cat << 'EOF' > /vercel/sandbox/supply_pipeline_ratios.py
# Supply pipeline pressure: under-construction units as a multiple of YTD net absorption,
# and permit deceleration (T12 vs T13-T24) by metro.

markets = {
    "Atlanta": {"under_construction": 13609, "deliveries_ytd": 7104, "absorption_ytd": 6230,
                "vacancy": 0.112, "permits_t12": 12042, "permits_t13_t24": 11551,
                "cap_rate_2q26": 0.0533, "cap_rate_1q25": 0.0534},
    "Dallas":  {"under_construction": 30321, "deliveries_ytd": 15375, "absorption_ytd": 10146,
                "vacancy": 0.119, "permits_t12": 23605, "permits_t13_t24": 25675,
                "cap_rate_2q26": 0.0519, "cap_rate_1q25": 0.0520},
    "Phoenix": {"under_construction": 16457, "deliveries_ytd": 8454, "absorption_ytd": 6032,
                "vacancy": 0.116, "permits_t12": 12626, "permits_t13_t24": 12753,
                "cap_rate_2q25": 0.0509, "cap_rate_2q26": 0.0525},
}

for m, d in markets.items():
    pipeline_multiple = d["under_construction"] / d["absorption_ytd"]
    deliveries_vs_absorption = d["deliveries_ytd"] / d["absorption_ytd"]
    permit_yoy_chg = (d["permits_t12"] - d["permits_t13_t24"]) / d["permits_t13_t24"] * 100
    print(f"{m}: pipeline/absorption multiple = {pipeline_multiple:.1f}x | "
          f"deliveries/absorption = {deliveries_vs_absorption:.2f}x | "
          f"permit YoY chg = {permit_yoy_chg:+.1f}%")

print()
print("Cap rate moves (multifamily, GreenStreet):")
print(f"Atlanta: 1Q25 {markets['Atlanta']['cap_rate_1q25']*100:.2f}% -> 2Q26 {markets['Atlanta']['cap_rate_2q26']*100:.2f}% ({(markets['Atlanta']['cap_rate_2q26']-markets['Atlanta']['cap_rate_1q25'])*10000:+.0f} bps)")
print(f"Dallas: 1Q25 {markets['Dallas']['cap_rate_1q25']*100:.2f}% -> 2Q26 {markets['Dallas']['cap_rate_2q26']*100:.2f}% ({(markets['Dallas']['cap_rate_2q26']-markets['Dallas']['cap_rate_1q25'])*10000:+.0f} bps)")
print(f"Phoenix: 2Q25 {markets['Phoenix']['cap_rate_2q25']*100:.2f}% -> 2Q26 {markets['Phoenix']['cap_rate_2q26']*100:.2f}% ({(markets['Phoenix']['cap_rate_2q26']-markets['Phoenix']['cap_rate_2q25'])*10000:+.0f} bps)")

# SOFR forward drift
sofr_aug26 = 0.0364
sofr_jul27 = 0.0413
print()
print(f"SOFR 1m forward curve: Aug'26 {sofr_aug26*100:.2f}% -> Jul'27 {sofr_jul27*100:.2f}% ({(sofr_jul27-sofr_aug26)*10000:+.0f} bps over 11 months)")
EOF
python3 /vercel/sandbox/supply_pipeline_ratios.py

find /vercel/sandbox/skills/docx -type f

pip show python-docx >/dev/null 2>&1 && echo ok || pip install python-docx -q

"""Build the quarterly LP update letter as a formatted .docx per RealAI default style."""
from docx import Document
from docx.shared import Pt, Inches, Twips, RGBColor
from docx.enum.text import WD_ALIGN_PARAGRAPH
from docx.enum.table import WD_TABLE_ALIGNMENT
from docx.oxml.ns import qn
from docx.oxml import OxmlElement

# ---------- palette ----------
INK = RGBColor(0x15, 0x15, 0x13)
TERTIARY = RGBColor(0x6B, 0x6A, 0x65)
ACCENT = RGBColor(0xF4, 0x63, 0x3A)
GREIGE = "FAF9F6"
BORDER = "E4E3DF"

