From coast to coast to coast, communities are grappling with questions of supply, affordability, and suitability, questions that housing needs assessments (HNAs) are designed to help answer. At Turner Drake, we have been conducting HNAs for communities across Canada for years, helping municipalities and provincial governments understand not just how much housing exists, but whether it is sufficient to meet demand, and whether (and to what extent) there is a need for affordable alternatives.
Our work has recently expanded considerably in scope. Through the Research & Knowledge Initiative, a program administered by Housing, Infrastructure and Communities Canada (HICC), we are leading the development of the HNA Blueprint – a standardized toolkit and resource package designed to make housing needs analysis accessible to every community in Canada. As part of this same project, we developed preliminary HNAs for each of Canada’s thousands of Census Subdivisions, giving communities a head start on their own assessments thanks to a ready-made foundation of secondary data and analysis.
Much of the Blueprint follows the typical expectations of what you would find in an HNA. However, we approached the project as being an opportunity to explore other housing-related datasets and issues, and advance the state of the art. One of these is the inclusion of climate change considerations; an area that, despite its growing urgency, has largely remained absent from assessments of current and future housing needs.
Housing needs assessments draw heavily on Statistics Canada data to evaluate whether communities have sufficient housing supply, appropriate unit types, and homes that households can actually afford. They are, by design, backward-looking tools grounded in census snapshots and trend data, supplemented by projection analysis to anticipate future need. What they have not traditionally captured is whether the housing that exists today will still be viable, safe, or insurable a generation from now.
For coastal and low-lying communities especially, that is a substantial blind spot. Flooding is already the costliest natural disaster in Canada,[1] and the risks are growing. Rising sea levels, intensifying storm surges, and more frequent heavy precipitation events are putting an increasing share of the residential housing stock in jeopardy, often in communities where housing options are already limited.
An Analysis of Flood Risk
Our analysis draws on two distinct but complementary measurements of flood exposure, both sourced from existing national datasets, though with varying degrees of adaptation by our team.
The first is the Flood Susceptibility Index (FSI), produced by Natural Resources Canada (NRCan). The FSI is a national, gridded dataset that is a ranking of flood-prone conditions across Canada, based on the physical characteristics of the land. It draws on a range of environmental inputs (including terrain, soil permeability, proximity to water bodies, and land cover) to produce susceptibility scores for both current conditions and future climate scenarios. In plain terms, it gives an understanding of risk exposure related to flooding, and how that picture changes as temperatures rise and precipitation patterns shift. It is not an actual flood map that can tell if your house is on the wet or dry side of a floodplain boundary.
The second is a Sea level Rise (SLR) Inundation Analysis that we added in to the mix on top of what we’ve built out in the HNA Blueprint, expecting it would capture a dimension of coastal flood risk that the FSI does not explicitly address. Using moderate-resolution digital elevation model (DEM) tiles sourced from NRCan’s open geospatial data infrastructure, we interpolated projected relative sea level change values from ClimateData.ca (SSP2-4.5 scenario, CMIP6) across the study area. These projections (drawn from the same federal climate data platform used by researchers and governments across Canada) were then compared against estimated parcel elevations to estimate what share of each parcel falls at or below projected inundation thresholds, incorporating a one-metre storm surge allowance.
A parcel was flagged as being flood prone if at least 25% of the parcel was deemed to be below the 2050 projected sea-level and/or its FSI score exceed the 66th percentile (i.e., is in the top third of parcels). Outputs from both approaches were applied to each residential parcel in Nova Scotia. Results suggest that precipitation is by far the dominant driver of exposure province-wide for both current and future scenarios. Sea level rise represents a concentrated and growing risk in specific coastal communities.
What We Found in Nova Scotia
Applying both methodologies to Nova Scotia’s residential parcel fabric, we estimated what quantity and proportion of residential units are located on properties exposed to flood risk under current and future conditions.
Across Nova Scotia’s more than 500,000 residential units, we estimate that roughly 39,000 units (~8%) are currently situated on parcels with high flood susceptibility. Under future climate scenarios, that figure nearly doubles, rising to approximately 73,000 units, or ~14% of the existing provincial housing stock. In absolute terms, this represents an additional 34,000 units becoming exposed to high flood risk as climate conditions shift, or an 87% increase relative to current levels.
The picture is not uniform across the province. Some municipalities already face acute exposure: several smaller coastal communities have more than 40% of their housing stock in high-susceptibility areas today, with future projections pushing that share above 50% in some cases. At the other end of the spectrum, a number of inland communities and larger urban centres show comparatively modest current exposure, though even these jurisdictions are not immune to meaningful increases under future scenarios.
