A Spreadsheet and the Paradox of Choice

Last year, I wrote about my plans to move away from the Washington, DC area. At the time, I was applying for jobs and was considering cities where I’d be willing to look for positions. Then, early this year, I decided to work for myself instead (eventually). And that completely changes the game–it means I can literally live anywhere (don’t worry; I’ll get to the spreadsheet).

My stress levels have ratcheted up by about 1 million since my boss has left, so I’ve been thinking more and more seriously about when/where to move. In fact (as of a meeting with my therapist earlier this week), I’m leaning toward moving this winter…maybe January? I might not actually stick to that timeframe, but for now, it feels really really good to have a solid date to aim for.

The problem with all this is that I can move literally anywhere. And I know that isn’t actually a bad problem, but the paradox of choice is real. The theory goes that the more choices we have, the less happy we are. We’re overwhelmed and paralyzed by our options.

So, knowing that, and being the Type-A, anxious, reformed Econ major that I am, I decided to narrow down my choices. Through the wonderful magic of spreadsheets!

The Oh-So Glorious Spreadsheet

Y’all, I am super excited about this spreadsheet. I started with a long list of cities I would consider (many of which have already been taken off the version of the spreadsheet I’ll share with you) and then quantified factors that I felt were important for wherever I end up. In this post, I’m going to go through the factors I considered, how to view the spreadsheet, how to submit additional cities for me to add (they likely won’t be considered for moving to, but it’s fun to see how other cities stack up), and how you can download and customize the spreadsheet to your own preferences.

Without further ado, here it is (the image links to the full spreadsheet–it’s way too big to embed here neatly):

The final score for each city is based on a weighted index of the factors I tracked. Here are the official rankings, along with scores:

1. Cleveland, OH – 5.56
2. Pittsburgh, PA – 5.41
3. St. Paul, MN – 4.90
4. Cincinnati, OH – 4.55
5. Minneapolis, MN – 4.23
6. Boise, ID – 1.48
7. Portland, ME – (0.82)
8. Columbus, OH – (1.06)
9. Manchester, NH – (1.18)
10. Iowa City, IA – (1.26)
11. Champaign, IL – (1.30)
12. Des Moines, IA – (1.60)
13. Omaha, NE – (2.17)
14. Bloomington, IN – (3.70)
15. Burlington, VT – (4.15)
16. Nashville, TN – (8.89)

I left Arlington, VA (where I currently live) out of the main ranking because it’s a massive outlier and skews the data pretty significantly. But I did rank it using the same standardization approach (with the mean and standard deviation from the main list) to roughly compare. No surprise, it comes in dead last, with a score of -11.19…

What I Tracked

I know what I like (and don’t like) about where I live now, and I want to make sure that my next location has more of the good stuff and less of the bad stuff. Here are the things I care about:

• Population Growth – a decent measure of a city’s growth and vitality. Growing cities are a good place to be!
• Median Home Price – This one is huge for me. I really want to buy as soon as I move. That means I want to move to an area where I can afford to buy.
• Price per Square Foot – I want to make sure that lower average prices aren’t due to small home size. I’m hoping for a place where I can have a home office to meet clients in, and possibly space to Airbnb as well (to help with that mortgage cost!).
• Walk Score – One thing I love about my current neighborhood is that I can easily walk to a park, restaurants, a library, a grocery store, and a dog park. I don’t want to go back to having to drive everywhere when I move.
• % of Population that is White – I think diversity in a city is wonderful–it means new ideas and cultural experiences. This factor doesn’t get the same weight as home price or cost of living, but I did think it was important enough to include.
• Cost of Living Index – Another way of tracking affordability in wherever I move. I don’t want to move somewhere awesome and then not be able to afford the awesome things happening there.
• Crime Rate – This should be obvious. But I wasn’t thrilled with the availability on data here, so if you know of a better source, let me know.
• Green Score – I want to live somewhere with abundant parks and outdoor spaces for StarDog.
• Distance to Airport – I love to travel, and I am not willing to drive forever to get to a major airport (or pay obscene prices to fly out of a smaller one).
• Healthcare Cost – Lower healthcare costs are great!
• Physicians per Capita – But so is access to good healthcare. I think this makes a great proxy variable for that.
• Effective Tax Rate (State and Local) – $$$
• Comfort Index – One of the websites I used had their own ranking of climate comfort levels based on days within the range of 60-70 degrees and discounting for higher humidity. But some places I think are really more comfortable than others ranked lower, so I didn’t give this as heavy of a weight.
• Average Dew Point in August 2018 – I hate humidity. Absolutely hate it. If I’m sweating at 6 am in 73-degree weather, something is wrong.
• Average Annual Temperature – I didn’t weight this one, but I did think it was useful to know. I focused more on the seasonal averages below.
• Average Summer Temperature – This is important to me–I hate absurdly hot summers, so a lower average summer temperature was worth including.
• Average Winter Temperature – Aaand I’m a wimp and hate the extreme cold too. But this was only weighted at 1/2 because I don’t hate it as much extreme heat in the summer.
• Sunny Days per Year – Yay sun! The more sun there is, the better I manage my depression. And sun is wonderful. Though I didn’t weight this as heavily because clouds are good too…I’m super pale.
• Snowfall per Year – Less snow means less work shoveling. But again, not a huge deal for me, so weighted at 1/2.

