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 && python3 -c "
import pdfplumber
with pdfplumber.open('term_sheet.pdf') as pdf:
print('pages', len(pdf.pages))
"
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.dimensions)
for r in range(1, 16):
row = [ws.cell(row=r, column=c).value for c in range(1, 115)]
print(r, [x for x in row if x is not None][:10])
"
cd /vercel/sandbox && python3 -c "
import openpyxl
wb = openpyxl.load_workbook('annex.xlsx', data_only=True)
ws = wb['Annex']
headers = [ws.cell(row=3, column=c).value for c in range(1, 115)]
for i,h in enumerate(headers,1):
print(i, h)
"
Excellent — this is a full loan-level data tape. Let me pull the key fields for all 9 properties and the PDF term sheet for structural detail.
cd /vercel/sandbox && python3 -c "
import openpyxl, json
wb = openpyxl.load_workbook('annex.xlsx', data_only=True)
ws = wb['Annex']
headers = [ws.cell(row=3, column=c).value for c in range(1, 115)]
rows = []
for r in range(5, 14):
row = {headers[c-1]: ws.cell(row=r, column=c).value for c in range(1, 115)}
rows.append(row)
keyfields = ['Loan Number','Property Name','Property City','Property State','County','Market','Year Built','Year Renovated','No. of Units','Occupancy %','Note Date','Maturity Date or Anticipated Repayment Date','Assumed Mortgage Rate','Trust Notes Original Principal Balance','Trust Notes Loan Level Cut-Off Date Balance','Amortization Term (Original)','Loan Term (Original)','IO Period','Loan Amortization Type','Aggregate Individual Appraised Value ($)','Portfolio Appraised Value ($)','Mortgage Loan Closing Date LTV (Aggregate Individual Appraised Value)','Mortgage Loan Closing Balloon LTV (Aggregate Individual Appraised Value)','Mortgage Loan Closing Date LTV (Portfolio Value)','UW NCF DSCR','Cut-Off Date UW NOI Debt Yield','Cut-Off Date UW NCF Debt Yield','UW NOI','UW NCF','Total Debt Original Principal Balance','Existing Mezzanine Debt (Y/N)','Future Mezzanine Debt Permitted (Y/N)','Seismic PML %','Lien Position']
for row in rows:
print('---', row['Property Name'])
for k in keyfields:
print(' ', k, '=', row.get(k))
"
cd /vercel/sandbox && python3 -c "
import openpyxl
wb = openpyxl.load_workbook('annex.xlsx', data_only=True)
ws = wb['Annex']
headers = [ws.cell(row=3, column=c).value for c in range(1, 115)]
row4 = {headers[c-1]: ws.cell(row=4, column=c).value for c in range(1, 115)}
for k,v in row4.items():
print(k,'=',v)
"
cd /vercel/sandbox && python3 -c "
import pdfplumber
with pdfplumber.open('term_sheet.pdf') as pdf:
for i, page in enumerate(pdf.pages, start=1):
text = page.extract_text() or ''
print(f'=== PAGE {i} ===')
print(text)
" > term_sheet_text.txt
wc -l term_sheet_text.txt
grep -n "Class\|Rating\|Credit Support\|Certificate Balance" term_sheet_text.txt | head -80
cd /vercel/sandbox && sed -n '1,240p' term_sheet_text.txt
cd /vercel/sandbox && grep -n "Allocated Loan Amount\|Historical\|Cap Rate\|Agency\|GSE\|Fannie\|Freddie\|conduit\|Cash Management\|Cash Sweep\|Release Price\|Mezzanine" term_sheet_text.txt | head -60
cd /vercel/sandbox && python3 -c "
noi = 45057306.24
ncf = 43546070.24
appr = 813000000
loan = 609750000
units = 3441
print('going-in cap rate on portfolio value:', noi/appr)
print('NCF cap rate:', ncf/appr)
print('loan per unit:', loan/units)
print('appraised value per unit:', appr/units)
print('equity cushion $:', appr-loan)
print('equity cushion %:', 1-loan/appr)
"
Now I have everything needed. Let me build the artifacts and write the analysis.
cd /vercel/sandbox && grep -n "Weighted Average Cost of Cap\|weighted average life\|WAL\|Certificate LTV\|Underwritten NCF Debt Yield\|397\|399" term_sheet_text.txt | sed -n '1,20p'
Good, I have the capital-stack table already at lines 398-407. Now let's write the analysis.
cd /vercel/sandbox && python3 -c "
classes = [
('A',258700000,'AAAsf/AAA(sf)',31.8,16.8),
('B',46100000,'AA-sf/AA(low)(sf)',37.5,14.3),
('C',62500000,'NR/A(low)(sf)',45.2,11.9),
('D',46600000,'NR/BBB(low)(sf)',50.9,10.5),
('E',47300000,'NR/BB(low)(sf)',56.7,9.4),
('F',53200000,'NR/B(low)(sf)',63.3,8.5),
('G',64862500,'NR/NR',71.3,7.5),
('HRR',30487500,'NR/NR',75.0,7.1),
]
total = sum(c[1] for c in classes)
print('total', total)
for c in classes:
print(c[0], c[1], round(c[1]/total*100,1),'%')
"
SCG 2026-MFP2: a $609.75M single-loan, nine-property Sunbelt multifamily conduit — 75% LTV, 1.18x DSCR, 7.1% debt yield, and a structure that's floating-rate bridge risk, not agency-eligible paper
This isn't a diversified conduit pool — it's one $609.75MM floating-rate mortgage loan secured by nine cross-collateralized garden-style apartment communities (3,441 units), carved into eight rated/unrated certificate classes plus the HRR risk-retention piece . Borrower is Starwood REIT (SREIT), refinancing a portfolio it's owned since 2021; Highmark Residential manages .
