Real Estate Pro Forma Model with AI Cash Flow Forecasting

Tested prompts for real estate pro forma template excel compared across 5 leading AI models.

BEST BY JUDGE SCORE Claude Haiku 4.5 8/10

You're looking for a real estate pro forma template in Excel because you need to underwrite a deal fast. Maybe it's a 12-unit multifamily you're bidding on this week, a small retail strip your broker just sent over, or a value-add flip where you need to know if the numbers work before you waste another weekend. A static Excel template gets you 60% of the way there: rent roll, operating expenses, debt service, NOI, cash-on-cash, IRR. But the moment you change an assumption, you're back to manually rebuilding rows.

This page shows a faster path. Instead of downloading someone else's rigid template, you use an AI prompt that generates a custom pro forma for your specific deal, then forecasts cash flow month by month with assumptions you can challenge in plain English. You still get the Excel output. You just skip the part where you spend three hours wiring formulas.

Below you'll find the tested prompt, four model outputs compared side by side, and guidance on when this approach beats a downloadable template and when it doesn't.

When to use this

Use AI-generated pro formas when your deal doesn't fit a standard template, when you need to run multiple scenarios quickly, or when you're screening deals and don't want to commit hours before knowing if the numbers pencil. It's strongest for early-stage underwriting, LP pitch prep, and stress-testing assumptions against rate shifts or vacancy spikes.

Screening 5+ deals per week where speed matters more than CAD-level precision
Small multifamily, mixed-use, or retail deals that don't fit REIT-grade templates
Value-add or reposition plays where rent bumps and capex timing drive returns
LP or lender pitches where you need clean monthly cash flow tables fast
Sensitivity analysis on cap rate exit, interest rate, or lease-up pace

When this format breaks down

Institutional acquisitions over $50M where Argus is the lender-required standard
Complex waterfalls with multiple GP promote tiers and catch-up provisions
Development deals with construction draws, interest reserves, and phased lease-up that need month-by-month CAD
Any situation where an auditor, lender, or LP will stress-test every formula cell

The prompt we tested

You are a commercial real estate analyst and Excel modeler. Build a complete real estate pro forma template in Excel format for the property and deal described below. Follow these rules strictly: Output a tab-by-tab breakdown with clear section headers, then a markdown table per tab showing Row | Label | Formula or Value | Notes. Use real Excel formulas (=PMT, =IRR, =XIRR, =NPV, =IF, =SUMIFS) with explicit cell references. Keep explanations concise and include a short 'How to use this template' section at the end with 3-5 bullet points.

Property and deal details:
24-unit multifamily value-add acquisition in Tampa, FL. Purchase price $4.2M, 70% LTV at 6.5% interest, 30-year amortization, 5-year hold. Current average rent $1,450/unit with plans to renovate 18 units over 24 months and push rents to $1,750. Current vacancy 8%, target stabilized 5%, exit cap 5.75%.

Deliver the pro forma as a structured Excel-ready specification including: (1) Assumptions tab (purchase price, loan terms, rent growth, vacancy, expense growth, exit cap, hold period), (2) Unit Mix & Rent Roll, (3) 10-year Monthly and Annual Cash Flow (GPR, vacancy, other income, EGI, OpEx line items, NOI, debt service, CapEx, levered cash flow), (4) Debt Schedule (amortization, interest, principal, balance), (5) Sale & Returns (exit value, sale costs, net proceeds), (6) Returns Summary (unlevered IRR, levered IRR, equity multiple, cash-on-cash by year, DSCR, yield-on-cost). For each cell, specify the exact Excel formula using A1 references and tab names so the user can paste it directly into Excel. Include an AI cash flow forecasting section that uses historical trends and market assumptions to project rent growth and expense inflation scenarios (base, upside, downside).

