Elizabeth Panella
UEP 232
May 3, 2010
A Regression Analysis of Energy Efficiency in Milwaukee
Project Description
For this final GIS project, I wished to do an analysis that might be helpful to
policymakers attempting to distribute weatherization funds to low-income renters.
Because many existing weatherization programs are available only to homeowners, renters
are often left in the cold, so to speak. This may be because of the emphasis on private
property and ownership in this country. Unfortunately, without government help, most
renters in all income groups will not weatherize their homes due to the conflicting
incentives between landlords, who do not pay energy bills, and tenants who do not wish to
spend significant sums to weatherize homes they may only live in for one year. This not
only hurts the families, whose already tight budgets are stretched further, but society as a
whole due to increased emissions of greenhouse gasses.
For this project, I used a regression model to estimate how much energy residential
buildings in Milwaukee use per square foot. These estimates were computed at the Block
Group level. The regression model used was developed by UEP student Neil Veilleux and is
based upon the RECS dataset, which is a national survey of household energy consumption.
About a dozen variables (household income, square footage, housing tenure, presence of
seniors, household size, number of heating and cooling degree days, various heating types,
age of structure, and a special variable for being located in the midwest) were run through
the regression model in order to generate coefficients for each variable, which were then
applied to Milwaukee specific census data. These variables were combined to get an
estimate of how much energy each Block Group uses and were then divided by the average
square footage of residential properties in each individual BG. This average number, KBTUs
per square foot per Block Group per year, was the main focus of the mapping portion of this
project. The poster also included maps of some of the individual variables that went into
the regression such as building age and housing tenure.
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Literature Review
Etzion, Y., B.A. Portnov, E. Erell, I. Meir and D. Pearlmutter. 2001. An open GIS framework
for recording and analyzing post-occupancy changes in residential buildingsa climate-related case study. Building and Environment 35: 1075-1090.
This article assesses different types of modifications made by homeowners to their own
dwellings and how these changes affect the energy output of each structure. The case study
is based in Israel, a very hot climate. The model inputs nine different types of structural
changes, such as location, orientation, adjacent space, building material, and size, and
assesses which ones have the highest impact on energy. The paper concludes by stating
that these modifications almost always harm the efficiency of the building because they are
not done by licensed carpenters. The authors recommend that Israel change its building tax
system because it is discouraging builders from adding cooling porches and such
structures. This information can easily be extrapolated to relate to Milwaukee and support
government sponsored weatherization, rather than residents attempting to do it
themselves.
Harmaajarvi, Irmeli. 2000. EcoBalance model for assessing sustainability in residential
areas and relevant case studies in Finland. Environmental Impact Assessment Review
20: 373-380.
This article discusses the Finnish EcoBalance model, which estimates the “total
consumption of energy…by residential areas and urban structures on a life-cycle basis.” It
works by inputting a large number of data about individual structures, such as size,
population, materials, transportation available, water consumption and treatment, and
several others. It then calculates energy consumption in a number of ways. Although this
model has apparently been very useful for Finnish policy makers, it is less useful for me in
trying to figure out how to approach my project since I don’t have access to the same level
of data. The paper concludes by stating that the model is actually very accurate and
efficient, which makes sense given the amount of inputs available to researchers.
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Jones, P.J., S. Lannon and J. Williams. 2001. Modelling building energy use at urban scale.
Paper presented at the Seventh International IBPSA Conference, August 13-15,
2001, in Rio de Janerio, Brazil.
This article discusses the Energy and Environmental Prediction (EEP) model, which is used
for “environmental auditing and decision making” by policy makers in Wales. This model
works under the assumption that urban planners must make decisions for large areas
based on relatively small amounts of data. It was created in response to the UK’s Home
Energy Conservation Act which sets targets for housing energy efficiency. The model
identifies properties with similar consumption by comparing the five following factors:
location, building dimensions, age, built form, and specific assumptions (this category is a
bit fuzzy in the write up). Of the four articles here, this model seems the closest to the type
of analysis I’d like to do. Fortunately for the Welsh policy makers and unfortunately for me,
they can get data on a much smaller scale due to the privacy concerns governing American
census data. The article does not present detailed conclusions but merely states that the
model is still in testing mode and should soon be utilized by planners.
Schotten, Kees, Roland Goetgeluk, Maarten Hilferink, Piet Rietveld and Henk Scholten.
2001. Residential construction, land use and the environment. Simulations for the
Netherlands using a GIS –based land use model. Environmental Modeling and
Assessment 6: 133-143.
This article discusses the LAND USE SCANNER MODEL, a GIS model which simulates 3
potential land use and population growth scenarios and assesses the “environmental
effects of alternative spatial policies.” Much like my project, it inputs several factors present
in a certain grid of the Netherlands and outputs potential needs for each of these grids. This
seems to be the most complex statistical model of the four I’ve looked at. The paper finds
that accessibility of natural areas, proximity to other residential areas, and accessibility of
work locations all positively predict new residential growth.
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Data Sources

