Some decisions are simple. For example, say you want to fly from Ottawa to Toronto on Monday, and you want to pay as little as you can. Your decision here will be straight forward: take a look at the ticket prices offered by the various carriers for that day and pick the least expensive one.
However, home buying decisions are rarely that straightforward. As discussed in the previous post, in the home-buying context, attaining the best outcome usually involves a bundle of criteria. Of course, you want to get your home for the right price, but you probably also care about the aesthetics of the house, number of bedrooms, the neighborhood, proximity schools, and so on.
In situations like that, picking out the best option is a little more difficult. Now why is that? I think the difficulty comes from the different bases of comparison. We measure price in dollars, but we measure proximity to schools in meters. Meters and dollars aren’t the same units, but at least we can express them with numbers. Home buying decisions can seem even foggier (at first) because many of the criteria aren’t gauged with numbers. Take aesthetic criteria, for an example. Say you want to live in a quaint neighborhood (lots of people do). I’m pretty sure there’s no unit of measurement for quaintness.
Now, maybe you’re thinking something like “if you can’t measure it, you can’t use it to rank something, so there’s just no way you can consider something like quaintness in a ranking of options.” That’s a fair concern. After all, if you can’t rank your options, how can you say which one is the best. Well, fortunately, we can gauge things like quaintness. Maybe not to a super high level of precision – but we do gauge it. It would seem kind of silly to say that something is whether quaint or not and that all quaint things are equally quaint. For instance, if I told you that there were exactly six quaint neighborhoods in Ottawa, and that each of those neighborhoods was equally quaint, you’d probably tell me to get out of town! “Equally quaint! Preposterous!” you might say. Things aren’t quaint or not, quaintness is a matter of degree. So then, some neighborhoods must be quainter than others. Now maybe you and I differ on what counts as quaint, but fortunately, as the client, all that matters is what you think. The point is, you can rank quaintness – even if you can’t assign a number to it.
So how do we rank options on the basis of multiple criteria?
First, we need to set out our options and our criteria. We can do this using a table, which we can fill out like this.
| House A | House B | House C | House D | |
| Price | 480,000 | 520,000 | 450,000 | 550,000 |
| School Proximity | 1200 M | 100 M | 600 M | 2400 M |
| Rooms | 3 | 4 | 4 | 2 |
| Quaintness | Quaint | Less quaint | Very quaint | Not quaint |
Now, how do we proceed from here?
First, we should simplify things. We can do that by getting rid of the options that are beat in every respect by at least one other option.
So, for example, House C is cheaper than house D, it’s closer to the school, it has more rooms, and it’s quainter than house D. If those four things are all we care about, then house C is clearly better than house D. (If we care about more than those four things, we could just add them to the table and follow the same process). So let’s scratch off house D.
| House A | House B | House C | House D | |
| Price | 480,000 | 520,000 | 450,000 | 550,000 |
| School Proximity | 1200 M | 100 M | 600 M | 400 M |
| Rooms | 3 | 4 | 4 | 5 |
| Quaintness | Quaint | Less quaint | Very quaint | Not quaint |
Anything else we can scratch off?
House C beats house A on every count, so we can scratch off house A too.
| House A | House B | House C | House D | |
| Price | 480,000 | 520,000 | 450,000 | 550,000 |
| School Proximity | 1200 M | 100 M | 600 M | 400 M |
| Rooms | 3 | 4 | 4 | 5 |
| Quaintness | Quaint | Less quaint | Very quaint | Not quaint |
Now we’re down to two options: House B and House C. House C wins price, and quaintness, but house B wins in proximity to schools. The houses tie for number of rooms. The number of rooms will be the same no matter which house we get, so we don’t need to keep them in our comparison. Let’s scratch them off and make things even clearer.
| House A | House B | House C | House D | |
| Price | 480,000 | 520,000 | 450,000 | 550,000 |
| School Proximity | 1200 M | 100 M | 600 M | 400 M |
| Rooms | 3 | 4 | 4 | 5 |
| Quaintness | Quaint | Less quaint | Very quaint | Not quaint |
Well, that still leaves us with the question of whether the savings and the extra quaintness is worth the extra distance from the school. At this point, we may have simplified things so much that the answer would be clear to the home buyer. If that’s the case, we’d just wrap it up there. But for the sake of explanation, let’s take the process all the way. Let’s continue from a simplified table.
| House B | House C | |
| Price | 520,000 | 450,000 |
| School Proximity | 100 M | 600 M |
| Quaintness | Less quaint | Very quaint |
Now, at this point, it’s helpful to estimate how much of one thing is worth trading for another. This is entirely up to the client (this is why, like I wrote in my last post, it’s so important to get clear about what matters to the client). That said, at this point, the decision can still seem daunting to some clients. After all, how much is quaintness worth? That takes some thinking, but with a bit of coaxing, most people can come up with a surprisingly clear idea. So how do we proceed? House C wins price and quaintness, so our question is ‘how much quaintness, or savings, would we give up to have house C located 100M from the school?’ If the answer isn’t immediately clear, we can proceed with a brief game of ‘hotter – colder’.
For example,
Agent: “Would you pay 70,000 more to have house C 100M from the school?”
Client: “No.”
Agent: “How about 30,000?”
Client: “Yes”
Agent: “50,000?”
Client: “Hmmm — no.”
Agent: “40,000?”
Client: “Yeah, I think I would.”
Agent: “How about 45,000 then?”
Client: “No, I was okay with 40, but just barely really.”
So there we have it, the shortest distance to the school is worth 40,000 to the client. We can rewrite our table accordingly.
| House B | House C | |
| Price | 520,000 | 450,000 490,000 |
| School Proximity | 100 M | 600 M 100M |
| Quaintness | Less quaint | Very quaint |
Next we eliminate equal criteria, and we get this table:
| House B | House C | |
| Price | 520,000 | 450,000 490,000 |
| Quaintness | Less quaint | Very quaint |
House C wins on price and quaintness. House C is the best choice.
To conclude, let’s briefly summarize the steps.
- Lay out all the options and all the relevant considerations in a table.
- Eliminate each option that is beat by another option on every consideration.
- Eliminate the considerations that all options are tied on.
- Use the ‘hot-cold’ technique to equalize the remaining criteria.
- Using the equalized table, repeat the process from step 2 until a clear best option emerges.
As I wrote in my last posts, what counts as the best is defined by the client; the job of an agent is to bring about the best outcome for the client as she defines it; accordingly, it’s important for agents to get clear about what the client considers the best outcome. The method demonstrated in this post is one way that agents can make sure they’re clear about what the client considers the best outcome.
