Rental market snapshot for Maineville, Ohio

Maineville, Ohio Rental Property Market Overview

The average rent for an apartment in Maineville is $2,034. The cost of rent varies depending on several factors, including location, size, and quality.

The average rent has increased by 5.0% over the past year.

Maineville is a village in Warren County, Ohio, United States. The population was 1,480 at the 2010 census. It is part of the Cincinnati–Middletown Metropolitan Statistical Area.

Last Updated December 23, 2022

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Average monthly rent graph in Maineville Ohio | Cost of Living

  • The most expensive ZIP Code in Maineville is 45039 with an average price of $1,995
  • The cheapest ZIP Code in Maineville is 45039 with an average price of $1,850

Maineville ZIP Codes with the highest, most expensive rent

#zip codeaverage rent
145039$1,995
245039$1,945

Live near Maineville, Ohio's Top Sights and Attractions

Fosters Landing Park is a beautiful park located on the banks of the Little Miami River. The park features a playground, picnic tables, a pavilion, and a walking trail. The park is a great place to relax and enjoy the scenery.

Testerman Park is a beautiful park located in Maineville, Ohio. The park features a playground, a pavilion, and a walking trail. The playground is perfect for children of all ages, and the pavilion is a great place to relax and enjoy the scenery. The walking trail is perfect for a leisurely stroll or a vigorous hike.

Maineville, Ohio area median rent change by ZIP Code map

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How does Maineville compare to other cities in Ohio ?

citymedian price
Mentor$1,475
Mentor$1,475
Columbus$1,585
Blue Ash$1,910
Madison$1,045
Ashtabula$1,200
Youngstown$725
Maple Heights$1,000

Average household income in Maineville area graph - US Census

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The median household income in Maineville in 2021 was $77,961. This represents a 28.9% change from 2011 when the median was $60,469.

#categorypercent
0Less than $10,0003.6%
1$10,000 to $14,9991.9%
2$15,000 to $19,9994.0%
3$20,000 to $24,9994.0%
4$25,000 to $29,9993.4%
5$30,000 to $34,9992.7%
6$35,000 to $39,9991.9%
7$40,000 to $44,9994.6%
8$45,000 to $49,9995.5%
9$50,000 to $59,9997.8%
10$60,000 to $74,9997.2%
11$75,000 to $99,99924.2%
12$100,000 to $124,99910.7%
13$125,000 to $149,9996.7%
14$150,000 to $199,9998.8%
15$200,000 or more3.0%

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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