A strong covenant can hide a weak store.

Commercial property analysis often splits the problem in two. Credit teams look at the retailer. Leasing teams look at the unit. Yet the rent is earned at one specific location, with its own pedestrian routes, customer mix, anchors, competitors and daily rhythm.

The Tenant Resilience Index proposed here stress-tests that location.

The question is practical: how much deterioration in local demand can a unit absorb before its economics become difficult to sustain?

We aren't trying to build another bankruptcy prediction model. The framework spots micro-location vulnerabilities early — before a retailer starts asking for concessions, refuses a renewal or hands back the keys.

For landlords, that extra lead time matters.

1. A Strong Tenant Can Still Have a Weak Store

Take two shops occupied by the same national retailer.

Same balance sheet. Same covenant. Similar rent.

The first unit sits beside a station exit and gets most of its trade from weekday office workers. The second has slightly lower footfall, but its demand is spread across residents, workers and weekend visitors.

The difference looks small until something changes.

Hybrid working hits the first store harder. A station entrance closure reroutes its passing trade. A neighbouring anchor leaves, and suddenly the people still walking past are not necessarily the people who used to shop there.

The trading backdrop is already harsh. The Insolvency Service's Business Insolvency Demography 2015 to 2025 recorded 3,860 wholesale and retail insolvencies in England and Wales during 2025. Accommodation and food services had the highest insolvency rate among the nine largest industries, at 268 per 10,000 businesses.

Store churn appears earlier than corporate insolvency. PwC and Green Street recorded 12,804 chain outlet closures across Great Britain in 2024, around 35 a day.

Once a unit goes dark, the landlord starts paying for time. In England, most commercial property receives 100% empty property relief for the first three months of vacancy. Full business rates are normally payable afterwards, subject to exemptions; qualifying industrial premises can receive six months.

The asset-management problem therefore starts before a tenant actually fails. The earlier a fragile unit is identified, the more time there is to avoid a void or prepare for one.

2. What Tenant Resilience Actually Measures

The Tenant Resilience Index isn't an off-the-shelf credit rating. It is a proposed diagnostic framework.

Location-level tenant resilience measures the amount of local demand deterioration a unit can absorb before its economics become unsustainable.

Here, “unsustainable” refers to the individual unit.

A national retailer can remain perfectly solvent while shutting a marginal branch. It may also keep an underperforming store open for strategic reasons. No mobility dataset can tell you what the board will decide.

Geodata can, however, expose the external demand structure supporting that branch.

That makes it useful alongside metrics landlords already understand. NewRiver's FY26 Annual Report, covering the year ended 31 March 2026 and published on 25 June 2026, reported an average Occupational Cost Ratio of 7.8%.

OCR describes current occupancy affordability. A resilience model tests how quickly that affordability could deteriorate if the local demand underneath it weakens.

3. Five Data Layers Behind the Index

Average footfall is a blunt instrument.

Two locations can attract almost the same number of pedestrians and still produce very different trading conditions. What matters is who those people are, when they arrive, how they move and what alternatives compete for their spending.

A working resilience model breaks down into five layers:

The data stack itself is not especially exotic.

BT Location Insights provides aggregated daily and hourly footfall, separates visitors, residents and workers, and includes demographic, income and home-catchment information. The ONS UK retail footfall dataset also uses BT Active Intelligence data.

The route layer needs more care.

A 300-metre radius tells you very little about whether someone can conveniently reach the shop. People follow streets. They choose station exits. They cross at particular junctions, avoid barriers and take shortcuts through arcades or passages.

A frontage can sit fifty metres from a heavy pedestrian flow and still capture very little of it.

That's where street-network topology matters. Entrances, crossings and station exits act as nodes; walkable segments become edges; barriers and poor connectivity add friction. Observed pedestrian flows can then calibrate the network where data are available.

The same hyperlocal logic sits behind Why London Must Model Density Before Building It: aggregate development numbers can conceal very uneven movement at street level.

For a landlord, that difference can exist between two neighbouring units.

4. Footfall Is Not Demand

A unit may sit in front of 20,000 pedestrians a day.

Fine. How many actually matter?

Effective Demand = Footfall × Category Fit × Spending Capacity × Route Capture × Temporal Fit × Competition Factor

This is an analytical scaffold, not a universal retail equation.

Footfall gives the available movement pool.

Category Fit estimates how much of that pool is plausibly relevant to the format. A pharmacy, discount grocer and premium fashion store should value the same crowd differently.

Spending Capacity uses aggregated purchasing-power or socio-demographic proxies.

Route Capture estimates how much surrounding demand reaches the frontage. Street topology, crossings, station exits, barriers and entrances all affect it.

Temporal Fit accounts for timing. Heavy commuter traffic at 8:30am can be excellent for coffee and almost useless for another format.

Competition Factor measures spatial cannibalisation from nearby alternatives competing for the same pool of demand.

The coefficients become useful when several of them move at once.

5. Finding the Breaking Point

A score of 72/100 sounds clean, but it masks the actual risk. A percentage margin is far more actionable.

Suppose a unit has baseline Effective Demand of 100. Sales data, occupancy costs or another internal benchmark suggest that its economics become problematic below 82.

Its Resilience Margin is 18%.

Resilience Margin = (Baseline Effective Demand − Break-Even Effective Demand) / Baseline Effective Demand

With unit-level sales data, the break-even point can be calibrated against turnover, margin or occupancy cost.

Without those data, the model can still rank locations by relative resilience. It should not pretend to predict closure with false precision.

