# Which Milwaukee neighborhood has the most hotels and inns?

It's a tie. Juneau Town and Kilbourn Town have the most hotels and inns of any City of Milwaukee neighborhood, with 13 hotels and inns.

Count by residents and Holler Park comes first, at 4.9 per 1,000, even though it ranks 5th by plain count. Among the suburbs, Oak Creek has the most, at 14.

Metric: Records in the hotels and inns category. Period: As of November 4, 2026. Source: [Little Milwaukee business records, from public licenses, filings and OpenStreetMap](https://littlemilwaukee.com/records).

## City of Milwaukee neighborhoods

| Rank | Neighborhood | Records in the hotels and inns category | Per 1,000 residents |
| --- | --- | --- | --- |
| 1 | [Juneau Town](https://littlemilwaukee.com/neighborhoods/juneau-town) | 13 | 2.3 |
| 1 | [Kilbourn Town](https://littlemilwaukee.com/neighborhoods/kilbourn-town) | 13 | 2.5 |
| 3 | [College Heights](https://littlemilwaukee.com/neighborhoods/college-heights) | 4 | 4.2 |
| 3 | [Timmerman West](https://littlemilwaukee.com/neighborhoods/timmerman-west) | 4 | 1.0 |
| 5 | [Avenues West](https://littlemilwaukee.com/neighborhoods/avenues-west) | 3 | 0.4 |
| 5 | [Holler Park](https://littlemilwaukee.com/neighborhoods/holler-park) | 3 | 4.9 |
| 5 | [Park Place](https://littlemilwaukee.com/neighborhoods/park-place) | 3 | 2.4 |
| 5 | [Yankee Hill](https://littlemilwaukee.com/neighborhoods/yankee-hill) | 3 | 0.8 |
| 9 | [Castle Manor](https://littlemilwaukee.com/neighborhoods/castle-manor) | 2 | 0.6 |
| 9 | [Dineen Park](https://littlemilwaukee.com/neighborhoods/dineen-park) | 2 | 0.6 |
| 9 | [Mitchell West](https://littlemilwaukee.com/neighborhoods/mitchell-west) | 2 | 0.6 |
| 9 | [Town of Lake](https://littlemilwaukee.com/neighborhoods/town-of-lake) | 2 | 0.4 |
| 9 | [Walker's Point](https://littlemilwaukee.com/neighborhoods/walkers-point) | 2 | 0.3 |
| 14 | [Capitol Heights](https://littlemilwaukee.com/neighborhoods/capitol-heights) | 1 | 0.2 |
| 14 | [Fair Park](https://littlemilwaukee.com/neighborhoods/fair-park) | 1 | 0.9 |
| 14 | [Florist Highlands](https://littlemilwaukee.com/neighborhoods/florist-highlands) | 1 | 0.9 |
| 14 | [Halyard Park](https://littlemilwaukee.com/neighborhoods/halyard-park) | 1 | 1.0 |
| 14 | [Historic Third Ward](https://littlemilwaukee.com/neighborhoods/historic-third-ward) | 1 | 0.6 |
| 14 | [Lincoln Creek](https://littlemilwaukee.com/neighborhoods/lincoln-creek) | 1 | 0.1 |
| 14 | [Maitland Park](https://littlemilwaukee.com/neighborhoods/maitland-park) | 1 | 1.5 |
| 14 | [Menomonee River Valley](https://littlemilwaukee.com/neighborhoods/menomonee-river-valley) | 1 | 0.4 |
| 14 | [Wilson Park](https://littlemilwaukee.com/neighborhoods/wilson-park) | 1 | 0.2 |

159 City neighborhoods aren't in this table: no record in this category is inside their boundary.

## Suburbs

| Rank | Neighborhood | Records in the hotels and inns category | Per 1,000 residents |
| --- | --- | --- | --- |
| 1 | [Oak Creek](https://littlemilwaukee.com/neighborhoods/oak-creek) | 14 | 0.4 |
| 2 | [Franklin](https://littlemilwaukee.com/neighborhoods/franklin) | 13 | 0.4 |
| 3 | [Wauwatosa](https://littlemilwaukee.com/neighborhoods/wauwatosa) | 10 | 0.2 |
| 4 | [Glendale](https://littlemilwaukee.com/neighborhoods/glendale) | 7 | 0.5 |
| 5 | [Brown Deer](https://littlemilwaukee.com/neighborhoods/brown-deer) | 4 | 0.3 |
| 6 | [West Allis](https://littlemilwaukee.com/neighborhoods/west-allis) | 3 | 0.1 |
| 7 | [West Milwaukee](https://littlemilwaukee.com/neighborhoods/west-milwaukee) | 2 | 0.5 |
| 8 | [Fox Point](https://littlemilwaukee.com/neighborhoods/fox-point) | 1 | 0.1 |
| 8 | [Greendale](https://littlemilwaukee.com/neighborhoods/greendale) | 1 | 0.1 |
| 8 | [Greenfield](https://littlemilwaukee.com/neighborhoods/greenfield) | 1 | 0.0 |
| 8 | [South Milwaukee](https://littlemilwaukee.com/neighborhoods/south-milwaukee) | 1 | 0.0 |

## How it was measured

We count the business records we've placed in the hotels and inns category, by the neighborhood boundary each address is inside.

## What would change the answer

A record's category comes from its license and from public sources, and we don't have a category for every record. So the true count can be higher where more records have no category.