Question

Assume you are interested in housing prices. You believe that the number of bedrooms will impact the price of a home. Let bedroom, represent the number of bedrooms in home i, and price, its price (in dollars). In this case the unit of observation is the home. a) Write the population model of interest. b) Name two factors not accounted by the model that could affect the price a home, i.e. two factors that are in the error term. Briefly explain why you believe these factors would affect your dependent variable b) Assume the following random sample from the Toronto housing market (bedroom,price): i-1,,4)) (3,500000), (2,300000), (7, 1000000), (4,600000)) Using the OLS estimator, calculate estimates of the intercept and the slope parameter. Yoru are to do it by hand and show your work d) Interpret the estimate of interest. e) Graph (to scale) the sample points, and the estimated line. Put the ber of bedrooms on the horizontal axis and the price on the vertical axis.

Answer 1

The econometric model for housing prices is a simple linear regression model with price of home as dependent variable and number of bedrooms as independent variable.It can be represented as Price = f(Bedrooms).

(a)

The econometric model for housing prices is a simple linear regression model with price of home as dependent variable and number of bedrooms as independent variable.It can be represented as Price = f(Bedrooms). (a) The population model of interest is price = beta_0 + beta_1 Bedrooms + E The sample model is price i = beta_0 + beta_1 Bedrooms i_1 + E_i (b)The factors that could affect the price of home are garage and age of the house.The presence of garage affects the price of home positively. A house with garage is mostly desired and hence commands high price.The age of a house would have a negative relationship with house price since an old house commands a lower price compared to a newly built house. (c) (b) price^^= beta^^_0 + beta^^_1 Bedrooms estimated The above is the regression line. Beta^^_1 = sigma^4_i = 1 (Bedroom_i minus Bedrooms) (price_1 minus price)/sigma (Bedrooms minus Bedrooms)^2

(b)The factors that could affect the price of home are garage and age of the house.The presence of garage affects the price of home positively. A house with garage is mostly desired and hence commands high price.The age of a house would have a negative relationship with house price since an old house commands a lower price compared to a newly built house.

(c)

(d)Here, the estimate of interest is the slope of regression line,$\beta_{1}$. The slope indicates that on an average the price of home increases by $135714, for every additional bedroom that the house contains.

(e)