Want Me to Add a Location to the Spreadsheet?

Via Twitter, several people mentioned other cities I should consider adding just for fun. I love that idea, but I’m kind of sick of data collection for a while. If you want me to add a city to this master spreadsheet, You can fill out this Google Form with the required data. The form (and the second tab on the spreadsheet) both have all of the links to my sources. Or, you can add your own cities to your heart’s content (along with new factors and different weights) by downloading the spreadsheet and customizing it.

So…Much…Data…

At a certain point when creating this spreadsheet, I had to narrow down the list before actually collecting all of the data, simply so I wouldn’t go crazy (crazy is, in fact, the opposite of the goal for making this spreadsheet). But I’m a data nerd at heart, and I would love to crowdsource more info so this can turn into something wonderful.

Want to Customize This Spreadsheet?

I know not everyone’s preferences will match up with mine. Some of you (the really really weird ones) would prioritize higher winter temperatures over lower summer ones (or would even prefer to rank FOR hot summers instead of against them). Some of you might want lots of snow, but my sheet assigns positive values for less snow. You get the drift (hahaha snow pun!).

If you download or duplicate my spreadsheet into your own Google Drive, you can make all of those changes. The big areas you’ll want to consider customizing:

The Weighting.

You only have to change the numbers at the bottom of the first half of the spreadsheet (row labeled “Weight”). Assign a higher weight to factors that are more important to you. For me, that was real estate cost, walkability, and green space. Assign a lower weight to factors that kind of matter but aren’t as important (like I did with Comfort Index, for example). Assign a weight of 1 to factors that are important but not your top considerations.

Note that in my spreadsheet, a couple of the weights are zero, either because I didn’t assign much of a positive or negative value to the data (in the case of total population) or because I felt that factor was captured elsewhere sufficiently (I didn’t weight annual average temperature because my weighting already included average summer temp and average winter temp, which more specifically captured my priorities).

The Positive/Negative Values of Your Standardized Numbers

This one is important. For some of these factors, lower numbers are generally good (like with crime rates or distance to an airport). But when you standardize the data based on mean and standard deviation, numbers below the average appear as negatives. The same is true when you have high numbers that are bad (crime rate again) being assigned a positive value.

To adjust for this, I added a negative sign in front of the Standardize formula where necessary (shown in the photo below). In some instances, that shouldn’t change. Unless you’re Batman, I’m guessing you don’t want a place with more crime (also, if you are Batman, OMG THANK YOU SO MUCH FOR READING MY BLOG). But with other factors (like summer temperature, snowfall, etc.), individual preferences might mean that a negative aspect for me is a positive one for you. So you’ll have to change that sign by adding (or deleting) the minus sign at the beginning of the formula. Here’s an example because as I write this, my own eyes are glazing over…

And, for reference, here’s what the full second half of the spreadsheet looks like with all of the standardized data…I really need to focus more on being actually productive with my time…

Note the weights carried over from the front half of the spreadsheet onto the bottom here. Those weights are multiplied by their respective value for each city and then added to get that final score. Voila! Now you understand the basic mechanics for when you replicate this on your own, either by adding new locations, new weights, or new factors to consider (I almost put a column in for “Number of native poisonous spiders/snakes” but decided that might be going too far…At some point, the spreadsheet just has to be finished.

My Next Steps

Now that I’ve narrowed down a few places I might want to live, I need to decide where I’m actually going! My plan over the coming months (likely August-September) is to visit my top cities and find out if I can really see myself there. I’m not going to all 16 on the list, but I’ll definitely go to the top 3. Are you from Cleveland, Pittsburgh, or St. Paul (or any of these other cities)? I’d love recommendations for neighborhoods to look at or avoid, and what I need to see to get a real sense of what it’s like to live there.

If any of you use this spreadsheet for your own purposes, I would be thrilled if you’d share with me how you used it! I hope this labor of love helps someone else with their location change like it’s helped me! And as this move progresses, I will, of course, be writing updates here on the blog. But until then, I’m taking this advice…