You are very interested in how to predict housing prices, as you are considering

Question

You are very interested in how to predict housing prices, as you are considering starting a housing price analysis firm. OLS, using observations 1 - 7 6 Dependent variable: Price Heteroskedasticity-robust standard errors, variant HC1 What variables are significant? Of those that aren't, why might they not be? How much of the variation in Price is explained by this model? Predict what the price will be for a 2200 sq ft, 5000 sq foot lot, 2 Bath, 4 Bed, 2 Garage Home, built in 1976, and zoned to Harris Elementary? What would be the price be if zoned to Edgewood instead.

Explanation / Answer

a) Considering an alpha = 0.05 , all variables who p values are less than 0.05 are considered signifcant in the regression output. please refer to the o/p table and . The variables are

const, lotsize, bed, garage and Edgewood versus .. they are also marked with an "asterisk"

b) The variables that are not able to explain the variation in the model are considered to be non significant , those are

sqft , bath , year ,,, this is primarliy because they do not statistically contribute to explaining the variation of the model

c) Look at the r square value , which is 0.304045 , this means that model is able to explain on 30.40% of the variation in the data

d) The regression equation can be formed using the coeffecients of the variables as

Y = 1243.96 + 49.46*sqft +8.3*Lotsize + 12.5*Bath -18.06*Bed -0.55*year +18.53*Garage+ 45.28*Edgewood

putting the values in the given equation

1243.96 + 49.46*2200 +8.3*5000 + 12.5*2 -18.06*4 -0.55*1976 +18.53*0+ 45.28*0 = 150421

e) if edgewood is present

then the equation becomes

1243.96 + 49.46*2200 +8.3*5000 + 12.5*2 -18.06*4 -0.55*1976 +18.53*0+ 45.28*1

= 150467

so the price increases if edgewood is present by the coefficeint value which is 45.28

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