How to choose where to live

Choose where to live

A spreadsheet to help choose where to live

In December 2012, my wife and I were planning to leave Singapore. We had been discussing it for months, actually. We kept coming back to the same conversation, though – Where were we going to go? We’re lucky, in the sense that we have dual citizenship options (US/EU) and even some additional options (pre-approval for immigration in another country, Nordic pact open labor markets). So that opened a lot of the world to us. I’ve discussed this with a lot of people over the last couple of years, and I’ve been asked time and again to share my spreadsheet. So here’s the spreadsheet. But importantly, here’s the approach we used to figure out where to live.

  1. Actually step 0: make your own copy of this spreadsheet. You can copy/paste into OpenOffice, LibreOffice, Excel, Numbers, etc.
  2. Add as many possible candidate locations to the list as you think you can manage. We started with about 30. Definitely include random, out-of-the-way places that you’ve maybe visited or vacationed in and always wondered about. This is the time to consider them in earnest.
  3. Go through the evaluation criteria. Feel free to add/remove items that are more relevant for you. We focused on total quality of life, and for us, that meant Education, Weather, Travel, Housing, Cost of living, Social factors, Economy. On Economy, we focused really only on one thing – were there realistic job opportunities.
  4. For other items, you can look for PISA scores, news articles about each place, etc. is good for climate information. Enter a place name, then choose monthly weather, and averages (usually a link at the bottom).
  5. Note that some items are more subjective, at least when we did it. We rated crime as high, moderate or low.
  6. Don’t get upset when you start having some disagreements here about the assessments. The only important thing is that you eventually come up with a scale that makes sense for all the people making the decisions.
  7. Next, figure out a weighting, absolute or implied, for the different factors. This will probably give you a good sense for how important each item is to you.
  8. Make an overall score. Or don’t. We actually didn’t, but I did set up the spreadsheet in a way that would make it easy and possible. Just add columns for weighting after each, and figure out a way to try to normalize to a score range, like possible points out of 100.
  9. Discuss.
  10. Make your first round of cuts. This was tough for us. Inevitably, you will have some emotional attachment to at least one place that gets cut. This is just how it goes, unfortunately. There are lots of great places around the world, but the reality is that many will lack good schools or economic opportunity or who knows what.
  11. Do another round of research. This is a tip from my father, who often said, “When you’re trying to make a big decision, and you don’t know what to do yet, gather information.” He’s a data visionary apparently.
  12. Go back into your ratings. Re-assess. Discuss.
  13. Refine your scores.
  14. Make your second round of cuts. Ideally, at this point, you’ll be down to just 3 locations, maybe 4. You need this round to be manageable.
  15. I recommend visiting all the places, if that’s at all feasible. And, beyond that – try to go local as much as possible.
  16. Use sites that let you stay at people’s homes/apartments, etc, so you get a feel for what real living environments are like. If you’re lucky, you’ll even get a chance to talk to the owner to understand what utilities cost, what transportation is like, etc. And do some shopping in local grocery stores.

Hopefully this helps. It’s not a firm approach, but it’s a set of guidelines that might work for you. Ping me if you have questions or want to talk personally about how my wife and I used this approach to make our own decision.

a troubling topic on my mind

it’s been over a year since i blogged, but i felt compelled to get back into it, after some reading i’ve been doing in the past few weeks.

every day, i wake up, get my kids off to school, and then try to get some work done. on a good day, i manage to sneak in a workout or get a good long walk going.

but in reality, work takes up most of my time. and what is that work? i split time between a few projects. like almost all work, these projects have cycles – some days are more interesting; some are less. some projects can be more interesting than others, while some are commercially phenomenal, but hold little interest for me outside of the pure work function of them. my work is almost exclusively in software, specifically web-based software to solve business problems.

and this is where the troubling topic first emerged.

if i’m only helping to solve business problems, am i ignoring the world’s problems?

it turns out, i’m far from the first person to ask this question. people on quora have been debating real-world problems since 2012. the summary arguments basically come down to:

  • not interested
  • there’s no money in it (cf. silicon valley people are just in it for the money, and are just greedy)
  • some companies are, but aren’t talking about it or aren’t on the web
  • silicon valley companies are solving real problems, just not the problems of the poor

and this last one really bugs me. consider, for instance that:

  1. mPesa is definitely helping solve a real problem for disadvantaged people.
  2. it’s a technology offering that clearly could have been built by silicon valley people (or this type)

in fact, while silicon valley is kind of ignoring these problems, it has gone ahead and made itself a bunch of enemies. what should be expected? trickle down clearly doesn’t work, as pointed out by planet money on npr. so while silicon valley continues in the newest bubble cycle, those working in lower-wage service industries are suffering from the overall price increases brought by new wealth.

and yes, i am, in a way, part of the problem. this is why my most interesting and exciting project is open-source data tracking and analysis. our goal is that any organization, from the poorest municipality to the wealthiest corporation, can learn from data to improve lives, outcomes, experiences, processes or whatever.