Why Location Risk Is the Part of Your CRE Model That Usually Goes Unmodeled
May 20th, 2026
Open any pre-development model. Rent, vacancy, exit yield, capex, debt terms, each has a row, a source, sometimes a whole tab dedicated to scenarios.
Now look for where the submarket story lives.
In most deals we've seen, it's either missing or collapsed into a broker comp and a label: "prime location," "high-growth submarket." That label then feeds directly into the rent assumption, which feeds into everything else. Nobody audits it separately. Nobody stress-tests it as its own variable.
This is where things often start to drift without anyone noticing.
Open any pre-development model. Rent, vacancy, exit yield, capex, debt terms, each has a row, a source, sometimes a whole tab dedicated to scenarios.
Now look for where the submarket story lives.
In most deals we've seen, it's either missing or collapsed into a broker comp and a label: "prime location," "high-growth submarket." That label then feeds directly into the rent assumption, which feeds into everything else. Nobody audits it separately. Nobody stress-tests it as its own variable.
This is where things often start to drift without anyone noticing.
The thing you can't fix after signing
Most underwriting errors are recoverable. Capex runs 15% over budget — you adjust. Vacancy comes in 200bps higher than projected — you extend the leasing timeline, renegotiate terms. These are execution problems, and execution problems have solutions.
The submarket story is harder. If you bought into the wrong location, or overestimated what a location could support, the rest of the model is pulling against that from day one. You can renovate the building. The catchment area, the transit access, the competitive supply pipeline — those stay.
This isn't unique to one asset class, and it plays out differently in retail, office, and logistics. But the common thread is that location-level assumptions usually don't get modeled with the same rigor as financial ones — even when they carry more of the return.
Most underwriting errors are recoverable. Capex runs 15% over budget — you adjust. Vacancy comes in 200bps higher than projected — you extend the leasing timeline, renegotiate terms. These are execution problems, and execution problems have solutions.
The submarket story is harder. If you bought into the wrong location, or overestimated what a location could support, the rest of the model is pulling against that from day one. You can renovate the building. The catchment area, the transit access, the competitive supply pipeline — those stay.
This isn't unique to one asset class, and it plays out differently in retail, office, and logistics. But the common thread is that location-level assumptions usually don't get modeled with the same rigor as financial ones — even when they carry more of the return.
Why most teams work with a single layer when there are at least three
When analysts do think about location, they tend to focus on the city level: employment trends, population growth, GDP. Most IC decks still rely on metro averages here — partly because submarket data is harder to pull, partly because the city story is cleaner to present. The macro picture matters, but it's also the easiest thing to get directionally right.
The harder work is at the submarket level. This is where rent growth actually plays out, and it varies a lot even within the same market. A submarket with 12% year-over-year growth in competitor foot traffic reads very differently from one with an 18% decline — that gap isn't visible from city-level data. You need to go closer.
Below that is the parcel itself. Zoning, overlay restrictions, utility capacity, ingress and egress. Teams often treat these as a checklist — permitted use confirmed, tick — rather than as variables with real cost and timeline consequences. A utility extension that adds six months and $800k to a project was technically "confirmed" in due diligence. It just wasn't modeled.
In practice, the submarket layer gets the least attention. The city story goes in the investment memo. The parcel constraints surface in legal. The block-level dynamics fall into the gap between the two.
When analysts do think about location, they tend to focus on the city level: employment trends, population growth, GDP. Most IC decks still rely on metro averages here — partly because submarket data is harder to pull, partly because the city story is cleaner to present. The macro picture matters, but it's also the easiest thing to get directionally right.
The harder work is at the submarket level. This is where rent growth actually plays out, and it varies a lot even within the same market. A submarket with 12% year-over-year growth in competitor foot traffic reads very differently from one with an 18% decline — that gap isn't visible from city-level data. You need to go closer.
Below that is the parcel itself. Zoning, overlay restrictions, utility capacity, ingress and egress. Teams often treat these as a checklist — permitted use confirmed, tick — rather than as variables with real cost and timeline consequences. A utility extension that adds six months and $800k to a project was technically "confirmed" in due diligence. It just wasn't modeled.
In practice, the submarket layer gets the least attention. The city story goes in the investment memo. The parcel constraints surface in legal. The block-level dynamics fall into the gap between the two.
What the post-COVID office split actually showed
The best recent example of location risk materializing at scale is the office market.
A 2021 study published in the Journal of Urban Economics estimated that post-COVID, commercial rent gradients fell by roughly 15% in transit-dependent cities, and the premium for proximity to rail stations also declined. Columbia Business School research cited by NBER projected NYC office values 39% below 2019 levels a decade out, on the path where hybrid work persists.
Meanwhile, PwC's 2025 outlook notes that CBD buildings with strong transit access and modern amenities are among the clearest winners of the current cycle — while the overall CMBS delinquency rate for office hit 11.66% in August 2025, the sector's worst-ever level.
The split was largely determined by location. And the signals were in location-level data before leasing began. Transit dependency, submarket tenant composition, distance from residential density — these variables were available. Most teams just hadn't modeled them as assumptions that could move.
