Find out the rental market snapshot for cities in minnesota

Explore in-depth rental data for every city in minnesota

albertville
anoka
apple valley
blaine
bloomington
brooklyn center
brooklyn park
buffalo
burnsville
champlin
chanhassen
chaska
circle pines
columbia heights
coon rapids
cottage grove
crystal
duluth
eagan
eden prairie
edina
elk river
forest lake
fridley
golden valley
hastings
hopkins
inver grove heights
isanti
lakeville
lexington
lino lakes
little canada
maple grove
maplewood
minneapolis
minnetonka
minnetrista
monticello
moorhead
mound
mounds view
new brighton
new hope
newport
northeast aitkin ut
northwest aitkin ut
oakdale
osseo
plymouth
ramsey
richfield
robbinsdale
rochester
rosemount
roseville
sabin
sargeant
sartell
savage
shakopee
shoreview
south end
spring lake park
st louis park
st paul
stillwater
white bear lake
white bearship
winona
woodbury

Our data is best categorized as "alternative data", which is a burgeoning sector. Through partnerships and direct feeds, we extract key factual elements that are publicly available within rental listings on internet listing sites and property websites. Once aggregated, we mine through the data to parse out relevant insights and calculate important metrics, benchmarks, and other KPIs. Each week, our system sifts through millions of listing observations and other pockets of market information to deliver the most comprehensive picture of rental housing available.

This is a metric that we try not to overthink. Simply, we take each unique listing observation within a geographic boundary and calculate a simple average. Of course, we're careful to filter for duplicates and other listings that aren't reflective of the market.

Yes, but please attribute us accordingly.

Yes. We can deliver bulk raw data in various formats. Please contact us to discuss - [email protected].

While some of our data is refreshed daily and other data comes in weekly, the bulk of it comes in on a biweekly basis.

Our coverage is nationwide! In our platform, we have data points for every ZIP code and neighborhood boundary in the country.

Every rental housing unit is differentiated by attributes such as its location, square footage, and amenity composition. Thanks to machine learning and natural language processing technologies we deploy, we're able to deconstruct our rental listing data points and identify key amenities for each listing. With this information, we're able to give signals around how certain amenities drive rental pricing value in certain areas.

Well, we think so! At the highest level, our process is simple. Listings data is ingested, cleaned (de-duplicated. etc.), analyzed for insights, and then presented to our users.
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