In 2023, the population of the Boston-Cambridge-Newton metropolitan area in the United States was about 4.92 million people. This is a slight increase when compared with last year's population, which was about 4.9 million people.

Graph and download economic data for Resident Population in Boston-Cambridge-Newton, MA-NH (MSA) (BOSPOP) from 2000 to 2024 about Boston, NH, MA, residents, population, and USA.

The gross domestic product (GDP) of the Greater Boston metro area has increased significantly since 2001. In 2022, the area's GDP amounted to ***** billion chained 2017 U.S. dollars, compared to ***** billion U.S. dollars in 2001.

Historical dataset of population level and growth rate for the Boston metro area from 1950 to 2025.

About this dataset

Context

The dataset tabulates the Boston population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Boston across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Boston was 653,833, a 0.09% increase year-by-year from 2022. Previously, in 2022, Boston population was 653,243, a decline of 0.61% compared to a population of 657,283 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Boston increased by 62,272. In this period, the peak population was 694,661 in the year 2019. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Boston is shown in this column.
• Year on Year Change: This column displays the change in Boston population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Boston Population by Year. You can refer the same here

2020 Census data for the city of Boston, Boston neighborhoods, census tracts, block groups, and voting districts. In the 2020 Census, the U.S. Census Bureau divided Boston into 207 census tracts (~4,000 residents) made up of 581 smaller block groups. The Boston Planning and Development Agency uses the 2020 tracts to approximate Boston neighborhoods. The 2020 Census Redistricting data also identify Boston’s voting districts.

For analysis of Boston’s 2020 Census data including graphs and maps by the BPDA Research Division and Office of Digital Cartography and GIS, see 2020 Census Research Publications

For a complete official data dictionary, please go to 2020 Census State Redistricting Data (Public Law 94-171) Summary File, Chapter 6. Data Dictionary. 2020 Census State Redistricting Data (Public Law 94-171) Summary File

2020 Census Tracts In Boston

2020 Census Block Groups In Boston

Boston Neighborhood Boundaries Approximated By 2020 Census Tracts

Boston Voting District Boundaries

This statistic displays the average physician-to-population ratio in select U.S. metropolitan areas as of 2013. During this year, there was an average of ***** physicians per 100,000 population in Detroit. Boston has one of the overall highest average wait times for a physician appointment. The average cumulative wait time is approximately **** days in 2014, which has decreased since 2004.

Resident Population in Boston-Cambridge-Newton, MA-NH (MSA) was 5025.51700 Thous. of Persons in January of 2024, according to the United States Federal Reserve. Historically, Resident Population in Boston-Cambridge-Newton, MA-NH (MSA) reached a record high of 5025.51700 in January of 2024 and a record low of 4402.61100 in January of 2000. Trading Economics provides the current actual value, an historical data chart and related indicators for Resident Population in Boston-Cambridge-Newton, MA-NH (MSA) - last updated from the United States Federal Reserve on August of 2025.

Demographic Data for Boston’s Neighborhoods, 1950-2019

Boston is a city defined by the unique character of its many neighborhoods. The historical tables created by the BPDA Research Division from U.S. Census Decennial data describe demographic changes in Boston’s neighborhoods from 1950 through 2010 using consistent tract-based geographies. For more analysis of these data, please see Historical Trends in Boston's Neighborhoods. The most recent available neighborhood demographic data come from the 5-year American Community Survey (ACS). The ACS tables also present demographic data for Census-tract approximations of Boston’s neighborhoods. For pdf versions of the data presented here plus earlier versions of the analysis, please see Boston in Context.