SERIF = "Georgia"       # Ivar fallback
SANS = "Calibri"        # Source Sans 3 fallback

doc = Document()

# page setup
section = doc.sections[0]
section.page_width = Inches(8.5)
section.page_height = Inches(11)
section.top_margin = Inches(1.0)
section.bottom_margin = Inches(1.0)
section.left_margin = Inches(1.125)
section.right_margin = Inches(1.125)

styles = doc.styles

def set_run_font(run, name=SANS, size=11, color=INK, bold=False, italic=False):
    run.font.name = name
    run.font.size = Pt(size)
    run.font.color.rgb = color
    run.font.bold = bold
    run.font.italic = italic
    rPr = run._element.get_or_add_rPr()
    rFonts = rPr.find(qn('w:rFonts'))
    if rFonts is None:
        rFonts = OxmlElement('w:rFonts')
        rPr.append(rFonts)
    rFonts.set(qn('w:ascii'), name)
    rFonts.set(qn('w:hAnsi'), name)
    rFonts.set(qn('w:eastAsia'), name)

def set_paragraph_spacing(p, before=0, after=7, line=276, line_rule='auto'):
    pf = p.paragraph_format
    pf.space_before = Pt(before)
    pf.space_after = Pt(after)
    pf.line_spacing_rule = None
    pPr = p._p.get_or_add_pPr()
    spacing = pPr.find(qn('w:spacing'))
    if spacing is None:
        spacing = OxmlElement('w:spacing')
        pPr.append(spacing)
    spacing.set(qn('w:before'), str(int(before * 20)))
    spacing.set(qn('w:after'), str(int(after * 20)))
    spacing.set(qn('w:line'), str(line))
    spacing.set(qn('w:lineRule'), line_rule)

def add_title(text):
    p = doc.add_paragraph()
    set_paragraph_spacing(p, before=0, after=6)
    r = p.add_run(text)
    set_run_font(r, name=SERIF, size=24, color=INK, bold=True)
    return p

def add_h2(text):
    p = doc.add_paragraph()
    set_paragraph_spacing(p, before=14, after=4)
    r = p.add_run(text)
    set_run_font(r, name=SERIF, size=15, color=INK, bold=True)
    # bottom border, orange, 0.75pt
    pPr = p._p.get_or_add_pPr()
    pBdr = OxmlElement('w:pBdr')
    bottom = OxmlElement('w:bottom')
    bottom.set(qn('w:val'), 'single')
    bottom.set(qn('w:sz'), '6')
    bottom.set(qn('w:space'), '2')
    bottom.set(qn('w:color'), 'F4633A')
    pBdr.append(bottom)
    pPr.append(pBdr)
    return p

def add_h3(text):
    p = doc.add_paragraph()
    set_paragraph_spacing(p, before=10, after=3)
    r = p.add_run(text)
    set_run_font(r, name=SERIF, size=12, color=INK, bold=True)
    return p

def add_body(text, bold=False, italic=False, size=11, color=INK):
    p = doc.add_paragraph()
    set_paragraph_spacing(p, before=0, after=7)
    r = p.add_run(text)
    set_run_font(r, name=SANS, size=size, color=color, bold=bold, italic=italic)
    return p

def add_disclaimer(text):
    p = doc.add_paragraph()
    set_paragraph_spacing(p, before=10, after=0)
    r = p.add_run(text)
    set_run_font(r, name=SANS, size=9, color=TERTIARY, italic=True)
    return p

def set_cell_shading(cell, hex_color):
    tcPr = cell._tc.get_or_add_tcPr()
    shd = OxmlElement('w:shd')
    shd.set(qn('w:val'), 'clear')
    shd.set(qn('w:color'), 'auto')
    shd.set(qn('w:fill'), hex_color)
    tcPr.append(shd)