Model comparison

Model	Quality	Cost	Latency	Tokens
Claude Opus 4.7	8	$0.1639	23.7s	688 / 2048
Claude Haiku 4.5	8	$0.0107	15.5s	476 / 2048
GPT-5.4	Not run yet
Gemini 2.5 Pro	Not run yet
Grok 4.1 Fast Reasoning	8	$0.0011	65.7s	575 / 2048

Outputs

Claude Opus 4.7 8/10

# Real Estate Pro Forma Template: 24-Unit Tampa Value-Add

## Tab 1: Assumptions

### Section A: Property & Acquisition
| Row | Label | Formula or Value | Notes |
|---|---|---|---|
| 3 | Property Name | "Tampa 24-Unit Value-Add" | Text |
| 4 | Units | 24 | |
| 5 | Purchase Price | 4200000 | |
| 6 | Price per Unit | `=B5/B4` | $175,000/unit |
| 7 | Acquisition Costs % | 2.0% | Closing, legal, DD |
| 8 | Acquisition Costs $ | `=B5*B7` | |
| 9 | Total Acquisition Basis | `=B5+B8` | |

### Section B: Loan Terms
| Row | Label | Formula or Value | Notes |
|---|---|---|---|
| 12 | LTV | 70% | |
| 13 | Loan Amount | `=B5*B12` | $2.94M |
| 14 | Interest Rate | 6.5% | |
| 15 | Amort (Years) | 30 | |
| 16 | Loan Term (Years) | 5 | Matches hold |
| 17 | Monthly Payment | `=PMT(B14/12,B15*12,-B13)` | |
| 18 | Annual Debt Service | `=B17*12` | |
| 19 | Equity Required | `=B9-B13` | |

### Section C: Operating Assumptions
| Row | Label | Formula or Value | Notes |
|---|---|---|---|
| 22 | Current Avg Rent/Unit | 1450 | |
| 23 | Post-Reno Rent/Unit | 1750 | |
| 24 | Units to Renovate | 18 | 75% of units |
| 25 | Reno Period (Months) | 24 | |
| 26 | Reno Cost/Unit | 12000 | |
| 27 | Total Reno CapEx | `=B24*B26` | $216,000 |
| 28 | Current Vacancy | 8% | |
| 29 | Stabilized Vacancy | 5% | |
| 30 | Rent Growth (Base) | 3.0% | Annual |
| 31 | Expense Growth | 2.5% | |
| 32 | Other Income/Unit/Mo | 50 | RUBS, laundry, fees |
| 33 | OpEx % of EGI (Yr1) | 42% | |

### Section D: Exit & Returns
| Row | Label | Formula or Value | Notes |
|---|---|---|---|
| 36 | Exit Cap Rate | 5.75% | |
| 37 | Sale Cost % | 2.0% | Broker + closing |
| 38 | Hold Period (Years) | 5 | |
| 39 | Discount Rate | 10% | For NPV |

---

## Tab 2: Unit Mix & Rent Roll

| Row | Label | Formula or Value | Notes |
|---|---|---|---|
| 3 | Unit Type | Header | |
| 4 | Classic (not renovated) | 6 | |
| 5 | Renovated | 18 | |
| 6 | Total Units | `=B4+B5` | Ties to Assumptions!B4 |
| 9 | Classic Rent | `=Assumptions!B22` | $1,450 |
| 10 | Renovated Rent | `=Assumptions!B23` | $1,750 |
| 11 | Monthly GPR (stabilized) | `=B4*B9+B5*B10` | $40,200/mo |
| 12 | Annual GPR (stabilized) | `=B11*12` | $482,400 |
| 13 | Current Annual GPR | `=Assumptions!B4*Assumptions!B22*12` | $417,600 |

---

## Tab 3: Annual Cash Flow (Years 1-10)

Columns: B=Yr1, C=Yr2, D=Yr3, E=Yr4, F=Yr5, G=Yr6... (extend through K=Yr10)

### Revenue Build
| Row | Label | Formula (Yr1 in Col B) | Notes |
|---|---|---|---|
| 3 | Year | 1 | Increment across row |
| 4 | Renovated Units (cum.) | `=MIN(Assumptions!$B$24, 9*B3)` | 9/yr ramp over 24 mo |
| 5 | Classic Units | `=Assumptions!$B$4-B4` | |
| 6 | Blended Avg Rent | `=(B4*Assumptions!$B$23+B5*Assumptions!$B$22)*(1+Assumptions!$B$30)^(B3-1)` | |
| 7 | GPR | `=B6*Assumptions!$B$4*12` | |
| 8 | Vacancy Loss | `=-B7*IF(B3<=2,Assumptions!$B$28,Assumptions!$B$29)` | |
| 9 | Other Income | `=Assumptions!$B$32*Assumptions!$B$4*12*(1+Assumptions!$B$30)^(B3-1)` | |
| 10 | **EGI** | `=B7+B8+B9` | |

