Why People Move in Canada & the USA: Comparing CHS, AHS, & CPS results

Why do people move? I’ve taken up this question in a series of recent posts (some co-authored), and though the available data to address the question remains sparse, it’s getting richer all the time. Today I want to compare three different sources of information, highlighting how much it matters just how we ask people about their reasons for moving.

The Canadian Housing Survey (CHS) is the newest source of information on reason for move. Its format borrows heavily from the American Housing Survey (AHS). But the Current Population Survey (CPS) also provides information on reason for move in the USA. Each survey asks about reason for move in slightly different ways.

In the USA, the CPS and the AHS ask about reason for move in different ways that might at first seem subtle, but have a big impact on results. The CPS tracks individuals, and asks where they lived one year ago. If they lived somewhere different from their current residence, they’re asked “what was your main reason for moving to this house?” This directs them to choose only one reason as their main reason, with options to specify reasons not on the list. The AHS, but contrast, tracks households, and asks only the reference person for the household if they moved in the last two years.  If so, they’re directed to a “recent movers” section, providing a little preamble and asking them repeated yes or no questions about their move, each of which might constitute one of multiple reasons to characterize their last move.

Reason4Move-A

There are a few major differences in these questions which I’ll detail in a moment, but one is worth talking about insofar as it’s especially subtle given its possible impact. Researchers often think of two separable but related processes as involved in moving. There are the “push” reasons you might leave a home and the “pull” reasons that might draw you to a new one. Reading the different questions carefully, the CPS clearly cues for “pull” reasons in specifying “reason for moving to this house.” The implicit comparison is “as compared to some other house” you might’ve moved to, rather than “why did you leave your old house.” The AHS more neutrally refers to moves overall, letting respondents sort through push or pull factors relevant to each option. I’ll come back to why this might be importantly in a moment. First let’s jump over to the Canadian Housing Survey question, which asks the responding member of each household about their previous residence and the move to their current residence, no matter how long ago it occurred.

Reason4Move-B

The set up is then quite similar to the AHS, except the CHS appears to provide all of the options at once instead of one at a time (people can still choose more than one). There is significant overlap (one might say “copying”) in the language of each option, though the CHS also provides a few extra options unavailable in the AHS, concerning moves for school, personal health, and to become a homeowner (all closely related to options available in the CPS).

Let’s quickly summarize major points of difference:

  1. individual (CPS) v. household (AHS, CHS)
  2. one-year (CPS) v. last move within two years (AHS) v. last move (CHS)
  3. different option lists (CPS, AHS, CHS)
  4. choose only “main” option (CPS) v. all relevant explanations (AHS, CHS)
  5. cued for place moving to (CPS) v. cued for many reasons for moving (AHS, CHS)

All of these differences create real problems for comparing results, but its also clear that the CHS and AHS are closest (rather than the CHS and CPS, which I’ve compared before). So let’s compare CHS (StatCan 46-10-0036-01) and AHS (Interactive Table) first. Here I’ll compare countries overall and also the four biggest metro areas within each country to get at some of the variation.

Reason4Move-C

The Canadian data is themed in the “cool” colors of blue, purple, and green, while the USA is in “hot” shades of red, orange, and yellow (“hot zone” references entirely unintentional, but perhaps apt). Here we see only the categories where AHS and CHS options map – almost identically – onto one another. For many options, the percentage of movers indicating the option at least partially explains their last move matches pretty closely. In particular, the “forced move;” “new job;” “change in household size;” and maybe “upgrade to bigger dwelling” all look like the AHS and CHS could plausibly be drawing upon the same distributions. But there are some big differences with the other options, with Americans reporting greater likelihood a move relates to “form own household;” “be closer to family;” “reduce commuting time;” “reduce housing cost;” and move to a “more desirable neighbourhood.” Are these real differences between countries or artifacts of the different surveys themselves?

Let’s zoom in on a few areas and add in the CPS comparison (here accessed via IPUMS for contemporary metro data) to provide more information. First up: forced moves!

