ECON 2P91: Business Econometrics with Applications
Fall 2010
Assignment 3 (Due Date: Friday November 26th 2010) 
SOLUTIONS
In assignments1 and 2, you investigated the relationship between residential housing
price and lot size by constructing hedonic price models for the City of Prince George,
British Columbia, Canada. In this assignment, you will refine the previous models
with a focus on the following questions. First, are there seasonal differences in
residential housing prices? Second, is the relationship between lot size and price
linear?
For this assignment, you will employ the data for 1011 residential houses sold in
Prince George during the period from the first quarter of 2001 to the first quarter of
2002. As in the previous assignments, LOT denotes lot size (acres), PRICE is the
residential housing price (thousands of dollars). We have added a binary variable
SSUMMER
(
1
=
SSUMMER
if the house was sold in Spring/Summer and
0
=
SSUMMER
otherwise).
ID
is the housing identification variable. The relevant
data are provided in the Excel data file
princegeorge_housing3.xls
, which is available
on Sakai.
1.
Run the regression
i
i
i
i
u
SSUMMER
LOT
PRICE
+
+
+
=
2
1
0
β
β
β
(recall that
SSUMMER
is a binary variable as defined above). Report your answer in the
format of equation 5.8 (Chapter 5, p. 152) in the textbook including
2
R
and
the standard error of the regression (SER). Interpret the coefficient of LOT.
Also, interpret the coefficient of the binary variable
SSUMMER
.
Model 2: OLS estimates using the 1011 observations 11011
Dependent variable: price
VARIABLE
COEFFICIENT
STDERROR
T STAT
PVALUE
const
113.764
1.91663
59.356
<0.00001 ***
lot
1.33425
0.204869
6.513
<0.00001 ***
SSUMMER
8.00840
2.71371
2.951
0.00324 ***
Mean of dependent variable = 119.911
Standard deviation of dep. var. = 44.1686
Sum of squared residuals = 1.87356e+006
Standard error of residuals = 43.1126
Unadjusted Rsquared = 0.04913
Adjusted Rsquared = 0.04724
Fstatistic (2, 1008) = 26.0417 (pvalue < 0.00001)
Loglikelihood = 5238.26
Akaike information criterion (AIC) = 10482.5
1
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Schwarz Bayesian criterion (BIC) = 10497.3
HannanQuinn criterion (HQC) = 10488.1
i
i
i
SSUMMER
LOT
CE
I
PR
0085
.
8
33425
.
1
764
.
113
ˆ
+
+
=
R
2
=0.049
SER=43.113
(1.91663)
(0.204869)
(2.71371)
Interpreting the LOT coefficient: A 1 unit (i.e. 1 acre) increase in lot size will lead to a
1.33425 (i.e. $1,334.00) increase in price, controlling for other variables.
Interpreting the SSUMMER coefficient: The mean price of a house sold in
spring/summer is 8.0084 units (i.e. $8,008.40) higher that that for a house sold in spring/
summer, controlling for lot size.
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 Fall '09
 Ogwang
 Econometrics, Regression Analysis, Null hypothesis, SSUMMER

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