### Operating Expenses
| Row | Label | Formula (Yr1) | Notes |
|---|---|---|---|
| 13 | Property Taxes | `=50000*(1+Assumptions!$B$31)^(B3-1)` | |
| 14 | Insurance | `=25000*(1+Assumptions!$B$31)^(B3-1)` | FL premiums |
| 15 | Property Mgmt (4%) | `=B10*0.04` | |
| 16 | Repairs & Maint | `=18000*(1+Assumptions!$B$31)^(B3-1)` | |
| 17 | Utilities | `=22000*(1+Assumptions!$B$31)^(B3-1)` | |
| 18 | Payroll | `=15000*(1+Assumptions!$B

Claude Haiku 4.5 8/10

# Complete Real Estate Pro Forma Template: 24-Unit Multifamily, Tampa FL

---

## TAB 1: ASSUMPTIONS

| Row | Label | Formula or Value | Notes |
|-----|-------|------------------|-------|
| 1 | **PURCHASE & LOAN ASSUMPTIONS** | | |
| 2 | Purchase Price | 4200000 | Total acquisition cost |
| 3 | Loan Amount (70% LTV) | =B2*0.7 | $2,940,000 |
| 4 | Equity Investment | =B2-B3 | $1,260,000 |
| 5 | Interest Rate (Annual) | 6.5% | Fixed rate |
| 6 | Amortization Period (years) | 30 | 360 monthly payments |
| 7 | Monthly Interest Rate | =B5/12 | 0.541667% |
| 8 | Monthly Loan Payment | =PMT(B7,B6*12,-B3) | Formula: PMT(rate, nper, pv) |
| 9 | | | |
| 10 | **OPERATING ASSUMPTIONS** | | |
| 11 | Total Units | 24 | Property size |
| 12 | Units to Renovate (Phase 1) | 18 | Over 24 months |
| 13 | Current Rent (non-renovated) | 1450 | $/unit/month, 6 units |
| 14 | Stabilized Rent (renovated) | 1750 | $/unit/month, 18 units post-reno |
| 15 | Current Vacancy Rate | 8% | Acquisition period |
| 16 | Stabilized Vacancy Rate | 5% | Year 2+ assumption |
| 17 | Other Income (% of Potential Rent) | 5% | Parking, late fees, pet rent |
| 18 | | | |
| 19 | **EXPENSE ASSUMPTIONS** | | |
| 20 | Year 1 Operating Expense Ratio (% of EGI) | 40% | Management, maintenance, utilities, insurance, taxes |
| 21 | Year 1 Operating Expenses (absolute $ for Year 1) | 180000 | Baseline; grows with inflation |
| 22 | Annual Expense Growth Rate | 3% | Inflation assumption |
| 23 | Annual CapEx Reserve (% of EGI) | 5% | Maintenance reserve |
| 24 | Renovation CapEx per Unit | 25000 | Hard costs only |
| 25 | Renovation Soft Costs (% of hard) | 10% | Design, permits, contingency |
| 26 | | | |
| 27 | **HOLD & EXIT ASSUMPTIONS** | | |
| 28 | Hold Period (years) | 5 | Investment horizon |
| 29 | Exit Cap Rate | 5.75% | Year 5 sale |
| 30 | Exit Year Sale Costs (% of Sale Price) | 6% | Broker commissions, closing |
| 31 | | | |
| 32 | **MARKET GROWTH SCENARIOS** | | |
| 33 | Base Case Rent Growth (annual) | 2.5% | Conservative market growth |
| 34 | Upside Rent Growth (annual) | 4.0% | Strong demand scenario |
| 35 | Downside Rent Growth (annual) | 1.0% | Weak demand scenario |
| 36 | Base Case Expense Growth | 3.0% | In line with inflation |
| 37 | Upside Expense Growth | 2.0% | Lower labor/input cost growth |
| 38 | Downside Expense Growth | 4.5% | Higher inflation/labor costs |
| 39 | | | |
| 40 | **DISCOUNT RATE ASSUMPTIONS** | | |
| 41 | Unlevered Discount Rate (WACC) | 8.0% | For unlevered IRR/NPV |
| 42 | Levered Equity Discount Rate | 12.0% | For levered IRR/equity returns |

