Term Paper – 2015
ECO 5520/5930/6520/Honors
It’s early, but …
• Several have asked.
• Deliverable is due Monday, March 30.
• Paper is due Wednesday, April 22.
Housing – Why is it Different?
• Why?
– Housing is heterogeneous
– Housing is immobile
– Housing is durable
– Housing is expensive
– Moving costs are high
– Neighborhood comes with housing … and it
matters!
3
Heterogeneous?
• Dwellings differ in:
– house size (sq. feet)
– lot size (sq. feet)
– configuration
– quality
• People seem to value these qualities
differently.
4
Immobile?
• It is where it is. Where you buy it, you get:
– Accessibility (to good and bad things)
– Package of local public services
– Environmental quality
• Further
– You can’t (really) “move” houses
– You can’t rebundle them (use half of two
different houses at the same time).
5
Price: The Hedonic Approach
• Hedonic approach looks at house as a
bundle of components.
• Analogy: Suppose that when you went to
the grocery store, all you could buy were
“filled” shopping carts (food, soaps, etc.),
and each one had a price.
• You know what’s in them, but you can’t
take things out or put things in.
6
Price: The Hedonic Approach
• How do you figure out what the individual
components are worth?
• A> If you had a large sample of carts, and
each had different amounts of goods in
them, then you could come up with the
value of the individual components.
7
• Suppose that
sq. feet of
living space
was ALL that
mattered in
the price of
house.
• You collect
data on lots of
houses.
Price
Example for Hedonic Prices
Sq. feet
8
• What does
this suggest?
– A> Bigger
houses have
more value.
Price
Example for Hedonic Prices
?
?
• Let’s draw a
line.
Sq. feet
9
Example for Hedonic Prices
• What does a
mean?
• What does b
mean?
slope = b
Price
• Line has a
form:
Price = a +
b*size
a
Sq. feet
10
Example for Hedonic Prices
• Although it is
hard to think of,
we could draw
this diagram in
n dimensions!
slope = b
Price
• Says that for
each additional
sq. ft., house
price is $b more.
a
b is the hedonic
price of house size.
Sq. feet
11
n dimensions?
Let’s look at a
house with
2000 sq.ft., 5
rooms for
$75,000
Let’s look at a
house with
3000 sq.ft., 6
rooms for
$100,000
Price
100
75
2000
3000 Sq. feet
5
6
12
Line has a form: Price = a + b*size + c*rooms
Your Task
• Use this analysis to say something about
taxes and what they buy.
• We have over 125,000 housing
transactions for Ohio for 1999.
• They have
– House variables
– Neighborhood variables
– Tax variables
Hypotheses
• Let’s look at the value of a house.
• All else equal, what do you think will be
the impact of better services?
• All else equal, what do you think will be
the impact of higher taxes?
Here is an example !!!
• Dependent variable is log of the
transactions price
• When we write
Log P = b0 + b1X1 + b2X2 + e
The coefficient of X is the percentage
change in P brought about by a one unit
change in X.
Here is an example
Variable
DF
Parameter Estimates
Parameter
Estimate
Standard
Error
t Value
Pr > |t|
Intercept
lotsize
Lot size in square feet
bedrooms
Number of bedrooms
brick
1 if brick; 0 otherwise
fullbath
How many full bathrooms
agehouse
Age of house in years
buildingsqft
Building square feet
effmills_sd
Effective tax rate in mills – What’s a mill?
cprate_sd
Percent in college prep track
Cincinn
1 if Cincinnati metro; 0 otherwise
Cleveland
1 if Cleveland metro; 0 otherwise
Columbus
1 if Columbus metro; 0 otherwise
Dayton
1 if Dayton metro; 0 otherwise
Toledo
1 if Toledo metro; 0 otherwise
Ytown
1 if Youngstown metro; 0 otherwise
One mill is
equivalent to onetenth of a cent or
$0.001.
Here is an example
Parameter Estimates
Variable
DF
Parameter
Estimate
Standard
Error
t Value
Pr > |t|
Intercept
1
10.92321
0.00909
1201.13
<.0001
lotsize
1
6.423393E-7
1.7071E-8
37.63
<.0001
bedrooms
1
0.01025
0.00177
5.80
<.0001
brick
1
0.08339
0.00253
32.94
<.0001
fullbath
1
0.10724
0.00255
41.99
<.0001
agehouse
1
-0.00434
0.00004152
-104.57
<.0001
buildingsqft
1
0.00039912
0.00000222
180.14
<.0001
effmills_sd
1
-0.00148
0.00020849
-7.08
<.0001
cprate_sd
1
0.00108
0.00003478
30.92
<.0001
Cincinn
1
0.02079
0.00458
4.54
<.0001
Cleveland
1
0.12323
0.00432
28.56
<.0001
Columbus
1
0.02669
0.00436
6.13
<.0001
Dayton
1
-0.12256
0.00513
-23.89
<.0001
Toledo
1
-0.05452
0.00533
-10.22
<.0001
Ytown
1
-0.20326
0.00656
-30.98
<.0001
Here is an example
Parameter Estimates
Variable
DF
Parameter
Estimate
Standard
Error
t Value
Pr > |t|
Intercept
1
10.92321
0.00909
1201.13
<.0001
lotsize
1
6.423393E-7
1.7071E-8
37.63
<.0001
bedrooms
1
0.01025
0.00177
5.80
<.0001
brick
1
0.08339
0.00253
32.94
<.0001
fullbath
1
0.10724
0.00255
41.99
<.0001
agehouse
1
-0.00434
0.00004152
-104.57
<.0001
buildingsqft
1
0.00039912
0.00000222
180.14
<.0001
effmills_sd
1
-0.00148
0.00020849
-7.08
<.0001
cprate_sd
1
0.00108
0.00003478
30.92
<.0001
Cincinn
1
0.02079
0.00458
4.54
<.0001
Cleveland
1
0.12323
0.00432
28.56
<.0001
Columbus
1
0.02669
0.00436
6.13
<.0001
Dayton
1
-0.12256
0.00513
-23.89
<.0001
Toledo
1
-0.05452
0.00533
-10.22
<.0001
Ytown
1
-0.20326
0.00656
-30.98
<.0001
How well does it fit?
Analysis of Variance
Number of Observations Read
101234
Source
Number of Observations Used
Number of Observations with
Missing Values
Root MSE
Dependent Mean
R-Square
Adj R-Sq
Coeff Var
DF
Sum of
Squares
Mean
Square
F Value
Pr > F
11973.0
<.0001
94010
Model
14
18235
1302.52
Error
93995
10226
0.10879
Corrected
Total
94009
28461
7224
0.3298
11.6561
0.6407
0.6407
2.8297
But …
• I’m sure you can do better than that.
Topic: Data-based Finance
Analysis
• I am creating a file for your use.
• You will create hypotheses and test them.
Topic: Data-based Health Analysis
• Student will form null hypotheses.
• Test them using appropriate data analysis.
• Write up findings in a format to be
provided by professor.
Specifics
• ECO 5520 – No less than 10 pages; no
less than 5 references from scholarly
journals.
• ECO 5930/6520 – No less than 15 pages;
no less than 10 references from scholarly
journals.
– Wikipedia is not a scholarly journal.
– Fisher is not a scholarly journal.