2.  Home value will be most affected by square footage, acreage, and property type  A bigger house will be more expensive than a smaller one  A house in an area zoned for farming will be less expensive per acre than a house zoned in a residential area.

3.  Variables Used: ◦ Adjusted sales price ◦ Land size ◦ Effective year built ◦ Living area ◦ Bedrooms ◦ Full bathrooms ◦ Half bathrooms ◦ Zone ◦ Stat Class (Residence Type) ◦ Study Area ◦ Zones  Models Tried  KNN  Regression  General Linear Regression  CHAID  Neural Networks  CART  SMV

4.  The Auto Numeric node was used to develop the 7 models  The RMS Error was used to shortlist the top 3 models:

10. Stat Class SA_3.0Living Area Land Size Full Bath BedRes. Zone Yr. Built House 1131114800.1532311977 House 2143131480.2373411980 Zillow.comRegressionKNNNeural Networks House 1$152,500$153,015$157,550$162,239 House 2$281,506$288,429$271,470$231,136

11.  2 main errors: ◦ Overpriced houses  Not of major concern as house prices can be brought down by gauging market interest ◦ Underpriced houses  Top 3 models underpriced houses by 5%, 36% of the time

12.  Use the model for different counties  Refinement of current model by adding appraised values  Make KNN more user-friendly

13.  We recommend the regression model  Generally, our hypothesis was accurate ◦ Variables seen as most important were square footage and lot size ◦ Certain property types were also seen as important  Half bath was seen as an important variable  Zoning was not seen as a very important variable in indicating house prices