---

## TAB 2: UNIT MIX & RENT ROLL

| Row | Label | Formula or Value | Notes |
|-----|-------|------------------|-------|
| 1 | **UNIT MIX & RENT ROLL** | | |
| 2 | Unit Type | Count | Current Monthly Rent | Target Monthly Rent | Notes |
| 3 | 1BR/1BA (Non-Renovated) | 6 | 1450 | 1450 | Remain at current rent |
| 4 | 2BR/1BA (To Be Renovated) | 18 | 1450 | 1750 | Reno Phase 1 (Months 1-24) |
| 5 | | | | | |
| 6 | **YEAR 1 RENT ROLL** | | | |
| 7 | Non-Renovated Units | 6 | 1450 | (Months 1-12 avg) |
| 8 | Monthly Rent Income (Non-Reno) | =C3*C7 | 8700 | 6 units × $1,450 |
| 9 | Partially Renovated (assume avg 12 units reno'd by end of Y1) | 12 | 1600 | Blended mid-year rents |
| 10 | Monthly Rent Income (Reno units) | =(18*1450 + 18*1750)/2/12*12 | 34200 | Weighted average over 12 months |
| 11 | Remaining Non-Reno Units | 6 | 1450 | |
| 12 | Monthly Rent Income (Non-Reno remaining) | 8700 | | |
| 13 | Year 1 Potential Gross Rent (avg) | =8700 + 34200 + 8700 | 51600 | Conservative: assumes linear reno |
| 14 | | | | |
| 15 | **YEAR 2-5 RENT ROLL (STABILIZED)** | | | |
| 16 | Non-Renovated Units | 6 | 1450 | |
| 17 | Renovated Units (2BR) | 18 | 1750 | |
| 18 | Total Potential Gross Rent (PGR) before growth | =(C16*C17 + C19*C20) | 45300 | 6×$1,450 + 18×$1,750 = $45,300 |
| 19 | | | | |
| 20 | **RENT GROWTH SCHEDULE (BASE CASE)** | | | |
| 21 | Year | PGR Growth Factor | Monthly PGR | Annual PGR |
| 22 | 1 (Blended) | 1.0 | 51600 | 619200 |
| 23 | 2 (Stabilized) | =(1+Assumptions!B33)^1 | 46407 | 556884 |
| 24 | 3 | =(1+Assumptions!B33)^2 | 47567 | 570804 |
| 25 | 4 | =(1+Assumptions!B33)^3 | 48754 | 585048 |
| 26 | 5 | =(1+Assumptions!B33)^4 | 49969 | 599628 |

---

## TAB 3: 10-YEAR ANNUAL CASH FLOW

| Row | Label | Formula or Value | Notes |
|-----|-------|------------------|-------|
| 1 | **ANNUAL OPERATING CASH FLOW - YEARS 1-5 (HOLD PERIOD)** | | |
| 2 | Year | 0 | 1 | 2 | 3 | 4 | 5 |
| 3 | | | | | | | |
| 4 | **REVENUE** | | | | | | |
| 5 | Potential Gross Rent (PGR) | — | ='Unit Mix'!

