Location, distance to the nearest train station, size of the room, number of windows, exposure to sunlight, room structure, the year the house is built, neighborhood vibe, and so on: there were countless factors to consider when I was looking for a new room. It is an important decision - my lifestyle and quality will be decided by that one choice. And there is no easy turning back - I will need to spend around 2 years there, no matter I like it or not after signing the deal. Yes, it was stressful.

Finding a room in Tokyo is not easy, especially for a foreigner. Out of the rooms that my agent and I filtered out from the database, I could only go see 20% because I could not speak Japanese. (the more annoying part was that I could find this out only after we called that management company)

How can I increase my chance of landing into a right room? What should be my best strategy?

Luckily, I was reading the right book to help me go through this stressful decision-making process: <Algorithms to Live by> by Brian Christian, Tom Griffiths. I will introduce some of the wisdom I adopted from this book and how it worked out in real life.

Finding my right room is an optimization problem

There are so many variables when playing this game. I had opinions about each one: 20 mins commute to work, higher floor, separated bedroom, south-facing windows, bigger than 30 square-meter, within 10 mins from train station, newly built, quiet but not too boring neighborhood, washroom with dryer function & toilet that cleans my ass automatically. (yes, it's a thing in Japan)

There is a website that you can input all of those conditions and shows rooms with that condition. And I did!

There are no properties that meet the conditions. Please change the conditions and search again.

OMG, so I loosened my conditions a bit.

Now there are 5,369 rooms to consider! Yay

Another possible approach is to create an Excel spreadsheet to put in scores for each variable, do a weighted sum (according to what I value most), and sort them in descending order! (this is called: ranking problem)

But how do I decide the weights? I need to be subjective and objective at the same time. And how much time would I have to organize data for 5,000 rooms? What if I get fired from my new job for doing non-business essential work by the time I reach a decision?

In computer science, these problems are called (NP-) HARD problems. It means that we cannot find the optimal solution by computing in polynomial time, but let's skip the nerdy part. The gist is that it is also hard for computers.

In theory, in order to solve these problems, firstly, we have to relax the constraints. Basically, give up some stuff and be realistic (be a grown-up). Secondly, we have to limit the search space. More choices sound good, but it is actually the worst enemy for decision making.

1) Relax the constraints

For relaxing the constraint, I flipped my thinking:

What are the conditions of a room that I would absolutely never want to live in?
  • No first floor, No loft
  • Commute time over 30 mins, more than 5 mins, less than 15 mins to station.
  • Room smaller than 30 m^2 (I had enough of smallness when living in HK)
  • No built-in air conditioner
  • No room over XXX monthly rent

Then everything becomes optional. Maybe I want a separate bedroom with a balcony. Or maybe I want to live in a mansion with proper lobby and security. Or maybe I want to live in a newly-built apartment. Or maybe I want to have a convenient store nearby. But those are nice-to-haves. By not enforcing these conditions, the constraints became more relaxed and the search became more reasonable.

2) Limit the search space

Tokyo is a huge city. My office is located in Shibuya, a major transportation hub that has several train lines go through. I had to choose a neighborhood to cut down the candidates. In the end, I choose the neighborhood where my temporary housing is at: Setagaya-ku.

The main reason I stuck to my status-quo is the train commute. Tokyo is notorious for its rush hour experience. I had been commuting from here for a month and found it quite bearable. This is because is that Meidaimae station has two lines, one going to Shibuya and another going to Shinjuku (also a major city center), so a lot of people get off the train to exchange lines.

I experimented with different times and locations of the platform and found the optimal way of commuting from this station. I heard so many horror stories about other train lines, so I decided not to take a risk of moving to another neighborhood and stick with the certainty that I managed to clear out during the last month.

So when I searched for rooms, I asked the agent to limit the search to this neighborhood. Another positive side-effect was that room viewing logistics were so much simpler as we only needed to drive around one area.

Photo by Karen Lau on Unsplash

When to decide?

Searching for a room is similar to finding your life-long marriage partner (if you are looking for one). You have to find an optimal stopping point. Is your current partner the best I would get in my life? What if your next future boy/girlfriend will be a better husband/wife material?

There are costs too - whether they are physical, mental, or monetary - for each search (not sure if I am talking about finding room or partner at this point). Also, the more time you spend in the game, your value may go down.

Luckily, I watched this TED talk a few years ago by Hannah Fry, The Mathematics of Love, introducing the 37% rule.

The book <Algorithms to Live By> again mentions this useful rule when you have to look at different options in a limited time window.

  • Define the maximum time window that you are willing (or bounded) to spend.
  • Explore options for the first 37% of that time, but don't accept any of it. Learn about what options there are in the game and what you can afford or take.
  • Accept the first option that is better than those you have seen in the first 37%.

Since this is a mathematically proven method to maximize your probability to land a good room (or partner), I did the same. I chose 3 weeks as my maximum time window. In the first week, I went to see around five rooms to get a sense of the game. For the following week, I refined my conditions and chose the first room that was the best so far.

"Giving yourself more time to decide about something does not necessarily mean that you’ll make a better decision. But it does guarantee that you’ll end up considering more factors, more hypotheticals, more pros and cons, and thus risk overfitting." Algorithms to Live By, Chapter 7.

Love the process, don't hate the result

The best part of the book is the concluding chapter. The authors argue that we should treat the process and the result separately. The domain of result is where luck squeezes in. Even if the winning chances are 99%, there is always one person that loses out of a hundred. Then would you say it was a bad decision to play the 99% win game? Of course not. Even if you can go back and make that decision again, you should probably play the same.

I have not moved into the room yet, but there is a non-zero chance that it was a bad choice somehow. Maybe my neighbor is very noisy, or I find the area too boring. Probably I cannot imagine the reason now until I experience it. Nevertheless, I should not blame myself too much, because I tried my best to maximize my chance of landing a good room. We are all emotional, so this separation of process and result is indeed very challenging.

Sure enough, a few days later, there was a problem: the landlord and agents were asking me for an unreasonable amount of initial costs! I guess in a capitalist society there is really no time to relax since there is always someone who wants to take your money.

For this reason, I had to spend some stressful time negotiating with them. Fortunately, this book has a chapter called "Game Theory". I will continue on my 2nd post!