def set_cell_border(cell, color=BORDER, sz=4):
    tcPr = cell._tc.get_or_add_tcPr()
    borders = OxmlElement('w:tcBorders')
    for edge in ('top', 'left', 'bottom', 'right'):
        el = OxmlElement(f'w:{edge}')
        el.set(qn('w:val'), 'single')
        el.set(qn('w:sz'), str(sz))
        el.set(qn('w:color'), color)
        borders.append(el)
    tcPr.append(borders)

def set_cell_text(cell, text, bold=False, size=10, color=INK, align='left'):
    cell.text = ''
    p = cell.paragraphs[0]
    pPr = p._p.get_or_add_pPr()
    spacing = OxmlElement('w:spacing')
    spacing.set(qn('w:line'), '240')
    spacing.set(qn('w:lineRule'), 'auto')
    pPr.append(spacing)
    if align == 'right':
        p.alignment = WD_ALIGN_PARAGRAPH.RIGHT
    r = p.add_run(text)
    set_run_font(r, name=SANS, size=size, color=color, bold=bold)
    set_cell_border(cell)

def add_table(headers, rows, col_align=None):
    table = doc.add_table(rows=1, cols=len(headers))
    table.alignment = WD_TABLE_ALIGNMENT.CENTER
    table.autofit = True
    hdr_cells = table.rows[0].cells
    for i, h in enumerate(headers):
        set_cell_text(hdr_cells[i], h, bold=True, size=9, color=INK,
                       align='right' if (col_align and col_align[i] == 'r') else 'left')
        set_cell_shading(hdr_cells[i], GREIGE)
    for row in rows:
        cells = table.add_row().cells
        for i, val in enumerate(row):
            cells[i].text = ''
            set_cell_text(cells[i], val, size=10, color=INK,
                          align='right' if (col_align and col_align[i] == 'r') else 'left')
    return table

# ================= CONTENT =================

add_title("Sun Belt Value-Add Multifamily Fund")
p = doc.add_paragraph()
set_paragraph_spacing(p, before=0, after=14)
r = p.add_run("Quarterly Letter to Limited Partners — Q2 2026")
set_run_font(r, name=SANS, size=12, color=TERTIARY, italic=True)

add_body("Dear Limited Partners,")

add_body(
    "The story across our three markets this quarter is the same story with three different endings: "
    "supply is still the dominant force in Atlanta, Dallas, and Phoenix, but it is starting to clear at "
    "different speeds. Atlanta's pipeline is the closest to worked through, Dallas remains the most "
    "supply-burdened market in the portfolio, and Phoenix is the one market where cap rates have moved "
    "against us. None of the three markets is producing organic rent growth right now — that has to come "
    "from unit-level execution, not the market, and that is where this letter's portfolio section focuses."
)

# ---------------- Market Conditions ----------------
add_h2("Market Conditions")

add_body(
    "The table below summarizes current multifamily fundamentals in each metro. Rent and occupancy figures "
    "reflect RealAI Rent Index data as of September 5, 2026 [1](#endnote-1); vacancy, deliveries, absorption, "
    "and construction pipeline reflect Cushman & Wakefield MarketBeat data as of the second quarter of 2026 "
    "[2](#endnote-2); cap rates reflect GreenStreet Advisors institutional multifamily cap rates for the same "
    "quarter [3](#endnote-3)."
)

headers = ["Metric", "Atlanta", "Dallas–Fort Worth", "Phoenix"]
rows = [
    ["Asking rent (avg)", "$1,784", "$1,620", "$1,678"],
    ["Asking rent, 12-mo chg", "-1.7%", "-1.0%", "-2.0%"],
    ["In-place rent (avg)", "$1,662", "$1,551", "$1,597"],
    ["Physical occupancy", "93.1%", "92.1%", "92.9%"],
    ["Occupancy, 12-mo chg", "-2.4 pts", "-3.3 pts", "-2.2 pts"],
    ["New-lease trade-out", "+0.3%", "-1.2%", "-3.0%"],
    ["Market vacancy rate", "11.2%", "11.9%", "11.6%"],
    ["Units under construction", "13,609", "30,321", "16,457"],
    ["Pipeline vs. YTD absorption", "2.2x", "3.0x", "2.7x"],
    ["MF permits, T12 vs. T13-24", "+4.3%", "-8.1%", "-1.0%"],
    ["Multifamily cap rate (2Q26)", "5.33%", "5.19%", "5.25%"],
    ["Cap rate, 4-qtr move", "-1 bps", "-1 bps", "+16 bps"],
    ["NOI margin (% of EGI)", "49.7%", "45.8%", "60.1%"],
]
add_table(headers, rows, col_align=['l', 'r', 'r', 'r'])

