Analyzing a rent roll means systematically evaluating unit-level tenant data to determine a property's current income, occupancy health, and upside potential. Start by calculating occupancy and average rent, then check for red flags like month-to-month concentration, below-market rents, and lease expiration clustering.
What Is Rent Roll Analysis?
Rent roll analysis is the process of evaluating a property's tenant and lease data to assess income quality, identify risks, and quantify upside potential. It is one of the first steps in underwriting a commercial real estate acquisition.
A rent roll is a snapshot. It shows who is paying what, when their lease expires, and which units are vacant at a single point in time. Analysis turns that snapshot into actionable intelligence: Is the income sustainable? Where is the upside? What could go wrong after you close?
The framework below works for any income-producing property. The specific metrics shift by asset class (multifamily, self-storage, industrial), but the analytical process is the same.
The 7-Step Rent Roll Analysis Process
Work through these steps in order. Each step builds on the previous one. By Step 7, you will have a complete picture of the property's income profile and know whether to continue diligence or move on to the next deal.
Step 1: Verify the Date
The rent roll must be dated within 30 days of your analysis. This is your first check and it takes 5 seconds.
A stale rent roll (60+ days old) may not reflect recent move-outs, new leases, rent changes, or concessions. If the broker provides a rent roll from 3 months ago, request a current one before spending time on analysis. Per Fannie Mae Multifamily lending requirements, rent rolls used for loan underwriting must be current. The HUD MAP Guide imposes similar standards for FHA-insured multifamily loans. Apply the same standard to your acquisition analysis.
Step 2: Calculate Physical and Economic Occupancy
Physical occupancy tells you how many units have tenants. Economic occupancy tells you how much of the potential income you are actually collecting.
Physical Occ = Occupied Units / Total Units x 100
Economic Occ = Collected Rent / Gross Potential Rent x 100
// 95 units occupied (Physical Occ = 95%)
// GPR = $150,000/mo, Collected = $132,000/mo
// Economic Occ = 88% (gap = concessions + bad debt)
A property can show 95% physical occupancy but only 88% economic occupancy. The 7-point gap reveals concessions, delinquencies, or below-market rents that eat into actual cash flow. Economic occupancy is the number that matters for your underwriting model.
Step 3: Compute Average In-Place Rent by Unit Type
Calculate average rent for each unit type separately. A blended property-wide average hides important variation.
Avg Rent (1BR) = Total 1BR Rent / Occupied 1BR Units
Avg Rent (2BR) = Total 2BR Rent / Occupied 2BR Units
Rent/SF = Monthly Rent / Unit Square Footage
Compare your averages to market data. The NAA Income & Expense Survey provides benchmarks by market and property vintage. If 1BR units average $1,200 but the market is at $1,400, you have quantifiable upside. If they are at $1,500 in a $1,400 market, tenants may not renew at current rents.
Step 4: Calculate Loss-to-Lease
Loss-to-lease quantifies the gap between what tenants currently pay and what the market supports. It is the single most important metric for evaluating rent upside.
LTL % = (Market Rent - In-Place Rent) / Market Rent x 100
LTL $ = (Market Rent - In-Place Rent) x Occupied Units
// LTL % = 14.3%, LTL $ = $10,000/month = $120,000/year
Calculate loss-to-lease per unit type. A 14% loss-to-lease on 1BR units tells a different story than a blended 7% across the property. The unit-level breakdown reveals where the rent growth opportunity actually lives.
Important caveat: loss-to-lease is theoretical upside, not guaranteed income. Tenants may leave rather than accept increases. Renovations may be required to justify market rents. Always stress-test your assumptions with turnover costs and a realistic lease-up timeline.
Step 5: Map the Lease Expiration Schedule
Group all leases by expiration month and year. The expiration schedule reveals rollover risk and your window for rent increases.
| Expiration Window | Units Expiring | % of Total | Risk Assessment |
|---|---|---|---|
| Month-to-Month (MTM) | 18 | 18% | Elevated: can leave in 30 days |
| Next 90 Days | 12 | 12% | Immediate renewal pressure |
| 91-180 Days | 22 | 22% | Normal: plan renewals now |
| 181-365 Days | 35 | 35% | Stable: locked in for near term |
| 12+ Months | 13 | 13% | Low risk: long-term committed |
In the example above, 30% of tenants (MTM + next 90 days) could leave within 90 days of closing. That is significant rollover exposure. If your business plan assumes rent increases, you need those tenants to renew, not vacate.
Watch for clustering. If 25 leases all expire in September, the prior owner may have done a mass lease-up with identical terms. You will face a mass renewal event that could spike vacancy if market conditions shift.
Step 6: Identify Renovated vs. Unrenovated Units
For value-add acquisitions, separating renovated and unrenovated units is critical. The rent premium between groups validates (or invalidates) the renovation business plan.
