly shareable methods and aggregate findings can support subsequent research. Synthetic examples can illustrate calculations when record-level data cannot be released, with their synthetic nature clearly identified.
Conclusions are limited to the roof types, buildings, events and regions represented in the study. Reporting distinguishes observed associations from causal interpretations and identifies unresolved sources of bias. A finding of lower losses in one population justifies further investigation, rather than establishing a universal effect across other materials or locations.
Evidence-based roof decisions require credible evidence about material performance and observed loss. Public research already supports investigating impact-rated roof upgrades, while also showing why laboratory results, modelled benefits and field outcomes must be distinguished. A focused Canadian evaluation can clarify the strength and limits of the evidence for loss reduction. Its value lies in the quality and transparency of the method, whether the final result is favourable, mixed or inconclusive.
Roof and material decisions on construction and restoration projects deserve the same discipline: clear evidence, stated assumptions and results you can audit. EcoClaim helps insurers, contractors and the wider claims supply chain compare standard materials with lower-carbon or more resilient alternatives, with cost differences shown at the claim level.
Contact us to apply this to your construction and restoration projects.
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