Jens von Bergmann

I'm confused. Isn't zoning meant to just describe the building that's currently on the lot 😜

Demand based zoning – Mountain Doodles What if zoning was responsive to underlying demand to live in an area? A simple model to estimate demand-based zoning in Vancouver.

When discussing reforms like multiplex zoning, demand estimates rarely enter the discussion (except as a force to be countered).
@lausterna.bsky.social and I think that’s unfortunate and we run some simple estimates what it would look like to zone for the demand to live in an area.

Figure from Ernie Tedeschi titled nonfarm payroll employment percent revisions

Important to remember that because of the incredible work of government statisticians, initial BLS employment estimates have become MORE accurate.

Its revisions are now smaller than those from years past

excellent figure from @ernietedeschi.bsky.social

Shows total crimes and property crime rates peaking in the mid-1990s and the declining, but slightly increasing in the oast 10 years. Shows ciolent crime and other crime oeaking jn the 1990s but then leveling off

Police reported crime in Canada 1962 - 2023

Yeah, I am a big fan of that one (except the meat ball) if I go early enough so that they aren’t out.

That’s fair. Although they do have some decently priced options and I was very happy with my salmon I kits bowl, I am a sucker for simple bowls like that.

No parking in either direction. Seems clear and to the point. No complaints from me, they should put more of these up in more places.

Haha, guilty as charged. Yeah, it can get slightly annoying but I don’t really mind.

Raisu is my favourite too. Toyokan Bowl is fairly new I think, I have only been once but really liked it.

New Fuji is great. (As are the other restaurants in that group.)

It is common for NIMBYs to give absurd reasons for opposing new housing.
Because they know their real reasons are unattractive.

YouTube video by Oh The Urbanity! The Problem With Left-Wing NIMBYism

The Problem With Left-Wing NIMBYism

Important point by @jensvb.bsky.social and @lausterna.bsky.social: when people move out to live on their own, their housing costs go up, but (by revealed preference) they are better off. Hence, housing costs alone are an incomplete picture.

doodles.mountainmath.ca/posts/2025-0...

Chicago has a market for parking reform The city’s new reforms will help build more units

Zak and I coded over 900 upzoning requests introduced in Chicago to analyze how home builders responded to the city’s parking reform (part of the Connected Communities ordinance)

Guess what: they responded by building less car parking 👏

No other way to slice it: this is incredibly fucked up.

Image of population growth for Vancouver 1921-2021 separating out region and adding note of bridges connecting each region.

Visualizing a century of Metro Vancouver growth by region and bridge (or other transportation connection) into the City proper.

Still playing around, and pretty rough, so tell me what I'm missing!

Fairly faithful rendering of the AI prompt “Draw a beautiful car that will make America great again and result in a Great Leap Forward, with our Dear Leader in the foreground.”

The housing shortage isn’t just about homelessness.

It’s also the young person who can’t afford to move out. It’s the couple who can’t get a place big enough to have children. It’s the people stuck with roommates (or even in abusive relationships) because they can’t get a place of their own.

Supply has to be ridiculously low to create our peculiar affordability problem. Maybe this is a repeat of some things I have written recently, but I think it can serve as a sort of summary statement of our problem.

“We don’t have a marginal or arguable housing shortage. We have a ridiculous housing shortage, and the patterns in rent and price inflation that it creates could only appear under ridiculous conditions.“

kevinerdmann.substack.com/p/supply-has...

Thanks for writing this resource. Increasingly more people are working with spatial data but quality and understanding of basic spatial concepts is … not very consistent. Resources like this are badly needed.

Reading further in the Glaeser and Gyourko paper, they seem to treat each (harmonized) census tract as an independent observation. 😱 Unless they can convince me that there is no spatial autocorrelation in their data, which requires work they don't show, their standard errors are uninterpretable.

I think the core problem here is much broader, there is a tendency to happily take data series done by others unquestioned. When people need data, they look through the shelf and grab a can with a label that (roughly) matches their needs. But don't check what's actually in the can. That's a problem.

Yeah, I think the takeaway is that their land holdings are worth a ton of money. Using assessed value to pin down how much each property is worth is not going to be all that useful.

Investing in definitions and framing – Mountain Doodles Taking a closer look at the CHSP data release and clearing up some misunderstandings.

So what exactly is the problem with articles like this? Glad you asked, @lausterna.bsky.social and I took a detailed look at this several years ago to explain why this does not mean what several newspaper articles (and a surprisingly high number of housing talking heads) made it out to be.

Headline from a very misleading G&M article.

Searching for outliers is always dangerous, that's where having a thorough understanding of the data really matters. By easy to misinterpret I mean that newspapers do exactly that and write bonkers wrong articles based on this without stopping to ask and carefully consider definitions and context.

library(tidyverse) library(cansim) get_cansim_connection("46-10-0095",refresh="auto") |> filter(Estimates %in% c("Percentage - investment status","Number of properties"), `Period of construction`=="Total, all periods of construction", `Property type`=="Condominium apartment", `Ownership type`=="Total, all ownership types", `Investment status`=="Investment property", REF_DATE=="2022", nchar(GeoUID)==3) |> # only CMAs collect_and_normalize() |> select(REF_DATE,GEO,Estimates,val_norm) |> pivot_wider(names_from=Estimates,values_from=val_norm) |> filter(`Number of properties`>=1000) |> # filter out ares with few condo apartments arrange(-`Percentage - investment status`)

Minor update of CHSP estimates out today, estimates are now on 2021 geographic boundaries. Using this as an opportunity to showcase the "search for outliers" data practice that's unfortunately all to common and yields results that are both unexpected and also easily to misinterpret.

Good to know. Was worried that it somehow went away.

Did this move? I was going to send a link to friend who mostly works in python and wanted a good resource for working with spatial data, but the link is dead.

Dasymetric approximation can improve on that, and that's why LTDB, and NHGIS preform a bit better. There is still a lot of room for improvements to be made on this front. At the same time, methods should pay more attention to also estimate confidence intervals and not just point estimates.