Key Takeaways:
- Most CRE underwriting still happens in Excel, but manual workflows slow down loan review and introduce data inconsistencies across teams.
- Lenders can automate underwriting in Excel using standardized templates, formulas, macros, scenario toggles, and structured data imports.
- Excel automation improves speed, but it cannot deliver real-time data, multi-user control, portfolio-level monitoring, or audit-grade consistency on its own.
- AI-powered platforms, such as Blooma’s Origination Intelligence and Portfolio Intelligence, expand automation beyond spreadsheets by extracting data, scoring deals, and monitoring risk continuously.
- Institutions that move from manual spreadsheets to intelligent automation gain faster time to LOI, stronger risk governance, and the ability to review more deals with the same team.
Excel remains the primary tool for CRE underwriting. It is flexible, approachable, and deeply embedded in lending workflows. But as deal volume grows and regulatory expectations tighten, manual spreadsheets create friction. Teams spend significant time cleaning data, reformatting templates, and validating formulas, which slows decisions and increases operational risk.
Automation in Excel can help. From formulas and macros to structured templates and integrated data imports, lenders can streamline many repetitive underwriting steps. Still, there is a ceiling to how far spreadsheets can scale.
Blooma’s perspective reflects what lenders see daily: Excel can be optimized, but true transformation comes when AI takes over data extraction, risk scoring, and monitoring. When analysts can spend less time gathering inputs and more time evaluating deals, underwriting becomes faster, more informed, and far more consistent.
Why Manual Excel Underwriting Slows CRE Lending
Manual underwriting using Excel has clear limitations for lenders who need accuracy, transparency, and scale.
- Manual data entry introduces delays and inconsistency
- Underwriters often spend hours keying in rent roll data, borrower information, historical financials, and market metrics. Each manual step introduces potential for miskeys and reduces analyst capacity. Analysts typically spend 60 - 80% of their time preparing data rather than analyzing it, constraining deal throughput.
- Decentralized templates weaken risk governance
- Even when teams share “standard” templates, small formula edits or outdated versions can create inconsistent calculations. This complicates audits and makes it difficult to benchmark deals across analysts or regions.
- Slow workflows impact responsiveness
- When underwriting preparation takes days, lenders miss opportunities to respond first. For banks and private lenders competing for quality assets, timing is often a decisive factor.
- These pain points are exactly where automation adds value
- Automation reduces human touchpoints, aligns logic across teams, and accelerates the path to a confident decision.
How to Automate CRE Underwriting in Excel
Even without advanced technology, lenders can significantly streamline Excel-based underwriting. These four steps introduce structure and consistency into standard workflows.
Step 1: Standardize Data Inputs
Creating consistency at the intake stage prevents rework and reduces data cleansing.
- Build a consistent underwriting template.
- Include dedicated fields for property details, borrower financials, market information, operating history, and loan assumptions. Standardized input formats allow formulas to reference the same cell locations in every deal.
- Use data validation and named ranges.
- Structured dropdowns prevent invalid entries. Named ranges make formulas more transparent and reduce risk when templates are updated.
- Reduce formatting overhead.
- Underwriters no longer need to rekey figures from differently formatted documents, which shortens the time from intake to analysis.
Step 2: Use Excel Functions & Macros
Excel is powerful when its automation features are used intentionally.
- Automate core underwriting calculations.
- Pre-program DSCR, cap rate, LTV, breakeven occupancy, sensitivity multipliers, and other standard metrics. This ensures consistent calculation logic across every deal reviewed.
- Build macros or Power Query imports for document processing.
- Macros can automatically clean data from rent rolls or operating statements. Power Query lets analysts import CSVs or recurring data files and refresh them with one click.
- Reduce repetitive work.
- Once formulas and macros are locked in, analysts avoid rebuilding the same workflows for every new deal.
Step 3: Add Risk Flags & Conditional Logic
Risk visibility improves when Excel templates proactively highlight issues.
- Use conditional formatting for policy variance.
- Color-coded flags can identify items like DSCR below threshold, high vacancy, major expense jumps, or loan sizing gaps. Analysts instantly see where deeper review is needed.
- Automate stress tests with scenario tabs.
- Create toggles for occupancy shifts, market rent changes, interest rate increases, or expense inflation. These scenarios help analysts evaluate resilience under multiple economic conditions.
- Strengthen early-stage risk assessment.
- Analysts can quickly determine which deals deserve full underwriting and which should be deprioritized.
Step 4: Connect External Data Sources
CRE Lenders have no shortage of tools, especially when it comes to data. Data availability is one of the biggest drivers of underwriting speed. However, so much time is spent going back and forth to these data tabs to paint that picture. Wouldn’t it be easier if your data & workflows connected?
- Use APIs, simple automation tools, or CSV imports for data ingestion.
- Market data, borrower information, rent comps, or historical performance can often be imported automatically.
- Reduce the risk of copy-paste errors.
- Direct imports eliminate manual manipulation of data and keep information consistent across the file.
- Understand the limits of Excel data connectivity.
- Even with these improvements, Excel cannot deliver live market updates or continuous monitoring on its own. That is why advanced lenders transition to intelligent automation solutions, such as Blooma’s Origination Intelligence, which processes documents, extracts financials, and scores deals in minutes instead of hours.
Advantages and Limits of Excel-Based Automation
Excel automation improves speed and structure, but it cannot fully support modern lending requirements.
Short-term gains are real
Macros, formulas, and structured templates save analysts hours per deal. They also create more consistent reporting and reduce rework.
