Town and Gown in Worcester, MA:
Measuring the Distance to Colleges and its Effect on House Prices
By
Gael Carter
December 2010
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I. Introduction
In a recent article in the Worcester Telegram and Gazette (Kotsopoulos, 2010), the
citizens of Worcester condemned local college students for holding out of control off-campus
parties. In the weeks following, many students from various colleges were arrested, including
students from College of the Holy Cross and Assumption College, at various off-campus
locations. In the news, it seems that the neighbors who live close to the colleges are the most
directly affected by student behavior, but do the homeowners in neighborhoods further away
from the schools feel the same effects? This study aims to answer the question: “To what extent
does the location of a house, in relation to a Worcester college, affect the price of the house?”
This paper also focuses on the separate effects of the colleges and universities in the city and
their differing effects on the sales price of a house.
In addition to the stereotype of wild, drunken behavior, students living in overcrowded
off-campus residences also cause increased traffic and parking issues from student ownership of
cars. Although it is easy to only see the negative impacts of living close to a college campus,
there are many positive externalities as well. Colleges often host free cultural events such as
speakers, art exhibitions, and movies on campus. They also hold athletic events, run summer
camps, and allow access to the facilities such as libraries, swimming pools, tennis courts, and
many more. Positive effects can also be seen in the community through student participation in
community outreach programs.
This balance of contradicting externalities has been referred to throughout history as
“Town and Gown” (Mayfield, 2001). The term “town and gown” dates back to medieval Europe
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identifying the town as the lay people in the communities and the gown as the universities. This
term refers to the separation of town and gown and also implies conflict between the two groups.
In 1862, the Morrill Act was passed in the United States, establishing a land-grant for colleges
who provided a public service in return for federal aid (Mayfield, 2001). In this situation, farmers
would identify an improvement, need, or specific problem and the experts at the university
would solve the problem for the farmers. This became one of the first of many kinds of
relationships between universities and the surrounding communities. Martin, Smith and Phillips’
(2005) paper explains that the separation of the universities in America began because of
geographical separation from the larger towns and cities. Universities were usually located in
rural areas and were often secluded. However, as the urban areas began to expand, the
universities found themselves in the midst of the economic and social problems of the
community around them. Throughout the history of American universities, there have been
examples of successful college and community relationships, but for the majority of college
towns the relationship continued to decline (Martin, Smith and Phillips, 2005).
Ten years ago, the city of Worcester faced a similar situation in its local college
neighborhoods. The debate at hand is whether or not colleges positively or negatively impact the
community. In my paper, I use the distance of a given house from the nearest college or
university in order measure a potential effect on the sales price of a house and if the effect is
positive or negative. I also examine the schools individually to test if there is a difference in the
effects between the diverse types of colleges in Worcester.
This study finds that that the location of a house in relation to a college in the city of
Worcester is significant. This distance is significant with and without college differentiation.
This effect is also significant when the distances for the different colleges are controlled for, and
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has a greater explanatory power than the original hedonic regression with the variable of interest
being the distance to any college. This implies that the differences in the colleges impact local
house values in Worcester, holding distance and all other neighborhood and house characteristics
constant.
II. Literature Review:
No study exists that exactly examines the impact of colleges in a “college town” on house
prices. However, the recent study conducted by Vandegrift, Lockshiss, and Lahr (2009)
examines the value of a college on house values at the aggregate level. Their study looks at the
entire state of New Jersey and they test to see if the presence of a college within the borders of a
municipality causes an effect on home municipality tax and also tested to see if the presence of a
college leads to higher house prices. They utilized a version of the hedonic pricing model to
regress their data, which is explained in the Hedonic Model section of this paper. The presence
of a college in the town corresponds with a tax base that is about 24% higher than that of a town
in New Jersey without a college. This implies that the presence of a college within a town border
causes a significant positive effect on the tax base per acre. This effect is greater in towns with a
four-year college than towns containing a community college. They also conclude that the
variables representing the size of the college and the degree to which the college is residential
have virtually no effect on the tax base of the town. Vandegrift, Lockshiss, and Lahr also found
that the presence of a college in a New Jersey town was associated with house prices that are
11% higher. They discovered that smaller colleges have a larger positive effect on house values
and this effect disappears when the enrollment of any particular college reaches 12,500 students.
This implies that while smaller colleges have an impact that increases house values in a town,
once the enrollment of the college surpasses 12,500 students; this effect reverses to a negative
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impact on house values. Similar to the influence of the tax base, the effects of a four-year college
are higher than that of a community college and the difference is derived from the degree to
which the college is residential. That is to say that a school with a larger percentage of the
student body living in off-campus housing would have a greater negative effect on the prices of
the surrounding houses in comparison to a college that has the entire student population in oncampus housing. They conclude that the overall effect for small sized, four-year colleges is a
positive impact on house values and the tax base per acre. The effect is similar, but to a lesser
degree in two-year colleges and the effect is negative for large schools.
Other studies deal with the effects of environmental externalities, open space, and the
quality of primary and secondary schools on house values. In Kiel’s (1995) research of the
impact of hazardous waste sites on house values, she examines the distance to the closest
hazardous waste site in the town of Woburn, MA. In Woburn there are two different hazardous
waste sites. Similar to my process, Kiel used the hedonic approach using variables such as price,
finished area in square feet, date of sale, year built, architectural style, and minimum distance
from house to the Superfund sites. In order to measure the effect of distance from the Superfund
sites on house values over time, the data were grouped by time periods. For example, the first
period was prior to announcements in the media about the Superfund sites, the second time
period represents the discovery of these sites, and the third time period represents the time period
in which the EPA added the sites in Woburn to the Superfund list. The possibility of cleaning up
the sites was made public during the fourth period, the fifth period was when the official cleanup
plan was announced, and the final period is when the cleanup actually began. The regression
results show that the information released on the toxic sites did impact the house prices. Unlike
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the work of Vandegrift, Lockshiss, and Lahr, Kiel’s approach allows for the data to be viewed at
a local level to gain the specific effects of the Superfund sites in Woburn over time.
Downes and Zabel (2002) quantified the impact of school characteristics on house prices
in Chicago from 1987-1991. In this study, they combined information from the American
Housing Survey and the Illinois School Report Cards to form their own data set. They also used
the hedonic method to estimate their findings. Due to the complexity of the data set, Downes and
Zabel used 33 variables in their regression and found that in the district-level results, the perpupil expenditures and test scores have similar positive impacts on house values. The schoollevel results implied that potential buyers respond to the racial composition of a school when
they are deciding the price that they are willing to pay for a given house.
My study looks specifically at the city of Worcester and the twelve colleges and
universities that it contains. The information that I have collected is on a microeconomic scale.
