You wish to predict the sale price of single-family residences in Massachusetts

Question

You wish to predict the sale price of single-family residences in Massachusetts using property features (commonly called a “hedonic pricing model”). You collect price and property features data on each property sold in the state for the years 2008 – 2014.

A. [6 points] How would you categorize, or label, this dataset? Defend your answer. B. [6 points] Using the above data, you plan to estimate the following model:
Pricei = 0 + 1*lotsizei + 2*houseagei + 3*bedroomsi + 4*bathroomsi + i Where:

lotsize = size of the house (in square feet) houseage = age of the house (in years) bedrooms = number of bedrooms in the house bathrooms = number of bathrooms in the house

What sign would you expect each of these coefficients to take? Explain why.

C. [8 points] After running the regression, you discover that both bedrooms and bathrooms are statistically insignificant at the = .1 level. What problem might there be with your equation that could explain this occurring? How could you fix this problem and how else could you test for the statistical significance of bedrooms and bathrooms?

Explanation / Answer

A) This data set is a hosing price model here variables are house size, bedroom size, bath room size and house age.

From the regression equation we have to see how these variables affedt the house price.

b) Except B2 all the B will be postitve since usually all of them are positively related with house price.

c) If some variables are not statistically significant but still we use it in equation then it affect estimator values in this case due to use of these insignificant variable house price will be inflated.(since B for these variables are positive).

For checking significance we can use R square and P value for the result.

Related Questions

Navigate

• Browse (All)
• Browse Y
• Subjects

Integrity-first tutoring: explanations and feedback only — we do not complete graded work. Learn more.