 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.

 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

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

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

 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

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

 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