How do you quantify the income at risk across an industrial portfolio?
You weight every line of the rent roll by how likely that tenant is to move, then add up the income behind the weakening covenants, the near-term breaks and expiries, and the volatile occupiers, because the roll alone records only contractual income, not its probability of holding. Scayled turns that static roll into a live income-at-risk view: it scores each tenancy's trajectory, attaches the evidence, and ranks the units by exposed income, refreshed every fortnight. For an industrial fund where one tenant can carry 15 to 30 percent of an asset's NOI, that figure is what should drive capital and disposal decisions.
- Why a rent roll shows income but never income at risk
- At-risk income is covenant trajectory times event proximity times concentration
- What Scayled computes and returns
- Where a static renewal-probability assumption stops
- Using a live income-at-risk view to drive capital and disposal decisions
Why a rent roll shows income but never income at risk
A rent roll is an accounting artefact. It tells you what each tenant is contracted to pay, when the lease ends, and where the breaks sit. What it does not tell you is the one thing an asset manager needs to size risk: the probability that each of those lines survives to its next event. A line paying on time today and a line paying on time today behind a tenant that just lost its anchor contract look identical on the roll, yet they carry completely different income risk.
Quantifying income at risk means converting that static schedule into an expectation. Not every pound of contracted rent is equally secure, and treating it as if it were is how a portfolio review misses the unit that is about to hand back. The number you want is not total income, it is the slice of income sitting behind tenancies whose trajectory, lease timing, or concentration makes a move materially more likely in the period you are underwriting.
At-risk income is covenant trajectory times event proximity times concentration
Three dimensions decide how exposed a line of income is. The first is covenant trajectory: is the tenant's business strengthening, holding, or deteriorating right now, ahead of any rating. The second is lease-event proximity: a weakening covenant two years from a break is a watch item, the same covenant four months from expiry is a live decision. The third is concentration: the same probability of moving matters far more on a tenant carrying a quarter of the asset than on one carrying two percent.
Multiply those together and the rent roll re-sorts itself. A large, healthy, long-dated single-let tenant may contribute enormous income and almost no risk. A mid-sized 3PL on a deteriorating trajectory, eight months from a break, on a unit it half-fills, may be a small line on the roll and the single largest piece of at-risk income in the portfolio. Income at risk is the product, not any one factor, which is exactly why a spreadsheet sorted by rent never surfaces it.
What Scayled computes and returns
Scayled watches each tenant entity's actual business for the operational events that move trajectory: contract wins and losses, M&A and parent-level consolidation, profit warnings, restructuring and administration of related entities, divestments, and changes to the distribution network. It scores each tenancy on that trajectory and combines it with the lease timing and the share of asset income that tenant carries, producing a ranked income-at-risk view across the portfolio.
The output is not a single headline number with nothing behind it. Each tenancy in the ranking shows the income exposed, the evidence driving its score, and an estimated action window, refreshed every fortnight so the figure tracks reality rather than the last review. An asset manager can read straight down the list from the largest exposed income to the smallest and know both how much is at stake on each unit and why.
Where a static renewal-probability assumption stops
A cash-flow model like ARGUS Enterprise can apply a renewal probability to each tenancy, and that is genuinely useful for valuation. But the probability is an assumption you set, usually a market average by sector or grade, and it does not change when this specific tenant loses the contract that funds the rent. The model will keep discounting that income at the same probability until you manually revise it, which means it is only ever as current as your last intervention.
That is the honest division of labour. ARGUS, Yardi, MRI, and VTS each do their job well, and Scayled assumes you run one of them. None of them watch the tenant's business for the change that should move the probability in the first place. Scayled supplies that live input, the entity-level trajectory with evidence, so the assumption in your model and the figure on your income-at-risk view reflect what is actually happening rather than a sector default.
Using a live income-at-risk view to drive capital and disposal decisions
Once exposed income is quantified and ranked, it becomes a planning instrument rather than a worry. It tells a fund which units to prioritise for an early regear or a quiet renewal conversation, where to sequence a disposal out of softening income before a buyer's diligence finds it, and how to give a lender or capital partner a current, defensible read on income security instead of a stale rating. A void of six to twelve months on a large distribution unit can dwarf a year of its rent, so acting on the right line two quarters early is the whole return.
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