Reason4Move-D

I’ve written about “forced moves” before, with special attention to those relating to landlords, banks and other financial institutions, and government actions in Canada and evictions and foreclosures in the USA. I puzzled over the differences between Canadian (CHS) and American (CPS) data. But looking across all surveys, we can see that the CHS and AHS data actually look very similar. It’s the CPS that seems to report an unusually low percentage of evictions and foreclosures rather than forced moves. So what’s happening? If one were reporting only the main “reason for move,” it would seem like being forced out of one’s previous residence would rise to the top, so it’s probably not just a matter of choosing a single “main” reason vs. multiple reasons. BUT let’s remember that the CPS also conditions peoples’ choices toward “pull” factors relating to the “main reason for moving to this house.” So CPS respondents are likely drawn toward considering why they ended up in their current residence, as opposed to other possible places they could’ve moved, rather than reporting on why they left their old place. Like I said, it’s a subtle difference in question wording, but here it probably has a big impact.

Returning to the AHS and CHS comparison, it looks like forced moves have been a little bit more common in the USA than in Canada, which matches with my rough expectations given differences in tenant protections, mortgage finance regimes, and economic turmoil. (If anything, I suspect these differences may become more stark, with more Americans experiencing forced moves as pandemic restrictions loosen). There remains big variation within each country, with Metro Vancouver topping forced moves in Canada and Chicago topping forced moves (and exceeding Metro Vancouver’s rate) in the USA. Of note, the CPS data is probably less reliable at distinguishing between Metros, but it’s notable that Chicago still stands out.

Let’s try moving for work!

Reason4Move-E

We can consider two different work-related options explaining moves: moving for a new job and moving to reduce commuting time. Interestingly, new jobs or job transfers account for more moves than reducing commutes in Toronto, Calgary, Vancouver, and Dallas. This is likely related to the high in-migration to these metro areas. Reducing commutes accounts for more moves in generally slower-growing metros (Montreal, NYC, LA, and Chicago). A notably smaller proportion of respondents in the CPS chose job transfer or reducing commute as the MAIN reason for moving to their current house, indicating lots of people considered a job-related move as likely just one of multiple reasons for moving – and possibly less related to why they chose a particular residence from multiple possibilities.

Let’s take a look at a suite of other, more housing-oriented reasons people might choose to move.

Reason4Move-I

“Form own household” as a reason to move is commonly thought of as capturing people like young adults (and/or divorcees) splitting off from existing households to start their own. This is a pretty regular demographic process, so it’s somewhat surprising that it seems to be related to so many more moves in the AHS than the CHS. Is this a Canadian-USA difference? Maybe, maybe not. Here the CHS and the CPS actually look more similar. What’s going on? One likely possibility is related to the fact that the AHS doesn’t have an option for people to choose “to become a homeowner” unlike both the CHS and the CPS. The closest SOUNDING option is “to form own home.” It seems entirely possible that this ambiguity in the meaning of “own home” – whether it means to become a homeowner or to separate from a previous household – explains much of the difference between the AHS results relative to both the CPS and the CHS.

Let’s compare moving for a larger dwelling with moving because of new household members.

Reason4Move-G

Change in household or family size and upgrading to a larger dwelling might be understood as related options. Again, very basic demographic processes – having children, partnering, etc. – often motivates a move to a larger home. Other demographic processes can result in smaller households, of course, but it’s less often people move in direct response. If a change in household size typically operates as a “push” (e.g. “this place is too small for us now”) then moving to a bigger dwelling operates as a “pull” (“this place is just right!”). What’s interesting here is that the CPS is predisposed to capture the “pull” part of this kind of move, and has no option at all for the “push” part. Perhaps as a result, here the CPS seems to “overperform” with “new or better home” as the MAIN reason for move almost reaching the prevalence of “upgrade to a larger of better dwelling” as one of many reasons for a move in the AHS.