Collateral geography. Two states, four markets, eight submarkets — but concentrated: North Carolina carries 66.6% of units / 53.9% of UW NCF (six Raleigh/Durham assets), Florida the other 33.4% of units / 46.1% of NCF (three Palm Beach/Fort Lauderdale assets, which run bigger and richer per unit) . Single-largest exposure is Arium Centro at Davie at 18.5% of UW NCF — real name risk if any one asset underperforms.
| Property | Market/State | Units | Occupancy % | Year Built | % of Total Units | % of UW NCF |
|---|---|---|---|---|---|---|
| Arium Centro at Davie | Fort Lauderdale, FL | 494 | 96.0%1 | 1989 | 14.4% | 18.5% |
| Arium Boca Raton | Palm Beach, FL | 392 | 96.7% | 1986 | 11.4% | 16.9% |
| Arium Lake Johnson | Raleigh, NC | 576 | 97.6% | 1985/renov 2022 | 16.7% | 12.3% |
| Arium Boynton Beach | Palm Beach, FL | 264 | 94.3% | 2002/renov 2016 | 7.7% | 10.7% |
| Arium Weston | Raleigh, NC — Morrisville | 380 | 97.9% | 2000 | 11.0% | 10.3% |
| Arium Kildaire | Raleigh, NC — Cary | 332 | 97.6% | 2000/renov 2020 | 9.6% | 9.8% |
| Arium Lake Lynn | Raleigh, NC | 423 | 98.3% | 1984 | 12.3% | 9.4% |
| Arium Trailwood | Raleigh, NC | 302 | 97.0% | 1981 | 8.8% | 6.8% |
| Arium Southpoint | Durham, NC | 278 | 96.0% | 19961 | 8.1% | 5.4% |
LTV / debt yield / DSCR — the headline credit metrics. Against an As-Is portfolio appraised value of $813.0MM (a ~2.0% portfolio premium over the sum of individual appraisals of $796.9MM), the $609.75MM loan prices at 75.0% LTV, a 7.4% UW NOI debt yield and 7.1% UW NCF debt yield, and 1.18x UW NCF DSCR (1.11x at the SOFR cap strike) . That implies a going-in cap rate of only ~5.5% on NOI against the appraised value — full pricing for a value-add-recovered, institutionally-owned Sunbelt portfolio, not a wide margin of safety. $203MM of appraised equity sits behind the loan (25% cushion), and the loan runs $177,201/unit against $236,269/unit of appraised value .
| Metric | Value |
|---|---|
| Original/Cut-off Balance | $609,750,000 |
| Portfolio Appraised Value | $813,000,0001 |
| LTV (Portfolio Value) | 75.0% |
| LTV (Sum of Individual Appraisals) | 76.5% |
| UW NOI | $45,057,306 |
| UW NCF | $43,546,070 |
| UW NOI Debt Yield | 7.4% |
| UW NCF Debt Yield | 7.1% |
| UW NCF DSCR | 1.18x |
| UW NCF DSCR at SOFR Cap | 1.11x |
| Implied Going-In NOI Cap Rate | 5.5% |
| Loan per Unit | $177,201 |
| Appraised Value per Unit | $236,269 |
The certificate structure spreads that risk across eight classes by subordination, not diversification — this is one loan, so every class is exposed to the same nine buildings; the tranching only allocates loss priority.
| Class | Balance | % of Deal | Expected Rating (Fitch/DBRS Morningstar) | Cumulative Certificate LTV | Cumulative UW NCF Debt Yield |
|---|---|---|---|---|---|
| A1 | $258,700,000 | 42.4% | AAAsf/AAA(sf) | 31.8% | 16.8% |
| B | $46,100,000 | 7.6% | AA-sf/AA(low)(sf) | 37.5% | 14.3% |
| C | $62,500,000 | 10.3% | NR/A(low)(sf) | 45.2% | 11.9% |
| D | $46,600,000 | 7.6% | NR/BBB(low)(sf) | 50.9% | 10.5% |
| E | $47,300,000 | 7.8% | NR/BB(low)(sf) | 56.7% | 9.4% |
| F | $53,200,000 | 8.7% | NR/B(low)(sf) | 63.3% | 8.5% |
| G | $64,862,500 | 10.6% | NR/NR | 71.3% | 7.5% |
| HRR (risk-retention) | $30,487,500 | 5.0% | NR/NR | 75.0% | 7.1% |
Why this wouldn't finance in agency (Fannie/Freddie) CMBS execution:
Net: SREIT chose conduit CMBS because the deal itself — leverage, coverage, floating short-term structure, and multi-borrower/release complexity — doesn't fit inside what Fannie Mae or Freddie Mac would underwrite; the private-label market was the only place this loan, at this balance and this leverage, could get done.
This is a read-only snapshot of a RealAI analysis.