Data report · 2026
Alabama Water Damage Risk Index
Where in Alabama is a house most likely to take on water? We combined Census housing age with ten years of daily weather for 67 of Alabama's 67 counties to rank them.
Key findings
- The five highest-scoring counties are Colbert, Marion, Lamar, Fayette, Jefferson. Each combines old housing with harsher-than-average winters or frequent downpours.
- Jefferson County has the oldest housing in the state, with 28% of homes built before 1960, against a county median of 16%.
- Colbert County averaged 7 nights a year at 20°F or colder, the most of any county in the data. The county median is 3.
- Baldwin County has the newest housing, with 48% of homes built since 2000, which pulls its index down even where winters are cold.
Weather: NOAA nClimGrid-Daily county averages, 2015–2024. Housing: ACS 2020-2024.
All counties
Select a column header to sort.
| Rank | County | Index | Freeze nights | Pre-1960 homes | Heavy-rain days | Muggy nights |
|---|---|---|---|---|---|---|
| 1 | Colbert | 77 | 7 | 23% | 16 | 89 |
| 2 | Marion | 73 | 6 | 17% | 17.2 | 94 |
| 3 | Lamar | 71 | 4 | 22% | 15.3 | 109 |
| 4 | Fayette | 70 | 4 | 20% | 15.8 | 105 |
| 5 | Jefferson | 70 | 4 | 28% | 14.9 | 106 |
| 6 | Clay | 69 | 4 | 21% | 16.6 | 85 |
| 7 | Franklin | 67 | 7 | 15% | 17.8 | 89 |
| 8 | Etowah | 66 | 4 | 28% | 14.8 | 103 |
| 9 | Jackson | 66 | 6 | 16% | 16.5 | 86 |
| 10 | Talladega | 65 | 3 | 21% | 16.1 | 100 |
| 11 | Walker | 64 | 4 | 19% | 15.2 | 102 |
| 12 | Lauderdale | 63 | 7 | 19% | 14.8 | 84 |
| 13 | Clarke | 61 | 2 | 15% | 18 | 131 |
| 14 | Mobile | 61 | 0 | 19% | 17.6 | 151 |
| 15 | DeKalb | 60 | 6 | 16% | 15.3 | 83 |
| 16 | Chambers | 59 | 3 | 28% | 14.9 | 93 |
| 17 | Geneva | 59 | 1 | 21% | 15.6 | 138 |
| 18 | Washington | 59 | 2 | 14% | 19.6 | 133 |
| 19 | Winston | 58 | 6 | 11% | 17.8 | 90 |
| 20 | Covington | 57 | 1 | 22% | 15.3 | 130 |
| 21 | Pickens | 57 | 3 | 17% | 15.2 | 118 |
| 22 | Blount | 56 | 4 | 13% | 16.2 | 105 |
| 23 | Conecuh | 56 | 1 | 17% | 16.9 | 130 |
| 24 | Escambia | 56 | 1 | 16% | 17 | 138 |
| 25 | Cullman | 54 | 4 | 13% | 16.2 | 103 |
| 26 | Greene | 54 | 3 | 17% | 14.9 | 119 |
| 27 | Choctaw | 53 | 2 | 14% | 16.7 | 129 |
| 28 | Coosa | 53 | 3 | 16% | 16.1 | 99 |
| 29 | Lawrence | 53 | 6 | 11% | 16.3 | 89 |
| 30 | Butler | 52 | 1 | 18% | 15.7 | 122 |
| 31 | Montgomery | 52 | 2 | 20% | 14.4 | 127 |
| 32 | Dallas | 51 | 2 | 23% | 13.5 | 124 |
| 33 | Madison | 51 | 6 | 9% | 15.9 | 95 |
| 34 | Marshall | 51 | 4 | 14% | 15.3 | 101 |
| 35 | Tallapoosa | 51 | 3 | 16% | 15.2 | 98 |
| 36 | Calhoun | 50 | 3 | 23% | 13.4 | 99 |