Finally, let’s consider neighbourhood desirability and reduced housing costs

Reason4Move-H

Comparing the CHS and the AHS alone would make it appear that neighbourhood desirability is much more important as a reason for move in the USA than in Canada. We could spin all kinds of possible reasons for this (e.g. greater neighbourhood segregation and inequality in the USA). But adding information from the CPS reveals that moving for a better neighbourhood is very seldom the MAIN reason for a move. People mostly don’t move in search of better neighbourhoods, it’s just a kind of side feature. So maybe it doesn’t actually tell us much that Americans mention this feature more often as describing their reason for moving (when presented with it as a “yes/no” option) than Canadians (provided as one of many options). By contrast, the CPS results more closely track both the AHS results (which still run higher) and the CHS results for moving to cheaper housing as a reason for moving.

LONG STORY SHORT: every move is a story in itself. We only partially capture this story with survey questions about why people move, and how we structure those survey questions really matters for the results we get. Compare with caution!

Metro Flows

Sometimes we talk about cities as if they’re settlements, where people become fixed to place. But in fact, if you track movements of people, cities look more like rivers. People churn through the urban landscape. Net migration numbers are really useful in some contexts, but also obscure the full extent of this churning. Fortunately, BC Stats has numbers that attempt to break down actual flows of people through regions. We can break out Metro Vancouver (a.k.a. Greater Vancouver) and see just how many people we think might be flowing through. Here’s a little graphic I made to highlight this churn, while I continue playing around with the best way to present it.

Flows-MetroVan-2

The numbers and categories for inflows and outflows are straight from the BC Stats regional district migration file for Greater Vancouver (which itself is derived from a more detailed version of Stats Can table 17-10-0140-01 on components of population change). Population, birth, and death figures similar come from BC Stats and StatCan files. I’ve rounded them off and expressed them in millions here both for ease of reading and in recognition of some of the underlying uncertainty in accounting for population shifts.

BC Stats figures divide up international flows into immigration, emigration, returning emigrants, net temporary emigrants, and net non-permanent residents. The many categories reflect both legal statuses and movements of people, which is part of why there are so many and starts to get at some of the complexities of international migration regimes. Then we get interprovincial in and out migration (to Metro Van from other provinces) and intraprovincial in and out migration (to Metro Van from elsewhere in BC). I find it super-cool to see all the flows laid out.

The basic takeaway for me is that over the course of thirteen years, from 2006 to 2019, we see enormous churn through Metro Vancouver. From a base population of 2.2 million, an additional 1.1 million arrivals came to the region. A smaller 0.7 million left. Wow! That’s a lot of turnover! The total 1.8 million moves into and out of the region over the thirteen year period nearly approaches the starting size of Metro Vancouver as a whole, and represents a much bigger number than the net migration of 0.4 million. Adding in 0.3 million births and subtracting 0.2 million deaths, and there’s your growth of roughly half a million people in Metro Vancouver through 2019.

What’s even more striking is that the moves into and out of the region are dwarfed by the moves within the region. That’s because, as I’ve previously discussed, local moves are a lot more common than regional ones.

Mobility1

Heck, most moves are within municipal boundaries, and well within metropolitan ones (see previous post for more discussion of this figure).

So the churn we see in metropolitan flows is only a small part of residential churn overall. People move! When we think of cities, we need to recognize this movement as fundamental to how they work. Our “settlements” really aren’t very settled at all.

UPDATE June 10th

For comparison’s sake, let’s update the figure above by adding an estimate of internal moves. These are moves from one location to another within the metro area of Vancouver, and as such they don’t add or subtract from the metro population as a whole. Instead they just highlight the centrality of mobility to urban life.

Migration_Flows_Mobility_Add_2006-19_MetroVan

We don’t have a straightforward estimate of these moves from StatCan data. So here I draw upon Census microdata from CHASS. I hold the internal moves constant by averaging the estimates of how many people recorded a move within the Vancouver CMA across three census years (2006, 2011, and 2016). The estimates vary a bit between years, dipping from 260,590 moves in 2006 to 251,635 in 2011, before rising again to 278,632 in 2016, but I don’t have data for every year and I like the graphic impact of treating it as a constant for comparison with in- and out- flows.

