Georgia Real Estate Data

Introduction of Data

This data set that I chose deals with Georgia Real Estate.I chose this data set because I have family that lives down in Georgia and I was curious on the results that I could find on different properties. The data set was full of interesting variables that could be looked into to see if any of them played a role in affected any of the other ones. The main ones that I worked with were zip code, Price per Square Foot, Home Type, Bedrooms, Living Area Value, and Building Area.

Count of Number of Homes by Type

From this bar graph we see that single family homes have the most at just over 8,000 properties and multifamily has the least with condo and townhouse just above. These three that have the least are all under 1,000.

Price of Properties by Price Per Square Foot by Home Type

Single family, condo, and townhouse are mostly clustered together and have similar prices. Whereas lot and multifamily are more spread out and have more variance. There seems to be a lot of properties with the Price per Square Foot being $0. This would have to be looked into further. Maybe just has not simply been put into the date yet.

Building Area Compared to the Living Area Value

Again, we see a cluster of a Building Area of 0. This could be that no one can build on the land yet or some other technicality. There are two different area units, acres and square foot so those are there respective colors. The main cluster shows that as the living area value increases the building area available is also increasing, which makes sense. The more land you have to building on the bigger the house can be.

Price per Square Foot and Living Area Value By Zip code

This graph results in a huge cluster with each point being relatively close to one another. It does not seem that one zip code has more expensive houses over the others which to me is somewhat shocking. I would think that different parts of the state would be consider “richer” and others “poorer” and we would see that when it came to breaking it down by zip code.

Number of Bedrooms and Type of Home Influencing Price?

I was thinking that the more bedrooms a property had the higher its value would be. The Lot type of property has a significant amount that have no bedrooms, which makes sense because people would build on that land. Multifamily has an outlier that has 999 bedrooms (was this just a number put in?) that skews that data facet. Single family and townhouses rise in price as the number of bedrooms rises.