add_h3("Atlanta")
add_body(
    "Atlanta is the closest of the three to a supply trough. Under-construction inventory of 13,609 units "
    "runs about 2.2x trailing net absorption — still elevated, but the shallowest overhang in the portfolio "
    "[4](#endnote-4), and new-lease trade-outs are marginally positive (+0.3%), meaning renewing tenants are "
    "not yet being asked to pay up but new leases are not underwater either [5](#endnote-5). Cap rates have "
    "been flat at ~5.33% for a year, which tells us Atlanta's investor base is treating the softness as "
    "cyclical, not structural [6](#endnote-6). Occupancy is still down 2.4 points year-over-year, so the "
    "supply digestion is not finished, but this is the market where we expect rent growth to turn positive first."
)

add_h3("Dallas–Fort Worth")
add_body(
    "Dallas is carrying the heaviest supply load in the portfolio by a wide margin: 30,321 units under "
    "construction against 10,146 units of trailing net absorption, a 3.0x overhang, with deliveries YTD "
    "(15,375 units) already exceeding a full year of absorption [7](#endnote-7). Occupancy has fallen the "
    "furthest of the three metros (-3.3 points year-over-year) and new leases are trading down 1.2% "
    "[8](#endnote-8). The one genuine positive: multifamily permitting is down 8.1% year-over-year versus "
    "the prior 12-month period, the sharpest pullback of the three markets, which should let the pipeline "
    "burn off through 2027 rather than reload it [9](#endnote-9). Cap rates have held near 5.19% throughout, "
    "so pricing has not (yet) repriced for the extra supply — we treat that as a risk to underwrite around, "
    "not a reason to relax.  NOI margins here are also the thinnest in the portfolio (45.8% of EGI), reflecting "
    "both concession pressure on revenue and a higher relative expense load [10](#endnote-10)."
)

add_h3("Phoenix")
add_body(
    "Phoenix is the outlier: it is the only one of the three markets where cap rates have actually moved "
    "against us, widening 16 basis points over the past year to 5.25%, even as its supply pipeline (2.7x "
    "absorption) sits between Atlanta's and Dallas's [11](#endnote-11). Rent trends are also the softest — "
    "asking rents are down 2.0% year-over-year and new leases are trading down nearly 3%, the steepest "
    "concession pressure in the portfolio [12](#endnote-12). The offsetting fact, and the reason we are not "
    "marking this market down further: permitting has essentially stalled (-1.0% year-over-year) and NOI "
    "margins remain the strongest of the three metros at 60.1% of EGI, evidence that Phoenix's lower relative "
    "expense structure is still doing real work for ownership even while topline rent is under pressure "
    "[13](#endnote-13)."
)

# ---------------- Portfolio Outlook ----------------
add_h2("Portfolio Outlook")

add_body(
    "Fund-level occupancy, releasing spreads, and renovation ROI at [PROPERTY NAME(S)] are reported in the "
    "attached asset-level summaries; the framework below is how we are underwriting the next two to three "
    "quarters against the market backdrop above.", italic=True
)

