In the analysis of housing prices, researchers often specify an econometric model of the following form: 5. Iprice, = B, + B,S, + ByN; + BspSD, +€, where Iprice, denotes the log of the sale price of house i, S, is a set of structural attributes of house i (such as living area, presence of a view, and number of bathrooms), N, is a set of neighborhood/community characteristics (such as the crime rate and air quality), SD,is a set of school district dummy variables that take the value of one if the ith house is located in that school distriet and €, is a random disturbance term. In a recent study, this specification was used to estimate the determinants of housing values using data on homes that sold within Los Angeles and Orange County. Definitions of the variables used in the study are reported below. Variable Definition Units of Measurement Structural Attributes PRICE Sale Price Dollars BATH Number of Bathrooms Number LIVAREA Interior Living Space Square Feet POOL Presence of Pool Zero/One VIEW Presence of a View Zero/One Neighborhood/Community Attributes BEACH Distance to Nearest Beach Miles BEACHSQ Distance to Beach Squared Miles CRIME Per Capita FBI Crime Index Crimes/Population TWORK Time to Work Minutes AIRQ Air Quality Miles of Visibility SCHOOL DISTRICT Location in Specific School Districts Zero/One Estimated Hedonic Equations Dependent Variable = Ln(Home Sale Price) Variable (Coefficient) Coefficient (St. Error) Structural Attributes BATH ( BI) .06 (.002) POOL (B2) .07 (.05) LIVAREA (B) 00035 (000002) VIEW (B4) .09 (.01) Neighborhood/Community Attributes BEACH (Bs) -015 4002) BEACHSQ (B6 ) 0.0002 (.0001) CRIME (B7) -.001 (.0001) TWORK (B) -.015 (.001) AIRQ (B») .02 (.001) School District Beverly Hills (Bi0) 39 (.002) Compton ( B1) 22 (.06) Orange (B12) .07 (05) Laguna Beach ( B13) .43 (.10) INTERCEPT ( Bo) 12.79 R-Square .55 Number of Observations 41,852
In the analysis of housing prices, researchers often specify an econometric model of the following form: 5. Iprice, = B, + B,S, + ByN; + BspSD, +€, where Iprice, denotes the log of the sale price of house i, S, is a set of structural attributes of house i (such as living area, presence of a view, and number of bathrooms), N, is a set of neighborhood/community characteristics (such as the crime rate and air quality), SD,is a set of school district dummy variables that take the value of one if the ith house is located in that school distriet and €, is a random disturbance term. In a recent study, this specification was used to estimate the determinants of housing values using data on homes that sold within Los Angeles and Orange County. Definitions of the variables used in the study are reported below. Variable Definition Units of Measurement Structural Attributes PRICE Sale Price Dollars BATH Number of Bathrooms Number LIVAREA Interior Living Space Square Feet POOL Presence of Pool Zero/One VIEW Presence of a View Zero/One Neighborhood/Community Attributes BEACH Distance to Nearest Beach Miles BEACHSQ Distance to Beach Squared Miles CRIME Per Capita FBI Crime Index Crimes/Population TWORK Time to Work Minutes AIRQ Air Quality Miles of Visibility SCHOOL DISTRICT Location in Specific School Districts Zero/One Estimated Hedonic Equations Dependent Variable = Ln(Home Sale Price) Variable (Coefficient) Coefficient (St. Error) Structural Attributes BATH ( BI) .06 (.002) POOL (B2) .07 (.05) LIVAREA (B) 00035 (000002) VIEW (B4) .09 (.01) Neighborhood/Community Attributes BEACH (Bs) -015 4002) BEACHSQ (B6 ) 0.0002 (.0001) CRIME (B7) -.001 (.0001) TWORK (B) -.015 (.001) AIRQ (B») .02 (.001) School District Beverly Hills (Bi0) 39 (.002) Compton ( B1) 22 (.06) Orange (B12) .07 (05) Laguna Beach ( B13) .43 (.10) INTERCEPT ( Bo) 12.79 R-Square .55 Number of Observations 41,852
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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Hi, Based on the attached:
1. Test the hypothesis that housing prices decrease at a decreasing rate as one moves farther away from the beach. Show all work.
2. Provide a graphical illustration of the implied relationship between log price and distance to the beach.
Transcribed Image Text:In the analysis of housing prices, researchers often specify an econometric model
of the following form:
5.
Iprice, = B, + B,S, + ByN; + BspSD, +€,
where Iprice, denotes the log of the sale price of house i, S, is a set of structural attributes of
house i (such as living area, presence of a view, and number of bathrooms), N, is a set of
neighborhood/community characteristics (such as the crime rate and air quality), SD,is a set of
school district dummy variables that take the value of one if the ith house is located in that school
distriet and €, is a random disturbance term.
In a recent study, this specification was used to estimate the determinants of housing values using
data on homes that sold within Los Angeles and Orange County. Definitions of the variables used
in the study are reported below.
Variable
Definition
Units of Measurement
Structural Attributes
PRICE
Sale Price
Dollars
BATH
Number of Bathrooms
Number
LIVAREA
Interior Living Space
Square Feet
POOL
Presence of Pool
Zero/One
VIEW
Presence of a View
Zero/One
Neighborhood/Community
Attributes
BEACH
Distance to Nearest Beach
Miles
BEACHSQ
Distance to Beach Squared
Miles
CRIME
Per Capita FBI Crime Index
Crimes/Population
TWORK
Time to Work
Minutes
AIRQ
Air Quality
Miles of Visibility
SCHOOL DISTRICT
Location in Specific School Districts
Zero/One
Transcribed Image Text:Estimated Hedonic Equations
Dependent Variable = Ln(Home Sale Price)
Variable (Coefficient)
Coefficient (St. Error)
Structural Attributes
BATH ( BI)
.06 (.002)
POOL (B2)
.07 (.05)
LIVAREA (B)
00035 (000002)
VIEW (B4)
.09 (.01)
Neighborhood/Community Attributes
BEACH (Bs)
-015 4002)
BEACHSQ (B6 )
0.0002 (.0001)
CRIME (B7)
-.001 (.0001)
TWORK (B)
-.015 (.001)
AIRQ (B»)
.02 (.001)
School District
Beverly Hills (Bi0)
39 (.002)
Compton ( B1)
22 (.06)
Orange (B12)
.07 (05)
Laguna Beach ( B13)
.43 (.10)
INTERCEPT ( Bo)
12.79
R-Square
.55
Number of Observations
41,852
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