1 1 v Based on what we reviewed in lecture, a hedonic price model is a regression model that identiﬁes the
value of a differentiated product (a residential house in this case) to its internal factors. For this assignment, you will construct both a simple and a multiple hedonic model for residential housing
and estimate its parameters using real Canadian data. Do bigger houses sell for higher prices? To answer this question empirically, we will construct a simple
hedonic value model for an unspeciﬁed Canadian city using real data on 832 residential houses that were
sold in this city during a particular calendar year. These data are reported in the Excel data ﬁle
h0usemodelhedonic.xls. For this hedonic data set, the following variables are measured:
1- Each residential house has been identiﬁed with (ID).
2- Price of the residential housing (VALUE), measured in thousands of Canadian dollars. 3- Area of the house as lot size (LOT), measured in acres. 4- Time of the year that the house was sold, (QUARTER). [QUARTER =1 denotes the ﬁrst quarter
(Winter); QUARYER = 2 denotes the second quarter (Spring); QUARTER = 3 denotes the third
quarter (Summer); and QUARTER = 4 denotes the fourth quarter (Fall)]. Moreover, to augment LOT with the purpose of debunking more of the omitted variables, we need to
construct a multiple linear regression (question 10), with four (4) additional regressors, namely: 5- Number of bedrooms (BDRM), measured in units.
6- Number of bathrooms (BTHRM), measured in units.
7- Number of parking spaces in the driveway (DRIVEWAY), measured in units. 8- A binary (dummy) variable representing the house’s basements (BSMT; BSMT=1 if the house has
basement and BSMT=0 if the house does not have basement). Activity numbers 1 to 9 in here focus on the construction, components and applications of simple regression
models. On activity #10, you will run a multiple regression model of VALUE on all important variables using data
ﬁom the spreadsheet.