Can you analyze this Conduit transaction? I want to understand collateral Geography, LTV, Debt Yield, DSCR, etc. KPIs on this 100% multifamily transaction.
Lastly, why would this not finance in the Agency CMBS market?
cd /vercel/sandbox && python skills/xlsx/scripts/workbook_profile.py annex.xlsx
cd /vercel/sandbox && python3 -c "
import openpyxl
wb = openpyxl.load_workbook('annex.xlsx', data_only=True)
ws = wb['Annex']
print(ws.max_row, ws.max_column)
for r in range(1, ws.max_row+1):
row = []
for c in range(1, ws.max_column+1):
v = ws.cell(r,c).value
row.append(v)
print(r, row[:20])
"
cd /vercel/sandbox && python3 -c "
import openpyxl
wb = openpyxl.load_workbook('annex.xlsx', data_only=True)
ws = wb['Annex']
headers = [ws.cell(3,c).value for c in range(1, ws.max_column+1)]
for i,h in enumerate(headers, start=1):
print(i, h)
"
Now let's pull all the relevant columns for the 9 loans plus the portfolio row.
cd /vercel/sandbox && python3 -c "
import openpyxl, json
wb = openpyxl.load_workbook('annex.xlsx', data_only=True)
ws = wb['Annex']
cols_wanted = [1,4,6,7,9,10,11,12,15,17,24,26,28,29,31,32,39,40,46,47,48,49,51,56,57,59,60,62,63,64,65,66,67,68,69,70,71,88,89,102,103,109]
headers = {c: ws.cell(3,c).value for c in cols_wanted}
rows=[]
for r in list(range(4,14)):
rowdata = {headers[c]: ws.cell(r,c).value for c in cols_wanted}
rows.append(rowdata)
print(json.dumps(rows, default=str, indent=1))
"
cd /vercel/sandbox && python3 -c "
import pdfplumber
with pdfplumber.open('termsheet.pdf') as pdf:
print(len(pdf.pages))
for i, page in enumerate(pdf.pages, start=1):
text = page.extract_text() or ''
print(f'=== PAGE {i} ===')
print(text)
" > termsheet_text.txt
wc -l termsheet_text.txt
cd /vercel/sandbox && cat > properties.json << 'EOF'
[
{"entity":"Arium Centro at Davie","state":"FL","market":"Fort Lauderdale","units":494,"year_built":1989,"occupancy":0.960,"avg_sf":986,"in_place_rent":2187,"rent_psf":2.21,"ncf":8070744,"pct_ncf":0.185,"appraised_value":154600000,"av_per_unit":312955,"capex_per_unit":6903},
{"entity":"Arium Boca Raton","state":"FL","market":"Palm Beach","units":392,"year_built":1986,"occupancy":0.967,"avg_sf":1134,"in_place_rent":2294,"rent_psf":2.03,"ncf":7342681,"pct_ncf":0.169,"appraised_value":131900000,"av_per_unit":336480,"capex_per_unit":6577},
{"entity":"Arium Lake Johnson","state":"NC","market":"Raleigh","units":576,"year_built":1985,"occupancy":0.976,"avg_sf":817,"in_place_rent":1251,"rent_psf":1.54,"ncf":5342984,"pct_ncf":0.123,"appraised_value":89600000,"av_per_unit":155556,"capex_per_unit":9264},
{"entity":"Arium Boynton Beach","state":"FL","market":"Palm Beach","units":264,"year_built":2002,"occupancy":0.943,"avg_sf":1097,"in_place_rent":2270,"rent_psf":2.08,"ncf":4674314,"pct_ncf":0.107,"appraised_value":89900000,"av_per_unit":340530,"capex_per_unit":12592},
{"entity":"Arium Weston","state":"NC","market":"Raleigh","units":380,"year_built":2000,"occupancy":0.979,"avg_sf":1138,"in_place_rent":1453,"rent_psf":1.28,"ncf":4502654,"pct_ncf":0.103,"appraised_value":84800000,"av_per_unit":223158,"capex_per_unit":7628},
{"entity":"Arium Kildaire","state":"NC","market":"Raleigh","units":332,"year_built":2000,"occupancy":0.976,"avg_sf":1156,"in_place_rent":1530,"rent_psf":1.32,"ncf":4256734,"pct_ncf":0.098,"appraised_value":77800000,"av_per_unit":234337,"capex_per_unit":8014},
{"entity":"Arium Lake Lynn","state":"NC","market":"Raleigh","units":423,"year_built":1984,"occupancy":0.983,"avg_sf":851,"in_place_rent":1238,"rent_psf":1.45,"ncf":4072538,"pct_ncf":0.094,"appraised_value":70000000,"av_per_unit":165485,"capex_per_unit":9720},
