Gathering Housing Insights From The Zillow Dataset (on Kaggle)

1 minute read

Intro

I’ve always been passively “interested in real estate” - but when I sat down and thought about what that meant, it dissolved mostly into, “I’d really like to just own a bunch of $REAL_ESTATE_PROPERTIES, rent them out, and live off the profit.” I imagine I’m not alone in that regard - there are probably a ton of “wannabe” real-estate moguls like me.

Whether or not I decide to develop that interest later, I’d still like to ask some questions and look at some numbers (using the Zillow Prize Dataset). To set an initial direction of this project, I’d like to know:

  • In general, what makes a house expensive?
  • Over time, what correlates with rising house values?
  • When’s the best time to buy a house?
  • Where’s the best place to buy a house?
  • How much of an impact do surrounding municipal/residential/commercial entities have on the value of a house?

I’ll be updating this as I make progress on it.

Update 1 (06/18/2018):

After taking a closer look at the data and what some people have done, it seems like most of what could be done with this dataset has already been done. I have to be honest, it does kill some of the motivation I have for this project, and makes it feel more like a regular school assignment. That being said, it’s still a great opportunity to touch up on data wrangling techniques for “regular” data.

I want to make this post both informative and novel, so I’ll import my favorite kernel (above) here and fill in any gaps I see. In other words, I’ll take advantage of any unfamiliarity by picking out stuff I either had to google or think about deeper and explaining it here.