| 37 | Morgan | 49 | 5 | 14% | 14.9 | 100 |
| 38 | Cleburne | 47 | 4 | 18% | 14 | 84 |
| 39 | Monroe | 46 | 1 | 12% | 17.7 | 134 |
| 40 | Perry | 46 | 2 | 22% | 12.9 | 115 |
| 41 | Randolph | 46 | 4 | 16% | 14.6 | 86 |
| 42 | Crenshaw | 44 | 2 | 15% | 15.1 | 119 |
| 43 | Limestone | 44 | 6 | 9% | 14.9 | 94 |
| 44 | St. Clair | 44 | 4 | 6% | 15.5 | 102 |
| 45 | Sumter | 44 | 2 | 16% | 14.6 | 123 |
| 46 | Bibb | 43 | 3 | 15% | 14.6 | 106 |
| 47 | Cherokee | 43 | 4 | 15% | 14.2 | 94 |
| 48 | Macon | 42 | 2 | 16% | 14.5 | 121 |
| 49 | Russell | 41 | 2 | 19% | 13.3 | 114 |
| 50 | Marengo | 40 | 2 | 17% | 12.7 | 122 |
| 51 | Barbour | 39 | 1 | 19% | 13.8 | 125 |
| 52 | Pike | 39 | 2 | 16% | 14.1 | 123 |
| 53 | Elmore | 38 | 2 | 8% | 15.7 | 119 |
| 54 | Henry | 38 | 1 | 17% | 12.7 | 136 |
| 55 | Wilcox | 38 | 2 | 15% | 14 | 127 |
| 56 | Baldwin | 37 | 0 | 5% | 16.9 | 154 |
| 57 | Chilton | 37 | 3 | 14% | 14.6 | 101 |
| 58 | Hale | 37 | 3 | 14% | 13.8 | 118 |
| 59 | Lowndes | 36 | 2 | 10% | 14.9 | 129 |
| 60 | Tuscaloosa | 35 | 3 | 12% | 14.2 | 112 |
| 61 | Shelby | 34 | 3 | 4% | 15.1 | 101 |
| 62 | Autauga | 33 | 2 | 7% | 15 | 120 |
| 63 | Houston | 31 | 1 | 14% | 13.9 | 144 |
| 64 | Dale | 30 | 1 | 13% | 14.3 | 135 |
| 65 | Coffee | 29 | 1 | 13% | 14.4 | 130 |
| 66 | Bullock | 28 | 2 | 11% | 14.1 | 121 |
| 67 | Lee | 26 | 2 | 7% | 14.6 | 108 |
Methodology
For each county with weather data, we convert four factors to a percentile rank among those counties (0 = lowest, 100 = highest), then take a weighted average:
- Pipe-freeze nights (nights ≤ 20°F / yr), weight 30%
- Homes built before 1960 (% of homes), weight 30%
- Heavy-rain days (days ≥ 1 in / yr), weight 25%
- Muggy nights (nights ≥ 65°F / yr), weight 15%
Freezing weather and old plumbing get the most weight because burst and failing supply lines are among the most common causes of household water damage. Heavy rain drives basement flooding and sewer backups, and warm, muggy nights mark the humid air that lets mold follow. The index is relative: a low score means lower risk than other Alabama counties, not no risk.
Limits: weather values are county-wide averages, so hollows, ridges and lakeshores can differ; ACS housing figures are survey estimates; the index doesn't account for floodplains, soil, or local sewer systems. Full details on our data sources page.
Cite this data
You're welcome to use these figures with attribution. Suggested citation:
Local Water & Mold, "Alabama Water Damage Risk Index 2026," https://localwatermold.com/alabama/alabama-water-damage-risk/. Data: U.S. Census Bureau ACS 2020-2024; NOAA nClimGrid-Daily 2015–2024.