Not every rent roll labels renovation status explicitly. Look for these signals: rent variation within the same unit type (renovated units typically command $100-300/month premiums), recent lease start dates on higher-rent units, and notes about upgrades like "stainless steel appliance packages" or "granite countertops."
| Unit Group | Count | Avg Rent | Rent/SF | Occupancy | Premium |
|---|---|---|---|---|---|
| Renovated 1BR | 28 | $1,475 | $2.15 | 96% | +$200 |
| Classic 1BR | 42 | $1,275 | $1.86 | 93% | base |
| Renovated 2BR | 15 | $1,850 | $1.98 | 100% | +$250 |
| Classic 2BR | 15 | $1,600 | $1.71 | 87% | base |
This table tells a clear story. Renovated units command a $200-250 premium and lease faster (higher occupancy). The renovation business plan is supported by the data. You can model the remaining 57 classic units as renovation candidates and project the premium with confidence.
If renovated units show no meaningful premium over classic units, the renovation program is not working. This is a red flag for any value-add thesis.
Step 7: Flag Outliers and Anomalies
The last step is looking for data that does not fit the pattern. Outliers often reveal the most important stories.
- Rent outliers: a unit renting at 30% below its peers may have a long-term legacy tenant, a lease concession, or a data error. Each explanation has different implications.
- Utility spikes: if one month shows utilities jumping from $10,000 to $22,000, investigate. It could signal a leak, a billing error, or a seasonal pattern the T12 will reveal.
- Recent move-ins at above-market rents: the seller may have inflated rents pre-sale. These tenants are likely to leave or demand concessions at renewal.
- Units with no lease dates: missing data is itself a red flag. It may indicate corporate housing, employee units, or tenants without formal leases.
- Security deposit mismatches: deposits significantly below rent amounts may signal tenants who signed leases years ago at lower rents, or simply sloppy record-keeping.
Cross-reference every outlier against the T12 operating statement. The rent roll is a point-in-time snapshot. The T12 shows the trailing 12-month reality and often explains what the rent roll cannot.
Key Rent Roll Metrics and Formulas
These are the core calculations you should perform on every rent roll you analyze. Use a rent roll template with these formulas built in to save time.
Physical Occ = Occupied Units / Total Units x 100
Economic Occ = Collected Rent / Gross Potential Rent (GPR) x 100
Avg Rent = Total Monthly Rent (occupied) / Occupied Units
Rent/SF = Monthly Rent / Unit SF
LTL = (Market Rent - In-Place Rent) / Market Rent x 100
MTM % = Month-to-Month Units / Occupied Units x 100
Rent Roll Red Flags Checklist
Use this checklist during every rent roll review. Each red flag includes what it means operationally and what action you should take.
| Red Flag | What It Means | Action Required |
|---|---|---|
| High MTM (>20%) | Over 20% of tenants on month-to-month leases. They can leave with 30 days notice, creating a turnover cliff. | Model higher turnover costs and vacancy in Year 1. Budget for lease-up concessions to convert MTM tenants to new terms. |
| Lease Clustering | 25%+ of leases expire within a single quarter. Mass lease-up or identical terms signal concentrated rollover risk. | Stagger renewals post-acquisition. Offer early renewal incentives to spread expiration dates across the calendar. |
| Below-Market Rents (No Renovation Plan) | Rents are 10-15% below market but units have not been renovated. Rents may be low because units are dated, not because of mismanagement. | Inspect units before assuming organic rent growth. Budget for renovations if upgrades are needed to justify market rents. |
| Vacancy in One Unit Type | All vacant units are the same type (e.g., all 2BR/2BA). The issue may be pricing, layout, or local demand, not general market softness. | Research submarket demand by unit type. Reprice affected units or evaluate conversion to a different configuration. |
| Stale Date (>60 Days) | The rent roll is outdated. Tenants may have moved, rents may have changed, and concessions may have been offered since the date shown. | Request a current rent roll before proceeding. Do not underwrite based on stale data. |
| Missing Security Deposits | Blank or $0 deposit fields for occupied units. Signals sloppy management or tenants placed without standard lease protections. | Verify deposit policy with the property manager. Confirm total deposits will transfer at closing (they are a liability). |
| Concessions Not Disclosed | Rent roll shows face rent but does not reflect concessions (free months, reduced rent periods). Effective rent is lower than face rent. | Request a concession report or lease abstracts. Calculate effective rent: (Total Lease Value) / (Lease Term in Months). |
How Rent Rolls Differ by Asset Class
The core analysis process is the same across property types, but the specific fields, metrics, and red flags shift by asset class. Here is what changes.
