Six Submarkets Rent Dynamics Analysis

For the Downtown Silver Spring, Hillcrest Heights/Marlow Heights, Beltsville/Laurel/South Laurel, Burke/Falls Church/Jefferson, Pikesville and Towson – Southwest submarkets, show 12-month in-place rent change, occupancy and its 12-month change, new-lease tradeout and median days on market. Which are firming and which are softening, and what does that mean for underwriting rent growth this year? Include data visualizations that will drive this home

8:53 PM

Resolving submarket IDs.

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{
 "_source": "query_data mf_rent_ts MONTH, submarkets toolu_bdrk_01G6s9vu5BiNZJDxiBdcKhdg; markets toolu_bdrk_01GhAz8a83tr8qTMLUqdFNtB; values rounded to cents",
 "months": ["2024-09-01","2024-10-01","2024-11-01","2024-12-01","2025-01-01","2025-02-01","2025-03-01","2025-04-01","2025-05-01","2025-06-01","2025-07-01","2025-08-01","2025-09-01","2025-10-01","2025-11-01","2025-12-01","2026-01-01","2026-02-01","2026-03-01","2026-04-01","2026-05-01","2026-06-01","2026-07-01","2026-08-01"],
 "series": {
  "Downtown Silver Spring": {"id":"f1b2ad23fb8eb5cdae748945b3e56a00","parent":"DC",
   "in_place":[1941.38,1949.62,1956.17,1960.80,1967.96,1970.41,1976.01,1982.15,1993.66,2002.06,2008.48,2012.49,2011.10,2012.07,2010.87,2009.43,2000.74,1996.50,1994.43,1993.80,1995.52,1996.61,1998.03,2000.89],
   "asking":[2287.92,2271.73,2263.47,2234.19,2221.98,2217.54,2232.39,2224.08,2217.66,2195.19,2201.20,2193.26,2185.40,2178.05,2133.43,2111.12,2102.49,2095.88,2089.36,2096.00,2116.66,2090.36,2125.75,2094.14]},
  "Hillcrest Heights/Marlow Heights": {"id":"e47195f73aae2bcd85562189691cd048","parent":"DC",
   "in_place":[1487.45,1489.31,1490.37,1490.35,1491.78,1493.49,1495.26,1496.41,1497.31,1497.40,1499.96,1500.18,1499.97,1499.95,1500.32,1499.96,1499.95,1500.42,1501.63,1503.57,1505.01,1504.81,1504.06,1503.85],
   "asking":[1522.85,1512.51,1519.26,1498.64,1543.87,1547.10,1526.37,1517.24,1506.96,1500.17,1488.22,1458.66,1496.54,1511.58,1489.80,1491.15,1522.97,1538.81,1529.98,1547.51,1527.47,1543.53,1539.81,1552.86]},
  "Beltsville/Laurel/South Laurel": {"id":"3c3a6171f6190e617afcee7f79f05fb5","parent":"DC",
   "in_place":[1834.69,1835.72,1850.56,1850.40,1851.14,1848.55,1848.82,1849.58,1846.29,1842.01,1838.19,1834.14,1831.84,1842.80,1843.20,1842.44,1842.16,1840.58,1840.05,1831.89,1829.00,1824.26,1820.70,1818.45],
   "asking":[1906.36,1864.53,1882.21,1881.22,1830.64,1821.13,1844.59,1847.13,1828.93,1852.47,1862.17,1852.46,1852.75,1865.83,1867.20,1868.06,1845.75,1849.90,1847.22,1860.33,1848.90,1886.00,1913.60,1936.44]},
