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Or the home you would like to buy. Using Zillow forecasts, I obtained the following predictions, clearly indicating strong recovery for the West Coast, according to Zillow.
You can easily retrieve this data by entering a city on Zillow's location box, then clicking on any home for sale on that city. The task could be automated with a web crawler or using an intern. And once you gathered data for 1,000 locations you can then interpolate for all 30,000 zip codes.
Expected home value increase 12 months from now:
Sacramento | 13.6% |
Phoenix | 9.8% |
Los Angeles | 9.7% |
San Francisco | 9.6% |
San Diego | 8.1% |
Seattle | 6.9% |
San Jose | 6.4% |
Portland | 6.0% |
Washington, DC | 5.9% |
Austin | 4.8% |
Boston | 3.9% |
Atlanta | 3.8% |
Oklahoma City | 3.3% |
Denver | 3.2% |
Minneapolis | 2.5% |
Pittsburg | 2.5% |
Philadelphia | 1.9% |
New Orleans | 1.9% |
Raleigh | 1.7% |
Des Moines | 1.5% |
Memphis | 1.4% |
Dallas | 1.2% |
Saint Louis | 0.9% |
Chicago | 0.5% |
Baltimore | 0.4% |
New York | 0.0% |
Las Vegas | 0.0% |
Cleveland | -0.2% |
Columbus | -0.4% |
Kansas City | -0.5% |
Detroit | -0.6% |
Charlotte | -1.4% |
Indianapolis | -1.7% |
Houston | n/a |
San Antonio | n/a |
The main questions, for a data scientist, are:
It would be interesting to plot this data (in a nice map), and maybe doing some interpolation to get a reliable forecast for each of the 30,000 US zip codes, broken down per type of property (expensive, medium, cheap).
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Another interesting web page about house valuation. Here's an extract:
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