Find out the rental market snapshot for cities in alabama

Explore in-depth rental data for every city in alabama

adamsville
alabaster
ardmore
athens
auburn
bessemer
birmingham
brookwood
calera
center point
chelsea
cullman
daphne
deatsville
decatur
elmore
fairfield
fairhope
florence
foley
forestdale
fultondale
gardendale
graysville
hartselle
harvest
hayden
hazel green
helena
homewood
hoover
hueytown
huntsville
irondale
jasper
killen
lake view
leeds
lincoln
madison
mcmullen
meridianville
millbrook
mobile
montevallo
montgomery
moody
morris
moundville
mount olive
mulga
new market
northport
odenville
owens cross roads
pelham
pell city
phenix city
pinson
pleasant grove
prattville
redland
silverhill
spanish fort
springville
sterrett
toxey
trussville
tuscaloosa
tuscumbia
vance
vestavia hills
warrior
west jefferson
wetumpka

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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