  "Burke/Falls Church/Jefferson": {"id":"bbe0808495a06e7dd554be9b0326fb0e","parent":"DC",
   "in_place":[2110.55,2120.96,2127.25,2132.01,2143.05,2153.48,2166.17,2175.48,2191.95,2210.84,2223.72,2234.49,2238.18,2241.71,2244.57,2248.37,2252.09,2254.82,2263.62,2289.29,2296.64,2307.44,2315.23,2312.98],
   "asking":[2407.57,2407.69,2428.67,2474.93,2575.46,2617.83,2574.79,2594.91,2572.77,2565.19,2505.77,2525.22,2513.34,2504.74,2494.64,2570.62,2546.68,2509.41,2480.86,2468.96,2459.57,2457.33,2459.86,2429.71]},
  "Pikesville": {"id":"9037127f4765c1f1a63e032712a8afd7","parent":"BAL",
   "in_place":[1669.56,1673.37,1675.59,1677.06,1679.54,1679.91,1686.54,1688.72,1692.85,1698.00,1696.14,1699.26,1700.01,1697.72,1698.43,1697.27,1695.78,1696.15,1699.60,1700.75,1705.31,1709.58,1714.15,1715.47],
   "asking":[1744.68,1726.08,1733.13,1726.51,1766.14,1764.76,1716.88,1725.58,1754.56,1729.42,1778.23,1788.57,1789.65,1784.18,1785.65,1760.04,1769.33,1762.66,1775.35,1787.98,1732.27,1785.95,1839.03,1803.99]},
  "Towson - Southwest": {"id":"4134e2fdc0999525ab430760605b9b95","parent":"BAL",
   "in_place":[1756.00,1758.30,1759.49,1759.85,1763.82,1765.19,1768.67,1765.29,1771.24,1777.24,1778.41,1778.56,1776.96,1778.61,1782.08,1783.91,1782.08,1782.71,1786.28,1787.84,1796.23,1802.81,1803.41,1805.79],
   "asking":[1999.18,2042.35,2020.38,2066.93,2002.62,1991.44,2005.09,2010.53,1869.41,1858.92,1886.06,1934.93,1954.96,1944.42,1976.32,1945.41,2013.80,1951.16,1926.16,1909.51,1890.76,1810.50,1875.68,1921.98]}
 },
 "parents": {
  "DC": {"id":"47900","label":"Washington, DC",
   "in_place":[2131.46,2140.06,2146.97,2149.95,2159.08,2163.67,2172.92,2180.76,2193.81,2206.30,2215.01,2220.76,2227.90,2230.19,2230.92,2229.89,2230.81,2232.44,2235.05,2243.71,2246.89,2250.24,2253.36,2253.29],
   "asking":[2464.13,2443.32,2432.43,2435.50,2457.85,2452.24,2473.29,2477.94,2475.75,2497.86,2489.98,2487.60,2454.81,2431.93,2420.98,2421.91,2420.12,2408.58,2418.67,2429.80,2420.34,2420.56,2413.54,2398.44]},
  "BAL": {"id":"12580","label":"Baltimore, MD",
   "in_place":[1780.90,1783.00,1783.28,1783.00,1780.26,1781.15,1783.56,1785.59,1790.96,1794.74,1800.43,1801.65,1800.36,1799.28,1799.57,1798.25,1794.37,1796.20,1798.31,1780.47,1781.32,1783.30,1786.38,1785.62],
   "asking":[1879.78,1883.58,1889.37,1898.52,1891.77,1887.46,1894.22,1898.15,1877.16,1880.71,1879.77,1878.01,1871.81,1879.57,1871.18,1872.97,1884.78,1884.17,1885.69,1873.16,1881.28,1891.72,1918.15,1928.02]}
 }
}

