Matthew Sarcione
Intro to GIS: Final Paper
Exploring Housing Data in Boston
Project Description
My final project seeks to understand and document housing trends for the City of Boston.
With the significant and transient student population, low and middle income residents, services
workers, biotech employees, professors, and numerous other populations clamoring to get into
Boston’s limited housing supply, it is an excellent place to explore changing conditions over
time. This is especially true considering the run-up, fall, and subsequent increase in housing
prices related to and caused by the housing bubble and ensuing recession/recovery. The project is
therefore exploratory in nature. It does not seek to perform any form of spatial analysis; it merely
is aimed at exploring trends in housing at the neighborhood level in Boston, if not more closely.
Although a major goal of this project is to display changes over time, this is difficult to
achieve due to changes between assessor’s data documentation, among other things. That being
said, many sources of information and data were collected to help visualize different
characteristics of housing in Boston. For example, data was collected from the Census and the
American Community Survey to display, at the census tract and block group level, household
income levels, differences in tenure, and differences in average household size based on tenure.
Additionally, the City of Boston’s Department of Neighborhood Development made it possible
to look at housing prices, for both rentals and sales, and subsidized housing parcels in the
neighborhoods of Boston.
Displaying this data and trying to uncover themes was the major reasoning behind this
project. Therefore, no overarching analysis for suitability or vulnerability was done. That being
said, this project could provide an excellent starting place for someone to do some form of
analysis, say about the vulnerability of neighborhoods to gentrification, in that it displays
information that would be useful for developing a necessary methodology.
Data Layers and Source
Data Layer
Boston
Neighborhoods
Census Block
Groups
Census Tracts
Boston Parcels
Source
City of Boston
(BRA)
2013
TIGER/Line
Shapefile
2013
TIGER/Line
Shapefile
City of Boston
(Assessor’s
Data)
Household
Size of
Occupied
Units by
Tenure
US Census
Bureau
Household
Tenure
US Census
Bureau
Roads
MassGIS
House Prices
Boston
Indicators
Project/Boston
Department of
Neighborhood
Development
Details
Boston planning
districts
Block Groups
for the city of
Boston
Census Tracts
for the city of
Boston
Includes the
parcel polygons
and joined
assessor’s data
for 1998 and
2012
Data at the Block
Group level, and
is taken from the
2010 Census
Summary File
(SF1)
Data at the Block
Group level
taken from the
2010 Census
Summary File
(SF1)
Major Roads
(class 1-4)
Includes median
sales prices for
all residential
property in 2010
and 2011 for
Links/ Where Found
Found on M Drive
http://www.census.gov/geo/mapsdata/data/tiger-line.html
http://www.census.gov/geo/mapsdata/data/tiger-line.html
Found on M Drive; Metadata can
be found at
http://www.cityofboston.gov/Tridio
nImages/2009_LITE_web_tcm13639.pdf
Data can be downloaded from
http://factfinder2.census.gov/faces/
nav/jsf/pages/searchresults.xhtml?r
efresh=t
Data can be downloaded from
http://factfinder2.census.gov/faces/
nav/jsf/pages/searchresults.xhtml?r
efresh=t
Found on M Drive; Metadata can
be found at
http://www.mass.gov/anf/researchand-tech/it-serv-andsupport/application-serv/office-ofgeographic-informationmassgis/datalayers/eotroads.html
Excel data was created from tables
within a report from 2011; This
report can be found at
http://www.cityofboston.gov/image
s_documents/RealEstateTrends_20
11_tcm3-31604.pdf
Advertised
Rental Prices
Boston
Indicators
Project/Boston
Department of
Neighborhood
Development
Household
Income Levels
US Census
Bureau
Race of
Householder
US Census
Bureau
Subsidized
Housing
Boston
Department of
Neighborhood
Development
neighborhoods in
the city
Includes median
rents for all
residential
property in 2010
and 2011 for
neighborhoods in
the city
Data at the
Census Tract
level, and is
taken from the
2011 ACS 5year estimates
Data at the Block
Group level
taken from the
2010 Census
Summary File
(SF1)
Parcels under the
40B program
were obtained;
Excel data was created from tables
within a report from 2011; This
report can be found at
http://www.cityofboston.gov/image
s_documents/RealEstateTrends_20
11_tcm3-31604.pdf
Data can be downloaded from
http://factfinder2.census.gov/faces/
nav/jsf/pages/searchresults.xhtml?r
efresh=t
Data can be downloaded from
http://factfinder2.census.gov/faces/
nav/jsf/pages/searchresults.xhtml?r
efresh=t
This data is not public and was
given to me for use only, and is not
to be distributed; Per email with
official, a public inventory is to be
issued in the coming year(s)
Major Preparation and Analysis Steps
1) Make sure all data was projected in NAD_1983_StatePlane_Massachusetts_
Mainland_FIPS_2001_Feet. This was a major concern for any data received from
MassGIS as their data tends to be in meters.
