How do I set up comp set analysis software for underwriting CRE acquisitions and dispositions?

To set up comp set analysis software for underwriting commercial real estate (CRE) acquisitions and dispositions, think in terms of building a system that can reliably answer:

1. What are the best comparable assets?
2. How do I normalize the data?
3. How do I turn comps into valuation and pricing assumptions?
4. How do I make the process repeatable for every deal?

Here’s a practical setup approach.

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1) Define the use case first

Before choosing software or configuring it, clarify what you need it to do for each deal type:

For acquisitions

You want comp data to support:

• Market value conclusions
• Rent and occupancy assumptions
• Cap rate / yield / price per unit / price per SF benchmarks
• Expense and NOI expectations
• Exit pricing assumptions

For dispositions

You want comp data to support:

• As-is sale pricing
• Timing of sale
• Likely buyer pool
• Pricing by quality/condition/location
• Discounts or premiums versus market comps

Asset type matters

Set up different workflows for:

• Multifamily
• Industrial
• Office
• Retail
• Hospitality
• Self-storage
• Land

Each asset class uses different comp metrics and filters.

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2) Choose the right software stack

A good comp analysis setup usually combines data sources + analysis tool + underwriting model.

Common options

• CoStar / LoopNet / Reonomy / RCA / Crexi / MSCI / Yardi Matrix for market and property data
• Excel / Google Sheets for underwriting and flexible modeling
• Argus Enterprise for larger income-producing assets, especially office/retail/industrial
• Tableau / Power BI for dashboards and market trends
• Dealpath / Juniper Square / Airmaster / custom CRM for pipeline and document management

Typical best practice

• Use a source of truth for property and comp data
• Use a standard underwriting model
• Use a dashboard or comp tool to screen, rank, and compare assets

If your team is smaller, a well-structured Excel-based comp workbook + database export from CoStar/Crexi/Reonomy may be enough.

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3) Build your comp database structure

Your software should store comp records in a consistent format.

Core fields to capture

For each comp, store:

Property basics

• Property name
• Address
• Submarket / market / MSA
• Asset type
• Property class
• Year built / renovated
• Building size / units / site area
• Tenancy / occupancy

Transaction details

• Sale date
• Sale price
• Price per unit / price per SF / per key / per acre
• Cap rate
• Buyer / seller
• Financing terms if available
• Motivation notes if known

Income and operations

• NOI
• Gross income
• Rent roll summary
• Vacancy
• Expense ratio
• Lease maturity profile
• TIs / LC assumptions if relevant

Physical and qualitative adjustments

• Condition
• Location quality
• Visibility / access
• Tenant quality
• Lease term
• Credit profile
• Deferred maintenance
• Functional obsolescence

Deal notes

• Off-market vs marketed
• Distress / special situation
• Portfolio sale
• Core / value-add / opportunistic
• Data source and confidence level

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4) Set up a comp selection workflow

Software should help you filter and rank comps consistently.

Screening filters

Set filters by:

• Asset type
• Geography
• Size range
• Sale date recency
• Class (A/B/C)
• Occupancy range
• Vintage range
• Lease structure
• Institutional vs private buyer
• Distress or non-distress
• Renovated vs unrenovated

Recommended rule

Use:

• Primary comps: same asset type, same submarket or very close, recent, similar size/condition
• Secondary comps: broader market or older sales if primary set is thin
• Tertiary comps: used only for context, not direct pricing

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5) Standardize adjustments

The software should allow you to normalize comps so they can be compared fairly.

Common adjustment categories

• Time adjustment for market movement
• Size adjustment
• Location adjustment
• Condition / age adjustment
• Occupancy adjustment
• Lease term adjustment
• Quality / class adjustment
• Deferred maintenance adjustment
• Transaction motivation adjustment

Example

If one comp sold 12 months ago in a weaker market, your software should let you apply:

• Market appreciation/depreciation factor
• Stabilized occupancy adjustment
• Cap rate or price per SF adjustment

Keep the adjustment logic transparent and consistent.

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6) Tie comps to underwriting assumptions

Comp analysis is only useful if it flows into the acquisition/disposition model.