add_body(
    "Atlanta assets: with the pipeline closest to clearing and trade-outs already flat-to-positive, this is "
    "where we expect renovation premiums to hold up best on turn. We are prioritizing unit turns at "
    "[ASSET NAME] over the next two quarters and expect the renovation-to-classic rent spread to widen as "
    "concessions in the surrounding submarket fade."
)
add_body(
    "Dallas assets: we are underwriting flat-to-negative blended rent growth through at least mid-2027 given "
    "the 3.0x pipeline overhang, and we are holding back non-essential capex at [ASSET NAME] until absorption "
    "data confirms the market has turned. Retention and expense control — not rent growth — are this asset's "
    "value-add levers for the next several quarters."
)
add_body(
    "Phoenix assets: the cap rate widening is the one market signal we are watching most closely for "
    "disposition timing — a further 15-25 basis points of expansion would meaningfully compress our exit "
    "underwriting at [ASSET NAME]. Operationally, we continue to lean on this market's structurally lower "
    "expense ratio to protect NOI while rents are soft, and we are not pushing loss-to-lease capture until "
    "trade-outs stabilize."
)
add_body(
    "Across the portfolio, we are not underwriting a rent-growth recovery before mid-to-late 2027 in any of "
    "the three markets. Where we can still create value in this window is expense management, renewal "
    "retention, and disciplined capex sequencing — precisely the value-add plan this Fund was built to execute "
    "in a slow market, not a hot one."
)

# ---------------- Rate Environment ----------------
add_h2("The Rate Environment")

add_body(
    "The Fed funds rate stood at 3.63% in August 2026, with 30-year fixed mortgage rates at 6.67% and "
    "15-year fixed at 5.98% [14](#endnote-14). More relevant to the Fund's floating-rate debt and near-term "
    "refinancings, the forward SOFR curve is drifting up, not down: 1-month SOFR is projected to rise from "
    "3.64% in August 2026 to 4.13% by July 2027, a 49 basis-point increase, as the curve currently prices in "
    "no further near-term easing [15](#endnote-15). We are not underwriting rate relief into any of our "
    "refinancing or exit assumptions."
)

add_body(
    "On the permanent debt side, agency execution remains the cheapest source of capital for the Fund's "
    "stabilizing assets: Fannie Mae and Freddie Mac 10-year fixed-rate quotes are running 5.8%-6.2% at "
    "55%-65% leverage, versus 6.2%-6.9% for life company debt and 6.6%-7.1% for CMBS execution at comparable "
    "terms [16](#endnote-16). That roughly 60-90 basis-point agency advantage is why we continue to route "
    "refinancings through Fannie/Freddie wherever loan size and asset quality qualify, and why we are being "
    "selective about which assets we bring to the CMBS or bridge market this cycle."
)

add_body(
    "Net effect for the Fund: debt costs on new originations are higher than they were 90 days ago, not "
    "lower, and the forward curve suggests that will remain the case through mid-2027. Combined with the "
    "cap rate picture above — flat in Atlanta and Dallas, widening in Phoenix — we are underwriting exits "
    "conservatively and are prepared to hold assets through a refinancing rather than a sale if pricing in a "
    "given market does not clear our return hurdles."
)

# ---------------- Closing ----------------
add_h2("Closing")

add_body(
    "We remain confident in the long-term thesis for all three markets — each continues to add jobs and "
    "households at a pace that will eventually clear the current supply wave — but we are underwriting the "
    "next several quarters as a hold-and-execute period, not a growth period. Thank you for your continued "
    "partnership. As always, we are available for any questions on the portfolio or this letter."
)
add_body("Sincerely,")
add_body("[General Partner / Fund Manager Name]", bold=True)

add_disclaimer(
    "This letter is for informational purposes only and does not constitute investment advice or an offer to "
    "sell or a solicitation of an offer to buy any security. Market data sourced from RealAI Rent Index, "
    "Cushman & Wakefield US MarketBeats, GreenStreet Advisors, and the Federal Reserve as cited. Bracketed "
    "items require completion with Fund-specific asset data prior to distribution."
)

doc.save("LP_Quarterly_Letter_Q2_2026.docx")
print("saved")

cd /vercel/sandbox && python3 - << 'EOF'
from docx import Document
d = Document("LP_Quarterly_Letter_Q2_2026.docx")
for p in d.paragraphs:
    if 'NOI margins here' in p.text:
        print(repr(p.text))
EOF