You Should Have Bought That Condo in 2012 and You Should Have Bought It in Oakland, Plus Other Findings About U.S. Home Value Over the Past Four Years

  • I looked only at 2-bedroom condos. Mostly for selfish reasons. If I were to purchase a place, a 2-bedroom would be the most likely type.
  • I concentrated on growth rate instead of the absolute value of housing prices, as for the purposes of this specific project I was primarily interested in how home values have changed over time (and there already seems to be a lot of studies on different cities’ absolute housing values). So for example I’m not digging into how high San Francisco’s housing value is (we already know it’s super high). Instead, I’m interested to know whether San Francisco’s housing value is rising faster or slower than other cities’.
  • One limitation here is Zillow doesn’t have data on every single state, city, and neighborhood in the country. For example, it’s missing data on Texas and Louisiana altogether for some reason but includes Washington, D.C. separately. It also has limited information on neighborhoods in Detroit and a few other cities. That said, the datasets are still super thorough overall.

This growth is not evenly distributed. Florida, Michigan, and the West have benefited the most.

The rise in home value over the past four years has benefited some parts of the country a lot more than others. 61% of U.S. states are seeing moderate growth in the 0–20% range, but two states’ housing value is actually declining while 22% of the states are experiencing massive growth over 30%.

Growth in housing value across states. Darker green means higher growth rate. For an interaction version of this chart, see here: https://public.tableau.com/shared/MGY2B4HRC?:display_count=yes.

The biggest winners of 2012–2016 are also the biggest winners of 2003–2007.

Interestingly, a similar roster of states seems to have reaped the biggest benefits from both of our most recent periods of growth. States that were growing fast in home value in 2003–2007 also tend to be growing fast now, and states that were growing slower in 2003–2007 tend to be seeing slower growth rates now as well. This correlation is actually statistically significant (for ye statisticians, the p-value is 0.02).

Correlation between a state’s home value growth rate in 2012–2016 and its growth rate in 2003–2007. For an interactive version of this chart, see here: https://public.tableau.com/views/property_value/2012-2016vs2003-2007growth?:embed=y&:display_count=yes.

The faster a state is growing, the more unequal that growth is.

It turns out the home value growth rate of a state is also related to how unequal that growth is within the state. In other words, the faster the state’s median growth rate, the bigger the difference between the growth rate of the state’s fastest-growing city and slowest-growing city.

Correlation between a state’s median home value growth rate and the spread in growth rate among cities in that state. For an interactive version of this chart, see here: https://public.tableau.com/views/property_value/stategrowthvsinequality?:embed=y&:display_count=yes.

You think San Francisco’s housing market is out of control? Take a look at Oakland.

To see how these trends play out on a more micro level, I dug into seven cities in the U.S. that I’ve lived in at some point or gotten to know on a deeper level. I was curious to know what the growth trajectory looks like for these cities and how unequal that growth is among neighborhoods in the same city. Here are the magnificent seven: Los Angeles, CA; Oakland, CA; San Francisco, CA; Chicago, IL; Detroit, MI; Boston, MA; New York, NY.

2012–2016 home value growth rates in seven different cities. For an interactive version of this chart, please see: https://public.tableau.com/views/property_value/cities-boxwhisker?:embed=y&:display_count=yes.
  • Oakland: 129% spread (fastest: Harringon | slowest: Lakewide)
  • Los Angeles: 71% spread (fastest: West Adams | slowest: West Hills)
  • New York: 68% spread (fastest: Williamsburg | slowest: Great Kills)
  • Chicago: 65% spread (fastest: Old Irving Park | slowest: East Hyde Park)
  • San Francisco: 59% spread (fastest: Bayview | slowest: Nob Hill)
  • Detroit: 44% spread (fastest: Grandale | slowest: Warrendale)
  • Boston: 25% spread (fastest: East Boston | slowest: West Roxbury)

Takeaways, recommendations, further exploration, learnings

To summarize the biggest takeaways from this analysis:

  • U.S. home value has increased a lot over the past four years. That rising tide has lifted most but not all boats, and it’s lifted them very unequally. This is true on every level—national, state, city, and neighborhood.
  • The biggest winners in this period of growth have been Florida, Michigan, and the West. Among the seven cities I dived deeper into, Oakland is growing by far the fastest, followed by San Francisco.
  • The regions of the country seeing the slowest growth (and sometimes negative growth) in 2012–2016 have been the Great Plains area, the Midwest, and the East. Among the seven cities I dived deeper into, Chicago’s median growth rate has been the most sluggish.
  • There is a strong, positive correlation between how fast a state has grown in 2012–2016 and how fast it grew in 2003–2007.
  • There is an even stronger, positive correlation between a state’s growth rate in 2012–2016 and how unequal that growth has been within the state (the faster the growth, the wider the difference in growth among cities in that state).
  • If you’re thinking about buying a home partly for investment purposes, make sure you do research into growth trends on the state, city, and neighborhood level. There could be wide variations on every one of those levels. This is probably not shocking.
  • A similar roster of states (e.g. California, Florida, Nevada) have done very well in both periods of recent growth. More longitudinal analysis is needed but this could mean that these are good states to consider investing in especially when we’re about to enter another period of housing market growth.
  • Regarding the correlation between a state’s growth rate and the inequality of that growth—does the same trend hold on the city level? In other words, is there also a strong positive correlation between how fast a city’s home value is growing and how big the difference in growth rate is among that city’s neighborhoods?
  • Similarly, does the correlation between growth rate in 2012–2016 and growth rate in 2003–2007 also apply on the city level?
  • Is there any relationship between how quickly a region is growing in 2012–2016 and how quickly that region declined in the post-2007 recession? Are certain states simply more volatile (win big in times of growth and lose big in times of decline), or have the same states been “winning” (/declining the least) through each period?
  • Why I want to learn more data science: I would love to be able to create a model that predicts which states/cities/neighborhoods will grow the most in the future.
  • The more data you have, the more disciplined you need to be about defining the focus of your analysis upfront. I spent about 20 hours total on this project, and 10+ of those hours were spent going down analytical rabbit holes, changing my mind about what direction to go in, and then going down more rabbit holes related to the new direction. Over the over and over again. The sheer volume and complexity of the Zillow datasets didn’t help, as they presented a lot of distracting “shiny objects” AKA possible questions I could have answered. I became an analyst without direction, spending a lot of time excavating answers with Excel and SQL before I knew which questions I really wanted to find answers for. A much better process would be for me to (1) get some initial, high-level understanding of my data, (2) decide what questions I most want to answer using my data and limit the number of questions to just a few (this step is usually already done for you if you’re doing data analysis for a company or class), and (3) do the actual analysis to get answers to those targeted questions (which is almost never the hardest part). And reiterate through those steps as the need arises.
  • Tools matter. In trying to figure out what regions of the country grew the fastest in 2012–2016, I first attempted to plot growth rates on a map of the United States by hand, using the custom Google Maps app. The app is fantastic in so many ways, but Tableau can (and ultimately did) get the job done in one-tenth of the time. Same goes for making scatterplots in Excel versus in Tableau.
  • Medium and Tableau are not friends yet. This was the first data project when I used Tableau extensively for my visualizations, for the reasons mentioned above. Tableau visualizations are fast, slick, and interactive. But unfortunately you can’t embed them into Medium posts yet. I ended up having to screenshot my visualizations in order to add them into this post as still graphics. So be sure to take a look here as well for the original, interactive visualizations!

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Annie L. Lin

Annie L. Lin

People & ops leader | data storyteller & nerd