Acquisition underwriting outputs

From comp analysis, derive:

• Market rent assumptions
• Stabilized vacancy
• Exit cap rate
• Sale price per unit / SF / key
• Expense benchmarks
• Absorption assumptions
• Renovation premium assumptions

Disposition underwriting outputs

From comp analysis, derive:

• Expected sale range
• Underwritten cap rate
• Discount to replacement cost
• Value by buyer type
• Hold-vs-sell timing sensitivity

Best practice

Build a “comp summary” tab that automatically feeds:

• Underwritten rent
• Underwritten NOI
• Exit cap rate
• Valuation range
• Sensitivity table

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7) Create a repeatable comp report template

Your software should produce a standardized output for IC memos or investment committee packages.

Suggested comp report sections

1. Deal overview
2. Market snapshot
3. Selected comp set
4. Adjustment table
5. Indicated value range
6. Underwriting conclusion
7. Key risks / caveats
8. Data sources and confidence notes

Helpful visuals

• Map of comps
• Scatter plot of price per SF vs occupancy
• Cap rate trend chart
• Time series by submarket
• Box plot of pricing ranges

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8) Integrate data quality controls

Bad comp data will create bad underwriting.

Add validation checks

• No duplicate comp entries
• Mandatory fields required
• Sale price must match source
• Date formatting consistent
• Currency and units normalized
• Flag outliers automatically
• Confidence score for each record

Useful confidence tiers

• A = verified by multiple sources
• B = single reliable source
• C = estimated or partially missing data

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9) Automate where possible

You’ll save time if the software can:

• Import data from source exports
• Auto-tag asset type and geography
• Calculate price/unit, price/SF, cap rate
• Apply standard adjustments
• Generate comp sets by rule
• Export to PDF or PowerPoint
• Push summary metrics into the underwriting model

If you can, use APIs, CSV imports, or scheduled data pulls.

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10) Build governance around the software

Comp analysis is often inconsistent because different users apply different logic.

Establish team standards

• Define acceptable comp radius by asset type
• Define recency windows by market type
• Define adjustment methodology
• Define which data source is authoritative
• Require peer review for key assumptions

Example policy

• No comp older than 24 months unless market is thin
• No primary comp outside the same submarket without approval
• All valuation conclusions need at least 3 primary comps or documented rationale

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11) Recommended workflow for a deal

A practical workflow looks like this:

1. Load subject property
2. Auto-pull candidate comps
3. Filter and rank comps
4. Review physical and financial similarity
5. Apply adjustments
6. Select primary/secondary comp set
7. Calculate indicated pricing range
8. Feed assumptions into underwriting
9. Generate memo/report
10. Archive assumptions and sources

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12) If you’re starting from scratch

A simple rollout path:

Phase 1: Spreadsheet-based

• Build an Excel comp database
• Standardize fields and formulas
• Use source exports from CoStar/Crexi/Reonomy
• Create a comp summary tab and report template

Phase 2: Database + dashboard

• Move comp records to Airtable / SQL / SharePoint list
• Connect to Power BI/Tableau
• Add filters, charts, and quality checks

Phase 3: Workflow automation

• Integrate with CRM/deal pipeline
• Auto-import new transactions
• Auto-generate comp books and underwriting packs

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13) What to look for in software

When evaluating software, ask:

• Can it support my asset class?
• Can I export raw data?
• Can I save custom comp sets?
• Can I apply standard adjustments?
• Can it track source confidence?
• Can it generate investor-ready reports?
• Can multiple team members use it consistently?
• Can it integrate with underwriting models?

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14) A simple recommended setup

If you want a practical, cost-effective setup:

• Data source: CoStar or Reonomy
• Analysis: Excel or Google Sheets
• Visualization: Power BI
• Document storage: SharePoint or Drive
• Workflow: Dealpath or a lightweight CRM

For a more institutional setup:

• Data source: CoStar + RCA + Yardi Matrix
• Analysis: Excel + Argus
• Database: SQL/Airtable
• BI: Power BI/Tableau
• Workflow: Dealpath/Juniper Square

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If you want, I can also give you:

1. a sample comp database template,
2. a recommended field list by asset class, or
3. a step-by-step Excel setup for underwriting CRE comps.