The Assessing Online application brings direct access for taxpayers, homeowners, real estate and legal professionals as well as business owners to property parcel data including assessed value, location, ownership and tax information for each piece of property in the city.

The information assists homeowners directly in their ownership responsibilities by providing the current value and tax status of their property. Professional real estate, business and legal entities access and draw upon Boston property parcel data to support and enhance their specific business operations. The GIS data appended to this application provides valuable graphical contexts for researchers, analysts and other professionals interested in demographical patterns, property usage and development.

Gives property, or parcel, ownership together with value information, which ensures fair assessment of Boston taxable and non-taxable property of all types and classifications. To preserve their integrity, the identifiers PID, CM_ID, GIS_ID, ZIPCODE, and MAIL_ZIPCODE all are marked with an underscore ("_") as the last character.

Year-specific documentation for the FY2008 through FY2013 files is not currently available, but the format of those files is equivalent to that described in the FY2014 documentation.

September 2025

This layer represents all the public and many of the private roadways in Massachusetts, including designations for Interstate, U.S. and State routes. Formerly known as the Massachusetts Highway Department (MHD) Roads, then the Executive Office of Transportation - Office of Transportation Planning (EOT-OTP) Roads, the MassDOT roads layer includes linework from the 1:5,000 road and rail centerlines data that were interpreted as part of the 1990s Black and White Digital Orthophoto project. The Massachusetts Department of Transportation - Office of Transportation Planning, which maintains this layer, continues to add linework from municipal and other sources and update existing linework using the most recent color ortho imagery as a base. The attribute table includes many "road inventory" items maintained in MassDOT's linear referencing system. The data layer published in November 2018 is based on the MassDOT 2017 year-end Road Inventory layer and results of a 2014-2015 MassDOT-Central Transportation Planning Staff project to conflate street names and other attributes from MassGIS' "base streets" to the MassDOT Road Inventory linework. The base streets are continually maintained by MassGIS as part of the NextGen 911 and Master Address Database projects. MassGIS staff reviewed the conflated layer and added many base street arcs digitized after the completion of the conflation work. MassGIS added several fields to support legacy symbology and labeling. Other edits included modifying some linework in areas of recent construction and roadway reconfiguration to align to 2017-2018 Google ortho imagery, and making minor fixes to attributes and linework.In ArcSDE this layer is named EOTROADS_ARC.From this data layer MassGIS extracted the Major Roads and Major Highway Routes layers.

City of Boston's parcel data created by the Assessing department in 2016. This data combines 2016 geometry data with 2016 ownership data.Data KeyProperty Occupancy Codes

Water body features within the City of Boston.

City of Boston sidewalk inventory data. Completed by the Boston Public Works Department (PWD) in 2014.

High resolution land cover dataset for City of Boston, MA. Seven land cover classes were mapped: (1) tree canopy, (2) grass/shrub, (3) bare earth, (4) water, (5) buildings, (6) roads, and (7) other paved surfaces. The primary sources used to derive this land cover layer were 2013 LiDAR data, 2014 Orthoimagery, and 2016 NAIP imagery. Ancillary data sources included GIS data provided by City of Boston, MA or created by the UVM Spatial Analysis Laboratory. Object-based image analysis techniques (OBIA) were employed to extract land cover information using the best available remotely sensed and vector GIS datasets. OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing. Following the automated OBIA mapping a detailed manual review of the dataset was carried out at a scale of 1:2500 and all observable errors were corrected.

High resolution land cover dataset for City of Boston, MA. Seven land cover classes were mapped: (1) tree canopy, (2) grass/shrub, (3) bare earth, (4) water, (5) buildings, (6) roads, and (7) other paved surfaces. The primary sources used to derive this land cover layer were 2013 LiDAR data, 2014 Orthoimagery, and 2016 NAIP imagery. Ancillary data sources included GIS data provided by City of Boston, MA or created by the UVM Spatial Analysis Laboratory. Object-based image analysis techniques (OBIA) were employed to extract land cover information using the best available remotely sensed and vector GIS datasets. OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing. Following the automated OBIA mapping a detailed manual review of the dataset was carried out at a scale of 1:2500 and all observable errors were corrected.

Credits: University of Vermont Spatial Analysis Laboratory in collaboration with the City of Boston, Trust for Public Lands, and City of Cambridge.