{"entity":"Arium Trailwood","state":"NC","market":"Raleigh","units":302,"year_built":1981,"occupancy":0.970,"avg_sf":987,"in_place_rent":1293,"rent_psf":1.32,"ncf":2945057,"pct_ncf":0.068,"appraised_value":49600000,"av_per_unit":164238,"capex_per_unit":12775},
{"entity":"Arium Southpoint","state":"NC","market":"Durham","units":278,"year_built":1996,"occupancy":0.960,"avg_sf":953,"in_place_rent":1299,"rent_psf":1.37,"ncf":2338363,"pct_ncf":0.054,"appraised_value":48700000,"av_per_unit":175180,"capex_per_unit":9887}
]
EOF
python skills/multi-entity-analysis/scripts/cross_check.py --check coverage properties.json
echo "---extremum occupancy max---"
python skills/multi-entity-analysis/scripts/cross_check.py --check extremum --field occupancy --direction max --claim "Arium Lake Lynn" properties.json
echo "---extremum occupancy min---"
python skills/multi-entity-analysis/scripts/cross_check.py --check extremum --field occupancy --direction min --claim "Arium Boynton Beach" properties.json
echo "---extremum ncf max---"
python skills/multi-entity-analysis/scripts/cross_check.py --check extremum --field ncf --direction max --claim "Arium Centro at Davie" properties.json
echo "---extremum av_per_unit max---"
python skills/multi-entity-analysis/scripts/cross_check.py --check extremum --field av_per_unit --direction max --claim "Arium Boynton Beach" properties.json
echo "---extremum av_per_unit min---"
python skills/multi-entity-analysis/scripts/cross_check.py --check extremum --field av_per_unit --direction min --claim "Arium Trailwood" properties.json
echo "---extremum rent max---"
python skills/multi-entity-analysis/scripts/cross_check.py --check extremum --field in_place_rent --direction max --claim "Arium Boca Raton" properties.json
cd /vercel/sandbox && python3 -c "
nc_units=2291; fl_units=1150; tot=3441
nc_ncf=23458331; fl_ncf=20087740; tot_ncf=43546070
print('NC unit share', nc_units/tot, 'NC NCF share', nc_ncf/tot_ncf)
print('FL unit share', fl_units/tot, 'FL NCF share', fl_ncf/tot_ncf)
loan=609750000
noi=45057306
ncf=43546070
av_incl_prem=813000000
av_excl_prem=796900000
print('LTV incl premium', loan/av_incl_prem)
print('LTV excl premium', loan/av_excl_prem)
print('NOI DY', noi/loan)
print('NCF DY', ncf/loan)
"
SCG 2026-MFP2: a $609.75M floating-rate, single-loan multifamily securitization — thin coverage, high leverage, and structured for flexibility Agency execution can't match
This isn't a "conduit" in the classic pooled-loan sense — it's one $609.75mm floating-rate mortgage loan, cross-collateralized and cross-defaulted across nine garden-style multifamily assets owned by Starwood's non-traded REIT (SREIT), tranched into an 8-class rated certificate stack. It's the refinance of a prior 2021 CMBS deal on the same portfolio, at meaningfully higher leverage and thinner coverage than that transaction.
Collateral geography
Nine assets, 3,441 units, two states, four markets, eight submarkets — but concentration is real: Raleigh alone carries 48.5% of UW NCF, and North Carolina in aggregate is 53.9% of NCF on 66.6% of units (the NC assets are older, smaller-unit, lower-rent product than the Florida assets) . Florida is only 33.4% of units but 46.1% of NCF, reflecting materially higher rents/SF ($2.03–$2.21 vs. $1.28–$1.54 in NC) . There's no single-market or single-asset dominance risk (largest property, Arium Centro at Davie, is 18.5% of NCF), but a NC apartment downturn (Raleigh/Durham both show negative trailing rent growth as new supply digests) would hit well over half the pool's cash flow.
