Co-authored with Jens von Bergmann and cross-posted at MountainMath
Young adults are increasingly relying upon parental help to navigate Canada’s difficult housing market. Here we have a look at how this manifests within co-ownership trends, as described by a recent CHSP release, with a special focus on variation across more and less expensive housing markets. We round this out with contextual data on other ways parents are helping and a brief consideration of how they all fit together.
A lot of my efforts in this blog go toward figuring out solutions to major housing problems. What works? But I also devote time to figuring out what not to do. What doesn’t work and/or just adds new problems? Last year I spent some time working with teammates to direct these efforts into two major reports. I’m linking these reports below.
(co-authored with Jens von Bergmann and cross-posted at MountainMath)
Almost everyone agrees that we have a housing crisis in Canada, and that it has gotten progressively worse over recent history. But there is a problem. The metrics most commonly used don’t reflect that.
Most commonly used metrics use existing households as the base of analysis, but households are a consequence of housing pressures. This kind of misspecification is a form of collider or selection bias that, especially in tight housing markets, misleads researchers toward faulty conclusions and policy recommendations. It blinds researchers to the struggles of people who are unhappy about their current household living arrangement, like young adults struggling to move out of their parent’s place or out of a bad roommate setup, as well as people who have left their desired region and moved away, or failed to move in, because of the lack of housing options.
This post explains the problem with analysis based solely on households in more detail, and explains why this will lead to incorrect diagnoses of our housing problems and misguided policy recommendations.
(co-authored with Jens von Bergmann and cross-posted at MountainMath)
Municipalities in BC are required to submit Housing Needs reports, and integrate these into Official Community Plans and Regional Growth Strategies in something resembling housing targets. The BC Housing Supply Act now sharpens this process and adds some teeth, effectively enabling the province to define housing targets, accompanied by new provincial enforcement mechanisms, where the province selects municipalities not meeting housing need. Left unstated are the details of precisely how we should go about calculating housing needs or housing targets.
Co-authored with Jens von Bergmann and cross-posted at MountainMath
With last week’s CHSP release of data on the investment status of residential properties and the framing of the accompanying article there has been a lot of rather uninformed and misleading news coverage.
The misleading reporting, combined with sometimes plainly wrong statements by people quoted in the news coverage, on one hand highlights the poor understanding of housing in the public discourse. On the other hand it highlights the importance of providing careful framing with data releases. In general it is good practice to accompany a data release with a brief analysis to provide framing and context. Analysts close to the raw data will have a much better understanding of what the data is measuring and can properly frame the data. Unfortunately, StatCan fell short of doing so, and the overview analysis provided by StatCan itself contains a number of problems. Given the public attention this has gotten it’s probably worthwhile to take a look at what the data does and does not say, and to correct some of the misinterpretations that have been circulating.
(Joint with Jens von Bergmann and cross-posted at MountainMath)
In May we estimated suppressed household formation across Canada using what we called the Montréal Method, finding strong evidence for suppression across many parts of Canada. As a reminder, we designed the Montréal Method to estimate housing shortfalls related to constraints upon current residents who might wish to form independent households but are forced to share by local housing markets. Now that we’ve got 2021 Census data out, it’s time to update our estimates. Given the data available, currently we can only estimate metro area effects of our previous Model 1 (crude household maintainer rates) and Model 2 (age-adjusted household maintainer rates). But that’s a start, and we’re also now enabled to extend the long timelines for Toronto, Montréal and Vancouver from our previous post to include 2021. Overall, current suppression of households alone suggests a shortfall of over 400,000 dwellings in Metro Toronto, and 130,000-200,000 across Metro Vancouver.
(Joint with Jens von Bergmann and cross-posted at MountainMath)
It can be really useful to count things, but sometimes numbers end up causing confusion and misunderstanding rather than helping. Often this has to do with how the number is presented and attached to claims. Other times it has to do with problematic procedures used to obtain the number. Here we want to explore these problems more in detail concerning a claim that “Canada lost 322,000 affordable homes” between 2011 and 2016. This stat is generally made in reference to “private” rentals, and is contrasted to the number of non-market units built between 2011 and 2016, pegged at 60k units.
(Joint with Jens von Bergmann and cross-posted at MountainMath)
Every now and then the topic of the GDP share of the “Real Estate Industry” comes up, often linked to the suggestion that an economy has become too dependent upon real estate. But this usually involves a fundamental misreading of the data. As people who pay attention know, the NAICS sector [53] “Real Estate Industry” of the expenditure based GDP produced by StatCan is mostly just rent and imputed rent.
(Written jointly with Jens von Bergmann and cross-posted at MountainMath)
We’re increasingly gathering lots of different measures of residential mobility in Canada. Which is great! Especially insofar as we want up-to-date information about demographic response through the pandemic. Here we want to add the CMHC Rental Market Survey (RMS) to the mix, comparing to Census and CHS (Housing Survey) results. Adding it in reveals a general decline in tenant mobility only recently (and partially) reversed. But it also raises a mystery worth solving about divergent CHS results and points toward the value of triangulation.
(Written jointly with Jens von Bergmann and cross-posted at MountainMath)
The newest trend in the search for reasons for rising home prices is to look toward investors. The Bank of Canada released a report showing that the share of investors has risen over time. For this they took mortgage data from federally regulated financial institutions and matched them with credit history to determine if some of the buyers already owned property before they bought (during roughly the past 10 years) and kept it after they bought.
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