But Excel lacks real-time data
Spreadsheets cannot automatically refresh market valuations, rent comps, or borrower information. Analysts must manually update files, which slows decisions and increases the risk of outdated assumptions.
Version control introduces operational risk
If analysts work from different versions of the same file, errors multiply. Audit teams often discover misaligned formulas or differing calculation logic across regions.
Regulatory expectations are rising
The OCC’s Model Risk Management guidance emphasizes transparency, validation, and governance. Excel lacks embedded audit trails, user tracking, and standardized logic, which makes compliance more burdensome.
AI-driven automation removes these constraints
Platforms like Blooma combine Excel-style familiarity with automated data extraction, scoring, and continuous monitoring.
Transitioning from Templates to True Automation
Lenders do not have to abandon Excel to benefit from automation. The most effective approach follows three phases.
Phase 1: Optimize Excel Workflows
Before adopting new technology, lenders should strengthen what they already use.
Audit existing spreadsheets.
- Identify outdated formulas, redundant steps, or inconsistent layouts. Document how metrics such as DSCR, NCF, and cap rate are calculated.
Clean and reorganize for consistency.
- A unified template provides a clear foundation for future automation.
Phase 2: Integrate Data Feeds
Data connections create meaningful efficiency gains.
Link Excel to property databases or internal systems.
- APIs or Power Automate workflows reduce copy-paste cycles and align underwriting inputs with centralized data sources.
Sync datasets across underwriting and portfolio review.
- Connecting front-end underwriting to portfolio monitoring improves continuity and makes deal lifecycle analysis easier.
Phase 3: Adopt Intelligent Automation (AI Layer)
This is where transformation occurs.
Blooma’s Origination Intelligence automates:
- Data extraction and spreading from rent rolls, financials, and offering memoranda.
- Borrower profiling using firm-specific lending criteria.
- Instant deal scoring based on institution-defined thresholds.
Blooma’s Portfolio Intelligence extends automation post-origination through:
- Continuous risk monitoring.
- Proactive alerts when performance or market conditions change.
- Dynamic portfolio-level stress testing.
This intelligence layer modernizes the entire workflow without requiring any system replacement.
Risk Reduction Through Underwriting Automation
Automation strengthens a lender’s entire risk framework by removing manual variability and giving teams continuous visibility into emerging credit signals. What begins as an efficiency gain quickly becomes a structural advantage for governance, consistency, and portfolio resilience.
Minimize human manipulation
- When data flows directly into the underwriting model, miskeys and spreadsheet logic errors drop dramatically.
Strengthen ongoing risk tracking
- Annual or semi-annual portfolio reviews leave lenders with delayed visibility. Automated monitoring provides real-time insight instead of retroactive reporting.
Receive early warning signals
- Blooma’s proactive alerts notify lenders when market, tenant, or financial conditions shift. Analysts can intervene before small issues escalate.
Improve audit alignment
Together, these improvements turn automation into a foundation for stronger decision-making, positioning lenders to manage risk proactively rather than reactively as their portfolios evolve.
The ROI of Automating CRE Underwriting
Automation creates clear operational and financial benefits for CRE lenders. Excel-based automation can shorten deal review cycles by hours through standardized templates and reduced manual entry. When AI enters the workflow, the impact scales quickly. Lenders using Blooma report an 85% faster time to LOI, three times more deals reviewed, and far less manual spreading and data preparation, all of which translate to faster responses and stronger competitive positioning.
Better data also leads to better decisions. With real-time, accurate information, analysts can evaluate deals with greater confidence and negotiate more effectively. By reducing manual touchpoints, teams shift their time toward higher-value work, lowering operational costs while improving the quality and consistency of underwriting.
The Future of CRE Underwriting: From Excel to AI Intelligence
Underwriting is evolving from spreadsheet-driven analysis to intelligent, continuous evaluation.
- AI will handle ingestion, scoring, validation, and monitoring.
- Analysts will focus on structuring, risk interpretation, and client relationships.
- Institutions that invest early will define the next decade of underwriting standards.
- Blooma’s intelligence layer gives lenders the ability to modernize without disrupting existing workflows.
Automation is becoming foundational to competitive CRE lending.
Turning Excel Automation into Intelligent Underwriting
Automation is now a necessity for lenders who want faster decisions, better risk visibility, and higher portfolio confidence. Excel templates are a valuable first step, but they cannot deliver the intelligence and scale today’s market demands.
Blooma’s Origination Intelligence and Portfolio Intelligence bring together the familiarity of spreadsheets with automated data extraction, scoring, and real-time monitoring. The result is underwriting that is faster, more consistent, and built for long-term growth.
See how Blooma’s Origination Intelligence and Portfolio Intelligence can modernize your underwriting process without disrupting your current systems. Request a demo to discover how your team can underwrite faster, smarter, and with greater confidence.
FAQs
- Can I automate my CRE underwriting process entirely in Excel?
- Not fully. You can streamline calculations and imports, but Excel lacks live data connectivity and audit control that AI platforms provide.
- What’s the difference between Excel automation and AI underwriting?
- Excel automation speeds up calculations; AI underwriting automates the entire workflow, from data extraction to scoring and monitoring.
- How does automation improve accuracy in underwriting?
- By removing manual data entry, automation reduces human error and applies consistent scoring criteria across every deal.
- Is Blooma compatible with my existing Excel-based workflow?
- Yes. Blooma integrates as an intelligence layer, enhancing your current process without replacing your spreadsheets or LOS.
