# Real Estate Rental Analysis > Rental cash flow analysis. 1% rule, 50% rule, cash-on-cash. - Skill: `loopyluci/real-estate-rental-analysis` (Agent Skill) - Install (CLI): `npx skillmds@latest add loopyluci/real-estate-rental-analysis` - Raw SKILL.md: https://api.skillmd.com/api/skills/loopyluci/real-estate-rental-analysis/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - License: MIT - Author: LoopyLuci (https://skillmd.com/u/loopyluci) - Updated: 2026-09-08 - Page: https://skillmd.com/skills/loopyluci/real-estate-rental-analysis --- # Real Estate Rental Analysis Deep-dive rental property analysis for buy-and-hold investors. Compute cash flow, apply the 1% and 50% rules, project depreciation (cost segregation), calculate amortization schedules, and determine true ROI across different financing scenarios. ## When to Use - Evaluating a property as a long-term rental investment - Comparing short-term rental (Airbnb/VRBO) vs. long-term rental returns - Analyzing a potential BRRRR (Buy-Rehab-Rent-Refinance-Repeat) deal - Presenting a rental investment proposal to a client or partner - Determining if a property meets your minimum cash flow thresholds ## Key Rental Metrics & Rules of Thumb | Metric/Rule | What It Tells You | Formula | Target | |---|---|---|---| | **1% Rule** | Quick rent-to-price sanity check | Monthly Rent ÷ Purchase Price | ≥ 1% | | **2% Rule** | Strong cash flow potential | Monthly Rent ÷ Purchase Price | ≥ 2% (multi-family) | | **50% Rule** | Operating expense estimate | Total OpEx ≈ 50% of Gross Rent | 45-55% | | **Cash-on-Cash Return** | Return on cash invested | Annual CF ÷ Total Cash In | 8-12%+ | | **Cap Rate** | Unlevered return | NOI ÷ Purchase Price | 5-10% | | **Gross Rent Multiplier** | Valuation multiple | Price ÷ Gross Annual Rent | 4-10 | | **Debt Coverage Ratio** | Lender safety margin | NOI ÷ Annual Debt Service | 1.25+ | | **Total ROI** | Full return including appreciation | (CF + Equity Growth) ÷ Total Cash | 15%+ annualized | | **CapEx % of Rent** | Reserve for replacements | 5-15% of EGI | 10% (recommended) | | **Cash Flow / Door** | Per-unit profitability | Net CF ÷ Units | $100-300+/door | ## Workflow ### 1. Gather Rental Property Data ``` Purchase Price: After Repair Value (ARV, if BRRRR): Number of Units: Monthly Rent Per Unit: Total Monthly Gross Rent: Other Monthly Income (laundry, parking, storage, pet fees, vending): Property Management (% of EGI or $): Property Taxes (annual): Insurance (annual): HOA Dues (monthly, if applicable): Maintenance/Repairs: ___ % of EGI or $____ / year CapEx Reserve: ___ % of EGI or $____ / year Vacancy Rate ___ % Utilities Paid by Owner (annual): Advertising/Marketing ($/year): Legal/Accounting ($/year): Pest Control ($/year): Trash/Snow Removal ($/year): Down Payment: ___ % Closing Costs: $____ or ___ % Interest Rate: ___ % Loan Term: ___ years Points / Origination Fees: $____ Renovation Costs (if BRRRR): $____ Refinance ARV (if BRRRR): $____ Appreciation Rate (annual): ___ % Rent Growth Rate (annual): ___ % Marginal Tax Rate: ___ % ``` ### 2. Apply the 1% Rule (Quick Filter) ```python def one_percent_rule(purchase_price, monthly_rent): ratio = monthly_rent / purchase_price * 100 passes = ratio >= 1.0 verdict = "PASSES ✅" if passes else "FAILS ❌" target_rent = purchase_price * 0.01 target_price = monthly_rent * 100 print("1% RULE ANALYSIS") print(f" Purchase Price: ${purchase_price:>10,}") print(f" Monthly Rent: ${monthly_rent:>10,}") print(f" Ratio: {ratio:>6.2f}%") print(f" Verdict: {verdict}") print(f" Need ${target_rent:,.0f}/mo rent or ${target_price:,.0f} price") return {'ratio': round(ratio, 2), 'passes': passes} ``` ### 3. Apply the 50% Rule (Expense Estimate) ```python def fifty_percent_rule(gross_monthly_rent): estimated_opex = gross_monthly_rent * 0.50 estimated_noi = gross_monthly_rent - estimated_opex print("\n50% RULE ESTIMATE") print(f" Gross Monthly Rent: ${gross_monthly_rent:>8,.2f}") print(f" Est. OpEx (50%): ${estimated_opex:>8,.2f}") print(f" Est. NOI (pre-debt): ${estimated_noi:>8,.2f}") print(f" Est. Annual NOI: ${estimated_noi*12:>8,.2f}") return {'gross_monthly': gross_monthly_rent, 'est_opex': estimated_opex, 'est_noi': estimated_noi, 'annual_noi': estimated_noi * 12} ``` ### 4. Full Cash Flow Analysis ```python def rental_analysis(**kwargs): p = { 'purchase_price': 300000, 'arv': 300000, 'down_payment_pct': 20, 'closing_costs': 6000, 'units': 1, 'monthly_rent': 3500, 'other_monthly_income': 100, 'mgmt_pct': 8, 'property_taxes': 3600, 'insurance': 1500, 'hoa': 0, 'maintenance_pct': 5, 'capex_pct': 8, 'vacancy_rate': 5, 'utilities': 600, 'advertising': 300, 'legal_accounting': 500, 'pest_control': 300, 'trash_snow': 500, 'interest_rate': 6.5, 'loan_term_years': 30, 'appreciation_rate': 3, 'rent_growth_rate': 2, 'holding_period_years': 5, 'rehab_costs': 0, 'marginal_tax_rate': 24, } p.update(kwargs) gross_monthly = p['monthly_rent'] + p['other_monthly_income'] gross_annual = gross_monthly * 12 vacancy = gross_annual * p['vacancy_rate'] / 100 egi = gross_annual - vacancy mgmt = egi * p['mgmt_pct'] / 100 maintenance = egi * p['maintenance_pct'] / 100 capex = egi * p['capex_pct'] / 100 total_opex = (mgmt + p['property_taxes'] + p['insurance'] + p['hoa']*12 + maintenance + p['utilities'] + p['advertising'] + p['legal_accounting'] + p['pest_control'] + p['trash_snow'] + capex) opex_ratio = total_opex / gross_annual * 100 if gross_annual else 0 loan_amount = min(p['purchase_price'] + p['rehab_costs'], p['arv']) * (1 - p['down_payment_pct'] / 100) monthly_rate = p['interest_rate'] / 100 / 12 n = p['loan_term_years'] * 12 monthly_pmt = monthly_rate * loan_amount * (1 + monthly_rate)**n / ((1 + monthly_rate)**n - 1) if monthly_rate > 0 else loan_amount / n annual_debt = monthly_pmt * 12 noi = egi - total_opex pre_tax_cf = noi - annual_debt total_cash = (p['purchase_price'] * p['down_payment_pct'] / 100 + p['closing_costs'] + p['rehab_costs']) coc_return = (pre_tax_cf / total_cash) * 100 if total_cash else 0 cap_rate = (noi / p['arv']) * 100 if p['arv'] else 0 one_pct_ratio = gross_monthly / p['purchase_price'] * 100 # Depreciation building_value = p['purchase_price'] * 0.80 annual_depreciation = building_value / 27.5 tax_savings = annual_depreciation * p['marginal_tax_rate'] / 100 # Projection cash_flows = [] current_rent = gross_annual current_value = p['arv'] for year in range(1, p['holding_period_years'] + 1): vac_y = current_rent * p['vacancy_rate'] / 100 egi_y = current_rent - vac_y mgmt_y = egi_y * p['mgmt_pct'] / 100 maint_y = egi_y * p['maintenance_pct'] / 100 capex_y = egi_y * p['capex_pct'] / 100 opex_fixed = (p['property_taxes'] + p['insurance'] + p['hoa']*12 + p['utilities'] + p['advertising'] + p['legal_accounting'] + p['pest_control'] + p['trash_snow']) noi_y = egi_y - (mgmt_y + opex_fixed + maint_y + capex_y) cf_y = noi_y - annual_debt cash_flows.append(cf_y) current_rent *= (1 + p['rent_growth_rate'] / 100) current_value *= (1 + p['appreciation_rate'] / 100) # Loan balance remaining = loan_amount for _ in range(p['holding_period_years'] * 12): interest = remaining * monthly_rate principal = monthly_pmt - interest remaining -= principal remaining = max(0, remaining) equity_gain = current_value - remaining - total_cash total_return = sum(cash_flows) + equity_gain total_roi_pct = (total_return / total_cash) * 100 if total_cash else 0 print("\n" + "=" * 70) print("RENTAL PROPERTY ANALYSIS") print("=" * 70) print(f"\n📋 QUICK RULES") print(f" 1% Rule: {one_pct_ratio:.2f}% {'✅ PASS' if one_pct_ratio >= 1 else '❌ FAIL'}") print(f" 50% Rule OpEx: {opex_ratio:.1f}% {'✅' if 40 <= opex_ratio <= 60 else '⚠️'}") print(f"\n💰 INCOME — Gross: ${gross_annual:>8,.0f} EGI: ${egi:>8,.0f}") print(f"\n📉 EXPENSES ({opex_ratio:.1f}%) — Total: ${total_opex:>8,.0f}") print(f"\n🏠 NOI: ${noi:>8,.0f} | Cap Rate: {cap_rate:.2f}%") print(f"\n🏦 DEBT — Loan: ${loan_amount:>8,.0f} Monthly P&I: ${monthly_pmt:>8,.2f}") print(f"\n💵 CASH FLOW — Monthly: ${pre_tax_cf/12:>8,.0f} Annual: ${pre_tax_cf:>8,.0f} /door: ${(pre_tax_cf/12)/p['units']:>8,.0f}") print(f"\n📊 RETURNS") print(f" Cash-on-Cash: {coc_return:.2f}%") print(f" Annual Depreciation: ${annual_depreciation:>8,.0f}") print(f" Tax Savings: ${tax_savings:>8,.0f}/yr") print(f"\n📈 PROJECTION ({p['holding_period_years']}yr)") print(f" Projected Value: ${current_value:>10,.0f}") print(f" Equity Gain: ${equity_gain:>10,.0f}") print(f" Total Return: ${total_return:>10,.0f}") print(f" Total ROI: {total_roi_pct:.1f}%") return { 'one_pct_ratio': round(one_pct_ratio, 2), 'opex_ratio': round(opex_ratio, 1), 'noi': round(noi, 2), 'cap_rate': round(cap_rate, 2), 'dscr': round(noi/annual_debt, 2) if annual_debt else 0, 'monthly_cf': round(pre_tax_cf/12, 2), 'annual_cf': round(pre_tax_cf, 2), 'total_cash': round(total_cash, 2), 'coc_return': round(coc_return, 2), 'tax_savings': round(tax_savings, 0), 'total_roi': round(total_roi_pct, 1), } ``` ### 5. BRRRR Analysis ```python def brrrr_analysis(**kwargs): p = { 'purchase_price': 200000, 'rehab_costs': 50000, 'closing_costs_buy': 5000, 'arv': 320000, 'monthly_rent_arv': 3500, 'refi_interest_rate': 7.0, 'refi_lvr': 75, 'refi_closing_costs': 4000, 'vacancy_rate': 5, 'mgmt_pct': 8, 'property_taxes': 4000, 'insurance': 1800, } p.update(kwargs) total_cash_in = p['purchase_price'] + p['rehab_costs'] + p['closing_costs_buy'] refi_loan = p['arv'] * p['refi_lvr'] / 100 cash_recouped = refi_loan - p['refi_closing_costs'] cash_left_in = total_cash_in - cash_recouped monthly_rate = p['refi_interest_rate'] / 100 / 12 n = 30 * 12 monthly_pmt = monthly_rate * refi_loan * (1 + monthly_rate)**n / ((1 + monthly_rate)**n - 1) annual_debt = monthly_pmt * 12 gross_annual = p['monthly_rent_arv'] * 12 egi = gross_annual - gross_annual * p['vacancy_rate'] / 100 mgmt = egi * p['mgmt_pct'] / 100 noi = egi - (mgmt + p['property_taxes'] + p['insurance']) cf = noi - annual_debt