Therefore, the effects of the colleges on the surrounding house prices will be much more
accurately identified than in Vandegrift, Lockshiss, and Lahr’s paper. In Worcester the tax base
will be constant for the entire city, so I do not include that variable in my study and I will simply
focus on house sale prices from the year 2000 and the census level data from the corresponding
year. Similar to their study, I also use the hedonic house price model in which I include variables
such as percentage of students in off-campus housing, number of undergraduate students, and
number of graduate students in my regression in order to account for differences in the various
schools that I include in my data set. Based on the study conducted by Vandegrift, Lockshiss,
and Lahr, the negative effects of the colleges on house values appear to be on a more local level,
or within the given municipality. In their regressions, they used municipal-level data for the
entire state of New Jersey to look at the effects on an aggregate level. The positive effects are
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conspicuous at the aggregate level in the state of New Jersey. However, by looking at the sale
prices of single family residences and their proximity to the nearest college, I am able to get a
more accurate idea of the positive and negative impacts caused by the colleges on the house sales
price.
My economic model is very similar the model from Kiel’s paper. This study uses the the
same house price hedonic method, and the price of the house as the dependent variable.
However, I use distance as the variable of interest in my regression analysis. Based upon the
assumption that effects of colleges on house prices would change very slowly over time, I only
use data from a single year in order to determine a generalized effect of the colleges on house
sale prices. Thus, by substituting in the distance from a given house to the nearest college in
Worcester, I am able to see the effects on the year 2000 sales price of the house.
Downes and Zabel present an interesting way to connect house prices and schools, but
what I have done is different in nature from their study. The difference lies in the fact that
homeowners who are choosing a home near a school are choosing based on the school itself.
People buy houses located in good school districts because they will most likely have children to
send to the schools. The purchase of a house near a particular primary or secondary school
permits enrollment in the schools. On the other hand, the purchase of a house near a college or
university is different because it does not guarantee enrollment in that particular college. For
most colleges, in-state tuition is much less, which could a potential draw to living in close
proximity to a certain college but not the sole reason. Also, the funding sources for colleges are
much different than those of primary and secondary schools, who draw from local taxes for
funding. Thus the decision to live near a particular college would not stem from the desire for the
homeowner’s children to attend the college. However, since the study conducted by Downes and
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Zabel proves that potential home buyers take school districts into account, it is reasonable to
assume that the school district that the house is located in is a neighborhood effect that would
affect the price of the house. They also include neighborhood variables such as the natural log of
the median income in the house’s census tract, median age of individuals in the house’s census
tract, proportion of nonwhite individuals in the census tract, and the proportion of blue-collar
workers in the census tract, along with many other variables. These neighborhood variables are
important for the hedonic method because they control for the differences in different parts of the
city, which affect the house prices. I also use census level data for neighborhood variables in my
regression, but my houses are divided up by census block groups rather than census tracts.
III. Data
In my research, I used information from the single family house sales from the year 2000
in the city of Worcester provided by the Warren Group. The Warren Group is a New England
based company that provides its clients with comprehensive studies, based on detailed property
and market information collected over time (www.thewarrengroup.com). The data set from the
Warren Group initially contained 1450 observations of houses. Some of the houses in the data set
were located outside of the Worcester city border; therefore they were eliminated from the usable
data. In the column for house sale prices, some of the sale prices seem abnormally low. This low
price may be a result of houses that were sold within a family for a price below market value. On
the other hand, some of the houses were significantly above the mean house price. In order to
correct for these outliers to some extent, I dropped the five lowest and the five highest sale
prices. Another problem with the data set arose in the column that described the year in which
the house was built. Thirty three of the houses in the data set were reported to be built after the
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year 2000. However, since the data describe houses sold in the year 2000, it is impossible for the
homes to be built in any year after the sale year. These houses have removed from the data set.
After cleaning the data set, 1394 observations remain. The number of observations remains
constant throughout the series of regressions.
Similar to Kiel’s (1995) house price hedonic method in Woburn, MA, I originally had
wanted to include variables such as lot size, interior square footage, number of floors, renovation
year, style of the house, type of heating fuel, central air conditioning, basement designation,
basement area, number of parking spaces, and if the house sale includes a garage. Unfortunately,
the data from the Warren Group was limited in the house descriptive variables and only
contained information on price, the year in which the house was built, the total number of rooms,
number of bedrooms, the number of bathrooms, lot size, and if the house is mortgaged.
Due to the restricted supply of house characteristic and information variables, I rely
heavily on neighborhood information. In order to incorporate neighborhood statistics, I layered
my house price data set with Census level data from the year 2000, collected from the
Massachusetts Office of Geographic Information (MassGIS) website. I used Census data at the
block group level, and each of the remaining houses in my data set was sorted into the Census
block groups of the city of Worcester. The U.S Census Bureau defines “A census block group
[as] a cluster of census blocks having the same first digit of their four-digit identifying numbers
within a census tract” (www.census.gov). Therefore, the block groups are smaller than the
census tracts and contain more specific neighborhood characteristics that I control for in my
regression analysis. In general, census block groups contain any number of observations
between 600 and 3,000 people, with an optimum size of 1,500 people; see Map 1 for a complete
map of the block groups of Worcester, MA.
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The information in the Census data that I collected included the total population, the
median value of owned houses, and the median household income for each block group. I also
included variables on the percentage of the population of the block groups broken down by race,
age, high school graduate, college graduate, and others. Similarly, I included percentages of the
total houses in each block group for variables such as occupied owned homes, occupied rented
homes, the type of power in the homes, rented and owned homes without full plumbing, and the
rented and owned homes without full kitchens. For a full list of variables and descriptive
statistics, see Table 1. I ran a correlation matrix of all of my independent variables, a few notable
correlations exist between the following neighborhood variables: the total population in a block
group and the total housing units in a block group, the median house value of a block group and
the median household income in a block group, and the percent of owned occupied houses and
the percent of rented occupied houses. The median household income for a given block group is
also highly correlated with the percent of owned occupied houses and the percent of rented
occupied houses. The house characteristic variables for the total number of rooms and the total
number of bedrooms were also highly correlated. The total number of rooms and the total
number of bathrooms is also correlated but to a lesser degree than the variable for bedrooms.
These correlations between the house characteristic variables are logical because the size of the
house depends on the total number of rooms, a fraction of which are made up of bedrooms and
bathrooms. I kept all of the variables in the regression because removing them would have
caused omitted variable bias. All other variables had correlations of less than 0.5, and a
correlation matrix of the significantly correlated variables is located in Table2.
Finally, in order to create my variable of interest I used GIS software to calculate the
distance of a given house to the nearest Worcester college or university. In order to do this, I
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used a data layer from the MassGIS website which contained information about the colleges
themselves. The data layer of colleges contained latitude and longitude locations for all of the
schools in the data set. From those locations, I converted their locations to the corresponding
coordinates on Massachusetts State Plane and calculated the distances from each house to the
closest college, measured in meters. This variable is labeled as “Dist1” and each house is
associated with the nearest college. The data layer of colleges contained variables such as the
number of undergraduate and graduate students, the percentage of students living on campus, the
length of the program, and if the college is classified as a liberal arts school. I then created
variables to represent other information about the schools that could theoretically affect the sales
price of the house. These variables are similar to the study conducted by Vandegrift, Lockshiss,
and Lahr (2009) in that they controlled for two year versus four year colleges and the percentage
of students living on campus. The correlation matrix of the college characteristic variables is
shown on Table 3. The variable representing the number of undergraduate students is highly
correlated with the following variables: the number of graduate students, the dummy variable for
programs of four years and longer in length, and the dummy variable for liberal arts schools.
