What Actually Drives Feasibility Before You Build a Model
The spreadsheet is not where feasibility is determined. It's where feasibility is confirmed — or disproven — after someone has already formed a judgment about whether a deal has a reasonable chance of working.
This distinction matters because teams that treat the model as the starting point tend to spend a lot of time building detailed underwriting for deals that were never viable. Teams that understand what actually drives feasibility before opening a spreadsheet make better screening decisions faster.
Here's what those pre-model drivers actually are — and how to think about them.
The rent ceiling
Every affordable housing deal has a revenue ceiling set by the program: restricted rents based on AMI, not market conditions. That ceiling is the first feasibility constraint, and it's knowable before you build anything.
If you know the HUD Area Median Income for a geography and the income targeting the deal requires — say, 60% AMI — you can calculate the maximum achievable rents almost immediately. Those rents, multiplied by a projected unit count and adjusted for vacancy, give you a top-line revenue figure. Everything else in the deal — debt service, operating expenses, required reserve contributions — has to fit under that ceiling.
Before you open a model, ask: given the AMI in this market and the unit count this site can support, is there a rent level that makes the capital structure workable? If the rent ceiling is too low to support any plausible combination of debt and equity, you don't have a deal. You have a site that looks interesting but isn't viable for this program.
The density floor
Affordable housing deals have minimum viable scale. Fixed costs — legal, financing, compliance, soft costs — don't shrink proportionally with project size. A 40-unit deal carries costs that are only modestly lower than a 90-unit deal, but with less than half the revenue.
This means there's a density threshold below which the numbers don't work for most program types. That threshold varies by market and deal type, but it's a number you should know for the programs your team typically pursues.
The pre-model question is: can this site support the density the deal needs? If the answer is clearly no under current zoning — and there's no realistic path to more units through a variance, density bonus, or rezoning — the deal probably doesn't work at this site, regardless of how attractive everything else looks.
The land basis constraint
Land cost in affordable housing is determined by what the program can support — not by what comparable land is selling for in the broader market. The maximum land basis a LIHTC deal can carry is derived from the capital stack: what equity and debt the deal can generate, minus total development cost excluding land, leaves a residual that represents the maximum you can pay for the site.
In markets where comparable parcels are trading at values well above what LIHTC deals can support, you have a structural land basis problem. No amount of creative structuring closes a $2 million gap between what the market wants for land and what the deal can support.
This isn't a model-dependent question. You can run a rough land basis check — what do deals of this type in this market typically support? what are comparables trading at? — before you've opened anything. If the gap is large, the deal probably doesn't advance.
The soft debt question
Most LIHTC deals need soft loans to close the gap between what equity and conventional debt produce and what the project costs. Whether that soft debt is realistically available — in the right amount, on the right timeline, from a source your organization has a real relationship with — is a feasibility question that's independent of the model.
A deal that's financially feasible assuming a $3 million soft loan from the city housing authority is only actually feasible if your team has a credible path to that soft loan. If the program is oversubscribed, if your organization hasn't worked with that authority before, or if the application cycle doesn't align with the credit round you're targeting — the soft debt assumption in your model may not reflect reality.
Before you commit to detailed underwriting, have a realistic answer to the question: where is the soft debt coming from, and how confident are we in that path?
The competitive context
In states with competitive 9% LIHTC allocation, a site that looks feasible in isolation may not be competitive in a given credit round. QAP scoring criteria — transit proximity, opportunity area designations, income targeting depth, local financing commitments — shape which deals get awards and which don't.
A deal that doesn't score competitively doesn't close, regardless of how clean the underwriting is. Before you build the model, have a working view of how this site would score under the current QAP and whether that score is competitive given what else you know about the market.
What these five drivers have in common
Rent ceiling, density floor, land basis constraint, soft debt availability, and competitive context are all assessable before a model is built. They don't require detailed financial analysis — they require market knowledge, program knowledge, and a structured way of applying both to a new site.
The teams that screen most effectively aren't doing more analysis before underwriting. They're doing different analysis — focused on the five questions that actually determine whether a deal works, in the sequence that surfaces the answer fastest.
That's what pre-model feasibility thinking actually is. Not a shortcut to underwriting. A more precise version of the question you're trying to answer.
Alpha Deal surfaces the pre-model feasibility signals — program eligibility, density supportability, rough capital stack viability — that let your team make better screening decisions before the underwriting begins.