About this project
Housing in Rockland has become significantly more expensive, with median home prices roughly doubling since 2020. Renters are vulnerable to being priced out, first-time buyers find starter homes out of reach, and local businesses struggle to find workers who can afford to live nearby.
City leaders have responded with zoning reforms, partnerships with affordable housing developers, and a $10 million housing bond approved by voters in June 2025. This housing plan guides policies, funding, and initiatives for the next ten years, establishing a production target of 60 new housing units per year to stabilize costs and preserve Rockland's socioeconomic diversity.
Housing Production Target
| 2,800 | Homes needed in Knox County by 2030 (State Housing Production Needs Study) |
| 17,979 | Households in Knox County (2023) |
| 3,386 | Households in Rockland (2023) |
| 19% | Rockland's share of Knox County households |
| 540 | Rockland new housing units needed 2021-2030 |
| 60 | Annual housing production target (units per year) |
| 233 | Rental units needed (apartments) |
| 307 | Ownership units needed (homes and condos) |
Maine's Housing Production Needs Study identified that Knox County requires 2,800 new homes from 2021 to 2030. Rockland's proportional share is 540 units over the decade, or 60 units per year.
Housing Production since 2021
| Type | Units | Buildings | Below 50% AMI | 50-80% AMI | 80-100% AMI | 100-120% AMI | Market Rate |
|---|---|---|---|---|---|---|---|
| Single Family | 14 | 14 | 2 | 1 | 3 | 4 | 5 |
| Two Family | 4 | 2 | 0 | 0 | 0 | 0 | 4 |
| 3-6 units | 4 | 1 | 0 | 0 | 0 | 0 | 4 |
| 6-12 units | 9 | 1 | 0 | 0 | 0 | 4 | 5 |
| 13+ units | 19 | 1 | 0 | 12 | 0 | 0 | 7 |
| TOTAL | 50 | 19 | 2 | 13 | 3 | 8 | 25 |
Rockland's production target is 60 units per year, up from a recent rate of approximately 25. This table tracks new housing units by building type and HUD AMI level.
How affordable is this community?
- ACS 5-year estimates · B19013, B25082, B25090 · 2010–2024
- Zillow ZHVI
- FRED 30-year fixed mortgage rate
- Insurance premium rates (CommunityScale)
- Congressional Budget Office CBO Per Capita Wage Growth · Per capita wage growth
- Federal Reserve FRED 30-Year Mortgage Rate · 30-year mortgage rate
- U.S. Bureau of Labor Statistics BLS CPI-U Inflation Ratios · Consumer price inflation factors
- U.S. Census Bureau ACS 5-Year Estimates · B01001, B19001, B19001A, B19001B +22 more ACS Public Use Microdata Sample (PUMS), 5-Year · Household records, Person records, Puerto Rico household records, Puerto Rico person records Census Address Count Listing · Address counts, December 2025 Census Building Permits Survey · Annual permits by county LEHD Origin-Destination Employment Statistics (LODES) · Block crosswalk, Jobs by workplace block
- U.S. Department of Housing and Urban Development HUD Income Limits · Income limits
- U.S. Department of the Treasury Homeowners Insurance Premiums by ZIP Code · Premiums by ZIP code
- Zillow Zillow Housing Data · Home values and rents by ZIP code
- Geography Geographic Crosswalks · Place to county, Tract crosswalk across Census years, ZIP code to ZCTA NTAD Metropolitan Planning Organizations · Metropolitan planning organizations TIGER/Line Boundaries · Boundaries
- CommunityScale Data CommunityScale App Datasets · Average homeowners insurance rates, Home values by geography, Household characteristics by income band, Household type by age group +6 more CommunityScale Population Forecasts · County population forecasts, Municipality population forecasts
This chart tracks whether housing prices are keeping pace with local incomes. The gap between what homes cost and what households can afford reveals affordability trends over time.
| $344,559 | Typical home price (Zillow, Aug 2026) |
| 6.86% | 30-year fixed mortgage rate (Freddie Mac, Sep 2026) |
| 1.66% | Effective property tax rate |
| $1,071 | Average annual homeowner's insurance |
| $87,663 | Median household income (Census, 2026) |
| 30% | Assumed max housing cost share of income |
This chart decomposes changes in housing affordability into two factors: the price-to-income ratio and mortgage rates. Each line shows the cumulative contribution of that factor to the change in the affordability gap (the difference between income required to purchase a typical home and median income). The decomposition uses Shapley value attribution to fairly allocate changes across correlated factors.
How are households changing by income?