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{"_source":"query_data mf_rent_and_occupancy_detail submarket toolu_bdrk_01BgoaupFEZPCVeXrCA18yNw; market toolu_bdrk_01BvsqCoLVhQombQcK4GiaYZ; as of 2026-09-26",
"records":[
{"entity":"Downtown Silver Spring","id":"f1b2ad23fb8eb5cdae748945b3e56a00","market":"Washington, DC","asking":2091.30,"asking_12mo":2176.18,"ip_avg":1994.33,"ip_avg_12mo":2011.21,"ip_med":1930.50,"ip_t12_med":0.0176,"ip_t3_med":0.0009,"occ":0.9455,"occ_12mo":0.9561,"occ_3mo":0.9538,"occ_t12":-0.0106,"tradeout":-0.0169,"tradeout_amt":-50.16,"tradeout_t12chg":-0.0309,"dom30":96,"leases30":343,"unleased":744,"ip_sample":6612},
{"entity":"Hillcrest Heights/Marlow Heights","id":"e47195f73aae2bcd85562189691cd048","market":"Washington, DC","asking":1594.10,"asking_12mo":1488.30,"ip_avg":1504.89,"ip_avg_12mo":1500.38,"ip_med":1455.00,"ip_t12_med":0.0051,"ip_t3_med":0.0003,"occ":0.9641,"occ_12mo":0.9685,"occ_3mo":0.9601,"occ_t12":-0.0044,"tradeout":0.0037,"tradeout_amt":6.80,"tradeout_t12chg":0.0007,"dom30":53,"leases30":277,"unleased":400,"ip_sample":5219},
{"entity":"Beltsville/Laurel/South Laurel","id":"3c3a6171f6190e617afcee7f79f05fb5","market":"Washington, DC","asking":1891.52,"asking_12mo":1849.92,"ip_avg":1823.64,"ip_avg_12mo":1832.00,"ip_med":1731.00,"ip_t12_med":0.0020,"ip_t3_med":0.0030,"occ":0.9634,"occ_12mo":0.9716,"occ_3mo":0.9629,"occ_t12":-0.0082,"tradeout":0.0069,"tradeout_amt":8.88,"tradeout_t12chg":0.0191,"dom30":110,"leases30":787,"unleased":728,"ip_sample":8487},
{"entity":"Burke/Falls Church/Jefferson","id":"bbe0808495a06e7dd554be9b0326fb0e","market":"Washington, DC","asking":2460.72,"asking_12mo":2505.72,"ip_avg":2311.38,"ip_avg_12mo":2237.30,"ip_med":2200.00,"ip_t12_med":0.0398,"ip_t3_med":0.0025,"occ":0.9554,"occ_12mo":0.9753,"occ_3mo":0.9621,"occ_t12":-0.0199,"tradeout":0.0151,"tradeout_amt":24.44,"tradeout_t12chg":-0.0753,"dom30":56,"leases30":284,"unleased":599,"ip_sample":6291},
{"entity":"Pikesville","id":"9037127f4765c1f1a63e032712a8afd7","market":"Baltimore, MD","asking":1817.26,"asking_12mo":1778.29,"ip_avg":1712.78,"ip_avg_12mo":1700.88,"ip_med":1680.00,"ip_t12_med":0.0243,"ip_t3_med":0.0079,"occ":0.9567,"occ_12mo":0.9429,"occ_3mo":0.9601,"occ_t12":0.0137,"tradeout":0.0910,"tradeout_amt":139.92,"tradeout_t12chg":0.0502,"dom30":82,"leases30":68,"unleased":156,"ip_sample":1675},
{"entity":"Towson - Southwest","id":"4134e2fdc0999525ab430760605b9b95","market":"Baltimore, MD","asking":1859.92,"asking_12mo":1979.64,"ip_avg":1809.15,"ip_avg_12mo":1776.81,"ip_med":1735.00,"ip_t12_med":0.0075,"ip_t3_med":0.0048,"occ":0.9848,"occ_12mo":0.9711,"occ_3mo":0.9806,"occ_t12":0.0137,"tradeout":0.0634,"tradeout_amt":94.35,"tradeout_t12chg":0.0355,"dom30":56,"leases30":54,"unleased":102,"ip_sample":1394}
],
"benchmarks":[
{"entity":"Washington, DC (MSA)","asking":2387.26,"ip_avg":2251.11,"ip_avg_12mo":2227.86,"ip_t12_med":0.0151,"occ":0.9511,"occ_12mo":0.9624,"occ_t12":-0.0113,"tradeout":0.0104,"dom30":70},
{"entity":"Baltimore, MD (MSA)","asking":1941.15,"ip_avg":1795.86,"ip_avg_12mo":1801.23,"ip_t12_med":-0.0121,"occ":0.9526,"occ_12mo":0.9707,"occ_t12":-0.0181,"tradeout":0.0302,"dom30":58}
]}

