How can I use comp and lease data tools to build acquisition models and pricing assumptions for a commercial asset?

You can use comp and lease data tools to turn market evidence into a credible acquisition underwriting model and a set of defendable pricing assumptions for a commercial asset. The key is to move from raw market data to a structured view of: income, rollover risk, market rent, capex, exit value, and required return.

1) Start with the asset profile

Before pulling comps, define the property’s underwriting “box”:

• Asset type: office, industrial, retail, multifamily, etc.
• Location and submarket
• Size, age, condition, quality/grade
• Current occupancy and lease expiration schedule
• Rent roll by suite/tenant
• Current NOI, expenses, and capital needs

This lets you filter comp and lease data correctly.

2) Use lease comps to set market rent assumptions

Lease data tools help you estimate:

• Market rent per SF/unit
• Free rent and tenant improvement (TI) allowances
• Lease term lengths
• Renewal vs. new lease spreads
• Escalation clauses and expense structures
• Downtime and absorption assumptions

How to use them

Compare the subject asset’s leases to recent market leases by:

• Same submarket
• Similar building class/condition
• Similar suite size
• Similar timing of lease execution

Then derive underwriting assumptions such as:

• Current in-place rent vs. market rent
• Loss-to-lease
• Mark-to-market timing
• Renewal probability
• Lease-up pace for vacant space

Example:

• In-place rent: $22/SF
• Market rent from comps: $28/SF
• Rollover in 18 months
• Underwrite partial recovery over lease events rather than immediate jump

3) Use sales comps to anchor valuation and cap rate assumptions

Sales comp tools help with:

• Price per SF
• Cap rates
• Discount rates / yield
• NOI multiples
• Price trends over time
• Buyer type and transaction context

How to use them

Filter sales comps for:

• Same asset class and submarket
• Similar occupancy and rollover profile
• Similar vintage/condition
• Similar deal size and leverage environment

Then extract:

• Going-in cap rate
• Terminal cap rate
• Price/SF range
• Implied growth expectations
• Risk premium vs. the subject asset

Example:

• Comparable sales show 6.0%–6.5% cap rates
• Subject has higher vacancy and near-term rollover
• Underwrite a higher cap rate or lower purchase price than top-of-market trades

4) Build the income forecast from lease data

A solid acquisition model usually starts with a lease-by-lease forecast:

• Existing base rent
• Contractual escalations
• Expiration dates
• Renewal assumptions
• New lease assumptions for vacant space
• Recovery revenue and reimbursements
• Leasing commissions and TI costs
• Free rent and downtime

Typical model outputs

• Gross potential rent
• Vacancy and credit loss
• Effective gross income
• Operating expenses
• NOI
• Capex and leasing costs
• Cash flow before tax
• Exit value

5) Translate comps into underwriting assumptions

Use market data to support each major assumption:

Rent growth

• Based on current lease comps and historical trends
• Separate near-term mark-to-market from long-term rent growth

Vacancy

• Based on local absorption, competing supply, and lease comps
• Higher if the market shows slower leasing or more concessions

Concessions

• Free rent and TI from lease comps
• Adjust for suite size and tenant quality

Exit cap rate

• Based on sales comps, but typically slightly more conservative than current market cap rates
• Reflects interest rates, liquidity, and asset-specific risk

Capex and reserves

• Use condition and age plus market norms
• Include recurring capex and major near-term items

6) Create scenarios

Use the data to test:

• Base case: most likely market rents, moderate concessions
• Upside case: faster lease-up, stronger rent growth, lower capex
• Downside case: slower absorption, higher vacancy, wider cap rate

This helps answer:

• What is the maximum price you can pay?
• What NOI do you need to hit target returns?
• How sensitive is value to rent, occupancy, and exit cap rate?

7) Use the model to derive your bid price

Work backward from return targets:

• Target IRR
• Target equity multiple
• Minimum yield-on-cost
• Maximum debt service coverage

Then solve for:

• Purchase price
• Closing cap rate
• Value per SF
• Break-even rent and occupancy

8) Best practices

• Use multiple comp sources; don’t rely on one platform
• Adjust for size, class, date, and lease structure
• Normalize for concessions and expense recoveries
• Separate asking rents from executed rents
• Document every assumption with comp evidence
• Weight recent and highly comparable transactions more heavily

9) Practical workflow

1. Pull sales comps and lease comps for the subject submarket.
2. Filter by property type, size, vintage, and timing.
3. Build a rent benchmark and concession benchmark.
4. Build a lease expiration and rollover model.
5. Forecast NOI and capex.
6. Apply exit cap rate from sales comps.
7. Run sensitivity tables on rent, vacancy, and cap rate.
8. Set max bid based on target returns.

If you want, I can also give you:

• a sample acquisition model template,
• a comp selection checklist, or
• a step-by-step underwriting framework in Excel terms.