A real estate company has built two predictive models for estimating the selling price of a house. Using a small test data set of 10 observations, it tries to assess how the prediction models would perform on a new data set. The following table lists a portion of the actual prices and predicted prices generated by the two predictive models.

House	Actual Price	Predicted Price 1	Predicted Price 2
1	230500	254000	256000
2	209900	215500	223400
3	258900	240000	228000
4	185500	204000	219400
5	169000	157500	159400
6	350500	325800	339800
7	399900	423600	452500
8	310000	324500	305500
9	195500	180750	193750
10	328900	340000	324500

a. Compute the ME, RMSE, MAD, MPE, and MAPE for the two predictive models. (Round intermediate calculations to at least 4 decimal places and your final answers to 2 decimal places. Negative values should be indicated by a minus sign.)

	Model 1		Model 2
RMSE
ME
MAD
MPE		%		%
MAPE		%		%

b. Are the predictive models over- or underestimating the actual selling price on average?

The predictive models

underestimate / overestimate the actual selling price.

c-1. Compare the predictive models to a base model where every house is predicted to be sold at the average price of all the houses in the training data set, which is $260,500. Compute RMSE for the base model. (Round intermediate calculations to at least 4 decimal places and your final answer to 2 decimal places.)

	RMSE

c-2. Do the predictive models built by the real estate company outperform the base model in terms of RMSE?

The predictive models. outperform/ do not outperform the base model.

Which predictive model is the better-performing model?

the first / or the 2nd is the better-performing model.