| Dimension | Multifamily | Self-Storage | Industrial |
|---|---|---|---|
| Tenant Identifier | Unit number, beds/baths | Unit size (5x5, 10x10, etc.), climate/non-climate | Suite/building, tenant name, SF |
| Key Rent Metrics | Rent/unit, rent/SF, concessions | Street rate vs. in-place rate, rate per SF | Base rent/SF, NNN charges, annual escalations |
| Lease Structure | 12-month leases, MTM common | Month-to-month standard (higher churn expected) | 3-10 year terms, NNN, annual escalations (2-3%) |
| Critical Red Flag | MTM concentration >20% | Street rate far above in-place (churn risk) | Single tenant >25% of NRA or income |
| Occupancy Benchmark | 93-96% physical | 85-92% physical (higher churn is normal) | 95%+ for single-tenant; 85-93% multi-tenant |
Multifamily Specifics
Multifamily rent rolls are the most granular. Expect unit type (beds/baths), square footage, lease dates, rent, deposit, and status for every unit. The key analysis focuses on rent-by-unit-type comparisons, loss-to-lease, and the renovated vs. unrenovated split for value-add deals.
Pay special attention to concessions. In lease-up or competitive markets, properties offer "2 months free on a 14-month lease." The rent roll may show the face rent ($1,400/month), but the effective rent is $1,200/month. Request a concession report separately.
Self-Storage Specifics
Self-storage rent rolls differ from multifamily in two important ways. First, nearly all tenants are month-to-month, so the MTM red flag does not apply the same way. Second, the key metric is "street rate vs. in-place rate." Long-tenured customers often pay below current street rates, creating loss-to-lease upside, but raising rates aggressively can trigger move-outs.
Segment by unit size (5x5, 10x10, 10x20, etc.) and climate control status. Each segment has different demand characteristics and pricing power.
Industrial Specifics
Industrial rent rolls focus on tenant quality and lease structure. Key fields include tenant name, credit quality, lease term, base rent per SF, NNN charges, annual escalation rate, and renewal options. Single-tenant concentration risk (one tenant occupying over 25% of net rentable area) is the primary red flag.
Industrial leases are typically NNN (triple net), meaning tenants pay taxes, insurance, and maintenance in addition to base rent. Analyze both base rent and total occupancy cost when comparing to market.
Common Rent Roll Formats and Why They Matter
Rent rolls arrive in every conceivable format. The format affects how quickly you can extract and analyze the data, which directly impacts your deal velocity.
Yardi Exports
Yardi is the dominant property management platform for institutional multifamily. Its rent roll exports are comprehensive but notoriously difficult to work with. Columns vary by Yardi version and owner configuration. Formatting inconsistencies, merged cells, and multi-page layouts are common. As one acquisitions analyst put it, "Yardi reports are the worst" when it comes to getting clean data into an underwriting model.
AppFolio Exports
Common for smaller operators (sub-500 units). Generally cleaner formatting than Yardi, but with less granular data. May omit fields like market rent or renovation status that larger systems track.
RealPage Exports
RealPage serves mid-to-large operators. Its rent roll output tends to be structured but may split data across multiple tabs or reports. Reconciling the "unit detail" report with the "lease summary" report adds analysis time.
PDF / Scanned Documents
The worst case scenario for data extraction. Rent rolls embedded in broker OMs or scanned from paper originals require manual retyping. A 200-unit rent roll in a scanned PDF can take 1-2 hours to transcribe, with a meaningful risk of data entry errors on every line.
Broker OM Tables
Rent rolls inside offering memoranda are often reformatted by the broker's design team. Columns may be renamed, reordered, or simplified. Some fields from the original PM export are dropped entirely. Always request the raw PM export in addition to the OM version.
Format inconsistency is the single biggest pain point in rent roll analysis. Every deal sends a different format, and your underwriting model expects a specific structure. The manual mapping process, copying data from one format into your template, consumes hours per deal.
How to Automate Rent Roll Analysis
Manual rent roll analysis works when you evaluate 2-3 deals per week. At 10+ deals per week, the manual process breaks. You spend more time retyping data than actually analyzing it.
Primer automates the extraction-to-analysis pipeline. Upload a rent roll in any format (Yardi export, scanned PDF, broker OM, AppFolio CSV), and Primer extracts every field, calculates the key metrics automatically, flags red flags, and maps the data to your underwriting model. Every extracted value is cited back to the source document, page, and table so you can verify accuracy with a single click.
The result: instead of spending 45 minutes per deal on data entry, your team spends that time on analysis, making better investment decisions from a larger opportunity set.
Stop sitting and typing in rent rolls
Primer extracts rent roll data from any format, calculates occupancy and loss-to-lease, flags red flags, and maps everything to your underwriting model. Every cell is cited to source.
Book a demo