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Two ANOVA for real estate data

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When purchasing real estate a buyer may have a number of important features they need in a home. Total square footage, number of bedrooms, bathrooms, attic or basement may be requirements when purchasing a home. A real estate agent collects information from surrounding home sales and compares features to price homes according to what the area selling prices are. Buyers expect to get the most features for their money. A buyer is browsing the real estate section to see what homes are on the market. What is the highest price home with 3 bedrooms and 2 bathrooms? Research was done in three different townships (3, 4 and 5 according to the Real Estate Data Set) and 2 different square feet homes (2100 and 2300). Does this chart demonstrate the square feet and township are significant predicators in the price of home?

Township
Square Feet 3 4 5
2100 \$292,400 \$182,400 \$209,300
2300 \$242,100 \$207,500 \$263,100

Hypothesis:
Price of home=f (square feet, township)
Linear Model:

Square Feet (square feet has no effect)
H :
H : Not all the A are equal to zero

Township (township has no effect)
H
H Not all the B are equal to zero