Grok 4.1 Fast Reasoning 8/10

## Assumptions Tab

| Row | Label | Formula or Value | Notes |
|-----|-------|------------------|-------|
| 1 | Assumptions | | Title, merge A1:F1, bold |
| 2 | | | Blank |
| 3 | Acquisition Assumptions | | Section header |
| 4 | Purchase Price | 4200000 | $4.2M |
| 5 | Acquisition Fees % | 0.02 | Broker/legal/etc. |
| 6 | Total Acquisition Cost | =B4*(1+B5) | ~$4,284,000 |
| 7 | Loan-to-Value (LTV) | 0.70 | 70% |
| 8 | Loan Amount | =B6*B7 | ~$2,998,800 |
| 9 | Interest Rate | 0.065 | 6.5% |
| 10 | Amortization Years | 30 | Fully amortizing |
| 11 | Loan Term Years | 5 | Balloon at end |
| 12 | Monthly Payment | =PMT(B9/12,B10*12,-B8) | ~$20,040 |
| 13 | Annual Debt Service | =B12*12 | ~$240,480 constant |
| 14 | | | Blank |
| 15 | Property Assumptions | | Section header |
| 16 | Number of Units | 24 | |
| 17 | Current Avg Rent ($/unit/mo) | 1450 | |
| 18 | Stabilized Rent ($/unit/mo renovated) | 1750 | |
| 19 | Units to Renovate | 18 | Over 24 months |
| 20 | Renovation Months | 24 | |
| 21 | Renov Cost per Unit | 15000 | $15k/unit total $270k |
| 22 | Renov Pace (units/mo) | =B19/B20 | 0.75 units/mo |
| 23 | Other Income ($/yr) | 50000 | 3% GPR approx., grows w/ rent |
| 24 | Yr1 Vacancy % | 0.08 | Current 8% |
| 25 | Yr2 Vacancy % | 0.07 | Ramp down |
| 26 | Stabilized Vacancy % (Yr3+) | 0.05 | 5% target |
| 27 | Rent Growth % (annual) | =IF(B45="Base",0.03,IF(B45="Upside",0.045,0.015)) | Scenario-driven |
| 28 | Expense Growth % (annual) | =IF(B45="Base",0.03,IF(B45="Upside",0.035,0.025)) | Scenario-driven |
| 29 | | | Blank |
| 30 | OpEx Assumptions (% of EGI unless noted) | | Section header |
| 31 | Property Taxes | 0.12 | 12% EGI |
| 32 | Insurance | 0.04 | 4% EGI |
| 33 | Utilities | 0.15 | 15% EGI (owner-paid) |
| 34 | Management | 0.05 | 5% PGR |
| 35 | Maintenance ($/unit/yr) | 1500 | Grows w/ exp growth |
| 36 | Admin/Other ($k/yr) | 25 | Grows w/ exp growth |
| 37 | Reserves ($/unit/yr, Yr3+) | 200 | |
| 38 | | | Blank |
| 39 | Exit Assumptions | | Section header |
| 40 | Hold Period (Years) | 5 | |
| 41 | Exit Cap Rate | 0.0575 | 5.75% |
| 42 | Sale Costs % | 0.03 | Broker/etc. |
| 43 | | | Blank |
| 44 | Scenario Selector | Base | Dropdown list: Base,Upside,Downside (Data Validation B44) |
| 45 | | | Blank |
| 46 | AI Cash Flow Forecasting (Tampa, FL Multifamily) | | Section header, based on CoStar/CBRE trends |
| 47 | Historical Rent Growth Avg (2019-2023) | 0.042 | 5.1%,4.8%,3.2%,2.9%,4.5%; moderating post-COVID |
| 48 | Hist Expense Inflation Avg | 0.028 | CPI-aligned |
| 49 | Base Rent Growth | 0.03 | Consensus forecast: steady economy |
| 50 | Upside Rent Growth | 0.045 | Strong job growth/tourism |
| 51 | Downside Rent Growth | 0.015 | Recession/high rates |
| 52 | Base Exp Growth | 0.03 | Inflation target |
| 53 | Upside Exp | 0.035 | Higher energy/labor |
| 54 | Downside Exp | 0.025 | Deflationary pressure |

## Unit Mix & Rent Roll Tab

| Row | Label | Formula or Value | Notes |
|-----|-------|------------------|-------|
| 1 | Unit Mix & Rent Roll | | Title |
| 2 | | | Blank |
| 3 | Unit Mix | | Header |
| 4 | Type | # Units | Current Rent ($/mo) | Post-Renov Rent ($/mo) |
| 5 | 1-Bedroom | =Assumptions!B16 | =Assumptions!B17 | =Assumptions!B18 | Assume uniform mix |
| 6 | | | | | |
| 7 | Renovation Schedule | | | | |
| 8 | Yr1 Avg New Units | =(Assumptions!B22*12)/2 | | 4.5 | (ramp avg) |
| 9 | Yr1 Units Renovated | =Assumptions!B22*12 | | 9 | Completed in Yr1 |
| 10 | Yr2 Avg New Units | =((Assumptions!B22*12)+(MIN(Assumptions!B19,Assumptions!B22*24)))/2 - Assumptions!B8 | | 13.5 | Cumulative ramp |
| 11 | Yr2 Units Renovated | =MIN(Assumptions!B19,Assumptions!B22*24)-Assumptions!B9 | | 9 | |
| 12 | Yr3+ Renovated Units | =Assumptions!B19 | | 18 total | 6 legacy units @ current rent |
| 13 | | | | | |
| 14 | Projected Blended Avg Rent ($/unit/mo, no growth) | | | | Links to CF ramp |
| 15 | Yr1 | ='Cash Flow'!B12 | | ~$1,524 | Pre-growth |
| 16 | Yr2 | ='Cash Flow'!C12 | | ~$1,669 | |
| 17 | Yr3+ Stabilized | ='Cash Flow'!D12/(1+Assumptions!B27) | | ~$1,750 blended | Legacy drags avg |

