This is a fun little passion project by Airbnb-engineer Andrew Pariser and someone known as Potch. The way it works is that it shows you a picture of a recently sold property, and you have to guess what it sold for. You get a bunch of guesses, and after each one, you are given more information about the property and some feedback on how close you are. To win, you need to get within 1%.

When I tried it out, my initial guess was way off (too high). Toronto has trained me well. I also wasn't sure where Evansville, Indiana was, so that bit of information didn't really help me. But the arrows telling me I was way too high, certainly did. The reality is that it's pretty hard to guess the value of a home if you don't know where it is, you can't see interior photos, and you generally don't have enough information.

But what if you were from Evansville, Indiana and what if you did have enough information? I bet that the guestimates would actually be pretty accurate. This idea of crowd-sourcing market information and pulling wisdom from crowds has long interested me, because price discovery is a major pain point for real estate. Sure you can look at comparable sales and current listings, but that is not an exact science. Neither are algorithms.

But what if there was a way to test the market and get pricing feedback before you actually list? Would you trust it more than Zillow's algorithm? This is something that I'm working on testing right now through a passion project called Unlyst. Myself and a few others are working on a very simple product that will be released this fall. If you'd like to follow along, sign up here.

Past performance, we are often told, is not necessarily indicative of future results. At the same time, history has a funny way of repeating itself. I recently stumbled upon this research paper by Marc Francke (University of Amsterdam) and Matthijs Korevaar (Erasmus School of Economics) looking at the impact of pandemics on housing markets. More specifically, it looks at the impacts of the bubonic plague on 17th-century Amsterdam and of cholera on 19th-century Paris. Here's an excerpt that summarizes what they found:

Our analyses for both cities point to substantial impacts of pandemics on property prices. We find that sales prices respond negatively to outbreaks, in particular in heavily affected areas, and that responses are short-lived, with the effects on sale prices being particularly significant in the first six months of an epidemic. Evidence from aggregate house and rent price indices suggests a smaller negative impact on rent prices. Amsterdam and Paris were very resilient to these outbreaks, with population and house price growth quickly reverting to prior trends.

This paper was first published at the beginning of 2021. A lot has changed since then and, in some ways, their findings now seem obvious. There was still a great deal of uncertainty in the market 12 months ago. While it seems like eons ago, I remember our team having discussions around when would be the right time to launch sales for One Delisle. Of course, 2021 turned out to be a record-setting year for housing and that includes the core/urban housing that the media was quick to write off at the onset of COVID.

This is not to say that certain things haven't changed or that there won't be further changes -- both positive and negative -- that come out of this. To give one just example, we all continue to hear anecdotal evidence that a lot of tech talent would now prefer to be in cities like Miami over San Francisco. (I'm not tech talent, but this would be my strong preference.) Did the pandemic help fuel this? Probably. It opened a door for the people who no longer wanted to live in a city with such a supply-constrained housing market. (I'm sure there were other reasons, too.)

These things, of course, happen. Cities are powerfully resilient, but they still need to compete. The bigger point is that cities continue to be our greatest centers of opportunity. And here we have centuries of data and housing records to support the fact that opportunity is both a powerful motivator and a centralizing force for urbanization. This is true even in the face of things like pestilence.

Happy new year, everyone. I think there's a lot to look forward to in 2022, including far less talk about pandemics and hopefully far more talk of places like Miami.

Photo by Adrien Olichon on Unsplash

The below graphs are taken from a recent (June 2019) report by Knight Frank on "prime" residential pricing across the world. They define "prime" as generally being the top 5% of each market by value. What these graphs show are the spread between the average price of a prime property and the top price achieved in that market.

The most expensive market is Hong Kong. The average price of a prime property in 2018 was USD 4,251 per square foot (or USD 45,760 per square meter) and the top price achieved was in 2016 at USD 28,154 per square foot (or USD 303,051 per square meter).

Using the 2018 average, a 350 square foot studio apartment would run nearly USD 1.5 million (or almost CAD 2 million), assuming there are "prime" studios available in the market. Remember, we are talking about the top end of the market.

If you'd like to download a copy of the full report, you can do that over here.

