Find out the rental market snapshot for cities in missouri

Explore in-depth rental data for every city in missouri

affton
arnold
ballwin
barnhart
belton
berkeley
black jack
blue springs
breckenridge hills
chesterfield
clayton
columbia
creve coeur
crystal city
excelsior springs
ferguson
florissant
grain valley
grandview
greenwood
hazelwood
imperial
independence
jennings
kansas city
kearney
kirkwood
lake st louis
lee s summit
liberty
maryland heights
north kansas city
o fallon
olivette
overland
parkville
platte city
pleasant hill
raymore
raytown
riverside
savannah
smithville
springfield
st louis
st peters
university city
warrensburg
webster groves
wentzville

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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