Both dummy variables for programs of four years and longer in length, and the dummy variable
for liberal arts schools are also highly correlated the percentage of students living in off-campus
housing. The other correlations are less significant, and I control for the high correlation of the
other variables in my regressions.
IV. Hedonic Model
The hedonic model originated in Rosen’s (1974) work on implicit markets. House price
hedonic models are now widely accepted in economic studies, especially in environmental
economics. The underlying foundation for the model uses the theory that the price of a house can
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be derived from considering a house as a bundle of goods; including characteristics of the house
and property itself, characteristics of the neighborhood and community, and environmental
characteristics. Kiel (2006) explains that “if transactions in the housing market reflect the
interaction of informed buyers and sellers, then the price that the house sells for is the sum of the
prices that the buyer is willing to pay for each individual characteristic of the house”. Therefore,
after controlling for non-environmental factors, any remaining differences in price can be
attributed to differences in environmental quality. In essence, it is possible to assign a price for
the said environmental quality. A general example of a hedonic regression would take the form:
Sales Price= β0+ β1i House Characteristics+ β2i Neighborhood Characteristics
+ β3i Environmental Quality + ε.
In order for this model to hold, four assumptions must be made. First, for the housing
market to reach equilibrium, the observed prices must be an outcome of the buyers maximizing
their utility functions and the sellers maximizing their profits. Even if this assumption is unlikely
in a housing market, the prices should still reflect the preferences of the buyers and sellers of the
houses. The next assumption is based upon the idea that the buyer of a house is aware of all of
the characteristics of the house, so all of the house characteristics are included in the price that
the buyer is willing to pay. In the case of bedrooms, bathrooms, square footage, and other similar
house variables, it is a safe assumption that the information is equally available to both the buyer
and seller. However, if there is radon in the basement or asbestos in the insulation, the buyer may
not be aware of this information at the time of the purchase. In this case, the buyer’s purchase
price would not reflect the true value of the house. The third assumption is that in the housing
market, discrimination cannot exist. If this assumption fails, then the characteristics of the buyer
affect the price of the house along with the house characteristics themselves and the resulting
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price would not reflect the true value of the house. The fourth and final assumption is that the
examined housing market cannot be a segmented market; it must be a single market. If a single
equation is estimated, it requires a single market for the equilibrium condition (Kiel 2006). In
order for the house price hedonic regression to hold and for the sales price of the house to fully
represent the house characteristics, all four of the assumptions must be correct.
V. Results:
In my study I used the basic house price hedonic model; however I included distance to
the colleges as one of my neighborhood characteristics in order to assist in the explanation of the
variation of the sales prices of the houses in the data set. In the first regression, I used Dist1 to
represent the minimum distance from the house to the nearest college or university, measured in
meters. In this regression, I assumed that effects of the colleges on house prices jointly and did
not separate out effects for the different schools. I regressed both price and ln(price) as the
dependent variables, however I chose to use price as my dependent variable because it had more
explanatory power than ln(price). Also, based on theory and the graph of price and dist1 in
Graph 1, my variables do not appear to have a linear functional form. Therefore, I have chosen to
use the variables dist1 squared and dist1 cubed, denoted dist1sq and dist1cubed respectively, in
order to better explain the variation in the mean of the dependent variable of price in the
regression.
For the first regression, the estimated coefficients are shown in Table 4, and all results are
represented with White’s Heteroskedasticity-Consistent Standard Errors. I used the dist1,
dist1sq, and dist1cube to represent the distance to any college; this is to determine if the distance
is significant without distinguishing between the colleges. The t-statistics imply that the variables
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dist1, dist1sq, and dist1cube are all statistically significant at the 95% level in the regression. The
coefficient for dist1 is negative, Therefore, being closer to a college has a positive effect on the
sales price of a house. For example, if two completely identical houses are located in the same
census block group, the house that is located closer to the college or university in Worcester
would have a higher sale price than an identical house further away from the school. The dist1sq
and dist1cube variables help to explain a more accurate picture of the impact that the distance
variable has on the sale price of a house. Including all three distance variables, the combined
coefficient is still negative, which implies that a house in close proximity to a college or
university in Worcester will have a positive impact on the sale price of the house. More
specifically, if a house is moved one meter closer any college in Worcester, the sales price of the
house would increase by $28.877. All of the other included independent variables have the
expected signs and the following variables have corresponding coefficients that are statistically
significant at the 95% level: mortgage, t_yearbuilt, totrooms, bathrooms, lotsize, vl_med_own,
p_pop_black, p_occ_owner, p_occ_renter, p_util_gas, p_tank_gas, p_elec, p_fuel, and p_other.
The coefficients for t_yearbuiltsq and p_rnt_vhcl_1 are both statistically significant at the 90%
level and all of the other coefficients of the remaining independent variables are statistically
insignificant. In order to avoid omitted variable bias, I have kept all of the independent variables
in the regression. The adjusted R-Squared for the regression is 0.6151, which implies that 61.5%
of the variation in the sales price of the houses in the data set is explained by the independent
variables, including distance to the nearest college.
It is possible that there are different effects on the housing prices from the different
universities in Worcester because of the many different types of colleges. For example, Bancroft
School of Massage therapy may have a different impact on nearby house prices than Clark
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University because of the characteristics of the schools. Therefore, after the initial regression I
tested the colleges separately to see if the distance from a given house to a specific college is
significant. In order to do this, first I created dummy variables for each college. Then, I
generated an interaction term between the college dummy variable and the distance variable in
order to test to see if the distance from a house to the nearest college is significant for each
college individually. Based on the statistically significant results of dist1sq and dist1cube from
the first regression, I also created interaction terms between the dummy variables of all of the
colleges and the dist1sq and dist1cube variables respectively. This helps to control for the
nonlinear functional form.
The results of this regression are shown in Table 5.1. The adjusted R-Squared for this
regression is 0.6309, which implies that this regression explains 63.09% of the variation around
the mean of the dependent variable, which is still price. The distance variables for WPI, Salter,
Clark, Becker, and Assumption are all statistically insignificant for the interaction terms between
the schools and dist1, dist1sq, and dist1cube. Although the coefficients are significant for the
schools, the overall signs of the coefficients are different for the different colleges. For example,
the overall signs for the coefficients of WPI are positive, but the signs switch to negative when
distance reaches 489.17 meters. The signs for the distance coefficients for WPI reverse again
when the distance reaches 2134.476 meters. This implies that a house within 489.17 meters of
WPI would have a lower sales price than an identical house that is greater than 489.17 meters
away from WPI. On the other hand, this effect switches again at 2134.476 meters and the exact
same house would have a lower price at any greater distance. A similar effect occurs in the prices
of the houses surrounding Becker College. The signs for the coefficients of Salter, Clark and
Assumption are generally negative. Holding all else constant, this implies that houses located in
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the vicinity of WPI or Becker College have lower house prices than houses located closer to the
Salter School, Clark University, and Assumption. More specifically, in the neighborhoods
around Clark University, the coefficient for the distance to Clark variable is negative for any
distance. This implies that any distance from a house to Clark is associated with an increase in
house price. In the example of Assumption College, any distance less than 2137.567 meters has
an increases house prices and any distance above 2137.567 meters causes a decrease in house
prices. A similar effect is seen in the houses near the Salter School.
