Apers vs. Excel Macros and VBA

Overview

Every institutional CRE firm has them: Excel templates with VBA macros built by a talented analyst who left two years ago. The macros auto-populate assumptions, format the cash flow tabs, generate sensitivity tables, and maybe even produce an IC memo summary. They work — mostly. And when they don't, nobody is quite sure how to fix them.

Building in-house Excel tooling is the most common "AI alternative" in institutional CRE. It's also the most fragile. This isn't a criticism — it's a pattern we've seen at every firm we've talked to. The question is whether the maintenance burden, knowledge concentration risk, and scaling limitations of in-house macros justify a purpose-built system.

The Appeal of Building In-House

There are real reasons firms build their own Excel tools:

• Total control. Every formula is yours. Every tab is arranged how your IC expects it. The model matches your firm's conventions exactly because your team built it.
• No vendor dependency. No subscription fees, no API changes, no vendor going out of business. Excel will be around as long as your firm is.
• Incremental investment. You don't buy macros — they accumulate. An analyst adds a sensitivity table this month, a waterfall calculator next quarter. The cost is embedded in salary you're already paying.
• Familiarity. Your team knows Excel. They don't need to learn a new tool or change their workflow. The macros extend what they already do.

These are legitimate advantages. The in-house approach fails not because it's wrong but because it doesn't scale — and the failure mode is slow and hard to notice until it's acute.

The Hidden Costs

Knowledge concentration

The analyst who built the waterfall macro understands the accrual logic. The analyst who built the debt sizing macro understands the DSCR constraints. When either of them leaves — and they will — the firm loses the ability to maintain or extend those tools. The macros still run, but nobody can fix them when they break or adapt them when deal structures change.

We've talked to firms running macros built by analysts who left five years ago. They work for standard deals. But when the firm needs to model a new structure — a C-PACE layer, a preferred equity tranche with an IRR lookback — nobody can modify the macro, so the analyst builds it manually. The macro that was supposed to save time now sits unused for half their deals.

Maintenance burden

Excel updates break macros. Operating system changes break macros. A team member saves the template with a different column structure and every macro reference breaks. Debugging VBA in a 50-tab workbook is not what you hired your analysts to do — but it's what they end up doing.

No document intelligence

Macros can automate calculations inside a spreadsheet. They cannot read a PDF offering memorandum, extract a rent roll, reconcile it against a T-12, or map extracted data to model assumptions via a document intelligence engine. The entire document-to-model pipeline — the part that consumes the most analyst time — is outside the scope of what VBA can do.

No learning

Macros don't improve with use. Your hundredth deal runs through the same logic as your first. There's no comp database building in the background, no assumption benchmarks refining over time, no institutional knowledge compounding. Every deal is independent.

Feature Comparison

Table 1 — In-house macros offer maximum control but create maintenance burden and knowledge concentration risk. Apers trades some customization for reliability, document intelligence, and knowledge compounding.

When Macros Win

• Highly specialized, stable workflows. If your firm models the same deal type repeatedly with minimal variation — say, exclusively stabilized multifamily acquisitions — and you have a well-maintained template, macros may be sufficient. The key word is "well-maintained."
• Regulatory or compliance requirements. Some firms have compliance requirements that mandate specific model formats or calculation methodologies. In-house templates built to those specifications may be non-negotiable.
• Small teams with a dedicated builder. If one person on your team actively maintains and extends the macros, and they're not planning to leave, the in-house approach can work. The risk is concentration — it works until it doesn't.

When Apers Wins

• The builder left. Nobody can fix the macros, extend them to new deal types, or debug them when they break. This is the most common trigger for firms evaluating Apers for underwriting.
• Deal type diversity. Your firm models multifamily, office, industrial, LIHTC, development, and mixed-use. Building and maintaining macros for every deal type is a full-time job. Apers' Excel modeling engine covers them all from a growing collection of templates.
• Document volume. Macros can't read PDFs. If your bottleneck is getting data from documents into models, no amount of VBA solves that problem.
• Team scaling. New analysts need weeks to learn your proprietary templates. With Apers, they describe the deal and get a model. The ramp-up time drops from weeks to hours.
• Knowledge compounding. If you want your comp databases and assumption benchmarks to improve with every deal, macros can't do that. They're static logic. Apers compounds.

Transitioning from Macros

Most firms don't abandon their macros overnight. The common transition pattern:

Phase 1: Run new deals through both your existing templates and Apers in parallel. Compare outputs. Build confidence that Apers handles your deal types correctly.

Phase 2: Use Apers for deal types your macros don't cover — the new structures, the edge cases, the deals that require manual model building today. Keep macros for your standard workflow.

Phase 3: As Apers output matches your firm's conventions and your team builds comfort, shift primary modeling to Apers. Keep your templates as reference and for any compliance-specific requirements.

We don't ask firms to throw away years of institutional knowledge embedded in their templates. We ask them to test whether a system that already knows their deal structures can produce the same quality output — faster, with document integration, and without the maintenance burden.

For more comparisons, see our full comparison overview.