Executive Summary
In this case, we condensed a large amount of data into a smaller, more useful data set. Jones Lang LaSalle (JLL) gave us sample data for 12 data points on 4,284 homes in Marion County (Indianapolis), Indiana. The firm wanted us to take these 12 attributes and combine them, based on similarities, into a smaller number of categories which would prove easier to make sense of, and thus make them more useful for analysis.
To accomplish this, we used a series of Simple Linear Regression (SLR) tests to determine which sets of variables were related to each other, and to what degree. This gave us a preliminary set of new attributes, although with a low R2. Finally, to confirm our groupings, and because of the lower R2, we ran a Correlation table of all the attributes.
We arrived at a conclusion of five new attributes:
1. Best Home Value – Incorporates Assessed Value, Annual Property Taxes, Annual Insurance, Neighborhood Association, and Policy Against Chain Link Fence.
2. Room to Grow – Incorporates Public School Quality, Square Footage, and Acreage.
3. Local Amenities – Proximity to Shopping and Proximity to Healthcare.
4. Age of House – Standalone category, no strong relationship
5. Closest Park – Standalone category, no strong relationship

We then suggested other data points that JLL could collect to strengthen some of the new categories.

Introduction
Jones Lang LaSalle (JLL), a firm that provides real estate services to a worldwide client base, has aggregated a wealth of data from real estate markets throughout the world. Their services are mainly of the commercial real estate variety, however, their expertise extends into residential markets as well. As such, JLL’s research is associated with a great range of issues related to real estate. In this specific case, they have a data set containing information about 4,284