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Data Science

Housing Turnover as a Metric for Home Buying

Casual data science

I was curious about housing prices over time for a neighborhood where I used to rent a house. I was curious how much housing turnover there was, and if any particular house type or area of the neighborhood differed from the statistical average. I thought this neighborhood was interesting because there were only a few types of houses in the plan.

I pulled the housing data from Zillow. Most of the houses were standardized in their design and square footage, so the comparison was pretty straight forward.

The houses sold per year was high in the first two years because that’s when the development was built. Yearly house sales since have been pretty steady.

When looking at the percent of home owners and their tenure in the neighborhood (or at least owning the home and possibly renting it), more than half have been there for a decade.

Breaking this tenure down by groups of houses, the average is brought down by Heritage Court and Tapestry Court/Somerset Drive. Those areas also have the most turnover.

I have never done a neighborhood analysis, but the one thing that stuck out to me was turnover. People move for all sorts of reasons, but people don’t move when they’re happy with where they live. So maybe turnover would be a good metric to use when trying to figure out if a particular house or neighborhood is desirable to live or not, or if it is an indication that you will have to move again in the near future.

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