Both coefficients for Holy Cross and the Bancroft School of Massage Therapy are
statistically significant at the 90% level for the interaction term between the school and dist1.
However, the interaction terms between the schools and dist1sq and dist1cube are both
statistically insignificant for Holy Cross and Bancroft. The overall sign of the coefficients for
Holy Cross is negative for distances under 977.62 meters and distances over 32,286.64 meters.
Therefore, Holy Cross has a positive impact on house prices for distances under 977.62 meters
and over 32,286.64 meters. This effect on house prices increases as distance from the college
decreases. On the other hand, the coefficient for Bancroft is negative for distances under 103.463
meters and for distances over 2323.232 meters. Then, intuitively, the coefficient switches to
positive between the 103.463 meters and 2323.232 meters. Therefore, the Bancroft School of
Massage Therapy has a positive impact on house prices for distances below 103.463 meters and
above 2323.232 meters and causes a negative effect on house prices for the distances located on
the interval in-between.
The coefficient for the interaction terms of Hair in Motion Beauty Academy, Worcester
State, UMass Medical Center, and Quinsigamond Community College and the distance variable
are all statistically significant, with the exception of worcesterstatedistcube coefficient, which is
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not statistically significant. The overall coefficients for the Hair in Motion Beauty Academy,
Worcester State, UMass Medical Center, and Quinsigamond Community College behave in the
same manner as the coefficient for Bancroft. For example, the coefficient for U. Mass Medical
Center is negative for distances below 791.419 meters and above 1755.195 meters. On the
interval in between, the coefficient is positive. This implies that U. Mass Medical Center has a
positive impact on house prices until distance from a house to the college reaches 791.419, when
this impact reverses to a negative impact. This again reverses to a positive effect on house prices
when distance reaches 1755.195 meters and all distances larger than this number are also
positive. This reversal of coefficient signs is the same pattern for the Hair in Motion Beauty
Academy, Worcester State, and Quinsigamond Community College, but the changes occur at
different distances for each college. The full table of inflection point distances is located in Table
5.2.
All three coefficients for the Rob Roy Academy are statistically significant at the 99%
level, and the coefficients are positive, however the sign switches to negative when distance
reaches 1263.59 meters. Therefore, the Rob Roy Academy has a general negative effect on house
prices for houses within 1262.92 meters of the school. In other words, as the distance to from a
house to the Rob Roy Academy decreases from 1262.92, the sales price of the house would
decrease as well.
Although the coefficient values of interaction terms are not statistically significant for all
of the colleges, it is clear that the impacts of the distance to each college are different. This
suggests that variations in the schools themselves have a diverse impact on the sales prices of
houses. Next, I grouped the schools into three categories; two-year community colleges,
programs of four-year and longer and non-academic career programs. I created an interaction
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variable between these three group variables with the distance variable to separate out the effects
of the different categories. In order to control for nonlinear functional form, I also included
interactions between the groupings of college variables and dist1sq and dist1cube individually.
Table 6 shows the regression results, the nonacademicdist and the nonacademicdistsq
coefficients are statistically significant at the 99% level. The coefficient for nonacademicdistcube
is also statistically significant at the 95% level; however the other two categories are not
statistically significant. This implies that the colleges had more explanatory power when they
were tested individually in the regression. The relationship between distance and house prices
from the community college and four year programs were very different, so their combined
effect is canceled out to some degree and not statistically significant. However, the nonacademic schools had similar effects on house prices from distance, which causes the additive
effect to be statistically significant. The adjusted R-Squared for this regression is 0.6154, which
implies that this regression explains 61.54% of the variation around the mean of the dependent
variable. This is slightly less than the adjusted R-Squared of the previous regression, which is
0.6309. This also implies that the independent variables, the grouped colleges interacted with
distance, distance squared, and distance cubed, have less explanatory power than the colleges
interacted separately with distance, distance squared, and distance cubed.
In the last set of regressions, I differentiated between the colleges and universities
because they have many diverse characteristics that may impact house prices. I used variables,
such as four-year colleges and community colleges, to account for the differences in school
attributes. I created dummy variables for private schools, religious affiliation, two-year and
under programs, liberal arts programs, and “non-academic” programs. I also included
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information such as the number of undergraduate students, the number of graduate students, and
the percentage of students living in off campus housing.
The results for the third set of regressions are shown in Table 7, I have included the
dummy variables for private, religious affiliation, programs of four years or longer, liberal arts
colleges, and non-academic schools. I have also included variables for the number of
undergraduate students, the number of graduate students, and the percent of the student
population that resides in off-campus housing. The only variable that is statistically significant at
the 95% level is the dummy variable for the non-academic schools. All other school variables are
found to be statistically insignificant. The adjusted R-Squared for this regression is 0.6217,
which implies that this regression explains 62.17% of the variation around the mean of the
dependent variable.
Since the variables for the four year programs and the liberal arts schools are highly
correlated, for the next regression I dropped liberal arts dummy variable along with the religious
affiliation dummy variable. These results are shown in Table 8. In this regression, the number of
undergraduate students, the number of graduate students and the dummy variable for nonacademic schools are statistically significant at the 99% level. The coefficient for the private
dummy variable is not statistically significant, but the presence of a private college increases
house prices by $6,857.04. The coefficient for the number of undergraduate students is negative
and significant at the 95% level. This is logical because it is the undergraduate students that are
usually associated with many of the negative externalities for people who live near a college. The
coefficient for the number of graduate students statistically significant at the 90% level and is
positive, which is also logical because graduate students are generally older and more mature
than undergraduate students. Also, graduate students usually live in off-campus housing, which
19
would cause an increase in demand in the surrounding neighborhoods of the colleges that have
graduate programs. As a result of this increase in demand, house prices would increase near
some colleges in Worcester. The dummy variable that represents the programs that are four years
or longer is not statistically significant; however it is highly correlated with the number of
undergraduate students, as seen in the correlation matrix in Table 3. The percentage of students
living off campus is not statistically significant. This is a surprising outcome of the regression;
however all of the students that attend the non-academic schools commute to the schools because
they do not have on-campus housing. Therefore, the non-academic schools are likely skewing
the results of this coefficient. The presence of a non-academic school has a negative impact on
house prices, which is surprising since the non-academic distance to a house is significant and
has a positive impact on house prices. This suggests that there are other amenities or
neighborhood characteristics near the non-academic colleges that would cause an increase in
house price since the presence of a non-academic school near a house has a negative impact on
the price of a house. The adjusted R-Squared for this regression is 0.6195, which implies that this
regression explains 61.95% of the variation around the mean of price. This R-Squared is slightly
less than that of the previous regression, however by dropping the highly correlated variables, the
effects of the remaining variables becomes more apparent.