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"""Submarket rent-momentum scorecard: derived metrics, threshold labels, composite.
Inputs: extracts/submarket_snapshot.json, extracts/monthly_rent_ts.json (DOM Sep-2025 embedded below from
mf_rent_ts MONTH toolu_bdrk_01G6s9vu5BiNZJDxiBdcKhdg).
Outputs: extracts/records.json, printed table."""
import json

snap = json.load(open("extracts/submarket_snapshot.json"))
ts = json.load(open("extracts/monthly_rent_ts.json"))

# Monthly median DOM, Sep-2025 .. Aug-2026 (from mf_rent_ts MONTH)
DOM_MONTHLY = {
 "Downtown Silver Spring": [60.5,69.5,85.5,89.5,87.5,89,91,84,57.5,51.5,78,61],
 "Hillcrest Heights/Marlow Heights": [57,62.5,70.5,75,83,60.5,74,60,71,49,55.5,54.5],
 "Beltsville/Laurel/South Laurel": [65,76,68,71.5,73,66,55,59.5,48,48,62,58],
 "Burke/Falls Church/Jefferson": [54,61.5,68,66.5,63,72.5,65,51,43,32,50,61],
 "Pikesville": [92,97,64.5,144.5,139,109,83,74,76,37.5,78,70.5],
 "Towson - Southwest": [45,72,77,161,78,69,23,45,76,32,64,70],
}
# Monthly tradeout sample sizes Aug-2026 (thin-sample flag)
TRADEOUT_N_AUG26 = {"Downtown Silver Spring":245,"Hillcrest Heights/Marlow Heights":145,
 "Beltsville/Laurel/South Laurel":210,"Burke/Falls Church/Jefferson":147,"Pikesville":59,"Towson - Southwest":37}
# Tradeout same month last year (Aug-2025 monthly) for trajectory
TRADEOUT_AUG25 = {"Downtown Silver Spring":0.009795,"Hillcrest Heights/Marlow Heights":0.003101,
 "Beltsville/Laurel/South Laurel":-0.013061,"Burke/Falls Church/Jefferson":0.077816,"Pikesville":0.037724,"Towson - Southwest":0.025663}

# Thresholds (leading indicators)
def s_tradeout(x): return 1 if x >= 0.03 else (-1 if x <= 0.01 else 0)
def s_occ(x):      return 1 if x >= 0.005 else (-1 if x <= -0.005 else 0)
def s_dom(x):      return 1 if x <= -5 else (-1 if x >= 10 else 0)   # YoY change in days; lower = better
def label(net):    return "Firming" if net >= 1 else ("Softening" if net <= -1 else "Flat")

recs = []
for r in snap["records"]:
    n = r["entity"]
    ser = ts["series"][n]
    dom_sep25 = DOM_MONTHLY[n][0]
    dom_yoy = r["dom30"] - dom_sep25
    ip_avg_t12 = r["ip_avg"]/r["ip_avg_12mo"] - 1
    asking_t12 = r["asking"]/r["asking_12mo"] - 1
    spread = r["asking"]/r["ip_avg"] - 1
    sig = {"tradeout": s_tradeout(r["tradeout"]), "occ": s_occ(r["occ_t12"]), "dom": s_dom(dom_yoy)}
    net = sum(sig.values())
    recs.append({
        "entity": n, "id": r["id"], "market": r["market"],
        "ip_t12_med": r["ip_t12_med"], "ip_avg_t12": round(ip_avg_t12,4), "ip_avg": r["ip_avg"],
        "occ": r["occ"], "occ_12mo": r["occ_12mo"], "occ_t12_pp": r["occ_t12"],
        "tradeout": r["tradeout"], "tradeout_amt": r["tradeout_amt"], "tradeout_aug25": TRADEOUT_AUG25[n],
        "tradeout_n_aug26": TRADEOUT_N_AUG26[n],
        "dom30": r["dom30"], "dom_sep25": dom_sep25, "dom_yoy": dom_yoy,
        "asking_t12": round(asking_t12,4), "spread": round(spread,4), "leases30": r["leases30"],
        "signals": sig, "net": net, "label": label(net),
        "neg_dom_yoy": -dom_yoy,
    })
json.dump(recs, open("extracts/records.json","w"), indent=1)

print(f"{'Submarket':34s} {'IPmed':>6s} {'IPavg':>6s} {'Occ':>6s} {'dOcc':>6s} {'TO':>6s} {'TO25':>6s} {'DOM':>4s} {'DOMyy':>6s} {'Ask12':>6s} {'Sprd':>6s} net label")
for x in recs:
    print(f"{x['entity']:34s} {x['ip_t12_med']*100:6.1f} {x['ip_avg_t12']*100:6.1f} {x['occ']*100:6.1f} {x['occ_t12_pp']*100:6.1f} "
          f"{x['tradeout']*100:6.1f} {x['tradeout_aug25']*100:6.1f} {x['dom30']:4.0f} {x['dom_yoy']:6.1f} {x['asking_t12']*100:6.1f} {x['spread']*100:6.1f} {x['net']:3d} {x['label']}")
for b in snap["benchmarks"]:
    print(b["entity"], "ip_avg_t12=%.1f%%" % ((b["ip_avg"]/b["ip_avg_12mo"]-1)*100), "ip_med_t12=%.1f%%" % (b["ip_t12_med"]*100),
          "occ=%.1f%%" % (b["occ"]*100), "dOcc=%.1fpp" % (b["occ_t12"]*100), "TO=%.1f%%" % (b["tradeout"]*100), "DOM=%d" % b["dom30"])

