Find out the rental market snapshot for cities in maryland

Explore in-depth rental data for every city in maryland

aberdeen
aberdeen proving ground
accokeek
annapolis
baltimore
bel air
beltsville
bethesda
bowie
camp springs
capitol heights
catonsville
cheverly
chevy chase
cockeysville
college park
columbia
crofton
dundalk
edgewood
elkridge
elkton
ellicott city
essex
forestville
fort meade
fort washington
frederick
gaithersburg
gambrills
germantown
glen burnie
greenbelt
hagerstown
hyattsville
joppatowne
landover
largo
laurel
lexington park
linthicum
manchester
middle river
milford mill
montgomery village
north bethesda
north east
north kensington
odenton
owings mills
oxon hill
parkville
pasadena
pikesville
potomac
randallstown
reisterstown
rockville
rosedale
salisbury
severn
silver spring
suitland
takoma park
taneytown
temple hills
towson
upper marlboro
waldorf
westminster
wheaton
willards

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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