2) Clip all data by the Boston Neighborhoods data layer. Mostly done to reduce the census
tracts and block groups to just include the city of Boston and not all of Suffolk County.
Also done to parcels layer and others that included the islands. For consistency, the
islands were not included in the maps since some data included a varying degree of some
or all of the islands.
3) Manipulate all census data as well as the rental and for sale housing prices so that they
could then be joined via a table join to the census tract, block group, or neighborhood
polygons. This included creating a percent change field in the rental and for sale housing
prices data, then using field calculator to figure out the amount the prices had changed
between 2010 and 2011 by subtracting the 2010 price from the 2011 price, then dividing
by the 2010 price.
4) Manipulate Assessor’s data for 2012 utilizing Microsoft Access to allow for a many to
one table join. This step was necessary due to the complications created with many
condos being found on 1 parcel. While originally this was to be done with a simple table
join, a database had to be created, resulting in a predetermined relationship join with the
parcel polygons.
5) Use the Kernel Raster tool to figure out the density in subsidized housing parcels per
square mile for Boston. A search radius of 3000 ft was used since it was roughly half the
distance observed between the parcels that were the farthest apart.
6) Select out all condominium parcels from the 1998 and 2012 layers, then create a layer file
from this selection to be exported as individual shapefiles. These shapefiles were then
converted into points so that a density per square miles of condominium parcels maps
could be made, utilizing Kernel Density again. Finally, these resulting rasters were then
subtracted from each other to create a change in density map.
Difficulties & Limitations
The most significant difficulty experienced was working with the Boston Assessor’s data.
While utilizing Microsoft access made it possible to create a table that dealt with the many to one
joining issue created by condominiums, getting the resulting table to join to the parcel data was
anything but simple. Many workarounds had to be tried, including opening a blank ArcMap file,
and several attempts at creating a geodatabase before a join was successful. Unfortunately, the
resulting data file was massive, making simple projections or clips extremely hard to undertake. I
experienced several instances where, when trying to clip the 2012 parcel data to the Boston
Neighborhoods Layer, ArcMap would create a blank layer file. I was able to finally make it work
by using the desktop as scratch work space. Upon completion, this clipped file alone was nearly
a full GB in size. Also related to the parcel data was my wish to document changes in assessed
value over time. Due not only to the size of the different data layers, but also because of
changing parcel ID’s within the assessor’s data itself, this simply was not possible.
Another issue/limitation of the project that I encountered was the format of the subsidized
housing data that I received from the Department of Neighborhood Development. Although they
did give me a pre-made shapefile of the locations of subsidized housing in Boston, it only
displayed the parcels where subsidized housing was, and not the number of units. I tried to get
around this by utilizing the kernel density tool with a large search radius to show where the
density of subsidized housing was the greatest but ultimately, it doesn’t show the true density in
that some parcels likely have many units on them.
The creation of the several density maps also brought with it complications. Due to the
format of the subsidized housing data and the condominium parcel data, I was not able to map
the true density of units that each parcel had on it. I was only able to map the density of parcels
themselves, making the maps not truly representative of actual density. Trying to figure out how
to make points out of all the individual units and then creating the density maps would have
created a more accurate depiction of density in Boston. Additionally, I chose wide search areas
in the Kernel Density tool settings, which makes subtle changes in information displayed less
accurately on the maps.
Unexpected challenges were also encountered due to the chosen method of the
deliverable. While I am glad that I didn’t make a poster in that it allowed me to create a greater
number of maps for the project, I had very little experience working with Wordpress. This meant
that not only did I have to overcome any difficulties I was having with ArcMap itself, but I also
had to deal with formatting issues of a surprisingly finicky internet application. For example, the
maps looked quite different in the ArcMap Layout view than when I imported them into the
wordpress pages. This meant that I had to try multiple exports and play with various aspects of
the maps in both programs in order to get them to some degree of consistency.
A final issue related to the project was the limited year to year data that I was able to
obtain from the Housing Trends report also published by the Department of Neighborhood
Development in 2011. Firstly, the data was only for the years 2010 and 2011, hence making any
observed changes documented occurring in a very small window of time. Furthermore, this data
was to be joined to the Boston Neighborhoods polygons layer. While this sounds straightforward, it was ultimately complicated by the fact that the Housing Trends report considered
Dorchester all one neighborhood, thus only having data only for Dorchester as a whole. On the
other hand, the Neighborhoods layer separates Dorchester into a northern and southern section. I
was able to get around it by creating an excel sheet that simply used the data for Dorchester
twice thereby filling in the same data for both North and South Dorchester from the Housing
Trends report.