Disclaimer: These data are updated by the author and are not an official product of the Federal Reserve Bank of Cleveland.This project provides two sets of migration estimates for the major US metro areas. The first series measures net migration of people to and from the urban neighborhoods of the metro areas. The second series covers all neighborhoods but breaks down net migration to other regions by four region types: (1) high-cost metros, (2) affordable, large metros, (3) midsized metros, and (4) small metros and rural areas. These series were introduced in a Cleveland Fed District Data Brief entitled “Urban and Regional Migration Estimates: Will Your City Recover from the Pandemic?"The migration estimates in this project are created with data from the Federal Reserve Bank of New York/Equifax Consumer Credit Panel (CCP). The CCP is a 5 percent random sample of the credit histories maintained by Equifax. The CCP reports the census block of residence for over 10 million individuals each quarter. Each month, Equifax receives individuals’ addresses, along with reports of debt balances and payments, from creditors (mortgage lenders, credit card issuers, student loan servicers, etc.). An algorithm maintained by Equifax considers all of the addresses reported for an individual and identifies the individual’s most likely current address. Equifax anonymizes the data before they are added to the CCP, removing names, addresses, and Social Security numbers (SSNs). In lieu of mailing addresses, the census block of the address is added to the CCP. Equifax creates a unique, anonymous identifier to enable researchers to build individuals’ panels. The panel nature of the data allows us to observe when someone has migrated and is living in a census block different from the one they lived in at the end of the preceding quarter. For more details about the CCP and its use in measuring migration, see Lee and Van der Klaauw (2010) and DeWaard, Johnson and Whitaker (2019). DefinitionsMetropolitan areaThe metropolitan areas in these data are combined statistical areas. This is the most aggregate definition of metro areas, and it combines Washington DC with Baltimore, San Jose with San Francisco, Akron with Cleveland, etc. Metro areas are combinations of counties that are tightly linked by worker commutes and other economic activity. All counties outside of metropolitan areas are tracked as parts of a rural commuting zone (CZ). CZs are also groups of counties linked by commuting, but CZ definitions cover all counties, both metropolitan and non-metropolitan. High-cost metropolitan areasHigh-cost metro areas are those where the median list price for a house was more than $200 per square foot on average between April 2017 and April 2022. These areas include San Francisco-San Jose, New York, San Diego, Los Angeles, Seattle, Boston, Miami, Sacramento, Denver, Salt Lake City, Portland, and Washington-Baltimore. Other Types of RegionsMetro areas with populations above 2 million and house price averages below $200 per square foot are categorized as affordable, large metros. Metro areas with populations between 500,000 and 2 million are categorized as mid-sized metros, regardless of house prices. All remaining counties are in the small metro and rural category.To obtain a metro area's total net migration, sum the four net migration values for the the four types of regions.Urban neighborhoodCensus tracts are designated as urban if they have a population density above 7,000 people per square mile. High density neighborhoods can support walkable retail districts and high-frequency public transportation. They are more likely to have the “street life” that people associate with living in an urban rather than a suburban area. The threshold of 7,000 people per square mile was selected because it was the average density in the largest US cities in the 1930 census. Before World War II, workplaces, shopping, schools and parks had to be accessible on foot. Tracts are also designated as urban if more than half of their housing units were built before WWII and they have a population density above 2,000 people per square mile. The lower population density threshold for the pre-war neighborhoods recognizes that many urban tracts have lost population since the 1960s. While the street grids usually remain, the area also nee

How racially diverse are residents in Massachusetts? This topic shows the demographic breakdown of residents by race/ethnicity and the increases in the Non-white population since 2010.

This map shows the percent of people who have low incomes who live in the Boston Region Metropolitan Planning Organization (MPO) area. The low-income population includes those whose family income is less than or equal to 200% of the poverty level for their family size. The data are from the 2010-14 American Community Survey, and are distributed to transportation analysis zones (TAZs) within the Boston MPO region. TAZs are approximately the size of Census block groups, but whose boundaries align more closely to roadways. They were developed for use in the Boston MPO's travel demand model, and are used for other MPO purposes as well, such as mapping.

This map shows a simple summary of the social vulnerability of populations in the United States. Using Census 2010 information, the map answers the question “Where are the areas of relatively greater potential impact from disaster events within the U.S.?” from the perspective of social vulnerability to hazards. In other words, all areas of the U.S. are assessed relative to each other. Local and regional assessments of social vulnerability should apply the same model to their multi-county or multi-state region. For emergency response planning and hazard mitigation, populations can be assessed by their vulnerability to various hazards (fire, flood, etc). Physical vulnerability refers to a population’s exposure to specific potential hazards, such as living in a designated flood plain. There are various methods for calculating the potential or real geographic extents for various types of hazards. Social vulnerability refers to sensitivity to this exposure due to population and housing characteristics: age, low income, disability, home value or other factors. The social vulnerability score presented in this web service is based upon a 2000 article from the Annals of the Association of American Geographers which sums the values of 8 variables as a surrogate for "social vulnerability". For example, low-income seniors may not have access to a car to simply drive away from an ongoing hazard such as a flood. A map of the flood’s extent can be overlaid on the social vulnerability layer to allow planners and responders to better understand the demographics of the people affected by the hazard. This map depicts social vulnerability at the block group level. A high score indicates an area is more vulnerable. This web service provides a simplistic view of social vulnerability. There are more recent methods and metrics for determining and displaying social vulnerability, including the Social Vulnerability Index (SoVI) which capture the multi-dimensional nature of social vulnerability across space. See www.sovius.org for more information on SoVI. The refereed journal article used to guide the creation of the model in ModelBuilder was: Cutter, S. L., J. T. Mitchell, and M. S. Scott, 2000. "Revealing the Vulnerability of People and Places: A Case Study of Georgetown County, South Carolina." Annals of the Association of American Geographers 90(4): 713-737. Additionally, a white paper used to guide creation of the model in ModelBuilder was "Handbook for Conducting a GIS-Based Hazards Assessment at the County Level" by Susan L. Cutter, Jerry T. Mitchell, and Michael S. Scott.Off-the-shelf software and data were used to generate this index. ModelBuilder in ArcGIS 10.1 was used to connect the data sources and run the calculations required by the model.-------------------------The Civic Analytics Network collaborates on shared projects that advance the use of data visualization and predictive analytics in solving important urban problems related to economic opportunity, poverty reduction, and addressing the root causes of social problems of equity and opportunity. For more information see About the Civil Analytics Network.

