Complete Guide to CRE Comps & Lease Data

• Sales comps and lease data form the backbone of pricing, valuation, and negotiation in commercial real estate. 
• A comp is only as good as its context. Sale price alone says little; cap rate, occupancy at sale, financing terms, and lease structure tell the real story.
• Lease data reveals what a property actually earns, not just what it last traded for. In-place vs. market rent comparisons are often where the real upside, or risk, lives.
• Verified, traceable data improves credibility with clients, lenders, and investment partners. Sourcing matters as much as the numbers themselves.
• Off-market prospecting becomes significantly more targeted when you layer comps with ownership records, lease rollover schedules, and hold period data.

Commercial real estate comps and lease data are at the core of every serious deal. Without reliable data, even experienced professionals are forced to rely on assumptions, and in competitive markets, assumptions are costly.

The challenge is that CRE data is often fragmented. Public records can be slow to update. Broker-reported deals often lack context. Lease terms are frequently unverified or incomplete. That makes it hard to know what's current, what's reliable, and what's actually comparable.

To evaluate data correctly, it helps to understand how these sources interact and where each can mislead if used in isolation. When layered together, comps, lease data, and property records support more accurate pricing, clearer risk assessment, and stronger decision-making.

Sales comps help set the tone for pricing and negotiation. When you know what similar assets have sold for, you can anchor expectations and defend your position. Cap rates, price per square foot, and recent sale dates all shape how buyers and sellers approach a deal and how lenders think about leverage.

Lease data has become just as important. In income-producing properties, value is ultimately a function of rent, lease term, and tenant strength. Knowing current market rents, lease expirations, and rent escalation structures helps investors understand both risk and timing. It also shows where upside may exist.

Consider a value-add multifamily asset trading at a 5.8 cap rate. On a sales comp basis alone, that might look fully priced or even expensive compared to recent trades. But if in-place rents are 20% below the current market, the reversion story changes the underwriting entirely. The same asset now looks underpriced once you model mark-to-market rents over the next few lease cycles. That gap between in-place and market rents becomes visible only when lease data is evaluated alongside transaction data.

The reverse is equally true. Relying only on strong lease data can mask valuation risk. A property with solid in-place rents and a long weighted average lease term ( WALT) might still be overpriced if comparable assets have been trading at compressed cap rates that have since expanded. Sales comps anchor the exit math. Together, both data types tell you where income and value are moving.

Not all commercial real estate data is created equal. Data volume alone doesn't create confidence. What matters most is source, recency, and traceability, especially when pricing and credibility are on the line.

A stale or unverified comp can undermine a deal in multiple ways. A broker who defends a listing price based on a comp that turns out to be a distressed sale — or one that occurred 30 months ago in a different rate environment — loses credibility with their client instantly. An investor who underwrites based on lease data from a marketing flyer that was never updated after a major tenant left faces a rude surprise at due diligence. Lenders conduct their own validation; when their comp set differs from yours, deals slow or collapse.Lenders run their own checks; when their comps don't match yours, deals slow down or fall apart.

Sales comps and property records are sourced from:

• Public filings
• Recorded transactions
• Verified broker-reported information 

Lease insights may be pulled from:

• Offering memorandums
• Marketing flyers
• Marketplace materials
• Direct broker input

The difference is transparency. When comps and lease records show their source, you can assess relevance instead of accepting numbers at face value.

Recency matters too, particularly in periods of rate movement. A cap rate from 18 months ago may reflect a fundamentally different financing environment. A lease signed in 2021 doesn't tell you much about what a tenant will pay to renew in today's market. Prioritizing verified and current data over raw volume reduces the risk of anchoring to numbers that no longer reflect market reality.

Property records provide the foundation. They typically include:

• Parcel and building characteristics
• Lot size, square footage, and zoning
• Ownership and transaction history

This information answers basic but critical questions. Who owns the asset? When did it last trade? Has it changed hands multiple times in a short window? This last question can itself be a signal worth investigating.

Sales comps build on that foundation. They include:

• Sale price and sale date
• Pricing metrics such as cap rate or price per square foot
• Asset type, size, and location

A raw comp is a starting point, not a conclusion. The ability to evaluate comp quality separates a useful data point from a misleading one.