For what it's worth: quantifying exactly how much of the value destruction was "location" versus "structural market shift" is genuinely hard, and not all analysts measure this the same way. But the directional story holds.
The best recent example of location risk materializing at scale is the office market.
A 2021 study published in the Journal of Urban Economics estimated that post-COVID, commercial rent gradients fell by roughly 15% in transit-dependent cities, and the premium for proximity to rail stations also declined. Columbia Business School research cited by NBER projected NYC office values 39% below 2019 levels a decade out, on the path where hybrid work persists.
Meanwhile, PwC's 2025 outlook notes that CBD buildings with strong transit access and modern amenities are among the clearest winners of the current cycle — while the overall CMBS delinquency rate for office hit 11.66% in August 2025, the sector's worst-ever level.
The split was largely determined by location. And the signals were in location-level data before leasing began. Transit dependency, submarket tenant composition, distance from residential density — these variables were available. Most teams just hadn't modeled them as assumptions that could move.
For what it's worth: quantifying exactly how much of the value destruction was "location" versus "structural market shift" is genuinely hard, and not all analysts measure this the same way. But the directional story holds.
A few things that tend to show up in practice
Broker comps are the most common input for rent assumptions, and they're usually fine as a starting point. The problem is when they become the only input — which happens more often than people admit. Comps reflect what was signed 6–18 months ago in buildings that may have a different profile, tenant mix, or access situation than yours. In a submarket that's moving, that lag matters more than it looks on a spreadsheet.
New supply pipelines get underweighted in a specific way: teams check what's been permitted, but less often track what's actively under construction or what a large landlord two blocks away has been quietly pre-leasing for the past year. We've seen deals where the submarket supply picture was already outdated at signing — not because anyone missed something obvious, but because that data lives across four different sources and nobody had aggregated it recently.
Exit assumptions are where the location story tends to get the least scrutiny. Most models use a spread over going-in yield, or a cap rate benchmarked at the metro level. Whether the specific submarket has actually traded — and at what volume, and who the buyers are — often doesn't make it into the model. For assets in thinner markets, that gap can be meaningful at exit.
Cushman & Wakefield's post-COVID analysis found that employees living within a mile of their workplace return to the office at over 90% of pre-pandemic levels, while those more than three miles away sit at around 70%. Whether that's a location variable or a tenant preference variable is a reasonable debate. But it's the kind of thing worth having in a pre-development analysis of an office asset, rather than assuming it away.
Broker comps are the most common input for rent assumptions, and they're usually fine as a starting point. The problem is when they become the only input — which happens more often than people admit. Comps reflect what was signed 6–18 months ago in buildings that may have a different profile, tenant mix, or access situation than yours. In a submarket that's moving, that lag matters more than it looks on a spreadsheet.
New supply pipelines get underweighted in a specific way: teams check what's been permitted, but less often track what's actively under construction or what a large landlord two blocks away has been quietly pre-leasing for the past year. We've seen deals where the submarket supply picture was already outdated at signing — not because anyone missed something obvious, but because that data lives across four different sources and nobody had aggregated it recently.
Exit assumptions are where the location story tends to get the least scrutiny. Most models use a spread over going-in yield, or a cap rate benchmarked at the metro level. Whether the specific submarket has actually traded — and at what volume, and who the buyers are — often doesn't make it into the model. For assets in thinner markets, that gap can be meaningful at exit.
Cushman & Wakefield's post-COVID analysis found that employees living within a mile of their workplace return to the office at over 90% of pre-pandemic levels, while those more than three miles away sit at around 70%. Whether that's a location variable or a tenant preference variable is a reasonable debate. But it's the kind of thing worth having in a pre-development analysis of an office asset, rather than assuming it away.
A practical check
Here's a way to test whether the location view is actually modeled or just assumed: ask what happens to the deal if the submarket story is wrong by 20%.
If the answer traces back to documented, submarket-specific data — great. If it traces back to "we used market rents from the broker," the location hasn't been modeled. It's been inherited.
This isn't about adding complexity for its own sake. It's about making the assumptions visible so they can be questioned, updated, and defended — by the team, by lenders, and by an investment committee.
Here's a way to test whether the location view is actually modeled or just assumed: ask what happens to the deal if the submarket story is wrong by 20%.
If the answer traces back to documented, submarket-specific data — great. If it traces back to "we used market rents from the broker," the location hasn't been modeled. It's been inherited.
This isn't about adding complexity for its own sake. It's about making the assumptions visible so they can be questioned, updated, and defended — by the team, by lenders, and by an investment committee.
Aino pulls submarket vacancy trends, competitive supply, infrastructure status, zoning, and demographic data for any address — usually before an analyst would finish pulling the same data manually. If you're working on a pre-development deal and want to see what that looks like for a specific site, you can run a free analysis here.
Aino pulls submarket vacancy trends, competitive supply, infrastructure status, zoning, and demographic data for any address — usually before an analyst would finish pulling the same data manually. If you're working on a pre-development deal and want to see what that looks like for a specific site, you can run a free analysis here.
Analyze your locations now → https://app.aino.world
Analyze your locations now → https://app.aino.world