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"""Run forecasting engine for in-place and asking rent, 6 submarkets, 1-year horizon.
Inputs: extracts/monthly_rent_ts.json. Outputs: extracts/forecasts/*.json + summary print."""
import json, subprocess, os
ts = json.load(open("extracts/monthly_rent_ts.json"))
months = ts["months"]
os.makedirs("extracts/forecasts", exist_ok=True)
summary = []
def t12(arr): return arr[-1]/arr[-13]-1
for name, s in ts["series"].items():
    par = ts["parents"][s["parent"]]
    div = t12(s["in_place"]) - t12(s["asking"])
    for metric in ["in_place", "asking"]:
        hist = [{"period": m, "value": v, "source": "datamart"} for m, v in zip(months, s[metric])]
        peer = [{"period": m, "value": v, "source": "datamart"} for m, v in zip(months, par[metric])]
        level_ratio = s[metric][-1]/par[metric][-1]
        payload = {
          "metric": {"name": f"mf_rent_ts.{metric}_rent_latest_avg", "units": "$", "family": "rent_or_occupancy"},
          "subject": {"entity_type": "submarket", "entity_id": s["id"], "label": name},
          "horizon": {"years": 1, "intervals": "annual"},
          "as_of": "2026-09-30",
          "history": hist,
          "history_check": {"count": len(hist), "sum": round(sum(s[metric]), 2), "last_value": s[metric][-1]},
          "peer_history": peer,
          "peer_history_check": {"count": len(peer), "last_value": par[metric][-1]},
          "caller_disclosures": {
            "peer_selection_basis": f"parent MSA ({par['label']}) default; level ratio {level_ratio:.2f}; subject T12 {t12(s[metric])*100:.1f}% vs parent T12 {t12(par[metric])*100:.1f}%",
            "sibling_series_note": "asking and in-place both run through engine (divergence > 100 bps)" if abs(div) > 0.01 else "sibling checked; within 100 bps",
            "sibling_divergence_pct": round(div, 4),
            "signals_omitted": [
              {"signal": "supply_pipeline", "reason": "no source exists at submarket grain (supply snapshot is market-grain only)"},
              {"signal": "migration", "reason": "not gathered"},
              {"signal": "employment", "reason": "not gathered"}],
            "lookback_note": "full 24 monthly points available in pull (Sep-2024 to Aug-2026)"},
          "scenarios": ["base", "upside", "downside"]}
        fn = f"extracts/forecasts/{name.replace('/','_').replace(' ','_')}_{metric}.json"
        json.dump(payload, open(fn.replace('.json','_payload.json'), "w"))
        out = subprocess.run(["python", "skills/forecasting/scripts/forecast.py", "--file", fn.replace('.json','_payload.json')], capture_output=True, text=True).stdout
        open(fn, "w").write(out)
        r = json.loads(out)
        summary.append({"entity": name, "metric": metric, "status": r.get("status"), "confidence": r.get("confidence"),
                        "base": r.get("base_case"), "up": r.get("upside_case"), "down": r.get("downside_case"),
                        "flags": r.get("data_quality_flags"), "sibling_div": round(div,4)})
json.dump(summary, open("extracts/forecast_summary.json","w"), indent=1)
for x in summary:
    print(json.dumps(x)[:1500]); print()

Only the two Baltimore-area submarkets, Pikesville and Towson – Southwest, are firming, and both are small. All four DC-area submarkets are cooling or softening. Burke is the trap: its 4.0% trailing rent growth comes from leases signed a year or more ago, and new leases are now priced only 1.5% above the previous tenant's rent.

Submarket rent momentum scorecard (data as of 9/26/2026)

Submarket	Metro	In-place rent T12 change (median)	Occupancy	Occupancy 12-mo change (pp)	New-lease tradeout	Median days on market (last 30d)	Days on market vs Sep-2025	Read
Submarkets
	Baltimore	+2.4%1	95.7%	+1.4	+9.1% ($140)2	82	−10	Firming3
	Baltimore	+0.8%1	98.5%	+1.4	+6.3% ($94)2	56	+11	Firming
	DC	+4.0%	95.5%	−2.0	+1.5% ($24)	56	+2	Cooling
	DC	+0.5%	96.4%	−0.4	+0.4% ($7)	53	−4	Cooling
	DC	+1.8%1	94.5%	−1.1	−1.7% (−$50)	96	+36	Softening
	DC	+0.2%1	96.3%	−0.8	+0.7% ($9)	110	+45	Softening
Benchmarks
	—	+1.5%	95.1%	−1.1	+1.0%	70	–	–
	—	−1.2%	95.3%	−1.8	+3.0%	58	–	–

