Clasificaciones 2026

Peor infraestructura: Delaware — Clasificaciones 2026

Última actualización: 19 de julio de 2026

84 códigos postales clasificados por Pipe Failure Risk N.º 1: código postal 19733 (Saint Georges, DE) - 68%

Out of 84 ZIP codes with infrastructure data in Delaware, these areas face the highest estimated risk of water pipe failure based on infrastructure age, funding gaps, and violation history.

Statistic Value
ZIP codes ranked 84
Highest risk 68%
Median risk 34%
Average risk 37%
State Delaware

Top 84 Highest Risk ZIP Codes

Rank ZIP Code City State Pipe Failure Risk Risk Level Safety Score
1 19733 Saint Georges DE 68% Very High 53/100
2 19710 Montchanin DE 62% Very High 75/100
3 19736 Yorklyn DE 61% Very High 55/100
4 19731 Port Penn DE 59% High 83/100
5 19730 Odessa DE 55% High 83/100
6 19931 Bethel DE 55% High 73/100
7 19706 Delaware City DE 53% High 78/100
8 19805 Wilmington DE 51% High 73/100
9 19806 Wilmington DE 51% High 78/100
10 19802 Wilmington DE 50% High 73/100
11 19809 Wilmington DE 48% High 78/100
12 19980 Woodside DE 48% High 66/100
13 19804 Wilmington DE 47% High 68/100
14 19703 Claymont DE 46% High 55/100
15 19801 Wilmington DE 46% High 73/100
16 19713 Newark DE 45% High 71/100
17 19807 Wilmington DE 45% High 90/100
18 19803 Wilmington DE 44% High 73/100
19 19732 Rockland DE 43% High 55/100
20 19810 Wilmington DE 43% High 73/100
21 19954 Houston DE 43% High 66/100
22 19711 Newark DE 41% High 71/100
23 19808 Wilmington DE 41% High 73/100
24 19956 Laurel DE 41% High 89/100
25 19720 New Castle DE 40% Moderate 73/100
26 19947 Georgetown DE 40% Moderate 93/100
27 19936 Cheswold DE 39% Moderate 94/100
28 19979 Viola DE 39% Moderate 97/100
29 19952 Harrington DE 38% Moderate 93/100
30 19963 Milford DE 38% Moderate 85/100
31 19973 Seaford DE 38% Moderate 84/100
32 19707 Hockessin DE 37% Moderate 72/100
33 19934 Camden Wyoming DE 37% Moderate 73/100
34 19944 Fenwick Island DE 36% Moderate 79/100
35 19950 Greenwood DE 36% Moderate 89/100
36 19961 Little Creek DE 36% Moderate 94/100
37 19971 Rehoboth Bch DE 35% Moderate 55/100
38 19850 Wilmington DE 34% Moderate 83/100
39 19880 Wilmington DE 34% Moderate 83/100
40 19884 Wilmington DE 34% Moderate 83/100
41 19885 Wilmington DE 34% Moderate 83/100
42 19886 Wilmington DE 34% Moderate 83/100
43 19887 Wilmington DE 34% Moderate 83/100
44 19890 Wilmington DE 34% Moderate 83/100
45 19891 Wilmington DE 34% Moderate 83/100
46 19892 Wilmington DE 34% Moderate 83/100
47 19893 Wilmington DE 34% Moderate 83/100
48 19894 Wilmington DE 34% Moderate 83/100
49 19895 Wilmington DE 34% Moderate 83/100
50 19896 Wilmington DE 34% Moderate 83/100
51 19897 Wilmington DE 34% Moderate 83/100
52 19898 Wilmington DE 34% Moderate 83/100
53 19899 Wilmington DE 34% Moderate 83/100
54 19943 Felton DE 34% Moderate 91/100
55 19958 Lewes DE 34% Moderate 55/100
56 19960 Lincoln DE 34% Moderate 91/100
57 19901 Dover DE 33% Moderate 80/100
58 19903 Dover DE 33% Moderate 93/100
59 19905 Dover DE 33% Moderate 93/100
60 19906 Dover DE 33% Moderate 93/100
61 19933 Bridgeville DE 32% Moderate 95/100
62 19939 Dagsboro DE 32% Moderate 83/100
63 19946 Frederica DE 32% Moderate 85/100
64 19951 Harbeson DE 32% Moderate 87/100
65 19938 Clayton DE 31% Moderate 87/100
66 19904 Dover DE 30% Moderate 85/100
67 19940 Delmar DE 30% Moderate 95/100
68 19977 Smyrna DE 30% Moderate 87/100
69 19702 Newark DE 29% Moderate 71/100
70 19953 Hartly DE 29% Moderate 95/100
71 19966 Millsboro DE 28% Moderate 80/100
72 19962 Magnolia DE 27% Moderate 94/100
73 19975 Selbyville DE 27% Moderate 79/100
74 19701 Bear DE 26% Moderate 70/100
75 19930 Bethany Beach DE 26% Moderate 75/100
76 19964 Marydel DE 26% Moderate 95/100
77 19734 Townsend DE 24% Moderate 83/100
78 19941 Ellendale DE 24% Moderate 94/100
79 19945 Frankford DE 24% Moderate 90/100
80 19967 Millville DE 24% Moderate 94/100
81 19968 Milton DE 24% Moderate 80/100
82 19955 Kenton DE 23% Moderate 98/100
83 19970 Ocean View DE 20% Low 84/100
84 19709 Middletown DE 16% Low 73/100

Methodology

Infrastructure risk rankings combine four data sources, each weighted equally (25%): (1) EPA Drinking Water Infrastructure Needs Survey (DWINS 2023) — state-level 20-year funding gap per capita for pipe replacement, treatment, and storage; (2) Census ACS housing vintage — percentage of homes built before 1980, when lead solder and galvanized pipes were common; (3) EPA SDWIS violation and enforcement data — system-level compliance history; (4) Lead and Copper Rule indicators — service line material inventories and 90th percentile lead testing results. The combined score is normalized to a 0-100% probability scale.

Data sources: EPA DWINS 2023, Census ACS, EPA SDWIS, Lead and Copper Rule data. Last updated: 2026-07-19.

Frequently Asked Questions

What does pipe failure probability mean?

Pipe failure probability estimates the likelihood of water main breaks, lead contamination from corroded pipes, or service disruptions based on infrastructure age, state funding gaps, violation history, and lead indicators. It is not a prediction of imminent failure, but a risk assessment based on known factors.

How is infrastructure risk calculated?

The risk score combines four equally weighted factors: state-level infrastructure funding gap per capita (from EPA DWINS 2023), housing vintage (Census ACS pre-1980 housing percentage), water system violation and enforcement history (EPA SDWIS), and lead exposure indicators (service line probability and testing results).

What can homeowners do about aging infrastructure?

Test your tap water annually, especially for lead. Check if your home has a lead service line through your water utility. Consider a whole-house water filter (NSF/ANSI 53 certified). Review your water system Consumer Confidence Report for violation history.

Does high risk mean my water is unsafe right now?

Not necessarily. Infrastructure risk indicates long-term vulnerability, not current contamination. However, areas with aging pipes and high violation counts have a statistically higher chance of water quality issues. Regular testing is the best way to verify your water safety.

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Cómo se calcula esta clasificación, fuentes de datos y limitaciones: Metodología de clasificaciones →


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