Larger municipalities tell an important story in absolute terms. Halifax Regional Municipality (HRM), which accounts for roughly 43% of Nova Scotia’s total housing stock, currently has approximately 10,900 units in high-susceptibility areas — around 5% of its total. Under future conditions, that rises to nearly 20,700 units, an increase of close to 9,800 homes. The relative rate of increase in HRM is lower than many smaller coastal municipalities, but the raw scale of additional exposure notable.
A full breakdown by municipality across the province is provided in the Appendix.
A Note on Methodology and Limitations (for the nerds)
These analyses are planning-level tools, not site-specific risk assessments. Several methodological considerations are worth noting:
- The NRCan FSI is a modelled susceptibility index at a 30m by 30m resolution, not a regulatory flood map. While this offers fine-grained identification of areas likely to experience flooding under changing precipitation and temperature conditions, it does not replace detailed hydrological modelling or official flood hazard designations. Results should be interpreted as approximate indicators of relative risk rather than precise flood exposure determinations.
- Our SLR inundation analysis applies a simple bathtub model: parcels where more than 25% of the area falls at or below the projected inundation threshold (including a one-metre storm surge allowance) are considered at risk. This approach does not account for the connectivity of inundated areas to open water, which means isolated low-lying inland parcels may be flagged even where coastal inundation is physically implausible. Results in such areas should be interpreted with caution.
- The SLR component compares DEM values referenced to CGVD2013 against projected relative sea level change values anchored to a 1995–2014 baseline. These reference points are not identical, though the offset between them is generally small relative to the uncertainty inherent in the SLR projection range itself.
Housing Needs Relate to Climate Needs
The implications of findings like these extend well beyond Nova Scotia. If a community’s housing stock is exposed to escalating flood risk, a conventional HNA may overestimate effective supply by counting units that could, in coming decades, become uninsurable, uninhabitable, or perhaps simply washed away. Planning for new supply without accounting for the attrition of existing stock due to climate impacts will increasingly compound the very shortfalls that HNAs are meant to address.
These concerns are not unique to Nova Scotia, nor are the methodological challenges in addressing them. Our results use chosen analysis thresholds (e.g., above the 66th percentile in FSI scores is classified as “high” flood susceptibility property), and some outputs may appear surprising. For instance, the Town of Truro demonstrates that virtually 0% of residential properties are currently highly susceptible (see the Appendix), which seems inconsistent with its flood history. Lowering the threshold to the 50th percentile dramatically raises that share to 14%. This sensitivity is a reminder that how we weight future scenarios, communicate risk to decision-makers, and integrate climate exposure into housing projections are all open questions. But imprecision is not a reason to hold back. We already rely on rules of thumb throughout housing analysis: the 3% vacancy rate as a proxy for balanced market conditions, or 30% of income as an affordability threshold. The uncertainty embedded in emerging climate screening tools is no different from that in the population, household, and economic models that routinely underpin housing supply targets. These are models that policymakers comfortably rely on despite their well-documented volatility. The FSI may not perfectly capture local flood dynamics where multiple factors, such as heavy rainfall coinciding with high tide, converge; but that is an argument for refinement, not abandonment.
Our climate risk estimates are built on established federal baselines and nationally standardised datasets, not unlike the census or labour force survey data that are highly regarded (and rightfully so). Treating climate-related analyses as too speculative to inform housing policy, while treating conventional demand projections and supply gap analyses as concrete insights, misunderstands the threat. The hazard of using imperfect, unfamiliar analyses to inform near-term strategies is minor next to the inertia in our social conscious that still sees climate risk as inactionable, hidden beyond a foggy horizon. The threat to the housing stock is current and compounding, the data and tools exist and will continue to be refined. What must catch up now is the practice of injecting the topic dispassionately into the routine policy conversations around housing and community planning.
Appendix: Flood Susceptibility by Municipality
Table 1: Units in high flood susceptibility areas, by municipality. Sorted by future exposure rate (highest to lowest). Current scenario reflects present-day FSI conditions; future scenario reflects 2050 projected conditions under mid-range climate change. Results are approximate and intended for planning-level interpretation only.