A strong comp generally meets several criteria: it sold last within 12 to 18 months, is located within a genuinely comparable submarket, matches the subject property on asset class and condition tier, and has a verifiable, traceable source. The further a comp deviates from any of these, the more judgment is required to apply it.

Common characteristics of weak comps include:

• Different vintage buildings that carry higher capex risk, 
• Distressed sales that don't reflect arm's-length market pricing, 
• Outlier financing terms (such as seller financing or assumable debt) that artificially compressed the cap rate, and 
• Trades that occurred during unusual market conditions. 

When using a weaker comp, document the adjustment rather than presenting it as a direct comparison.

Here, an important distinction is worth calling out. A lease-encumbered sale and a vacant sale tell fundamentally different stories, even on the same street. A fully leased asset trades on income certainty. A vacant building trades on replacement cost, land value, and the buyer's confidence in their own leasing assumptions. Mixing the two without adjustment produces unreliable benchmarks.

When property records and sales comps are merged in one place, research becomes more efficient. Instead of switching between tools, you can evaluate characteristics and transaction history together. That speeds up underwriting and client reporting.

Lease data is most powerful when it is tied directly to a specific property record rather than presented as a market-level abstraction.

At the building level, lease data may show overall occupancy, anchor tenants, and blended rent levels. At the space level, it may include individual suite information and tenant names.

CRE professionals often look for visibility into:

• Lease commencement and expiration dates
• Rent structure, including base rent and escalations
• Source documentation for the lease details

Traceable sources matter. When lease information is tied back to filings, broker-reported data, or marketplace materials, it adds credibility — not just for internal underwriting, but for investor meetings and lender conversations where your assumptions will be stress-tested.

For investors, lease expiration schedules are particularly important to analyze carefully. A major tenant with a lease expiring in 18 months and no renewal option in place is a fundamentally different risk profile than the same tenant with a 7-year term remaining. 

WALT provides a quick measure of asset stability. A low WALT signals near-term rollover risk, while a high WALT signals income durability but may also limit upside if in-place rents are below market.

In underwriting, solid lease data should answer this question: what is the gap between in-place net operating income (NOI) and market NOI? If in-place rents are materially below market, that gap represents embedded upside — but it also represents execution risk. If in-place rents are at or above market, the asset may be fully priced with limited room for rent growth. Lease data makes that calculation possible. Without it, underwriting relies on assumptions.

Lease data coverage can vary by asset type and geography. 

Major metros with active brokerage communities tend to have deeper reporting. Office and retail in gateway markets like New York, Los Angeles, and Chicago generally have stronger lease data availability than, say, industrial in secondary or tertiary markets where deals are often done quietly and never widely reported.

When data is thin, experienced professionals triangulate:

• Asking rents from comparable listings on the market provide a proxy for where the market is pricing space today. 
• Recent vacancy and absorption trends for the submarket can indicate directional rent movement even when specific lease terms are unavailable. 
• Local brokers who are active in the market often know what rents are clearing, even if those terms haven't shown up in any data platform yet. Building relationships with the two or three brokers who dominate local leasing activity is crucial to success.

Lease insights may include tenant information, lease term and duration, and context around rent and expenses such as common area maintenance (CAM). Together, these details help professionals understand how properties are performing in real time.

For investors comparing in-place rents to current market levels, this coverage(even when incomplete) highlights whether a property is under-rented, fully priced, or positioned for future rent growth.

The practical challenge with comps is not finding any data — it's finding the right data.

Data becomes actionable only when it can be filtered precisely. Depending on your goals, you may find that it is helpful to narrow results by:

• Record type, such as sales comps versus lease data
• Asset class
• Geography or custom-drawn boundaries
• Timeframe and pricing metrics

In practice, this means getting specific. If you're underwriting a 40,000 SF suburban office asset, the relevant comp set is not "office buildings that sold in this metro." It's “Office buildings in the 30,000 to 60,000 SF range, in the same submarket or a genuinely competing one, that traded within the last 18 to 24 months, and that are multi-tenant rather than single-tenant net-lease deals.”

Applying those filters might take a 200-record result set down to 12 meaningful comps, which is where the actual analysis begins. This approach can also save significant time.

