Data report · 2026

Minnesota Water Damage Risk Index

Where in Minnesota is a house most likely to take on water? We combined Census housing age with ten years of daily weather for 87 of Minnesota's 87 counties to rank them.

Highest index

Swift

Score 73 of 100

Most pipe-freeze nights

Cook

120 nights ≤ 20°F a year

Oldest housing

Faribault

62% built before 1960

Most heavy-rain days

Winona

9.6 days ≥ 1 inch a year

< 3939–4545–5353–62≥ 62 index (0–100)

Key findings

The five highest-scoring counties are Swift, Jackson, Faribault, Lac qui Parle, Chippewa. Each combines old housing with harsher-than-average winters or frequent downpours.
Faribault County has the oldest housing in the state, with 62% of homes built before 1960, against a county median of 34%.
Cook County averaged 120 nights a year at 20°F or colder, the most of any county in the data. The county median is 89.
Carver County has the newest housing, with 44% 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	Swift	73	93	52%	6.1	14
2	Jackson	72	83	52%	7.6	19
3	Faribault	70	79	62%	6.8	23
4	Lac qui Parle	70	89	60%	5.7	16
5	Chippewa	69	89	54%	5.7	16
6	Yellow Medicine	69	87	53%	5.7	18
7	Watonwan	68	81	48%	7.3	22
8	Brown	66	81	48%	6.4	22
9	Cottonwood	66	83	46%	6.8	20
10	Martin	66	81	50%	6.4	20
11	Freeborn	65	79	52%	7.6	18
12	Redwood	65	84	46%	6	21
13	Renville	64	85	51%	5.5	18
14	Mower	63	77	55%	7.7	13
15	Pipestone	63	86	55%	5.2	16
16	Pope	63	96	40%	5.9	11
17	Big Stone	62	95	51%	4.9	13
18	Lincoln	62	90	51%	5.1	14
19	Murray	61	85	47%	5.7	16
20	Nobles	60	83	43%	6.2	17
21	Waseca	60	81	40%	7.1	19
22	Norman	59	110	51%	3.5	5
23	Winona	58	71	42%	9.6	20
24	Houston	57	68	40%	8.7	22
25	St. Louis	57	118	48%	3.1	1
26	Ramsey	56	75	44%	6.2	25
27	Stevens	56	96	35%	5.4	12
28	Todd	56	96	34%	5.8	7
29	Traverse	56	95	48%	3.7	12
30	Grant	55	97	45%	4	11
31	Rock	55	80	50%	5	21
32	Steele	55	80	33%	8.3	18
33	Wilkin	54	100	41%	3.7	12
34	Fillmore	53	76	46%	7.5	12
35	Le Sueur	53	79	36%	6	20
36	Lyon	53	86	34%	5.4	18
37	Red Lake	53	115	40%	3.3	4
38	Blue Earth	52	79	32%	7.4	22
39	Kittson	52	114	42%	2.9	5
40	Lake	52	120	41%	3	0
41	Carlton	51	105	35%	5.2	1
42	Dodge	51	82	29%	8.1	12
43	Goodhue	50	77	36%	6.7	17
44	Polk	50	112	37%	3.2	5
45	Rice	50	79	30%	8.6	17
46	Sibley	50	80	47%	4.7	20
47	Marshall	48	115	38%	2.4	4
48	Wabasha	48	76	32%	7.7	18
49	Morrison	47	94	33%	4.8	9
50	Pennington	47	116	34%	2.7	4
51	Hennepin	46	78	36%	5.4	21
52	Stearns	46	92	21%	5.8	12
53	Kandiyohi	45	90	29%	5.2	12
54	Wadena	45	100	33%	3.8	6
55	Douglas	44	99	21%	5.1	9
56	Koochiching	44	115	32%	3.1	2
57	Mahnomen	44	113	32%	3.2	3
58	Meeker	44	89	33%	5	11
59	Otter Tail	44	101	27%	4.2	8
60	Clay	43	106	24%	4.1	8
61	Kanabec	43	93	25%	5.4	7
62	Pine	43	94	23%	5.6	4
63	Isanti	42	88	18%	5.7	13
64	Nicollet	42	79	29%	5.3	22
65	Becker	41	109	25%	3.4	5
66	Cook	41	120	21%	3.6	0
67	Olmsted	41	80	19%	8.6	11
68	Benton	40	91	18%	5.3	12
69	Mille Lacs	40	93	25%	4.6	9
70	Roseau	39	116	21%	3.2	2
71	Aitkin	38	103	22%	4.1	2
72	Anoka	38	81	13%	5.6	19
73	Clearwater	37	113	25%	2.5	3
74	Itasca	37	113	25%	2.7	2
75	McLeod	37	84	28%	4.1	16
76	Crow Wing	36	96	17%	4.7	6
77	Hubbard	35	108	20%	3	5
78	Scott	35	77	9%	5.9	22
79	Lake of the Woods	34	116	17%	2.9	2
80	Beltrami	33	112	19%	2.7	3
81	Chisago	33	85	16%	5	13
82	Washington	33	76	13%	5.6	23
83	Wright	33	86	13%	4.9	15
84	Cass	32	104	13%	3.5	4
85	Carver	31	80	11%	5.3	19
86	Dakota	31	75	11%	5.5	24
87	Sherburne	31	87	9%	4.7	14

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 Minnesota 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, "Minnesota Water Damage Risk Index 2026," https://localwatermold.com/minnesota/minnesota-water-damage-risk/. Data: U.S. Census Bureau ACS 2020-2024; NOAA nClimGrid-Daily 2015–2024.