Census 2000 SF1 and SF3 Demographic Data from American Fact Finder. Source: US
Census Bureau,
http://factfinder.census.gov/servlet/DatasetMainPageServlet?_program=DEC&_sub
menuId=datasets_1&_lang=en

Shapefile for Milwaukee County Block Groups, 2000 Census. According to ESRI, most
of the features were scanned directly from source maps (usually USGS 1:100,000
topographic quads). Source: ESRI Tigerline,
http://www.esri.com/data/download/census2000-tigerline/index.html

Shapefile for the corporate boundary of the city of Milwaukee. Metadata is
incomplete for this file. Source: City of Milwaukee website,
http://www.ci.mil.wi.us/MapMilwaukee3480.htm

Shapefile for all parcel boundaries within the city of Milwaukee. Metadata is
incomplete for this file. Source: City of Milwaukee website,
http://www.ci.mil.wi.us/MapMilwaukee3480.htm

MPROP (Master Property Record). This is the tax assessor’s database for all parcels
in the city of Milwaukee. Data current as of February 3, 2009. Source: City of
Milwaukee website, http://www.ci.mil.wi.us/MapMilwaukee3480.htm

City of Milwaukee Zoning Code. Source: www.mkedcd.org/czo/codetext

2005 Residential Energy Consumption Survey (RECS). Source: US Department of
Energy, Energy Information Administration.

Heating and Cooling Degree Days for Milwaukee. Source: www.degreedays.net

Neil Veilleux’s thesis, notes, regressions, and Excel spreadsheets.
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Data Preparation and Analysis Steps
GIS
1. Import shape files and project all to “NAD 1927 State Plane Wisconsin South FIPS
4803.”
2. Clip Milwaukee County Block Groups by city limits.
3. Table join census demographic data to this new Milwaukee Block Group shapefile.
4. Convert parcel shapefile into points.
5. Import MPROP dbf.
a. Remove any records that have building area or number of units equal to zero.
b. Remove any records that are not residentially zoned.
c. Create a new field “Unit Area” equal to total building area divided by number
of units.
6. Table join MPROP to parcel points.
7. Spatially join parcel points to Block Groups, ensuring that the new attribute table
has a field for mean “Unit Area.”
8. Export attribute table to Excel, specifically the mean “Unit Area” per Block Group.
9. Import “EUI” field from Excel (described below) and table join to Block Groups.
10. Make maps!
STATA and Excel
1. Import the RECS dataset into STATA and create a variable that isolates observations
from Census Division 3, which includes Milwaukee.
2. Run the regression with all of the variables Neil used, plus the newly created
Midwest. The STATA command is: regress log_btutot hhincome log_totsqft
inc_logsqft_interaction ownrent senior log_nhsldmem hd65 cd65 elwarm ugwarm
lpwarm fowarm krwarm wdwarm pre_1940 y1940_49 y1950_59 y1960_69
y1970_79 y1980_89 y1990_99 midwest
Imported into Excel, the information looks like this:
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The red numbers represent values that are not statistically significant. This means that
alone, they do not have a significant effect on energy usage. The fact that the Midwest
variable is not significant is interesting, because for Neil, the New England variable was
significant. I believe that this is the case because of the large proportions of homes in
New England that use fuel oil. Most homes in Milwaukee use natural gas, which is less
unique nationally.
Also note that some of the variables have “ln” in front of them. This means that the
people who made the RECS dataset took the log of each of these variables in order to get
a normal distribution. Later on, I will reverse this step in order to not skew the
Milwaukee specific data.
3. Multiply the coefficient of each variable as determined by RECS the corresponding
variable to each individual Block Group in Milwaukee. Most of this data came from
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the Census Bureau, with two exceptions: 1) The average square feet per Block Group
that was calculated in GIS and 2) The number of heating and cooling degree days
Milwaukee has experienced over the past year. This number is a constant, and
remains the same for each Block Group.
4. In the same formula, delog each column to reverse the normalization referenced
above. Here is a screen shot of the Excel table:
…and of the formula used:
The green numbers at the top of each column are the coefficients, which get
multiplied down the whole column, for all 678 Block Groups. Simply stated, each
coefficient is getting applied to a unique number for each BG. This was the step of
Neil’s project that I didn’t understand until I actually sat down and tried to replicate