Conclusions & Future Research
Housing in Boston will always be a very important issue. This project has shown that
housing prices are increasing, sometimes dramatically, in short periods of time in different
neighborhoods in the city. These areas, such as South Boston, have also experienced a dramatic
increase in the number of condominium parcels since the levels observed in 1998. Additionally,
while levels of renting are high in many areas of Boston, as is expected in a city of its size, this
"condo-ization" could potentially push housing prices in Boston out of the reach of many since
not all can afford a down-payment on a mortgage or condo fees in addition to a mortgage.
Not surprisingly, areas that are made up of non-white households that are lower in levels
of median income are also the site of the greatest density of subsidized housing parcels in the
city. While this is good in that housing prices are still increasing in these places and hence,
affordability is a key concern for the minority and low-income populations there, affordability is
clearly an issue throughout the city. This suggests that greater densities of subsidized housing for
all income levels and neighborhoods is a worthwhile consideration.
Overall, I would argue that this project was useful in displaying many different types of
information related to housing in Boston. If I were able to miraculously have more time and
storage space, I think I would have focused on the assessor’s data itself and compared the
different years available in some way. One such method would be to somehow rasterize the
parcel data for each year based on certain pertinent information so that then they could be
compared, subtracted, etc. This would be similar to what was done with the density maps of
condominium parcels. Additionally, I would have tried to improve the density maps to show the
actual intensity of units instead of just the parcels themselves.
As is mentioned in the conclusion section of my Wordpress site, I do think my project
could act as a starting point for future GIS work. Getting the 2012 assessor’s data in a joinable
form with Microsoft Access will alone be very useful to future projects. Also, a great idea for a
potential future study would be to use data from this project to evaluate areas in the city that are
in most need of affordable housing. This could readily be done by utilizing GIS to map
vulnerability of neighborhoods, trends, and overall suitability of sites and areas for potential
subsidized housing developments. Lastly, maybe focusing on one area of the city would allow a
finer-grained study of housing characteristics that could potentially be more revealing and
insightful. That being said, I am pleased with how the Wordpress site, my maps, and the overall
project turned out.
Literature Reviewed
Can, Ayse. (1998). GIS and Spatial Analysis of Housing and Mortgage Markets. Journal of
Housing Research, 9(1), 61-86.
Although this article is a bit dated, it provides an excellent rational about why housing is so
inherently tied to location, neighborhood, and other geographic factors and how this makes it an
ideal subject area to study using GIS. It then goes on to discuss major factors of an area that
contribute to housing price and overall desirability for residents. These include accessibility, the
physical environment, the social, economic and demographic context, and the provision or
availability of public services. Furthermore, it specifically mentions that when investigating
spatial structure of geographical data, the first necessary step to take is called the exploratory
spatial data analysis during which clustering and other measurements/quantifications of spatial
structure are found. Finally, it goes on to display methods of using GIS to undertake this first
step.
Perkins, D.D., Larsen, C., & Brown, B.B. (2009). Mapping Urban Revitalization: Using GIS
Spatial Analysis to Evaluate a New Housing Policy. Journal of Prevention & Intervention in the
Community, 37(1), 48-65.
This article looks at the impact on neighborhoods, blocks, and housing that results from urban
revitalization projects. While the final project will not be looking at the impact of certain new
projects on housing prices, value, tenure, etc., this article did inspire me to include certain data
layers that will serve as a proxy for revitalization and for areas that could be revitalized. Such
data layers include subway stops and housing prices around these places. As the article suggests,
revitalization and its impact on housing is geographically limited. It will be interesting to see if
this relationship holds in the negative sense, i.e. whether the presence of brownfields is also
where housing in Boston is priced lower.
Lee, S., Rosentraub, M. S., & Kobie, T. F. (2010). Race, class and spatial dimensions of
mortgage lending practices and residential foreclosures. Journal of Urbanism: International
Research on Placemaking and Urban Sustainability, 3(1), 39-68.
This article looks into various aspects of neighborhoods that have experienced foreclosures,
focusing on Cuyahoga County in Ohio. Not only does it look into low-income and minority
neighborhoods and their experiences with foreclosures, but it also looks into subprime loans and
whether these are also found in afflicted neighborhoods. Although my project will likely not
work with foreclosure data, this article is helpful in that it analyzes various other factors related
to housing using GIS, including income and demographics.
Vladeck, Abi. (2009) [Poster displaying final project with many maps showcasing housing data
for Boston.] An Exploration of Boston Housing Data. Retrieved from
https://wikis.uit.tufts.edu/confluence/download/attachments/24904863/poster+horizontal.pdf
This past GIS project is an excellent example of an exploration of different housing trends in the
Boston area. It mainly seeks to display the geographical distribution of different housing data
without performing any formal regressions or other statistical/spatial analyses. More importantly,
it provided a great resource that displays where to get certain housing data and potential subjects
to consider including in the final project. Ideally, the final project can work with more up to date
information than Vladeck was able to locate.