Flipping Properties: GIS Can Show You Where

Developed a new project for my graduate real estate students to chew on this semester. Working in teams of 2 to 4, the students need to identify the top two zip codes in the D/FW Metroplex to invest in residential properties in order to flip them over.

<-- Map showing the location of every residential sale in 2004.

I organized the course around three projects. The first project was focused on data acquisition and creation. The second project, this property flipping project, is focused on site selection and analysis. The third project will be a full market research project for a proposed new hotel development in the entertainment district of Arlington, TX.

So, what would make an area more suitable than another to invest in the residential real estate market? Well, these students must consider the following three variables:

1. Days on the market. This is the average number of days properties take to sell once the property is listed.
2. Property value change. This explores how much the values of the properties are increasing or decreasing.
3. Population change. This explores how much the population is increasing or decreasing.

The students then need to add a minimum of three additional variables and use ESRI's Spatial Analyst extension to identify the top two zip codes.

So, how can GIS help?

• First we had to geocode the local MLS data (which created the map above).
• Playing with MLS data in GIS is so much fun. The students were provided with a table containing every property sale in 2004 in the metroplex. There are over 45,000 sales during this year. For each sale, the address, days on the market, list price, sale price, interest rate, mortgage amount, and many, many more variables beyond what is normally in a standard appraisal district file.
• Second we had to aggregate the average number of days on the market and the average selling price during the year to the block group level.
• These attributes were aggregated so they could be correlated and analyzed with various demographic data on the block group level.
• Third the teams are all going in various directions with the project.
• Some are manipulating the datasets to explore fluctuations over the year. Some are acquiring additional sources to determine the number of vacant units and the number of nearby rental opportunities.
• Fourth the teams will have to use suitability analysis to locate the best spots that match their criteria.

Project is due in two weeks and I am eagerly looking forward to see what they come up with.

I have been pondering the idea of tailoring this project into a 2-hour workshop because of the attractiveness of performing this type of research. However, we can not distribute the MLS data and I would hate to hold a workshop and then tell everyone that they could not go out and repeat what they just learned how to do. Ah well...I'm still trying to think of some way to do it.