Client context
A mid-sized infill developer in a major Canadian metro was pursuing a multi-parcel assembly near an intensification corridor. The team had a familiar problem: dozens of plausible blocks, limited bandwidth, and high uncertainty around which clusters were worth serious outreach.
Land assembly is structurally hard because it concentrates negotiation and coordination risk—especially the “holdout” dynamic where a critical owner can delay or derail an otherwise viable assembly. Industry research on infill development likewise points to recurring barriers in the assembly process that slow execution and increase friction.
The challenge
The developer needed a repeatable way to answer three questions early—before spending months on outreach, letters of intent, and consultant work:
- Which contiguous parcel clusters are most likely to be financeable and buildable (in principle)?
- Which clusters justify immediate seller outreach vs. “monitor and wait”?
- Which sites carry hidden risk (marketability and climate exposure) that could create problems later?
What Gnowise data changed
The developer used Gnowise as a single source of property intelligence to turn an “eyes-on-the-map” search into a ranked shortlist.
Gnowise’s Wise1 API is positioned as a unified feed for residential property intelligence in Canada and Aegaia‘s climate resilience signals.
In this case, the developer used three categories of signals:
1) Valuation context to anchor assembly feasibility
Instead of treating every parcel as a bespoke underwrite, the team applied consistent valuation context to:
- sanity-check total “as-is” acquisition ranges across candidate clusters,
- spot outliers that could signal a high premium/holdout risk,
- prioritize clusters where value signals were coherent across adjacent parcels (a practical proxy for smoother negotiations).
(Importantly, this did not replace appraisal or broker opinion; it acted as an early-stage filter.)
2) Liquidity as a marketability lens
Assemblies fail not only on purchase price, but on exit assumptions (pre-sales velocity, takeout financing confidence, rental absorption). The team used Gnowise’s Liquidity Score concept—described as a 0–100 index intended to reflect how easily assets in a market can transact—to compare clusters and avoid “looks good on paper, hard to move in reality” locations.
3) Climate risk to avoid future surprises
For the long-lived nature of development projects, the team included property-level climate hazard signals—specifically flood, heat, wind, and wildfire risk—as a gating check. Gnowise’s climate-risk analytics offering explicitly covers these hazards at scale.
This was used to:
- flag clusters likely to require additional resilience design/cost contingencies,
- anticipate potential insurance friction,
- prioritize “cleaner” sites when two clusters were otherwise comparable.
The outcome
Within the first screening phase, the developer narrowed a large search area into:
- a short list of high-priority clusters for immediate outreach, and
- a watchlist of secondary clusters to revisit if negotiations stalled or pricing shifted.
The practical benefits were straightforward:
- fewer wasted outreach cycles on clusters that were unlikely to pencil,
- stronger internal alignment (development, acquisitions, and finance speaking from the same base signals),
- earlier identification of climate-exposure red flags that could have surfaced much later.
This aligns with broader commercial real estate research showing that data analytics can add material value to development decisions—especially through improved siting and project selection.
Key takeaways for developers doing land assembly
- Assembly is a probability game. Data is most useful when it helps you rank clusters by probability-adjusted feasibility, not when it pretends to “predict the one perfect site.”
- Consistency beats heroics. A unified intelligence layer reduces the variance between team members’ assumptions and speeds decision cycles.
- Climate is now an early filter, not a late-stage footnote. Flood/heat/wind/wildfire signals are most valuable when they prevent you from advancing the wrong site.
- Holdout risk is real. Academic work documents the holdout problem as a central friction in land assembly; reducing the number of “false-start” clusters is one of the highest-ROI uses of data.