- ACS 5-year estimates · B25007, B01001, B19001, B19113 · 2010–2024
- HUD Income Limits · FY 2025
- BLS CPI-U · 2010–2024
- Census PUMS 5-year · 2019–2023
- Congressional Budget Office CBO Per Capita Wage Growth · Per capita wage growth
- U.S. Bureau of Labor Statistics BLS CPI-U Inflation Ratios · Consumer price inflation factors
- U.S. Census Bureau ACS 5-Year Estimates · B01001, B19001, B19113, B25001 +6 more ACS Public Use Microdata Sample (PUMS), 5-Year · Household records, Person records, Puerto Rico household records, Puerto Rico person records Census Address Count Listing · Address counts, December 2025 LEHD Origin-Destination Employment Statistics (LODES) · Block crosswalk
- U.S. Department of Housing and Urban Development HUD Income Limits · Income limits
- U.S. Department of the Treasury Homeowners Insurance Premiums by ZIP Code · Premiums by ZIP code
- Zillow Zillow Housing Data · Home values and rents by ZIP code
- Geography Geographic Crosswalks · Tract crosswalk across Census years, ZIP code to ZCTA NTAD Metropolitan Planning Organizations · Metropolitan planning organizations TIGER/Line Boundaries · Boundaries
- CommunityScale Data CommunityScale App Datasets · Average homeowners insurance rates, Home values by geography, Household type by age group, Households by income band +3 more CommunityScale Population Forecasts · County population forecasts, Municipality population forecasts
Understanding how households at different income levels are growing or declining helps identify which groups face the greatest housing pressure and where new housing is most needed.
| AMI Group | Income Range | Households |
|---|---|---|
| <30% AMI | <$29K | 864 |
| 30-50% AMI | $29K - $49K | 688 |
| 50-80% AMI | $49K - $79K | 836 |
| 80-100% AMI | $79K - $98K | 410 |
| 100-120% AMI | $98K - $118K | 214 |
| >120% AMI | >$118K | 665 |
How is the population changing?
- Census Population Estimates Program (PEP) · 2010–2025
- ACS 5-year estimates · B01001 · 2010–2024
- CommunityScale population projection model
- U.S. Census Bureau ACS 5-Year Estimates · B01001
- Geography Geographic Crosswalks · Tract crosswalk across Census years NTAD Metropolitan Planning Organizations · Metropolitan planning organizations TIGER/Line Boundaries · Boundaries
- CommunityScale Data CommunityScale App Datasets · Population forecast summary CommunityScale Population Forecasts · County population forecasts, Municipality population forecasts
Population growth patterns reveal how communities are evolving. This chart shows historical population changes by age group and projects future trends based on demographic patterns.
The share of households with children indicates family housing demand, while the share with people over 65 reflects the growing need for senior-friendly and accessible housing options.
The share of people aged 25-34 indicates the pipeline of workers entering the labor force, while the share aged 65+ shows the pace of retirement. Comparing these trends reveals whether communities face a tightening or expanding labor supply.
Historical population comes from the U.S. Census Bureau's Population Estimates Program (PEP): counties directly from PEP's annual estimates by age, and municipalities from PEP sub-county totals with age detail from the American Community Survey (ACS). Projections use CommunityScale's PEP-anchored cohort-migration forecast model.
The model follows each five-year age cohort through the community's 2010–2024 population history and projects those cohort trends forward, launching from the most recent PEP estimate (2025). Group-quarters residents — college dorms, prisons, military barracks, nursing facilities — do not follow household demographics, so they are separated out, projected on their own trend, and added back. In small communities, where year-to-year cohort data is noisy, the model blends each cohort's trend toward the community's recent overall trend to keep projections stable.
How are household types changing?
- ACS 5-year estimates · B09021, B11015, B19131 · 2010–2024
- U.S. Census Bureau ACS 5-Year Estimates · B01001, B09021, B11015, B19131
- Geography Geographic Crosswalks · Tract crosswalk across Census years NTAD Metropolitan Planning Organizations · Metropolitan planning organizations TIGER/Line Boundaries · Boundaries
- CommunityScale Data CommunityScale App Datasets · Household types, Population forecast by age CommunityScale Population Forecasts · County population forecasts, Municipality population forecasts
This chart shows how household composition has changed over time, comparing the distribution of household types between 2015 and 2023. Changes in household types can indicate shifting demographics and evolving housing needs in a community.
Households may have different structure type preferences depending on characteristics such as household size, income, employment, presence of children, age of individuals, and lifestyle choices. Understanding the housing stock in corresponding terms helps assess how well existing units align with existing households' ideals.
Cost burden measures the share of household income spent on housing. Households paying more than 30% are considered cost burdened, while those paying more than 50% are severely cost burdened. Lower-income households typically face the highest rates of cost burden.
Household type data comes from the American Community Survey (ACS) 5-year estimates. Household types include family households (married couples, single parents) and non-family households (people living alone, unrelated roommates).
What does the housing stock look like?
- ACS 5-year estimates · B25024, B25032, B25041, B25042 · 2020–2024
- U.S. Census Bureau ACS 5-Year Estimates · B01001, B25024, B25032, B25041 +1 more
- Geography Geographic Crosswalks · Tract crosswalk across Census years NTAD Metropolitan Planning Organizations · Metropolitan planning organizations TIGER/Line Boundaries · Boundaries
- CommunityScale Data CommunityScale App Datasets · Housing stock, Population forecast by age CommunityScale Population Forecasts · County population forecasts, Municipality population forecasts
The mix of housing types and ownership patterns shapes a community's character. This chart breaks down housing units by structure type and whether they're owner-occupied or rented.
This chart shows rental and homeowner vacancy rates over time. A healthy rental vacancy rate is around 7.5%, and a healthy homeowner vacancy rate is around 1.5%. Rates well below these thresholds suggest a tight housing market with limited options for people looking to move.