VI. Conclusion:
The results suggest that there are definite and measurable effects from the different
schools in Worcester that can be quantified in house prices. This study finds that the location of a
house in relation to a college in the city of Worcester is significant. That is, the distance of a
house from any college or university is significant. This effect is also true when the distances for
the different colleges are controlled for, and has a greater explanatory power than the original
20
hedonic regression with the variable of interest being the distance to any college. When I
controlled for distance separately, I found that the characteristics of the schools were significant.
This implies that the schools themselves impact local house values in Worcester, holding
distance and all other neighborhood and house characteristics constant.
Unlike Vandegrift, Lockshiss, and Lahr (2009), this study yields results that are much
more specific. Their paper also found that colleges in general have a positive impact on house
prices. However, they only differentiate their colleges by the classification of a four-year college
or a community college. In the city of Worcester, the colleges have different impacts on house
prices within the categories of the four-year colleges. For example, I found that the College of
the Holy Cross has a positive impact on house prices for distances under 977.62 meters, and
switches to a negative impact on house prices until the distance of 32,286.64. On the other hand,
Assumption changes from a positive effect to a negative effect on house prices once the distance
of a house to the college surpasses 2137.567 meters. Both Holy Cross and Assumption are fouryear colleges, but they have different effects on house prices within the same city. Similar to this
paper, the study conducted by Vandegrift, Lockshiss, and Lahr also controls for the different
types of schools by including the number of students and the degree to which the college is
residential. While their study did control for enrollment numbers, they did not differentiate
between undergraduate and graduate students. As outlined in the results, there is a significant
difference in the impact of undergraduate and graduate students in this study. This suggests that
Vandegrift, Lockshiss, and Lahr’s paper does not capture the complete impact of students on the
surrounding community. Also, I include variables such as dummy variables for private schools
and non-academic schools. The differences between public and private colleges have a sizable
impact on house prices in the surrounding neighborhoods. The schools that are identified as non21
academic are the more statistically significant than the schools that are identified as academic,
and they are also classified as colleges by the state of Massachusetts in the data set. Therefore,
these schools must be included in the data for an accurate idea of the full effects of colleges on
house prices.
While it was interesting to see the effects of all of the colleges in Worcester on house
prices, I hope to continue my study and redefine what is meant by “colleges”. It is not a far
stretch to imagine that the average person would not assign the term “college” to a school such
as the Hair in Motion Beauty Academy. Therefore, I reconstructed my data set and to exclude the
non-academic schools in my regression analysis. The effects for the academic colleges in
Worcester are statistically significant and support the results of this study for the new definition
of “college”. Worcester is such a unique city of colleges; it would be interesting to see this model
applied to a city with fewer colleges to see if there is a similar effect.
22
References:
Downes,Thomas A., Zabel,Jeffrey E.. “The Impact of School Characteristics on House Prices:
Chicago 1987–1991”. J.Urban Econ., 2002, 52, 1, 1-25.
Kiel, Katherine A. 2006. “Environmental Contamination and House Values.” College of
The Holy Cross, Department of Economics research paper no. 06-01.
Kiel, Katherine A.. "Measuring the Impact of the Discovery and Cleaning of Identified
Hazardous Waste Sites on House Values". Land Economics, Vol. 71, No. 4 (Nov., 1995),
pp. 428-435.
Kotsopoulos, Nick. "Holy Cross President Tells Students Rowdy Behavior 'has to
Stop'"Telegram.com - An Edition of the Worcester Telegram & Gazette and Sunday
Telegram. Worcester Telegram and Gazette, 23 Nov. 2010. Web. 4 Dec. 2010.
<http://www.telegram.com/article/20101123/NEWS/101129891>.
Martin, L., H. Smith, and W. Phillips. 2005. Bridging 'Town & Gown' through
innovative University-Community Partnerships. The Innovation Journal: The Public
Sector Innovation Journal 10(2): Article 20.
Mayfield, L. (2001). Town and gown in America: Some historical and institutional
issues of the engaged university. Education for Health, 14, 231 – 240.
Vandegrift, Donald, Amanda Lockshiss, and Michael Lahr. "Town versus Gown: The Effect of a
College on Housing Prices and the Tax Base." Thesis. The College of New Jersey, 2009.
MPRA. Dec. 2009. Web. <http://mpra.ub.uni-muenchen.de/18998/>.
http://www.census.gov/geo/www/cob/bg_metadata.html
http://www.mass.gov/mgis/laylist.htm
23
0
200000 price 400000
600000
Graph 1- Correlation of Price and Dist1
0
1000
2000
DIST1
3000
4000
Table 1- Summary and Explanation of Variables
Variable
price
Explanation
Mean
Std. Dev.
Min
Max
136868.4
63886.14
5000
590000
1479.684
835.7461
49.60719
4386.892
dist1sq
sales price of single family homes in the year
2000
distance in meters from a house to the
closest college
distance squared
2887434
3185106
2460.873
1.92E+07
dist1cube
distance cubed
6.80E+09
1.18E+10
122077
8.44E+10
mtg
mortgage of the house
102584.5
71169.7
0
1424000
t_yearbuilt
2000-yearbuilt
52.2231
35.0857
0
210
t_yearbuiltsq
(2000-yearbuilt) squared
3957.375
4200.635
0
44100
totrooms
total number of rooms in the house
6.263271
1.387665
4
13
bedrooms
total number of bedrooms
3.022238
0.7711184
1
8
Bathrooms
total number of bathrooms
1.8967
0.8131744
0
7
Lotsize
the size of the lot
9787.54
9922.257
1573
240016
total_pop
total population in a given block group
1399.878
837.5041
424
3538
vl_med_own
median house value in a given block group
124432.1
27388.62
69100
261500
inc_med_hs
Median household income in a given block
group
percentage of the population that is white in
a given block group
percentage of the population that is black in
a given block group
percentage of the population that is native
american in a given block group
percentage of the population that is asian in
a given block group
percentage of the population that is of any
other race in a given block group
47910.01
14755.35
11887
91801
87.38704
10.98003
32.68156
100
4.61784
6.178511
0
44.34932
0.1731922
0.5049738
0
6.317689
3.068557
3.478741
0
18.23105
2.56002
4.230811
0
36.43533
dist1
p_pop_white
p_pop_black
p_pop_nat
p_pop_asn
p_pop_other
24
ptrav5_14
ptrav20_34
ptrav45_59
ptrav_gt90
Phsgrad
Pbach
p_poplt1_12
p_pop60_gt85
tot_hs_unt
p_occ_owner
p_occ_renter
p_util_gas
p_tank_gas
p_elec
p_fuel
p_coal
p_wood
p_solar
p_other
p_own_vhcl_0
p_own_vhcl_1
p_own_vhcl_2
p_rnt_vhcl_1
p_rnt_vhcl_2
p_hs_noplumb
p_own_noplumb
p_hs_noktch
p_own_noktch
Wpidist
Salterdist
Robroydist
Hairinmotiondist
percentage of the population who commute 5
to 14 minutes to work in a given block group
percentage of the population who commute
20 to 34 minutes to work in a given block
group
percentage of the population who commute
45 to 59 minutes to work in a given block
group
percentage of the population who commute >
90 minutes to work in a given block group
percentage of the population who have a
high school degree
percentage of the population who have a
bachelor degree
percentage of the population between the
ages of 0 to 12 in a block group
percentage of the population between the
ages of 60 to greater than 85 in a block
group
total number of housing units in a block
group
percentage of the owner occupied housing
units in a block group
percentage of the renter occupied housing
units in a block group
percentage of the houses in a block group
that
percentage of housing units heated by
Bottled, tank, or LP gas
percentage of housing units heated by
electricity
percentage of housing units heated by Fuel
oil, kerosene, etc.
