Monthly utility data for all City of Boston accounts. This data comes from Boston’s Enterprise Energy Management System. This software tool serves as the system of record for all municipal utility expenditures and energy/water use.

This data, maintained by the Mayor’s Office of Housing (MOH), is an inventory of all income-restricted units in the city. This data includes public housing owned by the Boston Housing Authority (BHA), privately- owned housing built with funding from DND and/or on land that was formerly City-owned, and privately-owned housing built without any City subsidy, e.g., created using Low-Income Housing Tax Credits (LIHTC) or as part of the Inclusionary Development Policy (IDP). Information is gathered from a variety of sources, including the City's IDP list, permitting and completion data from the Inspectional Services Department (ISD), newspaper advertisements for affordable units, Community Economic Development Assistance Corporation’s (CEDAC) Expiring Use list, and project lists from the BHA, the Massachusetts Department of Housing and Community Development (DHCD), MassHousing, and the U.S. Department of Housing and Urban Development (HUD), among others. The data is meant to be as exhaustive and up-to-date as possible, but since many units are not required to report data to the City of Boston, MOH is constantly working to verify and update it. See the data dictionary for more information on the structure of the data and important notes. The database only includes units that have a deed-restriction. It does not include tenant-based (also known as mobile) vouchers, which subsidize rent, but move with the tenant and are not attached to a particular unit. There are over 22,000 tenant-based vouchers in the city of Boston which provide additional affordability to low- and moderate-income households not accounted for here. The Income-Restricted Housing report can be directly accessed here:
https://www.boston.gov/sites/default/files/file/2023/04/Income%20Restricted%20Housing%202022_0.pdf

Learn more about income-restricted housing (as well as other types of affordable housing) here: https://www.boston.gov/affordable-housing-boston#income-restricted

This dataset provides information about the number of properties, residents, and average property values for Boston Lake Drive cross streets in Valley City, OH.

The population density picture of Boston is generally a story of two Bostons: the high density central and northern neighborhoods, and the low density southern neighborhoods.The highest density areas of Boston are particularly concentrated in Brighton, Allston, and the Fenway area, areas of the city with large numbers of college students and young adults. There is also high population density in areas such as the Back Bay, the South End, Charlestown, the North End, and South Boston. These are all relatively small areas geographically, but have housing stock conducive to population density (e.g. multi-family dwelling units, row housing, large apartment buildings). The southern neighborhoods, specifically Hyde Park and West Roxbury, have significant numbers of people living in them, but lots sizes tend to be much larger. These areas of the city also tend to have more single family dwelling units. In that, there are fewer people per square mile than places north in the city. Census data reveals that population density varies noticeably from area to area. Small area census data do a better job depicting where the crowded neighborhoods are. In this map, areas of highest density exceed 30,000 persons per square kilometer. Very high density areas exceed 7,000 persons per square kilometer. High density areas exceed 5,200 persons per square kilometer. The last categories break at 3,330 persons per square kilometer, and 1,500 persons per square kilometer.How to make this map for your city

U.S. Census Bureau QuickFacts statistics for Boston city, Massachusetts. QuickFacts data are derived from: Population Estimates, American Community Survey, Census of Population and Housing, Current Population Survey, Small Area Health Insurance Estimates, Small Area Income and Poverty Estimates, State and County Housing Unit Estimates, County Business Patterns, Nonemployer Statistics, Economic Census, Survey of Business Owners, Building Permits.