his spreadsheets.
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5. For each Block Group, multiply the value of each variable together. Here is the
formula:
6. This gives a total predicted KBTU usage per year, per block group. In order to get
the average per square foot, which allows for easier comparisons, divide this total
number by the average square foot figure that was computed in GIS. Here is a shot of
the final spreadsheet:
Notice that in the 7th row, there is an error message. This is because of the limitations of
my aggregated data. This will be discussed below.
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Difficulties and Limitations
Originally, I wanted to do this project in Chicago. Unfortunately, Cook County is very
reticent with their data. I called about 12 different agencies who knew nothing until finally
getting in touch with a man who said that parcel and tax assessor data for Cook County is
not available at this time. Milwaukee is much freer with their information, as parcel and tax
assessor data is posted on the city’s website, so I shifted my focus.
Milwaukee’s MPROP table was generally very complete, although many of the larger
apartment buildings were missing data regarding building area. Because of this limitation,
my sample is skewed towards single family homes and small apartment buildings, which
may tend to have larger areas. This may overestimate the amount of energy that Milwaukee
actually consumes. Also, when buildings were zoned mixed-use residential/office, I
included them in the sample. I have no way of knowing how many office buildings were
included, and since these might also have larger areas than would high rise apartment
buildings, this is another reason why my consumption estimate may be too high. Because of
these data limitations, some of the Block Groups showed up as having no residential
properties.
Finally, some of my Block Group data was not as accurate as I would have liked.
Although Neil used Mass GIS data, which has separate variables for structures built in the
1940s and 1950s, American Fact Finder groups these together. As such, I had to divide this
percentage in half in order to make the categories correspond to the RECS variables. I had
to do the same with wood, fuel oil, and kerosene heat, because I had previously combined
several fuel types into one category and did not have the time to redo it. Luckily, wood and
kerosene heat didn’t turn out to be significant, so I don’t think this oversight had much
influence on the outcomes of the regression.
Conclusions
The conclusions that can be drawn from this regression analysis are very similar to those
Neil found in regards to Boston. Although he posited that the presence of seniors would
increase energy consumption, both of us found this to not be the case. Other variables that
were not significant predictors of energy usage included: the Midwest location, use of wood
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or kerosene heat, and structures built after 1960. This means then, that all other variables
do impact energy use, including many that are generally assumed, such as income, number
of people, building size, other heating types, and older structures. These findings do not tell
much to policy makers that they did not already know, but they are reassuring in knowing
that the model might be on the right track. The final statistic, energy consumption per
square foot, can be used in conjunction with other variables to plan the weatherization
programs that I would like to see enacted.
Further Research
Unfortunately, due to the complex nature of the regression portion of this project, I
did not have time to take it as far as I would have liked into the policy implications of my
findings. Ideally, I would have liked to take the average energy consumption figure, EUI,
and overlay it with median income and tenure to find the areas with high energy costs, high
proportions of renters, and low median incomes. Luckily, though, I feel that the regression
itself shows many interesting findings and as such, should be highlighted before it is used
for further research.
In addition, I’d like to recreate this project with Chicago data if possible, replicating
what I’ve done and doing the extra overlay step to find the areas of the city most in need of
weatherization assistance for renters. This model could then be compared to the project
completed by the Center for Neighborhood Technology using real energy data to determine
how accurate the regression model is.
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