percentage of housing units heated by Coal
or coke
percentage of housing units heated by wood
percentage of housing units heated by solar
energy
percentage of housing units heated by other
energy
percentage of owner occupied housing units
with no vehicle available
percentage of owner occupied housing units
with one vehicle available
percentage of owner occupied housing units
with two vehicles available
percentage of renter occupied housing units
with one vehicle available
percentage of renter occupied housing units
with two vehicles available
percentage of housing units lacking complete
plumbing facilities
percentage of owner occupied housing units
lacking complete plumbing facilities
percentage of housing units lacking complete
kitchen facilities
percentage of owner occupied housing units
lacking complete kitchen facilities
interaction term between the dummy variable
for WPI and the distance variable
interaction term between the dummy variable
for Salter and the distance variable
interaction term between the dummy variable
for Rob Roy and the distance variable
interaction term between the dummy variable
for Hair In Motion and the distance variable
25
11.91695
4.251587
0
24.87961
11.38936
3.937049
1.71969
24.31843
2.38751
1.580721
0
7.38255
0.8149164
0.9451505
0
3.914989
19.89729
6.181187
4.042716
36.01236
11.32368
5.262218
0
25.9334
15.95041
4.66628
0.686499
41.62679
20.02132
6.985242
2.513967
47.29064
566.2131
325.2542
162
1566
63.39662
21.94621
4.761905
94.58599
33.20107
20.89421
4.249668
92.2619
50.4312
14.51052
14.97976
82.20641
1.254863
2.150942
0
17.02586
13.70377
11.40389
0
60.76191
30.18372
14.02496
0
65.84615
0.0867292
0.3142741
0
1.842105
0.3084518
0.8517046
0
3.523035
0.0750145
0.7450353
0
7.469306
0.3473939
0.9011939
0
7.279694
4.750337
5.164563
0
55
38.12109
12.73393
0
100
44.90005
12.94604
0
100
54.61199
13.51995
0
100
24.36987
13.6662
0
70.58823
0.5690895
1.322179
0
21.19403
0.5551353
4.041682
0
100
0.7322192
1.627741
0
21.19403
0.3408535
1.326941
0
18.29268
67.39843
338.0859
0
2392.836
98.81541
339.1233
0
2041.725
3.337636
55.74801
0
1191.862
255.0557
706.2583
0
4277.911
Hcdist
Clarkdist
Beckerdist
Bancroftdist
Assumptiondist
Worcesterstatedist
Umassmeddist
Quinsigamonddist
Wpidistsq
Salterdistsq
Robroydistsq
Hairinmotindistsq
Hcdistsq
Clarkdistsq
Beckerdistsq
Bancroftdistsq
Assumptiondistsq
Worcesterstatedistsq
Umassmeddistsq
Quinsigamonddistsq
Wpidiscube
Salterdistcube
Robroydistcube
Hairinmotiondistcube
Hcdistcube
Clarkdistcube
Beckerdistcube
Bancroftdistcube
interaction term between the dummy variable
for Holy Cross and the distance variable
interaction term between the dummy variable
for Clark and the distance variable
interaction term between the dummy variable
for Becker and the distance variable
interaction term between the dummy variable
for Bancroft and the distance variable
interaction term between the dummy variable
for Assumption and the distance variable
interaction term between the dummy variable
for Worcester State and the distance variable
interaction term between the dummy variable
for Umass Med and the distance variable
interaction term between the dummy variable
for Quinsigamond and the distance variable
interaction term between the dummy variable
for WPI and the distance variable squared
interaction term between the dummy variable
for Salter and the distance variable squared
interaction term between the dummy variable
for Rob Roy and the distance variable
squared
interaction term between the dummy variable
for Hair In Motion and the distance variable
squared
interaction term between the dummy variable
for Holy Cross and the distance variable
squared
interaction term between the dummy variable
for Clark and the distance variable squared
interaction term between the dummy variable
for Becker and the distance variable squared
interaction term between the dummy variable
for Bancroft and the distance variable
squared
interaction term between the dummy variable
for Assumption and the distance variable
squared
interaction term between the dummy variable
for Worcester State and the distance variable
squared
interaction term between the dummy variable
for Umass Med and the distance variable
squared
interaction term between the dummy variable
for Quinsigamond and the distance variable
squared
interaction term between the dummy variable
for WPI and the distance variable cubed
interaction term between the dummy variable
for Salter and the distance variable cubed
interaction term between the dummy variable
for Rob Roy and the distance variable cubed
interaction term between the dummy variable
for Hair In Motion and the distance variable
cubed
interaction term between the dummy variable
for Holy Cross and the distance variable
cubed
interaction term between the dummy variable
for Clark and the distance variable cubed
interaction term between the dummy variable
for Becker and the distance variable cubed
interaction term between the dummy variable
for Bancroft and the distance variable cubed
26
78.50254
386.237
0
3431.133
117.7479
497.3318
0
3010.97
15.00576
130.1521
0
1655.577
31.1403
203.4944
0
2287.123
273.9024
734.2812
0
4386.892
255.1696
644.8681
0
4361.995
60.88829
326.9612
0
2671.213
222.7197
589.2762
0
2604.93
118762.6
681239.9
0
5725664
124686.6
506355.4
0
4168641
3116.751
58582.47
0
1420536
563496.4
1989009
0
1.83E+07
155234.6
942603.9
0
1.18E+07
261026.1
1229283
0
9065942
17152.59
174529.5
0
2740935
42349.99
377240.8
0
5230933
613804.6
2274689
0
1.92E+07
480668.1
1691809
0
1.90E+07
110534.3
687798.8
0
7135381
396601.4
1227393
0
6785659
2.31E+08
1.47E+09
0
1.37E+10
1.78E+08
8.30E+08
0
8.51E+09
3214699
6.45E+07
0
1.69E+09
1.47E+09
6.40E+09
0
7.83E+10
3.57E+08
2.68E+09
0
4.04E+10
6.28E+08
3.20E+09
0
2.73E+10
2.23E+07
2.50E+08
0
4.54E+09
7.36E+07
7.91E+08
0
1.20E+10
Undergrad
interaction term between the dummy variable
for Assumption and the distance variable
cubed
