Rental market snapshot for Morrow, Georgia

Morrow, Georgia Rental Property Market Overview

The average rent for an apartment in Morrow is $1,780. The cost of rent varies depending on several factors, including location, size, and quality.

The average rent has increased by 0.67% over the past year.

Morrow is a city in Clayton County, Georgia, United States. The population was 6,445 at the 2010 census, up from 4,882 at the 2000 census. It is the home of Clayton State University.

Last Updated December 23, 2022

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Average monthly rent graph in Morrow Georgia | Cost of Living

  • The most expensive ZIP Code in Morrow is 30260 with an average price of $1,805
  • The cheapest ZIP Code in Morrow is 30260 with an average price of $1,795

Morrow ZIP Codes with the highest, most expensive rent

#zip codeaverage rent
130260$1,805
230260$1,800

Live near Morrow, Georgia's Top Sights and Attractions

Maddox Road Park is a 3-acre park that features a playground, a pavilion, and a walking trail.

The National Archives at Atlanta GA is a museum that houses documents and artifacts from the American Civil War. The museum is located in Morrow, Georgia, and is open to the public.

Paradise Park is an amusement park that features a variety of rides, games, and attractions. The park is located in Morrow, Georgia and is open year-round.

This park has a playground, a pavilion, and a walking trail.

J L Christian Park is a large park with a playground, a pavilion, and a walking trail. The park also has a pond with a fountain.

Morrow, Georgia area median rent change by ZIP Code map

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How does Morrow compare to other cities in Georgia ?

citymedian price
Richmond Hill$2,150
Gainesville$1,900
Marietta$2,032
Kennesaw$2,025
Roswell$2,290
Stone Mountain$1,795
Marietta$2,000
Ellenwood$1,975
Roswell$2,265

Average household income in Morrow area graph - US Census

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The median household income in Morrow in 2021 was $51,991. This represents a 5.0% change from 2011 when the median was $49,524.

#categorypercent
0Less than $10,0004.1%
1$10,000 to $14,9998.1%
2$15,000 to $19,9992.4%
3$20,000 to $24,9995.3%
4$25,000 to $29,9991.5%
5$30,000 to $34,9993.3%
6$35,000 to $39,9996.1%
7$40,000 to $44,9994.3%
8$45,000 to $49,99910.9%
9$50,000 to $59,99911.7%
10$60,000 to $74,9998.0%
11$75,000 to $99,99912.0%
12$100,000 to $124,99911.0%
13$125,000 to $149,9995.9%
14$150,000 to $199,9993.0%
15$200,000 or more2.4%

Frequently asked questions

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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