coc = (cf / cash_left_in) * 100 if cash_left_in > 0 else float('inf') print("\n" + "=" * 70) print("BRRRR ANALYSIS") print("=" * 70) print(f"\nBUY+REHAB: ${total_cash_in:>8,} total cash in") print(f"REFI: ARV ${p['arv']:>8,} → Loan ${refi_loan:>8,} → Recoup ${cash_recouped:>8,}") print(f"CASH LEFT IN: ${cash_left_in:>8,}") print(f"RENT: ${p['monthly_rent_arv']:>8,}/mo → CF ${cf:>8,}/yr → CoC {coc:.1f}%") print(f"Cash Out > All-In? {'✅ YES' if cash_recouped > total_cash_in else '❌ NO'}") return {'cash_left_in': round(cash_left_in, 0), 'coc': round(coc, 1)} ``` ### 6. Short-Term vs. Long-Term Comparison ```python def str_vs_ltr_analysis(**kwargs): p = { 'purchase_price': 350000, 'monthly_rent_ltr': 3500, 'str_daily_rate': 250, 'str_occupancy_rate': 65, 'str_management_pct': 20, 'str_cleaning_fee': 85, 'str_avg_stay_nights': 3.5, 'down_payment_pct': 25, 'closing_costs': 7000, 'interest_rate': 6.5, 'property_taxes': 4200, 'insurance': 2000, 'hoa': 0, 'utilities': 2400, 'maintenance_pct': 5, 'capex_pct': 8, 'vacancy_rate_ltr': 5, } p.update(kwargs) gross_annual_ltr = p['monthly_rent_ltr'] * 12 vac_ltr = gross_annual_ltr * p['vacancy_rate_ltr'] / 100 egi_ltr = gross_annual_ltr - vac_ltr fixed_opex = p['property_taxes'] + p['insurance'] + p['hoa']*12 + p['utilities'] total_opex_ltr = (egi_ltr * 8/100 + fixed_opex + egi_ltr * p['maintenance_pct']/100 + egi_ltr * p['capex_pct']/100) noi_ltr = egi_ltr - total_opex_ltr str_gross_nights = 365 * p['str_occupancy_rate'] / 100 str_gross_rev = str_gross_nights * p['str_daily_rate'] str_cleaning_rev = (str_gross_nights / p['str_avg_stay_nights']) * p['str_cleaning_fee'] str_total_rev = str_gross_rev + str_cleaning_rev str_total_opex = (str_total_rev * p['str_management_pct']/100 + fixed_opex + str_total_rev * p['maintenance_pct']/100 + str_total_rev * p['capex_pct']/100) str_noi = str_total_rev - str_total_opex loan_amount = p['purchase_price'] * (1 - p['down_payment_pct'] / 100) monthly_rate = p['interest_rate'] / 100 / 12 n = 30 * 12 monthly_pmt = monthly_rate * loan_amount * (1 + monthly_rate)**n / ((1 + monthly_rate)**n - 1) annual_debt = monthly_pmt * 12 total_cash = p['purchase_price'] * p['down_payment_pct'] / 100 + p['closing_costs'] cf_ltr = noi_ltr - annual_debt cf_str = str_noi - annual_debt coc_ltr = (cf_ltr / total_cash) * 100 coc_str = (cf_str / total_cash) * 100 print("\n" + "=" * 70) print("SHORT-TERM vs. LONG-TERM RENTAL COMPARISON") print("=" * 70) print(f"\n{'Metric':<35} {'Long-Term':>15} {'Short-Term':>15}") print(f"{'-'*65}") print(f"{'Gross Revenue':<35} ${gross_annual_ltr:>10,.0f} ${str_total_rev:>10,.0f}") print(f"{'Net Income':<35} ${noi_ltr:>10,.0f} ${str_noi:>10,.0f}") print(f"{'Cash Flow/yr':<35} ${cf_ltr:>10,.0f} ${cf_str:>10,.0f}") print(f"{'Cash Flow/mo':<35} ${cf_ltr/12:>10,.0f} ${cf_str/12:>10,.0f}") print(f"{'Cash-on-Cash':<35} {coc_ltr:>10.1f}% {coc_str:>10.1f}%") print(f"\n{'✅ STR wins' if cf_str > cf_ltr else '✅ LTR wins'} by ${abs(cf_str-cf_ltr):,.0f}/yr") return {'ltr_coc': round(coc_ltr, 1), 'str_coc': round(coc_str, 1)} ``` ### 7. Amortization Schedule ```python def amortization_schedule(loan_amount, annual_rate, term_years): monthly_rate = annual_rate / 100 / 12 n = term_years * 12 monthly_pmt = monthly_rate * loan_amount * (1 + monthly_rate)**n / ((1 + monthly_rate)**n - 1) balance = loan_amount total_interest = 0 print(f"\nAMORTIZATION — ${loan_amount:>8,} @ {annual_rate}% / {term_years}yr") print(f" Payment: ${monthly_pmt:>8,.2f}/mo") print(f"\n {'Year':>5} {'Payment':>10} {'Principal':>10} {'Interest':>10} {'Balance':>12}") for year in range(1, term_years + 1): ap, ai = 0, 0 for _ in range(12): i = balance * monthly_rate pr = monthly_pmt - i balance -= pr ap += pr; ai += i total_interest += i print(f" {year:>5} ${monthly_pmt*12:>8,.0f} ${ap:>8,.0f} ${ai:>8,.0f} ${max(0,balance):>10,.0f}") print(f"\n Total Interest: ${total_interest:>10,.0f}") amortization_schedule(200000, 6.5, 30) ``` ## Common Pitfalls - **Over-relying on the 1% rule**: Market conditions vary. In HCOL areas, 0.5-0.7% may be the norm. Use it as a filter, not a decision metric. - **Ignoring the 50% rule**: Actual expenses vary, but the 50% rule is remarkably accurate over time. Budgeting <45% for OpEx likely means underestimating. - **Forgetting vacancy between tenants**: Even 5% vacancy budgeted, a 3-week turnover costs ~6% annually. Budget for both vacancy AND turnover. - **Neglecting payroll taxes on self-management**: Self-managing means active income for IRS. LLC structure matters. - **Not accounting for STR regulations**: Many cities ban/restrict STRs (NY, LA, Austin, SF). Verify local laws before projecting STR returns. - **Underestimating STR costs**: STRs have 20-30% management fees, higher utilities, supplies, licensing. Expect 60-70% expense ratios. - **Skipping cost segregation**: Can accelerate depreciation from 27.5yr to 5-15yr on personal property. Saves $5-10k/yr on typical rental. - **Ignoring self-management labor value**: Your time has economic value. At $50/hr, 5 hrs/week = $13k/yr opportunity cost. - **Not running rent comps**: Projected rent must be based on actual market comps, not optimistic assumptions. Pull current listings. - **Forgetting the 3.8% NIIT**: Net Investment Income Tax applies over $200k/$250k MAGI. Rental income may trigger this. - **Overleveraging in rising rate environments**: CoC looks great at 5% rates but can go negative at 8%. Model at current rates. ## Verification Checklist - [ ] 1% rule calculated: monthly rent ≥ 1% of purchase price - [ ] 50% rule checked: operating expenses within 45-55% of gross rent - [ ] Cash-on-cash return ≥ 8% (or personal minimum) - [ ] Cap rate compared to market average for property type - [ ] DSCR ≥ 1.25 for conventional financing - [ ] Monthly cash flow per door documented (target $100+/door) - [ ] All operating expenses itemized (management, taxes, insurance, HOA, maintenance, CapEx, utilities, advertising, legal) - [ ] CapEx reserve included (minimum 8% of EGI) - [ ] Vacancy rate reflects local market (not ideal 0%) - [ ] Depreciation calculated (residential 27.5 years, land excluded) - [ ] Tax savings from depreciation estimated - [ ] Financing terms verified with actual lender quote - [ ] Rent projections based on actual market comps - [ ] Appreciation and rent growth rates conservative (2-3%) - [ ] For BRRRR: cash-out refi covers all-in costs - [ ] For STR vs LTR: local STR regulations verified - [ ] Amortization schedule confirmed for loan term - [ ] Equity growth and total ROI projected over hold period - [ ] At least one downside scenario modeled