interaction term between the dummy variable
for Worcester State and the distance variable
cubed
interaction term between the dummy variable
for Umass Med and the distance variable
cubed
interaction term between the dummy variable
for Quinsigamond and the distance variable
squared
interaction term between the dummy variable for
the community colleges and the distance variable
interaction term between the dummy variable
for the four year and longer programs and
the distance variable
interaction term between the dummy variable
for non-academic schools and the distance
variable
interaction term between the dummy variable
for academic schools and the distance
variable
dummy variable: 1 if the college is private, 0
if the college is public
number of undergraduate students
Grad
number of graduate students
d_religaff
dummy variable: 1 if the college is religiously
affiliated, 0 if the college is not religiously
affiliated
dummy variable: 1 if the college is a program
of four years or longer, 0 if the college is two
years or fewer
dummy variable: 1 if the college is classified
as liberal arts, 0 if the college is not a liberal
arts school
dummy variable: 1 if the college is a "nonacademic school", 0 if the college is an
academic school
percentage of the students residing in offcampus housing
percentage of the students residing in oncampus housing
Assumptiondistcube
Worcesterstatedistcube
Umassmeddistcube
Quinsigamonddistcube
d_communitcolldist
d_4yeardist
d_nonacademicdist
d_academicdist
d_private
d_4year
d_libarts
d_nonacademic
p_off_campus
p_on_campus
1.71E+09
8.50E+09
0
8.44E+10
1.12E+09
5.79E+09
0
8.30E+10
2.24E+08
1.58E+09
0
1.91E+10
7.88E+08
2.74E+09
0
1.77E+10
222.7197
589.2762
0
2604.93
868.6148
1001.704
0
4386.892
289.5337
724.921
0
4277.911
1190.15
928.7803
0
4386.892
0.6312769
0.4826318
0
1
1543.224
1482.541
0
3982
288.1011
373.9063
0
1004
0.2080344
0.4060473
0
1
0.5502152
0.4976506
0
1
0.510043
0.5000785
0
1
0.197274
0.3980837
0
1
69.10689
38.04495
11
100
30.89311
38.04495
0
89
*Note: "non-academic" schools include: Salter, Rob Roy, Hair in Motion, and Bancroft School of
Massage
27
Table 2- Correlation Matrix for Significant Correlations
totrooms
totrooms
bedrooms
bathrooms
vl_med_own
inc_med_hs
p_tot_occ_hs
p_occ_owner
p_occ_renter
1
bedrooms
0.7828
bathrooms
0.5566
0.457
1
vl_med_own
0.3004
0.2295
0.2954
1
inc_med_hs
0.2008
0.1503
0.2299
0.6038
1
p_tot_occ_hs
-0.0548
-0.0436
-0.0094
0.0037
0.245
1
p_occ_owner
0.0561
0.0227
0.1033
0.4025
0.7742
0.4569
1
p_occ_renter
-0.0657
-0.0292
-0.1096
-0.4223
-0.7829
-0.3565
-0.994
1
1
Table 3- Correlation Matrix for College Variables
d_private
d_private
undergrad
grad
p_off_campus
d_4year
d_libarts
d_nonac.
1
undergrad
-0.2305
1
grad
-0.1987
0.8259
1
p_off_campus
-0.4477
-0.4985
-0.4013
1
d_4year
-0.0574
0.8616
0.6969
-0.7344
1
d_libarts
0.0481
0.8991
0.7555
-0.7962
0.9225
1
d_nonacademic
0.3789
-0.4934
-0.3821
0.4027
-0.5483
-0.5058
28
1
Map 1 – Worcester Block Groups
29
Map 2- Worcester Colleges
The Salter School-Worcester
Quinsigamond Community College
Assumption College
UMass Med
Worcester Polytechnic Institute
Becker College
Worcester State College
Bancroft School Of Massage Therapy
Rob Roy Academy-Worcester
Hair In Motion Beauty Academy
Clark University
College of the Holy Cross
30
Map 3- Worcester Colleges in relation to the Single Family House Sales in 2000
The Salter School-Worcester
(!
(!
Quinsigamond Community College
Assumption College
(!
Worcester Polytechnic Institute
UMass Med
(!
(!
Bancroft School Of Massage Therapy
Becker College
!
(
(!
Worcester State College
(!
(! Rob Roy Academy-Worcester Hair In Motion Beauty Academy
(!
Clark University
(!
College of the Holy Cross
(!
*Houses represented by dots, * Colleges represented by stars,
* Block Groups shown by black lines
31
Table 4 – First run regression
Regression Number of obs = 1394
F(47, 1344) = 22.82
Prob > F = 0
R-squared = 0.6151
Root MSE = 40346
price
dist1
dist1sq
dist1cube
mtg
t_yearbuilt
t_yearbuiltsq
totrooms
bedrooms
bathrooms
lotsize
total_pop
vl_med_own
inc_med_hs
p_pop_white
p_pop_black
p_pop_nat
p_pop_asn
p_pop_other
ptrav5_14
ptrav20_34
ptrav45_59
ptrav_gt90
phsgrad
pbach
p_poplt1_12
p_pop60_gt85
tot_hs_unt
p_occ_owner
p_occ_renter
p_util_gas
p_tank_gas
p_elec
p_fuel
p_coal
p_wood
p_solar
Coef.
Std.err
t
-28.90634**
0.014652**
-2.10E-06**
0.294217***
-355.374***
1.627906*
8739.121***
749.1157
13503.23***
1.023087**
-0.2246738
0.5016043***
0.3016507
832.0357
1207.852**
-2038.844
132.1647
927.0958
335.4461
-19.14247
-722.8094
372.3882
-48.14107
422.6611
-31.45517
313.1355
-8.304475
3593.661**
3620.567**
-3896.682***
-3942.5**
-3988.075***
-3981.261***
-355.948
-1811.334
1827.4
12.53236
0.006808
1.03E-06
0.077051
113.6759
0.885238
1889.661
2788.253
2355.713
0.416871
6.874454
0.113288
0.211827
512.4422
555.7801
2197.948
570.7085
729.0399
313.0824
311.3587
862.5688
1384.403
330.882
361.012
329.7619
307.0879
15.81036
1623.358
1632.174
1485.385
1676.037
1520.413
1509.477
3494.379
2052.896
1208.122
-2.31
2.15
-2.02
3.82
-3.13
1.84
4.62
0.27
5.73
2.45
-0.03
4.43
1.42
1.62
2.17
-0.93
0.23
1.27
1.07
-0.06
-0.84
0.27
-0.15
1.17
-0.1
1.02
-0.53
2.21
2.22
-2.62
-2.35
-2.62
-2.64
-0.1
-0.88
1.51
32
p_other
-6262.768***
2306.535
-2.72
p_own_vhcl_0
-357.8544
265.4306
-1.35
p_own_vhcl_1
36.51372
199.3504
0.18
p_own_vhcl_2
194.7495
185.6174
1.05
p_rnt_vhcl_1
179.8301*
95.255
1.89
p_rnt_vhcl_2
125.6967
134.3859
0.94
p_hs_noplumb
-1755.571
1157.142
-1.52
p_own_noplumb
-213.1511
204.5949
-1.04
p_hs_noktch
955.9639
1203.072
0.79
p_own_noktch
254.711
789.3183
0.32
p_hshld15_64
-43.88334
224.9979
-0.2
_cons
-110573
85302.62
-1.3
Note: *significant at the 90% level, ** significant at the 95% level, *** significant at the 99% level
Table 5.1 - College-Distance Variables, Second Regression 1
Regression Number of obs. = 1394
F( 79, 1315) = 26.40
Prob > F = 0.0000
R-squared = 0.6309
Root MSE = 39961
price
Coef.