| Property | Market/State | Units | Year Built | Occupancy % | In-Place Rent/Unit | Rent PSF | UW NCF ($) | % of Pool NCF | Appraised Value per Unit |
|---|---|---|---|---|---|---|---|---|---|
| Centro at Davie | FL | 4941 | 1989 | 96.0% | $2,187 | $2.21 | $8,070,744 | 18.5% | $312,955 |
| Boca Raton | FL | 392 | 1986 | 96.7% | $2,294 | $2.03 | $7,342,681 | 16.9% | $336,480 |
| Lake Johnson | NC | 576 | 1985 | 97.6% | $1,251 | $1.54 | $5,342,984 | 12.3% | $155,556 |
| Boynton Beach | FL | 264 | 2002 | 94.3% | $2,270 | $2.08 | $4,674,314 | 10.7% | $340,530 |
| Weston | NC | 380 | 2000 | 97.9% | $1,453 | $1.28 | $4,502,654 | 10.3% | $223,158 |
| Kildaire | NC | 332 | 2000 | 97.6% | $1,530 | $1.32 | $4,256,734 | 9.8% | $234,337 |
| Lake Lynn | NC | 423 | 1984 | 98.3% | $1,238 | $1.45 | $4,072,538 | 9.4% | $165,485 |
| Trailwood | NC | 302 | 1981 | 97.0% | $1,293 | $1.32 | $2,945,057 | 6.8% | $164,238 |
| Southpoint | NC | 278 | 1996 | 96.0% | $1,299 | $1.37 | $2,338,363 | 5.4% | $175,180 |
Occupancy is tight and consistent (94.3%–98.3%, portfolio-wide 96.9% vs. a 93.9% five-year historical average — this rent roll is a cyclical high) . Arium Boynton Beach is both the weakest occupancy (94.3%) and the highest appraised value/unit ($340,530) — worth watching if that FL submarket's elevated supply persists.
Loan-level KPIs (this is one loan, not nine)
Because it's a single cross-collateralized mortgage, LTV/DY/DSCR exist only at the pool level — there's no separate loan-level coverage per property to rank.
| Metric | Value |
|---|---|
| Loan amount | $609,750,000 ($177,201/unit) |
| Appraised value | $813.0mm (incl. ~2.0% portfolio premium); $796.9mm ex-premium |
| LTV | 75.0% (76.5% ex-premium) |
| UW NOI / UW NCF | $45.1mm / $43.5mm |
| UW NOI Debt Yield | 7.4% |
| UW NCF Debt Yield | 7.1% |
| UW NCF DSCR (at pricing coupon) | 1.18x |
| UW NCF DSCR at SOFR cap strike | 1.11x |
| Rate | 1-mo Term SOFR + ~2.25% spread (all-in ~5.95% assumed); SOFR floor 0.00% |
| Term | 2 years IO, three 1-year extensions (max 5 yrs); SOFR cap strike 4.10% initial, 1.10x DSCR floor on extension |
Versus the loan's own 2021 predecessor on this same portfolio, leverage moved from 65.0% LTV / 5.4% DY to 75.0% LTV / 7.1% DY — proceeds grew ~26% even as coverage tightened, funded by the portfolio's real NCF growth (+26.2% since 2021, on 18.5% in-place rent growth) rather than by conservatism.
The rated stack shows where risk is actually priced: Class A attaches at 31.8% cumulative LTV / 16.8% DY (AAAsf), and coverage erodes fast down the stack — Class G (unrated) sits at 71.3% LTV / 7.5% DY, and the HRR first-loss piece (retained by a third-party purchaser to satisfy risk retention) absorbs the last 3.7 points down to 75.0% LTV / 7.1% DY .
| Class | Initial Balance | Rating (Fitch/DBRS) | Cumulative LTV % | Cumulative UW NCF Debt Yield % |
|---|---|---|---|---|
| A | $258,700,000 | AAAsf/AAA(sf) | 31.8% | 16.8% |
| B | $46,100,000 | AA-sf/AA(low)(sf) | 37.5% | 14.3% |
| C | $62,500,000 | NR/A(low)(sf) | 45.2% | 11.9% |
| D | $46,600,000 | NR/BBB(low)(sf) | 50.9% | 10.5% |
| E | $47,300,000 | NR/BB(low)(sf) | 56.7% | 9.4% |
| F | $53,200,000 | NR/B(low)(sf) | 63.3% | 8.5% |
| G | $64,862,500 | NR/NR | 71.3% | 7.5% |
| HRR1 | $30,487,500 | NR/NR | 75.0% | 7.1% |
Why this wouldn't finance through Agency (Fannie Mae/Freddie Mac) execution
Three structural mismatches rule it out, not one:
In short: SREIT is borrowing against real, executed NCF growth (properties still mid-business-plan, occupancy at a cyclical high) at leverage and coverage the GSEs wouldn't approve for that growth story, in a rate/term structure the GSEs don't offer at this scale. This loan was built for the private-label CMBS market by design, not as a fallback from Agency financing.
Happy to go deeper on the NC/FL market fundamentals, the extension/SOFR-cap mechanics, or run a maturity-refi stress test on the 1.18x/1.11x DSCR if useful.
This is a read-only snapshot of a RealAI analysis.