Thank you for all your guesses! We got 25 sale estimates for 1662 Union Street, the first ever attempt at a crowdsourced home sale price estimate that I know of. If you posted early you should go back and check out all the comments – some people shared a few sentences on their reasoning, which was really educational.

I’ve included the raw data at the end of the post, but if you just want the highlights a box plot is going to be a great way to understand what we collectively think about the market for this home. This box plot shows us the low estimate, high estimate, first & third quartiles, and median. I’ll explain all of those briefly.

The short lines on the left and right edge represent our low and high estimate, minus one low outlier, which is represented as a dot. The line through the middle of the box is our median (middle) estimate. If our estimates were 3.0m, 3.2m, and 100m, the median would be 3.2m because the median is not an average, it’s simply the middle value in our ordered list of guesses. The left and right edge of the box are the first and third quartiles. The first quartile is the value for which 25% of guesses are smaller and 75% are larger. And the third quartile is the value for which 75% of guesses are smaller and 25% are larger. As such, the “box” in the box plot ends up being a nice way to understand how wide around the median you have to go to capture 25% of the guesses on either side.

I’m fascinated by the location of the first and third quartile, relative to the listing price. The space between those two quartiles is the sweet spot where I think you can drive a competitive sale. My intuition is that it’s a bad sign when you are priced above the third quartile because you look expensive to 75% of the population. You will probably have to take offers as they come, because multiple buyers are unlikely to bid you up beyond this level.

From a buyer/bidder perspective, I also find this very useful. If I am interested but not in love with the property, I might bid something between the first and second quartile. It’s low but not unreasonably low. If I win I got the home at a great price. On the other hand if I love the property, I might bid something closer to the third quartile. This is a better than average outcome for the seller, which should merit consideration. Before you say “but what if lots of people like it even more than that” we have to go back to what these quartiles mean – by definition, lots of people *don’t* like it more than the third quartile otherwise the third quartile value would be higher.

I’m excited to keep doing this! If you enjoyed guessing please cast a vote on 71 Curtis Street, the home I toured on Sunday, and I’ll keep writing up the results. I think we’re exploring something pretty cool.

Raw data:

Guesses = [2.2,2.75,2.8,2.8,2.9,3,3,3.1,3.1,3.12,3.2,3.2,3.2,3.25,3.25,3.26,3.3,3.3,3.3875,3.4,3.4,3.4,3.5,3.505,3.575]

Population size: 25

Median: 3.2

Minimum: 2.2

Maximum: 3.575

First quartile: 3

Third quartile: 3.39375

Interquartile Range: 0.39375

Outlier: 2.2

If you want to play with the dataset yourself, here is a nice box plot calculator that you can plug the above values into (it’s the one I used). And if you’re wondering where that custom box plot came from – that was 30 minutes in Sketch, which is the gift that keeps on giving (thanks Blake Reary!)

Heang

I wonder what is the impact on bid price when a buyer uses a real estate to place the bid on his or her behalf since the real estate agent may try to convince the buyer to bid higher.