ZIP 10913 Property Distress & Foreclosure Data

Composite property distress in 10913 (Rockland County, New York) lands at 25/100 — low on DLRadar's public-record scoring. Structural exposure scores 57 and live distress 2 on the 0–100 scale. It additionally carries heavy environmental risk: climate & FEMA risk (87/100), flood (NFIP) exposure (84/100). The sharpest non-environmental signals are construction/permit lag (58/100), structural risk (57/100), institutional ownership (19/100). By contrast, institutional ownership (19/100) and mortgage stress (8/100) register low.

The market reads expansion — home values rose 6.3% year on year, and 16% higher over three years, at 39/100 phase confidence. Appreciation rarely lifts every parcel — the laggards are the opportunity.

The typical home is worth about $640,100 (3.5× income, relatively affordable). 97% of housing is owner-occupied. About 5,333 people live here, median age 39. There are about 1,530 housing units across 10913. Around 49% of adults hold a bachelor's degree or higher. The demographic-stress sub-score lands at 34/100. At $175,000, median income runs above typical U.S. levels. Rent burden reaches 96% of tenant households. Vacancy runs 3.4%. The poverty rate is 9.5%.

Net-net, 10913 is middle-of-the-pack, where the deals are specific addresses rather than the whole ZIP. Every signal above traces to a verifiable public dataset, refreshed continuously and scored the same way in every ZIP nationwide.

25/100
Composite stress
57/100
Structural risk
2/100
Distress activity

Distress signal breakdown — ZIP 10913

Foreclosure activity0
Mortgage stress8
Climate / FEMA risk87
+9 more distress dimensions scored for this ZIP

Tax delinquency, institutional ownership, insurance pressure, NFIP/flood, construction lag, price dislocation and auction velocity — plus the 0 individual distressed properties (owner, address, APN, per-property score and exit read) are in the full DLRadar report.

Nearby ZIPs in Rockland County

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Deterministic. Every signal traces to a public dataset · methodology