TO: Dr. Janet Fraser

FROM: Veronique Nedeau

Nicholas Gmernicki

Hanna Traggiai

DATE: September 30, 2023

SUBJECT: Home Sale Pricing Trends in Ames, IA

 While there is no single variable that will determine the value of a home, there are variables that have more bearing than others. Accordingly, Wise Group Analytics decided to focus on the following variables to develop an effective predictive model:

• Overall Quality of the Build: Quality of the home construction can factor into long term costs and maintenance that homeowners must perform. A home with a higher quality will likely not need as much maintenance in the short term as a low-quality home, thus higher quality homes may sell at a higher price.

• Square Footage: Size may be a major factor in home choice, due to the needs of the purchaser(s). A larger home may be needed if the purchaser(s) has a large family or a smaller home if the purchaser is an individual.

  • • Note: Ground Living Area is equated with Square Footage throughout this analysis. Ground Living Area is defined as: Above grade (ground) living area square feet. Definition found here (move to Citations Section, add citation)

• Year Remodeled/Additions: Remodels and additions can bring value to the home with updated floor plans, designs, and more space. More modern materials, appliances, and amenities may increase the appeal of an older home.

• Lot Area: Generally speaking, land is a finite resource, leading to a certain level of scarcity.  As populations grow within a region, land is purchased to develop communities or businesses, thus demand for land increases. This balance between supply and demand can be directly attributed to increased home sale prices due to the basic requirement that homes require land to be built on.

After investigating multiple models with different variables, the model was chosen based on the strong collinear relationship between the variables mentioned above (Figure 1). Wise Group Analytics noted the possible redundancy by including both square footage and lot area, where lot area includes the land the home is on. However, further investigation yielded results that demonstrated removing either of the variables reduced the strength of the model. It was determined that since square footage included living area above grade (above ground level) it was not necessary to remove either variable from the model.

The results of the model show that for every additional point gained in overall quality, there is a 17 percent increase in the sale price of the home (Table 1). An increase in lot area by 1 percent leads to a 15 percent increase in home sale price. For every year the remodel is done closer to the sale date, there is a 0.3 percent increase in the sale price. An additional square footage of living space (above grade level), there is a 0.003 percent increase in sale price.

The independent variables contained within the linear regression model discussed throughout this memo, are pertinent determinants of model performance. The Wise Group Analytics team determined overall quality, year remodeled, square footage, and lot area to have the greatest influence on home sale price in Ames, IA between the years of 2002-2011. The independent variables within the model are also known as predictor values because they are used to make predictions. The coefficients for each of these variables indicate how much the dependent variable is expected to change relative to a one-unit change in the predictor values. Before the team at Wise Group Analytics made a final decision regarding the variables to be included within their prediction model, additional statistical tests were performed to ensure significance.

• De Cock, D. (2011). "Ames, Iowa: Alternative to the Boston Housing Data as an End of Semester Regression Project," Journal of Statistics Education, Volume 19, Number 3.