Rental market snapshot for Hazel Green, Alabama

Hazel Green, Alabama Rental Property Market Overview

The average rent for an apartment in Hazel Green is $1,356. The cost of rent varies depending on several factors, including location, size, and quality.

The average rent has increased by 1.3% over the past year.

Hazel Green is a town in Madison County, Alabama, United States. The population was 3,158 at the 2010 census. It is part of the Huntsville-Decatur Combined Statistical Area.

Last Updated December 23, 2022

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Average monthly rent graph in Hazel Green Alabama | Cost of Living

  • The most expensive ZIP Code in Hazel Green is 35750 with an average price of $1,565
  • The cheapest ZIP Code in Hazel Green is 35750 with an average price of $1,375

Hazel Green ZIP Codes with the highest, most expensive rent

#zip codeaverage rent
135750$1,565
235750$1,500

Live near Hazel Green, Alabama's Top Sights and Attractions

Hazel Green Recreation Center is a recreation center that has a basketball court, a swimming pool, and a playground.

Hats & Boots Dance Hall is a country-themed dance hall located in Hazel Green, Alabama. The dance hall features a large dance floor, a stage for live music, and a bar. The dance hall is decorated with country-themed items, including cowboy hats and boots.

Hazel Green Park is a beautiful park located in Hazel Green, Alabama. The park features a playground, picnic tables, a walking trail, and a pond.

Hazel Green, Alabama area median rent change by ZIP Code map

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How does Hazel Green compare to other cities in Alabama ?

citymedian price
Auburn$2,200
Harvest$1,600
Calera$1,560
Pinson$1,325
Ashville$1,445
Phenix City$840
Mount Olive$1,240
Sylvan Springs$955

Average household income in Hazel Green area graph - US Census

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The median household income in Hazel Green in 2021 was $48,105. This represents a -4.9% change from 2011 when the median was $50,568.

#categorypercent
0Less than $10,0007.5%
1$10,000 to $14,9992.8%
2$15,000 to $19,9995.7%
3$20,000 to $24,9996.6%
4$25,000 to $29,9995.8%
5$30,000 to $34,9994.8%
6$35,000 to $39,9998.5%
7$40,000 to $44,9994.6%
8$45,000 to $49,9995.3%
9$50,000 to $59,9994.2%
10$60,000 to $74,9994.9%
11$75,000 to $99,99921.0%
12$100,000 to $124,9995.7%
13$125,000 to $149,99910.5%
14$150,000 to $199,9992.1%
15$200,000 or more0.0%

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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