Std. Err.
t
mtg
0.287202***
0.074909
3.83
t_yearbuilt
-336.278***
115.496
-2.91
37.23044
-50.9942***
-77.7228
641.9893***
-42.4932*
-20.0464
47.65893
-93.9076*
-13.4585
-95.8464***
-50.5661***
-63.5568**
-0.04682
0.05644
-1.83473***
72.91212
18.6238
54.32912
235.8619
22.59825
29.4129
90.5115
54.22715
23.32681
37.0027
19.08062
25.53092
0.084735
0.0691
0.477568
0.51
-2.74
-1.43
2.72
-1.88
-0.68
0.53
-1.73
-0.58
-2.59
-2.65
-2.49
-0.55
0.82
-3.84
### all other variables###
wpidist
hairinmotindist
Salterdist
Robroydist
Hcdist
Clarkdist
Beckerdist
Bancroft dist
Assumptiondist
Umassmeddist
worcesterstatedist
quinsigamonddist
Wpidistsq
Salterdistsq
Robroydistsq
33
hairinmotiondistsq
0.025865**
0.01145
2.26
Hcdistsq
0.022287
0.017698
1.26
Clarkdistsq
0.012721
0.024777
0.51
Beckerdistsq
-0.08219
0.151979
-0.54
bancroftdistsq
0.065515
0.077141
0.85
assumptiondistsq
0.00135
0.014598
0.09
worcesterstatedistsq
0.020374*
0.011107
1.83
umassmeddistsq
0.087858**
0.038082
2.31
quinsigamonddistsq
0.059418**
0.023559
2.52
Wpidiscube
1.19E-05
2.36E-05
0.5
salterdistcube
-6.22E-06
2.27E-05
-0.27
robroydistcube
0.001102***
0.000246
4.49
hairinmotiondistcube
-3.78E-06**
1.89E-06
-2
hcdistcube
-3.71E-06
3.72E-06
-1
clarkdistcube
-3.01E-06
5.60E-06
-0.54
beckerdistcube
0.000027
6.48E-05
0.42
bancroftdistcube
-1.3E-05
0.000025
-0.52
assumptiondistcube
1.70E-07
2.29E-06
0.07
worcestersstate
-2.00E-06
1.75E-06
-1.14
umassmeddistcube
-2.3E-05**
1.05E-05
-2.16
quinsigamonddistcube
-1.6E-05***
6.18E-06
-2.62
_cons
-153195
96085.49
-1.59
Note: All house and neighborhood characteristic variables were included in the regression, but not shown on the
table. All variables not shown have the similar significance level as the first regression in Table 4. Full results are
vailable upon request.
Note: *significant at the 90% level, ** significant at the 95% level, *** significant at the 99% level
Table 5.2 Coefficient Signs for Statistically Significant College-Distance Variables
Distance
Worcester State
Quinsigamond
Hair in Motion
U Mass Med
0
0
0
0
between
coefficient sign
negative
negative
negative
negative
Rob Rob
Academy
0
positive
distance
distance
sign
1634.430
781.570
1440.956
791.419
between
coefficient sign
positive
positive
positive
positive
5157.926
1694.17
3120.804
1755.195
negative
negative
negative
negative
1263.590
negative
∞
negative
*interpretation for Worcester State:
Coefficient is negative from distance 0 to 1634.430
Coefficient is positive from 1634.430 to 5157.926
Coefficient is negative for all distances over 5157.926
34
Table 6- Grouped College-Distance Variables, Second Regression 2
Regression Number of obs = 1394
F( 52, 1341) = 21.37
Prob > F = 0
R-squared = 0.6154
Root MSE = 40379
price
mtg
t_yearbuilt
Coef.
Std. Err.
t
0.292293***
-354.271***
0.07771
117.3168
3.76
-3.02
### All other Variables ###
d_4yeardist
-11.7131
10.35436
-1.13
d_nonacademicdist
-38.4366***
11.40585
-3.37
d_communitycolldist
-20.616
17.30407
-1.19
d_4yeardistsq
0.003766
0.006605
0.57
d_nonacademicdistsq
0.022581***
0.008451
2.67
d_communitycolldistsq
0.025495
0.019176
1.33
d_4yeardistcube
-2.30E-07
1.08E-06
-0.21
d_nonacademicdistcube
-3.46E-06**
1.49E-06
-2.32
d_communitycolldistbube
-8.49E-06
5.36E-06
-1.58
_cons
-131446
87409.67
-1.5
Note: All house and neighborhood characteristic variables were included in the regression, but not shown on the
table. All variables not shown have the similar significance level as the first regression in Table 4. Full results are
vailable upon request.
Note: *significant at the 90% level, ** significant at the 95% level, *** significant at the 99% level
35
Table 7- College Variables, Third Regressions 1
Regression Number of obs. = 1394
F( 54, 1337) = 20.86
Prob > F = 0
R-squared = 0.6217
Root MSE = 40107
Price
Coef.
Std. Err.
t
dist1
-21.6345*
12.49811
-1.73
dist1sq
0.008861
0.00687
1.29
dist1cube
-1.06E-06
1.06E-06
-1
mtg
0.289417***
0.076748
3.77
t_yearbuilt
-368.745***
113.7281
-3.24
d_private
5143.946
5042.404
1.02
undergrad
-5.09264
4.88857
-1.04
grad
8.163082
13.86759
0.59
p_off_campus
98.04768
220.5341
0.44
d_religaff
-2779.32
14385.28
-0.19
d_4year
-4261.03
6478.868
-0.66
d_libarts
5589.648
14445.91
0.39
-11868.9**
5221.336
-2.27
-157877
90908.06
-1.74
###All other variables###
d_nonacademic
_cons
Note: All house and neighborhood characteristic variables were included in the regression, but not shown on the
table. All variables not shown have the similar significance level as the first regression in Table 4. Full results are
vailable upon request.
Note: *significant at the 90% level, ** significant at the 95% level, *** significant at the 99% level
36
Table 8– College Variables, Third Regressions 2
Regression Number of obs = 1394
F( 52, 1341) = 21.17
Prob > F = 0
R-squared = 0.6195
Root MSE = 40163
price
dist1
dist1sq
dist1cube
mtg
t_yearbuilt
Coef.
Std. Err.
t
-21.0707*
0.008508
-1.01E-06
0.292217
-372.481
12.57311
0.006929
1.07E-06
0.077346
115.0718
-1.68
1.23
-0.94
3.78
-3.24
6875.036
-4.67581**
11.05664*
-83.018
-2037
-13145***
-156087
4492.823
2.08444
5.780309
80.74098
6016.986
4881.313
90947.79
1.53
-2.24
1.91
-1.03
-0.34
-2.69
-1.72
### All other
variables ###
d_private
undergrad
grad
p_off_campus
d_4year
d_nonacade~c
_cons
Note: All house and neighborhood characteristic variables were included in the regression, but not shown on the
table. All variables not shown have the similar significance level as the first regression in Table 4.
Note: *significant at the 90% level, ** significant at the 95% level, *** significant at the 99% level
37