Rental market snapshot for Ingalls, Indiana

Ingalls, Indiana Rental Property Market Overview

Ingalls, Indiana is a small town located in Madison County with a population of 2,506 as of 2021, according to the US Census. The town is home to a variety of businesses and services, including a post office, a library, and several restaurants. It is also home to a number of parks and recreational areas, making it a great place to live and visit.

The average rent for an apartment in Ingalls is $1,516. The cost of rent varies depending on several factors, including location, size, and quality.

The average rent has decreased by -11.3% over the past year.

Last Updated January 31, 2023

Average monthly rent graph in Ingalls Indiana | Cost of Living

  • The most expensive ZIP Code in Ingalls is 46048 with an average price of $1,645
  • The cheapest ZIP Code in Ingalls is 46048 with an average price of $1,585

Ingalls ZIP Codes with the highest, most expensive rent

#zip codeaverage rent
146048$1,645

Live near Ingalls, Indiana's Top Sights and Attractions

Ingalls Park is a large outdoor recreational area located in a small town in Indiana. It features a variety of activities and amenities, including a large playground, a walking trail, a basketball court, a picnic area, and a pond. The park also has a pavilion and a gazebo, perfect for hosting events or just relaxing. The park is surrounded by lush green trees and is a great place to spend a day outdoors.

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Ingalls, Indiana area median rent change by ZIP Code map

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How does Ingalls compare to other cities in Indiana ?

citymedian price
Valparaiso$2,695
Monrovia$1,965
Lafayette$1,060
Granger$1,618
Madison Township$1,900
New Castle$750
Pittsboro$1,649
Indianapolis$1,565

Average household income in Ingalls area graph - US Census

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The median household income in Ingalls in 2021 was $55,043. This represents a -2.6% change from 2011 when the median was $56,510.

#categorypercent
0Less than $10,0006.5%
1$10,000 to $14,9994.0%
2$15,000 to $19,9995.1%
3$20,000 to $24,9991.7%
4$25,000 to $29,9996.0%
5$30,000 to $34,9990.6%
6$35,000 to $39,9996.6%
7$40,000 to $44,9994.8%
8$45,000 to $49,9997.8%
9$50,000 to $59,99913.5%
10$60,000 to $74,99916.2%
11$75,000 to $99,99916.2%
12$100,000 to $124,9996.1%
13$125,000 to $149,9992.5%
14$150,000 to $199,9992.1%
15$200,000 or more0.4%

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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