| Municipality | Total Units | Current Units | Current % | Future Units | Future % |
| Lockeport | 330 | 210 | 64% | 270 | 81% |
| Barrington | 3,720 | 1,505 | 40% | 2,015 | 54% |
| Clark’s Harbour | 390 | 170 | 43% | 210 | 54% |
| Oxford | 620 | 245 | 40% | 295 | 48% |
| Argyle | 4,285 | 1,245 | 29% | 1,990 | 46% |
| St. Mary’s | 1,750 | 295 | 17% | 775 | 44% |
| Shelburne (District) | 3,145 | 885 | 28% | 1,385 | 44% |
| Richmond | 5,670 | 895 | 16% | 2,210 | 39% |
| Queens | 7,510 | 1,645 | 22% | 2,770 | 37% |
| Guysborough | 3,345 | 470 | 14% | 1,190 | 36% |
| Chester | 7,795 | 1,705 | 22% | 2,600 | 33% |
| Annapolis Royal | 410 | 135 | 33% | 125 | 31% |
| Yarmouth (District) | 5,550 | 790 | 14% | 1,650 | 30% |
| Lunenburg (District) | 16,205 | 2,600 | 16% | 4,755 | 29% |
| Shelburne (Town) | 900 | 215 | 24% | 265 | 29% |
| Victoria | 4,885 | 605 | 12% | 1,330 | 27% |
| Mahone Bay | 620 | 150 | 25% | 165 | 27% |
| Cumberland | 13,150 | 2,430 | 18% | 3,480 | 26% |
| Clare | 5,195 | 465 | 9% | 1,210 | 23% |
| Pictou (County) | 12,110 | 1,725 | 14% | 2,715 | 22% |
| Digby (District) | 4,715 | 520 | 11% | 955 | 20% |
| Lunenburg (Town) | 1,360 | 150 | 11% | 225 | 17% |
| Annapolis | 10,985 | 1,135 | 10% | 1,755 | 16% |
| Antigonish (County) | 7,345 | 350 | 5% | 1,155 | 16% |
| Inverness | 8,655 | 445 | 5% | 1,340 | 15% |
| Cape Breton (RM) | 50,000 | 3,970 | 8% | 6,445 | 13% |
| Colchester | 18,520 | 750 | 4% | 2,225 | 12% |
| East Hants | 11,390 | 260 | 2% | 1,270 | 11% |
| Kentville | 3,515 | 175 | 5% | 385 | 11% |
| Truro | 7,460 | 15 | 0% | 815 | 11% |
| Mulgrave | 370 | 5 | 1% | 40 | 10% |
| Digby (Town) | 1,175 | 115 | 10% | 120 | 10% |
| Pictou (Town) | 1,715 | 80 | 5% | 170 | 10% |
| Antigonish (Town) | 2,455 | 245 | 10% | 235 | 10% |
| Halifax (HRM) | 215,525 | 10,890 | 5% | 20,690 | 10% |
| Middleton | 1,090 | 80 | 7% | 100 | 9% |
| Port Hawkesbury | 1,515 | 70 | 5% | 125 | 8% |
| Kings | 23,890 | 705 | 3% | 1,900 | 8% |
| Wolfville | 3,095 | 60 | 2% | 185 | 6% |
| West Hants | 9,770 | 230 | 2% | 545 | 6% |
| Yarmouth (Town) | 3,585 | 80 | 2% | 190 | 5% |
| Bridgewater | 4,390 | 115 | 3% | 220 | 5% |
| New Glasgow | 4,985 | 130 | 3% | 235 | 5% |
| Stewiacke | 900 | 5 | 0% | 40 | 5% |
| Stellarton | 2,075 | 20 | 1% | 40 | 2% |
| Trenton | 1,185 | 15 | 1% | 15 | 1% |
| Amherst | 4,485 | 5 | 0% | 55 | 1% |
| Westville | 1,685 | 0 | 0% | 15 | 1% |
| Berwick | 1,190 | 0 | 0% | 10 | 1% |
Sources: NRCan Flood Susceptibility Index (precipitation-driven risk); Turner Drake & Partners sea level rise inundation analysis using NRCan HRDEM, ClimateData.ca RSLC projections (SSP2-4.5, CMIP6), and Nova Scotia residential parcel fabric. Results are approximate and intended for planning-level interpretation only.
Important note: Results reflect the chosen analysis thresholds; specifically, properties above the 66th percentile in FSI scores are classified as “high” flood susceptibility. Some outputs may appear surprising. For example, Truro shows virtually 0% of residential properties at high susceptibility, which seems inconsistent with its flood history. Lowering the threshold to the 50th percentile raises that share to 14%, suggesting the FSI may not fully capture local flood dynamics where multiple factors, such as heavy rainfall coinciding with high tide, drive risk.
[1] Government of Canada. (March 23, 2026). Floods.
Andrew Scanlan Dickie is Manager of our Planning Division. For more information about how you can benefit from the unique expertise of our Planning & Economic Intelligence team, contact Andrew at (902) 429-1811 or .