Useful filters typically include record type (sales comp vs. lease data), asset class, geography or custom-drawn submarket boundaries, timeframe, and pricing metrics. 

Map-based search tools support submarket analysis particularly well. By zooming into a corridor or drawing a boundary, users can isolate comps that truly compete with a subject property. That level of control makes analysis more precise.

Comps and lease data show up differently depending on your role in a deal:

• Brokers use comps to shape pricing and guide client conversations, involving repricing, counteroffers, and marketing strategies tied to market evidence.
• Investors compare in-place rents to market levels. They assess lease rollover schedules and evaluate stability versus upside. Sales comps help them determine whether pricing aligns with recent trades.
• Analysts and Appraisers rely on comps and lease trends for market studies and valuation support. With clean data you can reduce back-and-forth and support stronger, more defensible reports.
• Developers use sales and lease data to test feasibility. They evaluate site selection and determine whether projected rents support new construction costs.

In each case, access to verified, centralized data improves speed and accuracy.

Comps and lease data also support prospecting by replacing generic outreach with data-informed conversations. Deep market knowledge demonstrates preparation and expertise long before the conversation starts.

With comps data in hand, certain timing signals can indicate opportunity:

• Long-held assets where an owner may be approaching a decision point
• Lease rollovers approaching that create operational pressure or exit motivation
• Pricing gaps compared to current market levels

The most targeted outreach combines multiple signals simultaneously. An owner who acquired an asset 12 or more years ago, whose largest tenant has a lease expiring within the next 18 to 24 months, and whose in-place rents sit below current market levels may be approaching a decision point.

That owner faces a decision point. They can either execute the renewal and reposition at market rents, or sell before the rollover risk reprices the asset downward. A broker who surfaces that conversation with specific comp data and lease analysis in hand is offering something useful, not just making a cold call.

Layering comps with ownership records and listing history adds further context. If a property has been listed and withdrawn multiple times, that history tells you something about pricing expectations. If an owner has never listed but has held through significant appreciation, there may be a capital gains conversation worth having.

Once you’ve narrowed down relevant sales comps and lease data, the next step is getting that information into a format you can actually use.

On Crexi, exports typically fall into two categories:

• Property and transaction data only: This includes parcel details, building characteristics, sale price and date, cap rates, lease terms, rent levels, and transaction history. These exports are ideal for underwriting models, valuation analysis, internal deal memos, and client presentations. They focus strictly on asset-level and transaction-level information.
• Property data with ownership and contact details: In some workflows, the goal is outreach over analysis. In those cases, exports can include ownership records, mailing addresses, and available contact information. This allows brokers and investors to move from research to action without rebuilding lists manually.

Having both options matters. One supports pricing and valuation work. The other supports prospecting, relationship-building, and off-market conversations.

One best practice is to document the export date and applied filters directly in the file name or worksheet. Comps go stale, and a six-month-old comp set used in a current pitch, without that context, can undermine your credibility if someone on the other side of the table has more recent data. A simple notation in the file name takes 30 seconds and avoids that problem entirely.

Exports also make collaboration easier. Teams can:

• Share comps with partners or analysts for deeper modeling
• Build presentation materials for clients
• Maintain offline tracking spreadsheets
• Create targeted outreach lists tied to specific deal criteria

Instead of retyping data from multiple sources, everything can be organized in one structured file. That saves time and reduces errors, especially when you’re working across several assets or markets at once.

On Crexi, property records, sales comps, and lease data are connected inside one research environment. They’re designed to work together around the property record rather than existing in separate tools.

When you open a property record in Crexi Intelligence, you can see parcel details, ownership history, transaction data, and available lease information in one place. Sales comps are layered into the same view, so you can compare past trades without switching platforms. Lease data appears at the property level, often with source documentation for added transparency.

Search filters allow you to narrow results by asset type, geography, timeframe, and pricing metrics. Map-based tools help define submarkets visually, making it easier to pinpoint relevant comps quickly.

Because everything lives within the same system, your workflow stays organized. Instead of piecing together data from multiple sources, you’re working from a centralized set of verified information. This leads to faster analysis and better decision-making.

In competitive markets, speed matters. But so does precision. Leveraging comps and lease data within Crexi supports faster, more informed deal analysis.