ZIP 49896 Property Distress & Foreclosure Data

ZIP code 49896 in Menominee County, Michigan carries a composite property-distress score of 11/100 — a minimal reading on DLRadar's deterministic public-record index. What sets it apart are the readings on construction/permit lag (42/100), structural risk (24/100), mortgage stress (5/100). On the quiet end sit mortgage stress (5/100) and institutional ownership (3/100). Structural exposure scores 24 and live distress 2 on the 0–100 scale.

The expansion-phase market in 49896 posted values that rose 5.6% over the year, at 34/100 phase confidence. Rising prices can mask pockets of distress, where per-parcel scoring earns its keep.

Home values center near $164,800, an affordability ratio of 2.9× — accessible. Rent burden reaches 16% of tenant households. Population is roughly 1,718 with a median age of 41. DLRadar's demographic-stress index for the area reads 29/100. Owners hold 73% of homes, renters 27%. There are about 714 housing units across 49896. Vacancy runs 23.7%, above the national norm and a classic distress-and-opportunity signal. Median household income is $56,979, below the U.S. median near $78,000. 15.6% of residents fall below the poverty threshold. Educational attainment sits at 17% bachelor's-or-above.

On the whole, 49896 leans distressed, with opportunity clustered in specific stressed parcels. Every signal above traces to a verifiable public dataset, refreshed continuously and scored the same way in every ZIP nationwide.

11/100
Composite stress
24/100
Structural risk
2/100
Distress activity

Distress signal breakdown — ZIP 49896

Foreclosure activity0
Mortgage stress5
Climate / FEMA risk42
+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 Menominee County

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Deterministic. Every signal traces to a public dataset · methodology