This is a fun little passion project by Airbnb-engineer Andrew Pariser and someone known as Potch. The way it works is that it shows you a picture of a recently sold property, and you have to guess what it sold for. You get a bunch of guesses, and after each one, you are given more information about the property and some feedback on how close you are. To win, you need to get within 1%.

When I tried it out, my initial guess was way off (too high). Toronto has trained me well. I also wasn't sure where Evansville, Indiana was, so that bit of information didn't really help me. But the arrows telling me I was way too high, certainly did. The reality is that it's pretty hard to guess the value of a home if you don't know where it is, you can't see interior photos, and you generally don't have enough information.

But what if you were from Evansville, Indiana and what if you did have enough information? I bet that the guestimates would actually be pretty accurate. This idea of crowd-sourcing market information and pulling wisdom from crowds has long interested me, because price discovery is a major pain point for real estate. Sure you can look at comparable sales and current listings, but that is not an exact science. Neither are algorithms.

But what if there was a way to test the market and get pricing feedback before you actually list? Would you trust it more than Zillow's algorithm? This is something that I'm working on testing right now through a passion project called Unlyst. Myself and a few others are working on a very simple product that will be released this fall. If you'd like to follow along, sign up here.

Past performance, we are often told, is not necessarily indicative of future results. At the same time, history has a funny way of repeating itself. I recently stumbled upon this research paper by Marc Francke (University of Amsterdam) and Matthijs Korevaar (Erasmus School of Economics) looking at the impact of pandemics on housing markets. More specifically, it looks at the impacts of the bubonic plague on 17th-century Amsterdam and of cholera on 19th-century Paris. Here's an excerpt that summarizes what they found:

Our analyses for both cities point to substantial impacts of pandemics on property prices. We find that sales prices respond negatively to outbreaks, in particular in heavily affected areas, and that responses are short-lived, with the effects on sale prices being particularly significant in the first six months of an epidemic. Evidence from aggregate house and rent price indices suggests a smaller negative impact on rent prices. Amsterdam and Paris were very resilient to these outbreaks, with population and house price growth quickly reverting to prior trends.

This paper was first published at the beginning of 2021. A lot has changed since then and, in some ways, their findings now seem obvious. There was still a great deal of uncertainty in the market 12 months ago. While it seems like eons ago, I remember our team having discussions around when would be the right time to launch sales for One Delisle. Of course, 2021 turned out to be a record-setting year for housing and that includes the core/urban housing that the media was quick to write off at the onset of COVID.

This is not to say that certain things haven't changed or that there won't be further changes -- both positive and negative -- that come out of this. To give one just example, we all continue to hear anecdotal evidence that a lot of tech talent would now prefer to be in cities like Miami over San Francisco. (I'm not tech talent, but this would be my strong preference.) Did the pandemic help fuel this? Probably. It opened a door for the people who no longer wanted to live in a city with such a supply-constrained housing market. (I'm sure there were other reasons, too.)

These things, of course, happen. Cities are powerfully resilient, but they still need to compete. The bigger point is that cities continue to be our greatest centers of opportunity. And here we have centuries of data and housing records to support the fact that opportunity is both a powerful motivator and a centralizing force for urbanization. This is true even in the face of things like pestilence.

Happy new year, everyone. I think there's a lot to look forward to in 2022, including far less talk about pandemics and hopefully far more talk of places like Miami.

Photo by Adrien Olichon on Unsplash

The below graphs are taken from a recent (June 2019) report by Knight Frank on "prime" residential pricing across the world. They define "prime" as generally being the top 5% of each market by value. What these graphs show are the spread between the average price of a prime property and the top price achieved in that market.

The most expensive market is Hong Kong. The average price of a prime property in 2018 was USD 4,251 per square foot (or USD 45,760 per square meter) and the top price achieved was in 2016 at USD 28,154 per square foot (or USD 303,051 per square meter).

Using the 2018 average, a 350 square foot studio apartment would run nearly USD 1.5 million (or almost CAD 2 million), assuming there are "prime" studios available in the market. Remember, we are talking about the top end of the market.

If you'd like to download a copy of the full report, you can do that over here.