Takeaway: when you add in local moves, the city looks even more like a river. In fact, the total number of moves between 2006 and 2019 adds up to roughly 5.2 million. The population in motion more than doubles the population of the “settlement” at the start (2.2 million) and nearly doubles it at the end (2.7 million) of the period in question. You say settlement, I say river.

 

Projections and Self-Fulfilling Prophecies

jointly authored with Jens von Bergmann at MountainMath

 

When people want to live in your city, how many should you let in? On the one hand, this is a moral question. Do you have an obligation to people who don’t already live here? On the other hand, it’s a moot question. At least in Canada, cities don’t have the power to control migration.

BUT WAIT! Cities DO have power over how many new dwellings to allow. This actually changes our moral question a bit. Cities can’t keep people out, but because they have power over dwellings, municipalities can control how many people get to remain in. As a result, if you don’t allow any new dwellings when people want to live in your city then rich people will generally outbid poor people for the housing that’s left.

It may be the case that municipal politicians are fine with rich folk replacing the poor folk in their cities while their own housing rapidly appreciates in price. Why let any new housing get built? “No thanks, we’re full!” But they can’t always SAY this. Especially in cities full of renters that generally support progressive and inclusive values.

So what to do? Two paths are readily available. One: transform the moral question (“isn’t it terrible that developers make money off building housing?”) Two: turn the moral question into a narrow technocratic one instead. Let’s explore this latter option a bit more, because it’s really interesting and sits well within our wheelhouse (mathematician and demographer).

Here in the City of Vancouver, a new motion was just launched, titled Recalibrating the Vancouver Housing Strategy (RVHS). There are some good initiatives in this motion, but the main thrust and motivation is to turn the moral question of how many people get to remain in Vancouver into the narrow technocratic question of how do we forecast population growth? As any demographer can tell you, this can be tricky, especially when it comes to forecasting for municipalities. But there’s a naive kind of work-around some people use when they don’t follow demographic techniques and concerns very closely and don’t want to think too hard about the question at hand. They simply turn the population forecast into a projection forward from how a city grew in the past.

This is a neat trick! Especially if you’re in a city that’s limited new dwellings in the past and thereby kept its population growth to a minimum and you want to keep it that way. “The evidence suggests we haven’t been growing very fast, so we shouldn’t add much more housing.” With a little bit of hand-waving, the number of dwellings allowed by the city is reimagined as something that can be tailored to meet the forecast rather than the central determinative factor of the forecast.

Is this the kind of thing that could happen in Vancouver? Before we get into the motion, let’s just quickly look at Vancouver’s recent past. We know prices and rents rose rapidly through 2016 (and beyond), which is pretty good evidence that we didn’t add enough housing for the people who wanted to live here all by itself. But how did the City of Vancouver grow relative to the rest of the region? It grew more slowly. (“No thanks! We’re full!”) Did we lose poor people and replace them with rich people as a result? Yap, this is exactly what has happend in the City of Vancouver, which has lost lower and middle income people, and gained high-income people, at a faster pace than the surrounding Metro area.

2005-2015_rel_change-1

 

The Motion

Now let’s get back to that RVHS motion, starting with part A:

THAT Council direct staff to revisit the Housing Vancouver Strategy targets to align with historical and projected population growth based on census data.

This is a vague statement. There are, of course, many ways to “align” something (Dungeons and Dragons fans may be immediately reminded of the nine different alignments readily found therein). There are also many ways to project population growth. These often rely upon multiple sources of data. Birth rates, death rates, age structure, labour market statistics, and net migration rates serve as typical baseline sources of information for demographers, and are usually gathered from all manner of data (e.g. vital statistics, surveys, policy-based immigration projections, etc.) rather than simply historical census data. So how is the author of this particular motion imagining more specific alignments and projections? The answer can probably be found in the WHEREAS sections 4 and 5:

Population growth has been consistent at approximately 1% per annum over the past 20 years according to Statistics Canada census data. Based on this historical trend, a similar growth rate for the coming decade would amount to a population increase of around 66,000. In the City of Vancouver, the average household size is 2.2 individuals per dwelling unit (or “home”);

The target of 72,000 new homes across Vancouver in the next 10 years multiplied by 2.2 would mean a population increase of 158,400 – more than twice the historical rate. A projected historical rate of population growth would imply instead a need for roughly 30,000 new housing units over the coming decade;

We’ve left the refined techniques of demography behind here, as well as the determinative forces of births, deaths, and moves. Indeed, people pretty much disappear and their dwellings get only scare-quotes as homes. But let’s follow the math we do get and try and understand what projecting past trends means in terms of numbers (leaving aside if we agree that things went splendid and we should just keep going the same way). Let’s try and reproduce the estimation of new housing units assuming we hold the 20 year trends in the two mentioned metrics, population and household size, constant.

The 1% annual growth rate roughly checks out, although there have been variations.

cov-vs-metro-pop-growth-1

 

And population in the City has grown consistently at a lower rate than overall Metro Vancouver population. In fact, if the City of Vancouver had grown at the same rate as Metro Vancouver over those 20 years, Vancouver would have had 60,000 more people within city limits in 2016. But maybe people would just rather live farther out in the surrounding suburbs? Again, there are variations, but overall that is not what the price and rent data tell us.

rent-unnamed-chunk-3-1

 

People want to live in Vancouver. But they often settle for living farther out, based on the specifics of what they want and can afford. The competition for the limited number of dwellings in Vancouver drives up prices here relative to surrounding municipalities.

So what to make of the close relationship between population growth and dwelling units added? It’s a real relationship.

dwelling-pop-unnamed-chunk-4-1

 

The motion, as presented, seems to suggest that this close relationship is evidence that we’re projecting population growth really well, thereby allowing almost perfectly enough new housing to meet population needs. Is this what we’re doing? Well, no. In fact, the amount of new housing allowed sets a cap on population growth that can only be exceeded by increasing household size (which in many cases cities have also made illegal)1 or decreasing the number of empty dwellings.

There is broad support for decreasing the number of empty dwellings, and both the City of Vancouver and the Province of British Columbia have put in place taxes on vacant properties and their owners to do just that. Have they succeeded? Quite possibly! But compared to other municipalities, Vancouver’s vacancies (as recorded in the Census) looked relatively normal prior to the new taxes, despite persistent rumours of some mythical oversupply. After the new taxes, administrative data reveals there aren’t many taxable units left vacant at all (~1%).

What about household size? The motion suggests imposing a constant for Vancouver, expecting 2.2 people per household. But household size is not staying constant. It’s falling all across Canada, due to a combination of forces (aging of the population, declining childbearing, changes in partnership, the rise of people living alone). We also know that as people get richer, they tend to occupy more space. And, as pointed out above, Vancouver’s been getting richer.

hh-size-chunk-5-1

 

As we see, household size in the City of Vancouver has continuously declined over the years, a trend that has significant impact on the relationship between housing and population growth. Sticking with the bad assumption that past population growth should be predictive of future housing needs, we can see that we’re still going to need more housing per person than in the past. Projecting these trends forward, lazily anchored at the 2016 census data, gives an increase in population in private households of about 67,000 and a corresponding increase in 41,000 households (aka occupied dwelling units). And that is not yet accounting for the increase in population in non-private households that Vancouver has experienced, like retirement homes or similar institutional housing.

So if the RVHS motion points us toward a bad way to do population projections, then how should one do it? There are lots of models to look at, but given that people want to live in Vancouver, a key ingredient in any model should be how much housing will be allowed. Conditional on allowing a given amount of housing, we can attempt to forecast how many people will come. But this moves us back from narrow technical questions (which we’re more than happy to continue exploring in depth!) toward the central moral question at hand. How many people are we comfortable allowing to live in Vancouver? Because if we allow more housing, more people will come. And if we allow more housing, we’ll also allow more of those currently at risk of feeling unwanted in Vancouver to stay.