Who's firming and who's softening

Pikesville is the clearest firming case. New leases are signing 9.1% ($140) above the prior tenant's rent , the highest of the six . Occupancy is up 1.4pp over 12 months , and median days on market fell 10 days year over year . It is gaining ground while the Baltimore metro as a whole lost 1.8pp of occupancy .
Towson – Southwest is firming on a thin base. It has the highest occupancy of the six at 98.5% , and new leases are up 6.3% . But it signed only 54 leases in the last 30 days , and asking rents are 6.0% below a year ago . The strength is real but narrow.
Burke/Falls Church/Jefferson is cooling from a high level. Its 4.0% in-place rent gain is the highest of the six . Leading signals have turned, though: occupancy fell 2.0pp, the steepest drop in the set, and the 12-month change in its tradeout is −7.5pp .
Hillcrest Heights/Marlow Heights is stalled. Asking rents rose 7.1%, the most of any of the six , but new leases are only 0.4% above prior rents . Landlords are listing higher, but signed leases aren't following.
Downtown Silver Spring is softening. It is the only submarket where new leases sign below the prior rent, at −1.7% (−$50) . It also has the lowest occupancy (94.5%), and days on market jumped from about 61 a year ago to 96 .
Beltsville/Laurel/South Laurel is also softening. Its 110-day median days on market is the slowest leasing in the set and the largest year-over-year slowdown (+45 days) . This is despite heavy leasing volume: 787 leases in the last 30 days .

New-lease tradeout: which submarkets have pricing power

Monthly new-lease tradeout, Jan 2025 – Aug 2026

Occupancy trend by submarket, Sep 2024 – Aug 2026

What this means for underwriting rent growth this year

Base next year's rent growth on what new leases are actually signing at, not on trailing in-place growth. In-place rent reflects leases signed 6–18 months ago, so it lags. The forecasting engine, which works from that lagging series, gets the order backwards: it has Burke highest at +4.4% and Pikesville near the bottom at +0.9%.

Year-1 in-place rent growth: engine trend vs. recommended underwriting

Submarket	Engine base (downside / upside)	Engine confidence	Recommended Yr-1 underwriting (analyst judgment)	Why
Pikesville	+0.9% (−1.1% / +2.9%)	Medium	2.5–3.0%1	A 9% tradeout and rising occupancy should flow into in-place rent; stay below the tradeout because of the thin sample and 82 days on market.
Towson – Southwest	+1.0% (−1.0% / +3.0%)	Medium	2.0–2.5%	98.5% occupancy and a 6.3% tradeout, but low volume and asking rents down 6%.
Burke/Falls Church/Jefferson	+4.4% (+2.4% / +6.4%)	Medium	1.5–2.0%	Do not carry the trailing 4%: tradeout is 1.5% and occupancy fell 2pp.
Hillcrest Heights/Marlow Heights	+1.3% (−0.7% / +3.3%)	Medium	0.5–1.0%	Tradeout near zero; the 7% asking increase isn't being realized in signed leases.
Downtown Silver Spring	+1.9% (−0.1% / +3.9%)	Medium	0% plus a concession allowance	Negative tradeout, lowest occupancy, days on market up 36.
Beltsville/Laurel/South Laurel	+0.5% (−1.5% / +2.5%)	Low	0–0.5%	Slowest leasing (110 days) and occupancy down 0.8pp.

About the engine numbers above. Every run came back with the flag "boom extrapolation suspected" or, for Beltsville, "cyclical drawdown suspected." All runs also flagged "peer mismatch suspected," because each submarket's growth deviates 7.0–40.4 sigma from its metro . Supply, migration and jobs data weren't included in the forecasts.

The spread between the groups is the point. Two Baltimore submarkets that are currently strong can justify about 2.5%. Four DC submarkets can't justify much above 1–2%, and Silver Spring can't justify anything above zero. A single DC-wide growth rate would overstate Silver Spring and Beltsville and understate Pikesville.

What I'd act on now: underwrite Silver Spring and Beltsville flat for year 1, and cut Burke to 2% or below whatever the trailing numbers say. What would change this: Burke's monthly tradeout climbing back above 3% while occupancy recovers above 96.5%. That would mean its slowdown was seasonal rather than a real turn.