Change Indicator

High housing cost burden by owner/renter households and county in Maine

High housing cost burden by owner/renter households and county

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Why This Indicator Matters

High housing costs make it difficult for households to meet their other essential expenses such as for food and healthcare and transportation. "The Rent Eats First" by Harvard Joint Center for Housing Studies. Nationally, rents are rising and homes for sale are rising even more quickly. It is increasing difficult for young families to bear the burdens of both high rents and high costs to enter the home ownership market. Up until the beginning of 2022, low mortgage rates made home ownership more affordable, but now mortgage interest rates have risen.

In Maine, for 2017-2021, 21.2% of owners and 46.1% of renters paid more than 30% of their income for rent. This is slightly lower than 25.6% of owners and 50.5% of renters for the non-overlapping period of 2012-2016. 

For the most recent period, the counties of Oxford, Sagadahoc, and Washington had the highest percentage of renter households paying over 30% of their income at 57.1%, 49.9% and 48.9% respectively. The two counties with the lowest rental housing burden were Lincoln and Piscataquis Counties, at 35.2% and 35.7% respectively.
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Definition and Source



High housing cost burden by owner/ renter households is defined as the percent of households paying more than 30 percent of monthly household pretax income on housing. The numerator is the number of households who are paying more than 30% of their income and the denominator is all households for whom the percent of income spent on housing could be determined according to the US Census American Community Survey, Table DP04, 5-year estimates. The year 2021 represents data for 2017-2021, while the year 2020 represents data from 2016-2020 and so forth. All households, not just households with children under age 18, are included.

Data Source

US Census American Community Survey 5-year estimates, Table DP04.


2021 represents data for the years 2017-2021, 2020 for the years 2016-2020 and so on. Care should be used in interpreting data that is in overlapping intervals.

Updated December 2022.

Last Updated

January 2023