Census block groups are created by the U.S. Census Bureau as statistical geographic subdivisions of a census tract defined for the tabulation and presentation of data from the decennial census and the American Community Survey. Block groups generally contain between 600 and 3,000 people. Census block groups are split or merged every 10 years, depending on population change, with local feedback through the Participant Statistical Areas Program (PSAP). These shapefiles provide the boundaries for the 2020 block groups for Boston.

This dataset underlies a choropleth map of Boston area communities in which areas are shaded according to the percentage of the population that was foreign-born during each decade. The data was drawn from the US Census of Population, as well as the American Community Survey.

The TIGER/Line Files are shapefiles and related database files (.dbf) that are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line File is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Census tracts are small, relatively permanent statistical subdivisions of a county or equivalent entity, and were defined by local participants as part of the 2010 Census Participant Statistical Areas Program. The Census Bureau delineated the census tracts in situations where no local participant existed or where all the potential participants declined to participate. The primary purpose of census tracts is to provide a stable set of geographic units for the presentation of census data and comparison back to previous decennial censuses. Census tracts generally have a population size between 1,200 and 8,000 people, with an optimum size of 4,000 people. When first delineated, census tracts were designed to be homogeneous with respect to population characteristics, economic status, and living conditions. The spatial size of census tracts varies widely depending on the density of settlement. Physical changes in street patterns caused by highway construction, new development, and so forth, may require boundary revisions. In addition, census tracts occasionally are split due to population growth, or combined as a result of substantial population decline. Census tract boundaries generally follow visible and identifiable features. They may follow legal boundaries such as minor civil division (MCD) or incorporated place boundaries in some States and situations to allow for census tract-to-governmental unit relationships where the governmental boundaries tend to remain unchanged between censuses. State and county boundaries always are census tract boundaries in the standard census geographic hierarchy. In a few rare instances, a census tract may consist of noncontiguous areas. These noncontiguous areas may occur where the census tracts are coextensive with all or parts of legal entities that are themselves noncontiguous. For the 2010 Census, the census tract code range of 9400 through 9499 was enforced for census tracts that include a majority American Indian population according to Census 2000 data and/or their area was primarily covered by federally recognized American Indian reservations and/or off-reservation trust lands; the code range 9800 through 9899 was enforced for those census tracts that contained little or no population and represented a relatively large special land use area such as a National Park, military installation, or a business/industrial park; and the code range 9900 through 9998 was enforced for those census tracts that contained only water area, no land area.

Comparison of 1930's HOLC grades with contemporary demographics in greater Boston.Credits:US Census Bureau ACS 5-year Estimates, 2014-2018.Robert K. Nelson, LaDale Winling, Richard Marciano, Nathan Connolly, et al., “Mapping Inequality,” American Panorama, ed. Robert K. Nelson and Edward L. Ayers. Harvard T.H. Chan School of Public Health Public Health Disparities Geocoding Project

The BPDA Research Division prepared Census data on total population, population by race and ethnicity, voting-age population, group quarters populations, and housing occupancy for use in the 2022 City Council redistricting process. These data reflect 2020 census block-level data from the 2020 Decennial Census P.L. 94-171 Redistricting Data aggregated to the 275 precincts (as amended April 6, 2022) and the 9 current City Council Districts. Also included are 2010 estimates for these geographies based on 2010 census block-level.

Notes on coding of Race and Ethnicity:

The data presented here follow the conventions recommended by the Department of Justice in their September 1, 2021 guidance on the use of race and ethnicity data in redistricting. This differs from other commonly reported race and ethnicity groupings in that it groups those reporting 2 races, one White and one non-White, as being members of the non-White race reported. Thus a person reporting White and Black would be categorized here as Black. All residents of Hispanic or Latino origin, regardless of reported race, are grouped together. This coding appears on page 12 of the guidance that can be found here: https://www.justice.gov/opa/press-release/file/1429486/download

Notes on 2010 data:

For 2010 data the BPDA Research Division crosswalked 2010 census block data to 2020 boundaries using a combination of block assignment and areal interpolation based on Census Tiger shapefiles and the publicly available boundary files for Boston electoral geographies. For blocks split across 2020 boundaries the entire 2010 population was assigned to one side of the boundary if no residential structures within that block existed on the other side of the boundary. In cases where residential structures were present on both sides of the boundary, areal interpolation was used to assign the block's population and housing units based on the share of the land area of the block falling on either side of the boundary. These numbers will differ from those produced using different crosswalking methods.

About this dataset

Context

The dataset tabulates the population of New Boston town by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for New Boston town. The dataset can be utilized to understand the population distribution of New Boston town by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in New Boston town. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for New Boston town.

Key observations

Largest age group (population): Male # 45-49 years (449) | Female # 45-49 years (414). Source: U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the New Boston town population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the New Boston town is shown in the following column.
• Population (Female): The female population in the New Boston town is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in New Boston town for each age group.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for New Boston town Population by Gender. You can refer the same here