The Multi-City Study of Urban Inequality was designed to broaden the understanding of how changing labor market dynamics, racial attitudes and stereotypes, and racial residential segregation act singly and in concert to foster contemporary urban inequality. This data collection comprises data for two surveys: a survey of households and a survey of employers. Multistage area probability sampling of adult residents took place in four metropolitan areas: Atlanta (April 1992-September 1992), Boston (May 1993-November 1994), Detroit (April-September 1992), and Los Angeles (September 1993-August 1994). The combined four-city data file in Part 1 contains data on survey questions that were asked in households in at least two of the four survey cities. Questions on labor market dynamics included industry, hours worked per week, length of time on job, earnings before taxes, size of employer, benefits provided, instances of harassment and discrimination, and searching for work within particular areas of the metropolis in which the respondent resided. Questions covering racial attitudes and attitudes about inequality centered on the attitudes and beliefs that whites, Blacks, Latinos, and Asians hold about one another, including amount of discrimination, perceptions about wealth and intelligence, ability to be self-supporting, ability to speak English, involvement with drugs and gangs, the fairness of job training and educational assistance policies, and the fairness of hiring and promotion preferences. Residential segregation issues were studied through measures of neighborhood quality and satisfaction, and preferences regarding the racial/ethnic mix of neighborhoods. Other topics included residence and housing, neighborhood characteristics, family income structure, networks and social functioning, and interviewer observations. Demographic information on household respondents was also elicited, including length of residence, education, housing status, monthly rent or mortgage payment, marital status, gender, age, race, household composition, citizenship status, language spoken in the home, ability to read and speak English, political affiliation, and religion. The data in Part 2 represent a telephone survey of current business establishments in Atlanta, Boston, Detroit, and Los Angeles carried out between spring 1992 and spring 1995 to learn about hiring and vacancies, particularly for jobs requiring just a high school education. An employer size-weighted, stratified, probability sample (approximately two-thirds of the cases) was drawn from regional employment directories, and a probability sample (the other third of the cases) was drawn from the current or most recent employer reported by respondents to the household survey in Part 1. Employers were queried about characteristics of their firms, including composition of the firm's labor force, vacant positions, the person most recently hired and his or her salary, hours worked per week, educational qualifications, promotions, the firm's recruiting and hiring methods, and demographic information for the respondent, job applicants, the firm's customers, and the firm's labor force, including age, education, race, and gender.

This data set contains a series of administrative geographies utilized by the City of Boston, including: traditional neighborhoods defined by the Boston Planning and Development Agency (BPDA Neighborhood Statistical Areas, BPDA Planning Districts); election board regions (City Council Districts, Election Precincts, Election Wards); and districts for City operations (Fire Districts, ISD Neighborhoods, Police Districts, Public Works Districts, and ZIP Codes). For each we include a shape file with unique identifiers. These geographic files were obtained from the City of Boston Department of Innovation and Technology’s Analyze Boston data hub site: https://data.boston.gov/. Information pertaining to these files can be found in the most recent documentation for the "Geographical Infrastructure for the City of Boston." Note: These geographies are the most recent updated versions at the time of the release of the most recent Geographical Infrastructure for the City of Boston.

This dataset provides information about the number of properties, residents, and average property values for Boston Road cross streets in Valley City, OH.

This dataset provides information about the number of properties, residents, and average property values for Boston Avenue cross streets in Bessemer City, NC.

Ever wondered which city in Greater Boston reigns supreme? 🏆 Look no further! This dataset ranks 141 cities and towns in the Greater Boston area based on a variety of factors, including:

🏡 Housing – Where can you actually afford to live? (Trick question, it’s Boston.)

🚔 Safety – Which towns are as secure as Fort Knox?

🚆 Mobility – How easy is it to get around without losing your mind?

🏥 Health – Where are the healthiest, happiest Bostonians?

🎭 Entertainment – Because life is more than just Dunkin' runs.

🌍 Diversity – The melting pot of cultures in each town.

🎓 Education – Where future Einsteins are born.

💼 Employment – Who's hiring and where are the best career opportunities?

This dataset is perfect for data exploration, visualizations, and even some lighthearted city rivalry. Whether you're a data analyst, a real estate enthusiast, or just looking to settle the debate with your friends on which Boston suburb is the best—this dataset has you covered!

🔍 Insights Await! Can you uncover hidden trends, build an interactive ranking map, or find the best place for your next move? Let's find out!

Example notebook: https://www.kaggle.com/code/michaeldelamaza/boston-city-rankings-linear-regression

Boston MA city boundary including water features.

New Boston City Council districts for 2023-2031 municipal elections. Passed by the City Council on May 24th, 2023.The City Council Districts data layer reflects Chapter 9 of the Ordinances of 2022.

This dataset provides demographic and socioeconomic information for Boston neighborhoods compiled from the U.S. Census Bureau’s American Community Survey (ACS) 5-Year Estimates (2013–2017). It includes variables such as population, race, age, income, education, housing characteristics, and household composition.

The data was originally published through the City of Boston Open Data Portal and is publicly available under the City of Boston’s Open Data Policy. This version is provided on Kaggle for educational and research purposes.

