Still Short: Suppressed Households in 2021

(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.

Continue reading

Where did all the cheap rents go?

(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.

Continue reading

Rent Growth in GDP

(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.

Continue reading

Tumbling Turnover

(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.

Continue reading

A Brief History of Canadian Real Estate Investors

(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.

Continue reading

Estimating Suppressed Household Formation

(Written jointly with Jens von Bergmann and cross-posted at MountainMath)

TL;DR

We develop and elaborate a Montréal Method for estimating housing shortfalls related to constraints upon current residents who might wish to form independent households but are forced to share by local housing markets. Applying simple versions of the Montréal Method to Metro Areas across Canada suggests that Toronto has the biggest shortfall, which we estimate at 250,000 to 400,000 dwellings, depending upon assumptions. For Vancouver, the estimated shortfall range is narrower, from roughly 75,000 to 100,000 dwellings. But models suggest housing shortfalls remain widespread, and there is much room for further elaboration. Note: shortfalls estimated in this post only account for those due to suppressed household formation among residents and do not account for e.g. migration pressures, which means that overall housing shortfalls are likely much larger.

Continue reading

What’s Up With Squamish?

(Written jointly with Jens von Bergmann and cross-posted at MountainMath)

In our previous post we have outlined the broad problems with the recent UBCM report, in this post we return to one particular one, the comparison of dwelling growth to population growth for “BC Major Census Metropolitan Areas” (Figure 2 in the report), paying particular attention to Squamish as the largest outlier. To start out, let’s take a comprehensive look at how dwelling and population growth play out across BC’s CMAs and CAs.

Continue reading

Unoccupied Canada

(Joint with Jens von Bergmann and cross-posted at MountainMath)

TLDR

Canadian Census data on “Dwellings Unoccupied by Usual Residents” are frequently misunderstood. Now that data from 2021 are out, we provide a timely explainer and draw upon a variety of resources, including comparisons with US data, Empty Homes Tax data, and zooming in on census geographies, to help people interpret what we can see.

Continue reading

First Peek at Population and Household Data During COVID & Caveats

co-authored with Jens von Bergmann and cross-posted at MountainMath

In this post we look at the most recent population (and household) estimates to see if we can detect any signals concerning how the COVID-19 pandemic may have impacted how (and where) we live. This is inherently tricky; lots of things changed during COVID times, including how well our normal methods of estimation work. That makes time series less reliable, even as we’re especially concerned with how conditions have changed. So in this post we attempt to pay special attention to what we can and can’t glean from the signals we’re receiving so far.

Continue reading

Satellites, Sprawl, and City Six-Packs

Co-authored with Jens von Bergmann and cross-posted at MountainMath

We’re getting better and more accessible datasets for exploring land use change all the time. We have played with the Global Human Settlement Layer (GHSL) data in the past, where we looked at the population data on a 250m grid to compare how different city’s population distribute spatially, as well as the 1975, 1990, 2000, 2015 time series to see how it changed over time. These GHSL population datasets take a variety of input data to build, one part is census or other population-based datasets, the other is the built-out area derived from satellite data that is used to estimate population data at the fine 250m grid.

Continue reading