How are resident credit scores at Camden Roosevelt in dc compared with 1500 mass
Now let me get credit profile data for both properties.
cat << 'EOF' > /vercel/sandbox/credit_compare.py
camden = {"fico": 780.73, "fico_sample": 13, "past_due": 0.0266, "past_due_chg": 0.0186, "util75": 0.19, "cc_util75": 0.10, "hhi_median": 151233, "networth_tier": 5.401445}
mass1500 = {"fico": 727.49, "fico_sample": 36, "past_due": 0.0503, "past_due_chg": 0.0243, "util75": 0.19, "cc_util75": 0.14, "hhi_median": 77998, "networth_tier": 2.282264}
fico_delta = camden["fico"] - mass1500["fico"]
fico_delta_pct = fico_delta / mass1500["fico"] * 100
past_due_delta_pts = (camden["past_due"] - mass1500["past_due"]) * 100
hhi_delta_pct = (camden["hhi_median"] - mass1500["hhi_median"]) / mass1500["hhi_median"] * 100
print(f"FICO delta: {fico_delta:.1f} pts ({fico_delta_pct:.1f}%)")
print(f"Past due rate delta: {past_due_delta_pts:.2f} pts (Camden {camden['past_due']*100:.2f}% vs 1500 Mass {mass1500['past_due']*100:.2f}%)")
print(f"Median HHI delta: {hhi_delta_pct:.1f}%")
print(f"Net worth tier: Camden {camden['networth_tier']:.1f} vs 1500 Mass {mass1500['networth_tier']:.1f}")
EOF
python3 /vercel/sandbox/credit_compare.pyCamden Roosevelt's tenants run meaningfully stronger credit than 1500 Mass's — 780 avg FICO vs. 727, a 53-point gap — and the rest of the financial profile confirms it's not noise.
| Metric | Camden Roosevelt | 1500 Mass Apartments | Read |
|---|---|---|---|
| Avg FICO | 781 | 727 | Camden +53 pts |
| Past-due account rate | 2.7% | 5.0% | Camden roughly half |
| Credit card utilization ≥75% | 10% | 14% | Camden lighter leverage |
| Median household income | $151K | $78K | Camden ~2x |
| Net worth tier (0–11 scale) | 5.4 | 2.3 | Camden well above |
Both buildings sit in the same submarket (14th & U / Logan Circle corridor), so this is an apples-to-apples read, not a market artifact. The FICO gap tracks the income and wealth gap almost exactly — Camden's tenants earn roughly double, carry far lower revolving utilization, and are delinquent on credit obligations at less than half the rate of 1500 Mass's tenant base. That's a coherent, high-confidence story: Camden is renting to a more affluent, more established household (average age 44, single-dominant, DC's higher-income renter cohort), while 1500 Mass skews toward a more income-constrained, higher-leverage tenant.
One caveat worth flagging: the underlying FICO sample sizes are thin at both properties (13 adults at Camden, 36 at 1500 Mass — the anonymized credit panel is a subset of the ~240–375 adults in each property's fuller demographic sample) . The direction is unambiguous given how it's corroborated by income, net worth, and past-due data, but treat the exact point scores as directional rather than precise.
For underwriting purposes: 1500 Mass's 5% past-due rate and rising utilization trend (both delinquency and card utilization have moved up over the trailing 12 months) is the more consequential number here — it suggests real collections/bad-debt risk at that asset that Camden simply doesn't carry.
This is a read-only snapshot of a RealAI analysis.