Then comes the stress test.

A basic version can run Effective Demand at −5%, −10%, −15%, −20% and −30%.

In the real world, shocks do not hit neatly down a straight line.

An anchor shock can damage several variables at once. A department store closes. Footfall falls. Browsing visitors disappear. The centre retains more transit traffic. Category Fit drops too.

A transport shock behaves differently. Closing one station exit may barely change district-wide footfall while cutting Route Capture for one frontage.

A competitive shock can leave total movement unchanged. The new operator simply captures part of the available demand.

A behavioural shock, such as hybrid working, may reduce office traffic while increasing daytime residential activity.

Each scenario needs to follow its actual transmission mechanism. Otherwise the stress test becomes another spreadsheet where every risk is translated into “minus 20%”.

6. A Tale of Two Stores

Two synthetic stores. Same retailer.

On paper, Unit A wins out: 18,000 daily passersby versus Unit B's 17,500.

Traditional site selection could easily rank Unit A first.

Then the next-door anchor shuts down.

Footfall drops 12%.

Worse, the remaining crowd is less relevant to the retailer, cutting Category Fit by 8%. Pedestrian routing shifts too, reducing Route Capture by another 5%.

Because these friction factors compound multiplicatively:

100 × 0.88 × 0.92 × 0.95 ≈ 77

Effective Demand falls by roughly 23%.

Unit A only had an 11% resilience margin. It is now well beyond the stress threshold.

Unit B began with slightly weaker baseline demand but far less concentration: lower route dependency, less reliance on one anchor and a 26% margin.

This does not prove that Unit B will outperform indefinitely. It shows something more useful: the extra 500 daily passersby at Unit A were masking a much more fragile demand structure.

7. From Resilience to Lower Churn

At single-asset level, the model can be used throughout the lease cycle.

Before signing, it can expose a poor fit between a strong brand and a fragile micro-location.

During the lease, it can flag deterioration in visitor mix, anchor dependency or Route Capture before the problem appears as arrears or a closure notice.

At renewal, it adds context to rent negotiations. A proposed increase may look defensible against ERV while consuming most of the unit's remaining resilience headroom.

Fragility isn't always a tenant problem. Sometimes it is a physical asset problem.

Poor visibility, an awkward entrance, weak signage, a pedestrian barrier or the wrong neighbouring uses can all depress Route Capture. If relatively modest capex can materially improve access or visibility, replacing the occupier may be the more expensive solution.

Portfolio analysis exposes another layer.

A landlord may have dozens of tenants and believe its income is diversified, then discover that 30% of rent depends heavily on weekday office-worker demand.

Several assets may rely on the same Underground corridor.

A group of unrelated tenants may share one anchor exposure.

The tenant schedule looks diversified. The underlying geography may not be.

8. The Second Problem: Empty Property Risk

Preventing churn is only part of the job.

Then comes the void.

Reletting takes time: marketing, negotiations, approvals, incentives, fit-out and reopening. In a weak location, every incoming tenant also needs convincing that the unit can trade.

Early warning changes the timetable.

If resilience starts deteriorating before lease expiry, the landlord can test replacement categories against the updated catchment in advance.

Perhaps fashion demand has weakened while residential growth has strengthened convenience, health or everyday services.

Perhaps commuter traffic has fallen while evenings and weekends have improved.

Those shifts affect tenant selection, achievable rent and potentially the physical specification of the unit.

A mature resilience system could eventually support two separate outputs:

Probability of Vacancy

and

Expected Vacancy Duration

That second variable is where many portfolio models still have a blind spot. A unit with a high probability of vacancy but strong reletting demand presents a very different risk from one likely to sit empty for eighteen months.

9. Where the Model Stops

Let's be honest: location models break down the moment analysts treat synthetic indices as gospel.

Geodata will not show you a tenant's exact margin structure. It will not reveal head-office strategy. Pretending otherwise is a quick way to misprice lease risk.

Without sales data, the output remains a location resilience proxy.

Historical sales, occupancy costs and category-specific calibration make the stress model much stronger. Even then, traffic and turnover do not move one-for-one.

A 10% footfall decline may barely matter if the lost visitors had low conversion.

Another store may lose only 5% of traffic but lose its highest-spending segment.

Same headline shock. Very different P&L effect.

Category elasticity needs to be calibrated rather than guessed.

Privacy deserves the same pragmatism.

BT states that its Location Insights data are anonymised and aggregated and that reported volumes below ten are suppressed. The UK's ICO guidance on effective anonymisation also warns that k-anonymity has limitations and can remain vulnerable to inference attacks in some circumstances.

Jack Borie has covered on HackerNoon the broader privacy trade-off around commercial location data in How Your GPS Trail Became a Commodity — and What You Deserve in Return.

Individual movement trajectories are overkill here anyway. Aggregated cell-level dynamics are sufficient for this use case.

10. Tenant Risk Is Local

Covenant analysis still matters. It tells you whether the company behind the lease is financially capable of paying.

Location analysis tells you how exposed one particular unit is to a fragile demand structure.

That may mean dependence on an anchor, a station exit, one customer segment or one short window of the day. At portfolio level, the same model can uncover common geographic exposures hidden behind apparently diversified tenant names.

Early detection gives the landlord room to act before the keys come back: adjust the lease, improve access or frontage, reconsider capex, or start testing the replacement category before expiry.

That is where the Tenant Resilience Index earns its keep.

A strong tenant can still have a weak store. The expensive part is finding out too late.