## Cash Flow Tab

| Row | Label | Formula or Value | Notes |
|-----|-------|------------------|-------|
| 1 | 10-Year Annual Cash Flow Pro Forma | | Title (monthly debt aggregated to annual) |
| 2 | | | Blank |
| 3 | | Year | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | Headers B3:K3 |
| 4 | Year # | | =B$3+1 | Drag across C4:K4 | | |
| 5 | | | | | | | | | | | | Blank row |
| 6 | Rent Ramp Helpers | | | | | | | | | | | |
| 7 | Cum Renov End of Year (units) | | =MIN(Assumptions!$B$19,Assumptions!$B$22*12) | =MIN(Assumptions!$B$19,B7+Assumptions!$B$22*12) | =C7 | Drag D7:K7 | Yr1=9, Yr2=18 |
| 8 | Renov Units Completed (Yr) | | =B7 | =C7-B7 | =D7-C7 | Drag=0 | Yr1=9, Yr2=9 |
| 9 | Avg New Units (Yr) | | =B7/2 | =(B7+C7)/2 | =C7 | Drag | Yr1=4.5 ramp avg |
| 10 | Revenues | | | | | | | | | | | Section |
| 11 | Blended Avg Rent ($/unit/mo) | | =((Assumptions!$B$16-B9)*Assumptions!$B$17 + B9*Assumptions!$B$18)/Assumptions!$B$16 | =B11*(1+Assumptions!$B$27) | =C11*(1+Assumptions!$B$27) | Drag | ~$1,524 Yr1 → grows |
| 12 | Gross Potential Rent (PGR) | | =B11*Assumptions!$B$16*12 | =C11*Assumptions!$B$16*12 | Drag | ~$440k Yr1 |
| 13 | | | | | | | | | | | | Blank |
| 14 | Vacancy % | | =Assumptions!$B$24 | =Assumptions!$B$25 | =Assumptions!$B$26 | Drag 5% | 8%→7%→5% |
| 15 | Vacancy Loss ($) | | =B12*B14 | =

What makes these work

01

Feed it your actual rent roll
Paste unit-level data directly into the prompt: unit number, beds, current rent, market rent, lease end. The model builds a realistic lease-up curve instead of assuming blanket occupancy. Garbage in, garbage out applies harder here than with a static template.
02

Specify the output format up front
Tell it you want monthly columns for 60 months, a summary table of IRR and equity multiple, and a sensitivity grid. Without that, you get a generic annual summary. Naming the exact tabs and columns you want saves a follow-up round.
03

Lock your debt assumptions separately
Give the loan terms as a distinct block: LTV, rate, amortization, IO period, prepay. Models often blend these into the opex line if you bury them in a paragraph. Keeping debt isolated also makes it easier to swap in a refinance scenario.
04

Always request a sanity check
Ask the model to flag any assumption that looks aggressive for the submarket. This catches things like 4% vacancy in a market running 9%, or rent growth above the local 5-year average. It's not perfect but it catches obvious errors.

More example scenarios

#01 · 12-unit Class B multifamily acquisition

Input

Underwrite a 12-unit apartment building in Columbus OH. Purchase $1.65M, in-place rents average $950, market rents $1,150, 5% vacancy, $4,200 per unit per year opex, 70% LTV at 7.25% on a 30-year amort, 3% rent growth, exit cap 6.75% in year 5. Output monthly cash flow and return metrics.

Expected output

A 60-month pro forma showing Year 1 NOI of $92,400 rising to $108,600 by Year 5, monthly debt service of $7,880, Year 1 cash-on-cash of 4.8%, levered IRR of 14.2%, and equity multiple of 1.87x. Includes a sensitivity table on exit cap and rent growth.

