Rental market snapshot for Chapin, South Carolina

Chapin, South Carolina Rental Property Market Overview

The average rent for an apartment in Chapin is $1,630. The cost of rent varies depending on several factors, including location, size, and quality.

The average rent has decreased by -2.3% over the past year.

Chapin is a town in Lexington County, South Carolina, United States. The population was 1,554 at the 2010 census. Chapin is part of the Columbia, South Carolina metropolitan area.

Last Updated December 23, 2022

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

  • The most expensive ZIP Code in Chapin is 29036 with an average price of $1,919
  • The cheapest ZIP Code in Chapin is 29036 with an average price of $1,510

Chapin ZIP Codes with the highest, most expensive rent

#zip codeaverage rent
129036$1,919
229036$1,760

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

Melvin Park is a small, family-friendly park located in Chapin, South Carolina. The park features a playground, picnic tables, a walking trail, and a small pond.

Crooked Creek Park is a beautiful park located in Chapin, South Carolina. The park features a variety of amenities including a playground, picnic pavilions, a walking trail, and a fishing pier. Crooked Creek Park is a great place to spend a day with family and friends.

Derrick Park is a beautiful park located in Chapin, South Carolina. The park features a variety of amenities including a playground, picnic tables, a walking trail, and a pond.

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

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How does Chapin compare to other cities in South Carolina ?

citymedian price
Conway$1,900
Easley$1,680
North Charleston$1,500
Spartanburg$1,495
Boiling Springs$1,617
Greer$1,718
Moore$1,949
Roebuck$1,750

Average household income in Chapin area graph - US Census

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The median household income in Chapin in 2021 was $59,514. This represents a 25.9% change from 2011 when the median was $47,287.

#categorypercent
0Less than $10,0004.9%
1$10,000 to $14,9990.7%
2$15,000 to $19,9996.8%
3$20,000 to $24,9991.7%
4$25,000 to $29,9993.4%
5$30,000 to $34,9994.0%
6$35,000 to $39,9994.0%
7$40,000 to $44,9993.5%
8$45,000 to $49,9995.8%
9$50,000 to $59,99916.2%
10$60,000 to $74,99916.4%
11$75,000 to $99,99910.0%
12$100,000 to $124,9998.8%
13$125,000 to $149,9995.1%
14$150,000 to $199,9996.5%
15$200,000 or more2.3%

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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