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.

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.)

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

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 $257,200. Compute RMSE for the base model. (Round intermediate calculations to at least 4 decimal places and your final answer to 2 decimal places.)

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

d. Which predictive model is the better-performing model?

House	Actual Price	Predicted Price 1	Predicted Price 2
1	230332	253890	255837
2	209977	215463	223331
3	258810	239836	228050
4	185482	203957	219368
5	168976	157405	159410
6	350451	325918	339676
7	399919	423789	452410
8	309828	324640	305393
9	195338	180572	193752
10	329020	340125	324503