#02 · Single-tenant NNN retail

Input

Triple-net Dollar General, 15-year lease with 10 years remaining, $112,000 base rent, 10% bumps every 5 years, purchase at 6.5% cap, 60% LTV at 7% on 25-year amort. Show 10-year hold.

Expected output

Annual cash flow table with flat rent for years 1-5 then stepped to $123,200 years 6-10, constant expenses near zero under NNN structure, Year 1 cash-on-cash of 7.1%, exit at 7.0% cap producing 1.54x equity multiple and 9.8% levered IRR.

#03 · Short-term rental conversion

Input

Buying a 4BR beach house for $680K to run as STR. Projected ADR $340, 62% occupancy, $48K annual opex including cleaning and management, 25% down conventional loan at 7.75%. Model monthly with seasonality weighted toward May-September.

Expected output

Monthly revenue ranging from $4,200 in January to $18,500 in July, annual gross of $77,000, NOI of $29,000, debt service of $43,800, negative Year 1 cash flow of $14,800 turning positive Year 3 after two rate-adjustment scenarios.

#04 · Value-add office to medical conversion

Input

15,000 SF suburban office at $1.8M, currently 40% occupied at $18/SF gross. Plan $600K capex to convert to medical, lease at $28/SF NNN within 18 months. 65% LTV acquisition plus capex loan at 8%. 7-year hold.

Expected output

Month-by-month lease-up schedule reaching stabilization month 20, negative cash flow through month 18 totaling $142K, stabilized NOI of $378K, refinance in Year 3, exit at 7.25% cap producing 22% levered IRR and 2.4x equity multiple.

Common mistakes to avoid

Treating AI output as audit-ready
The numbers are directionally useful for screening, not ready for lender submission. Always export to Excel and verify debt service and tax calculations manually before sending to any capital source.
Skipping capex reserves
Most first-pass prompts forget to include a replacement reserve line, usually $250-$350 per unit per year for multifamily. Returns look 50-100 bps better than reality until you add it back in.
Using list-price rents as market rents
If you tell the model current rents are market, it won't underwrite the value-add upside or the risk that in-place rents are already stretched. Always provide both in-place and verified market comps.
Ignoring exit cap rate drift
Using today's entry cap as your exit cap five years out is the single biggest error in beginner underwriting. Add 25-75 bps to exit cap versus entry, and run the sensitivity both ways.
Forgetting closing and financing costs
A real pro forma includes 2-3% of purchase price in closing costs plus 1-2 points in loan fees. Leaving these out inflates Year 1 cash-on-cash and understates the total capital stack needed.

Related queries

Frequently asked questions

Can AI actually output a working Excel file with formulas?

Yes, the better models generate a downloadable .xlsx with live formulas for NOI, debt service, and IRR rather than just static values. You can open it in Excel or Google Sheets and change any assumption to see downstream effects, just like a template you built yourself.

How is this different from free pro forma templates from BiggerPockets or Adventures in CRE?

Free templates are excellent but rigid. They assume a specific deal structure, so adapting them to a mixed-use property or an unusual waterfall means rebuilding half the sheet. AI generates a structure matched to your specific deal in minutes, though it lacks the battle-tested formula integrity of a CRE veteran's template.

What metrics should a real estate pro forma always include?

At minimum: gross potential rent, vacancy, effective gross income, operating expenses, NOI, debt service, cash flow before tax, cash-on-cash return, levered IRR, and equity multiple. For hold periods over three years, add a sale proceeds calculation using an exit cap rate and year-by-year rent and expense growth.

Does this work for commercial deals or only residential?

It works for both. Commercial underwriting with NNN expense reimbursements, CAM, and TI/LC allowances requires more explicit prompt detail since the model won't default to commercial assumptions. Give it the lease structure, recovery terms, and rollover schedule and it handles office, retail, and industrial deals well.

How accurate are the cash flow forecasts?

The math is accurate. The forecast quality depends entirely on your inputs. If you give realistic market rents, vacancy, and expense ratios, the output matches what you'd build manually. The AI doesn't magically know your submarket, so verify comps independently before relying on projections.

Can I use this for a 1031 exchange or syndication pitch?

For initial screening and investor teaser decks, yes. For the final PPM, offering memo, or lender package, have a CPA or analyst rebuild the numbers in a validated template. Regulators and institutional LPs expect sourced assumptions and formula traceability that AI output alone doesn't provide.