Find out the rental market snapshot for cities in texas

Explore in-depth rental data for every city in texas

abilene
addison
aledo
allen
alvarado
alvin
amarillo
angleton
anna
argyle
arlington
atascocita
aubrey
austin
azle
bacliff
balch springs
bastrop
bay city
baytown
beaumont
bedford
benbrook
blue mound
boerne
brownsville
bryan
buda
bulverde
burleson
caddo mills
canyon
canyon lake
carrollton
cedar hill
cedar park
celina
channelview
cibolo
cleburne
cleveland
clute
college station
colleyville
commerce
conroe
converse
coppell
copperas cove
corinth
corpus christi
corsicana
cove
crandall
crosby
cross roads
crowley
dallas
deer park
dell city
denton
desoto
dickinson
dripping springs
duncanville
el paso
elgin
ennis
euless
fairview
farmers branch
farmersville
fate
ferris
flower mound
forest hill
forney
fort worth
fresno
friendswood
frisco
fulshear
galveston
garland
georgetown
glenn heights
granbury
grand prairie
grapevine
greenville
haltom city
harlingen
haslet
heath
helotes
highland village
highlands
hondo
hooks
houston
hudson oaks
humble
huntsville
hurst
hutchins
hutto
irving
jarrell
jersey village
justin
katy
kaufman
keller
kemah
kennedale
killeen
kingsbury
kingsland
kingsville
krugerville
krum
kyle
la marque
la porte
lago vista
lake dallas
lake jackson
lake worth
lakeway
lancaster
laredo
lavon
league city
leander
lewisville
liberty hill
little elm
live oak
livingston
longview
lorena
lubbock
mabank
madisonville
magnolia
manor
mansfield
manvel
marble falls
mcallen
mckinney
meadows place
melissa
mesquite
midland
midlothian
missouri city
mont belvieu
montgomery
murphy
nacogdoches
nederland
nevada
new braunfels
new deal
new hope
newark
north richland hills
northlake
odessa
orange
pantego
pasadena
pearland
pflugerville
pinehurst
plano
ponder
port arthur
porter heights
prairie view
princeton
progreso lakes
prosper
red oak
rhome
richardson
richland hills
richmond
roanoke
rockport
rockwall
roman forest
rosenberg
rosharon
round rock
rowlett
royse city
sachse
saginaw
saline o
san angelo
san antonio
san marcos
sanger
savannah
schertz
seabrook
seagoville
seguin
selma
shenandoah
sherman
south houston
southlake
splendora
spring
springtown
st hedwig
stafford
sugar land
taylor
temple
terrell
texas city
the colony
the woodlands
tomball
trophy club
troy
tyler
universal city
venus
victoria
waco
watauga
waxahachie
weatherford
webster
westworth village
white settlement
wichita falls
willis
willow park
wilmer
wylie

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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