Rental market snapshot for Easley, South Carolina

Easley, South Carolina Rental Property Market Overview

The average rent for an apartment in Easley is $1,586. The cost of rent varies depending on several factors, including location, size, and quality.

The average rent has increased by 0.73% over the past year.

Easley is a city in Pickens County, South Carolina, United States. The population was 19,993 at the 2010 census. It is the principal city of the Greenville–Mauldin–Easley Metropolitan Statistical Area.

Last Updated December 23, 2022

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Average monthly rent graph in Easley South Carolina | Cost of Living

  • The most expensive ZIP Code in Easley is 29642 with an average price of $1,700
  • The cheapest ZIP Code in Easley is 29640 with an average price of $1,550

Easley ZIP Codes with the highest, most expensive rent

#zip codeaverage rent
129642$1,700
229640$1,680

Live near Easley, South Carolina's Top Sights and Attractions

Family Skate Center is an indoor skating rink that offers public skating sessions, birthday party packages, and private rentals. The rink is also home to a pro shop, snack bar, and arcade.

Hagood Park is a beautiful park located in the heart of Easley, South Carolina. The park features a playground, picnic pavilion, walking trails, and a pond.

Alice Park is a beautiful park located in the heart of Easley, South Carolina. The park features a playground, picnic tables, a walking trail, and a pond.

Ball Park is a baseball stadium located in Easley, South Carolina. The stadium is home to the Easley Pioneers, a minor league baseball team. The stadium has a capacity of 4,000 people.

Kings Park is a large park with a playground, a pond, and a walking trail. The park also has a pavilion that can be rented for events.

The U S Army National Guard is a large, green building with many windows. It is surrounded by a parking lot and a grassy area.

Easley, South Carolina area median rent change by ZIP Code map

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How does Easley compare to other cities in South Carolina ?

citymedian price
Lexington$1,695
Bluffton$2,300
Goose Creek$1,750
Easley$1,680
Boiling Springs$1,617
Rock Hill$1,275
Lexington County$1,749
Greenville$1,450
Moore$1,795

Average household income in Easley area graph - US Census

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The median household income in Easley in 2021 was $55,655. This represents a 24.6% change from 2011 when the median was $44,683.

#categorypercent
0Less than $10,0007.9%
1$10,000 to $14,9994.0%
2$15,000 to $19,9996.3%
3$20,000 to $24,9994.8%
4$25,000 to $29,9995.7%
5$30,000 to $34,9995.5%
6$35,000 to $39,9994.4%
7$40,000 to $44,9994.4%
8$45,000 to $49,9993.3%
9$50,000 to $59,9996.1%
10$60,000 to $74,99910.0%
11$75,000 to $99,9999.8%
12$100,000 to $124,99912.1%
13$125,000 to $149,9995.3%
14$150,000 to $199,9994.0%
15$200,000 or more6.4%

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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