This is the Boston Housing Dataset, copied from: https://www.kaggle.com/datasets/vikrishnan/boston-house-prices

Each record in the database describes a Boston suburb or town. The data was drawn from the Boston Standard Metropolitan Statistical Area (SMSA) in 1970. The attributes are defined as follows (taken from the UCI Machine Learning Repository1): CRIM: per capita crime rate by town

CRIM per capita crime rate by town ZN proportion of residential land zoned for lots over 25,000 sq.ft. INDUS proportion of non-retail business acres per town CHAS Charles River dummy variable (= 1 if tract bounds river; 0 otherwise) NOX nitric oxides concentration (parts per 10 million) RM average number of rooms per dwelling AGE proportion of owner-occupied units built prior to 1940 DIS weighted distances to five Boston employment centres RAD index of accessibility to radial highways TAX full-value property-tax rate per 10 000 USD PTRATIO pupil-teacher ratio by town B 1000 (Bk - 0.63)^2 where Bk is the proportion of black people by town LSTAT % lower status of the population MEDV Median value of owner-occupied homes in $1000's Missing values: None

Duplicate entries: None

This is a copy of UCI ML housing dataset. https://archive.ics.uci.edu/ml/machine-learning-databases/housing/

It has then been amended to include multiple different correlations:

Directly Derived Features - New features created by applying direct transformations to existing features. For example a scaled version of another (e.g., CRIM_dup_2 = CRIM * 2), or adding some noise to an existing feature (e.g., RM_noisy = RM + random_noise).

Linear Combinations - Combining existing features linearly. For instance, a feature that is a weighted sum of several other features (e.g., weighted_feature = 0.5 * CRIM + 0.3 * NOX + 0.2 * RM).

Polynomial Features - Creating polynomial transformations of existing features. For example, square or cube a feature (e.g., AGE_squared = AGE^2). These will have a predictable correlation with their original feature.

Interaction Terms - Generating features that are the product of two existing features. Revealing interactions between variables (e.g., TAX_RAD_interaction = TAX * RAD).

Duplicate Features with Variations: Duplicate some existing features and add small variations. For example, copy a feature and add a random small value to each entry (e.g., LSTAT_varied = LSTAT + small_random_value).

These have been done by taking the dataset in python and transforming it, for example:

``import pandas as pd import random import numpy as np

List of original column names

original_columns = ["CRIM", "ZN", "INDUS", "CHAS", "NOX", "RM", "AGE", "DIS", "RAD", "TAX", "PTRATIO", "B", "LSTAT"]

Generate features

for col_name in original_columns: # Linear Combinations other_cols = random.sample([c for c in original_columns if c != col_name], 2) df[f"{col_name}_linear_combo"] = 0.5 * df[col_name] + 0.3 * df[other_cols[0]] + 0.2 * df[other_cols[1]]

# Polynomial Features
df[f"{col_name}_squared"] = df[col_name] ** 2

# Interaction Terms
other_col = random.choice([c for c in original_columns if c != col_name])
df[f"{col_name}_{other_col}_interaction"] = df[col_name] * df[other_col]

# Duplicate Features with Variations
df[f"{col_name}_varied"] = df[col_name] + (np.random.rand(df.shape[0]) * 0.05)

Display the DataFrame

print(df) ``

This dataset is a daily export of all moving truck permits issued by the city. Both the raw data and the interactive map are updated daily with the latest available data.

Please note that not all permit locations in the raw data can be geocoded automatically, and these permits are therefore not included in the interactive map. These permits are still included in the tabular dataset.

Graph and download economic data for Resident Population in South Boston City, VA (DISCONTINUED) (VASBOS0POP) from 1970 to 1989 about south, Boston, VA, residents, population, and USA.

Each year, the City of Boston publishes payroll data for employees. This dataset contains employee names, job details, and earnings information including base salary, overtime, and total compensation for employees of the City.

See the "Payroll Categories" document below for an explanation of what types of earnings are included in each category.

About this dataset

Context

The dataset tabulates the Boston town 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 town 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 town was 7,927, a 0.35% decrease year-by-year from 2022. Previously, in 2022, Boston town population was 7,955, an increase of 0.05% compared to a population of 7,951 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Boston town increased by 30. In this period, the peak population was 8,079 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 town is shown in this column.
• Year on Year Change: This column displays the change in Boston town 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 town Population by Year. You can refer the same here

Graph and download economic data for All-Transactions House Price Index for Boston, MA (MSAD) (ATNHPIUS14454Q) from Q3 1977 to Q3 2025 about Boston, MA, appraisers, HPI, housing, price index, indexes, price, and USA.