ZIP 39345 Property Distress & Foreclosure Data

Composite property distress in 39345 (Newton County, Mississippi) lands at 16/100 — low on DLRadar's public-record scoring. The sharpest non-environmental signals are construction/permit lag (94/100), structural risk (37/100), mortgage stress (4/100). On the quiet end sit mortgage stress (4/100) and institutional ownership (3/100). Structural risk reads 37/100 against active distress of 1/100.

The peak-phase market in 39345 posted values that rose 4.7% over the year, at 26/100 phase confidence. Near a top, distress surfaces unevenly, so parcel screening beats headline strength.

A median home runs $89,200 here, or 2.0 times local income. The tenure split is 69% owner-occupied to 31% rented. Vacancy runs 16.7%, above the national norm and a classic distress-and-opportunity signal. Rent burden reaches 27% of tenant households. There are about 2,749 housing units across 39345. Roughly 24.0% live below the poverty line, elevated and often tied to deferred-maintenance inventory. Population is roughly 5,571 with a median age of 42. About 16% have a four-year degree. Households earn a median $41,982 — below the roughly $78,000 national figure. On demographic stress specifically, 39345 scores 29/100.

On the whole, 39345 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.

16/100
Composite stress
37/100
Structural risk
1/100
Distress activity

Distress signal breakdown — ZIP 39345

Foreclosure activity0
Mortgage stress4
Climate / FEMA risk43
+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 Newton County

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Deterministic. Every signal traces to a public dataset · methodology