That begs the question: What would be the problem with allowing more housing? The last WHEREAS of the RVHS motion holds an answer to that.

A revised and more accurate understanding of demographic needs and demand will assist in properly planning for the post COVID-19 reality. Setting excessively high targets will pressure the City of Vancouver to grant significant amounts of density at a low price, in an attempt to induce housing construction approaching the HVS targets. This will cost the City of Vancouver potential revenue, and will mean that the City abandons its commitment to having growth pay for itself.

In short, housing might get cheaper. Which incidentally is quite in line the goals of the Vancouver Housing Strategy.

But there are a couple things here that need a bit more unpacking. First, from the title throughout the motion and showing up here again are mentions of planning for a “post COVID-19 reality.” To put it bluntly, this is odd. These parts of the motion caution us against assuming what comes next will reflect what came before. But, as discussed above, this is exactly the assumption the rest of the motion says we should make, resting as it does upon a very selective reading of Vancouver’s recent population growth. Weird contradiction. But then again, pretty much the same language has been employed way before COVID-19 was on anyone’s radar, suggesting that COVID-19 has just been tacked on for extra effect.

Second, the notion that “growth pay for itself” sounds quite reasonable, but it’s not clear what that means in practice. In Vancouver, new housing projects pay a variety of municipal fees, DCLs, CACs and additional engineering fees upfront, and annual property taxes thereafter. How much of the overall cost of living in the city should be charged upfront, and how much should be charged over the lifetime of the housing as property taxes? That’s a political question that Vancouver should have a discussion on.

Charging high entry fees keeps prices high, not just of new housing but of all housing. It encourages treating housing as an investment, with low holding costs (property taxes) and high barriers to increasing housing even as population pressures keep prices and rents rising.

Charging a lower entry tax and collecting a higher portion as property taxes later can lower the entry point to housing and spreads the costs out over the lifetime of the dwelling unit. This treats housing as a place to live, lowering the barriers to new housing construction and asking people to pay for city services and amenities over their time living in the city.

The (sort of) good parts of the motion

Let’s end with a few bright notes. There are some good parts to the motion! We like data and Part B asks:

THAT Council direct staff to provide annual historical data since 2000 on the number of units approved through rezoning, the breakdown of housing types that have been approved, housing starts and net housing completions, and estimated zoned capacity for the City of Vancouver.

This part of the motion is asking for better data, but it needs refinement. As it is right now it is hard to see what it will accomplish.

Number of units approved through rezoning is hard to interpret unless it is accompanied by more detail on how many of these units actually got built. Take the approved first version of the Oakridge development for example. A massive number of units got approved, yet the project died when drilling found an aquifer that precluded the project from going forward as approved. Several years later, a different proposal got approved, for the data on approvals to be useful we need to know what happened to those units.

Monthly data on housing starts is already easily available, asking the data be reproduced adds zero value and amounts to a waste of staff time.

Net housing completions is an important number, but very hard to do in Vancouver, given our high reliance on informal housing. It is still worthwhile to try and approximate this, but the motion should be clearer what part staff should focus on beyond the data on completions, demolitions and secondary suite estimates that we already have.

Estimates of zoned capacity is a great stat to get clarity on. Some vague estimate has been making the rounds for a while after surfacing in a consultant report, with next to no detail how it was derived. Having an estimate with a clear methodology would be a great addition to inform Vancouver housing policies.

Part B is a good and simple ask:

THAT Council direct staff to clarify whether the Vancouver Housing Strategy targets refer to net housing completions or gross housing completions.

Part E is mostly redundant:

THAT Council direct staff to provide detailed inventory data through the Open Data Portal4 of housing starts, development projects anticipated in the pipeline (including form and type of units), and existing zoned capacity (disaggregated by local area) to inform this work.

The open data portal already has detailed information on housing units in the pipeline. The information could be improved, but this ask is useless unless it specified how. As mentioned before, detailed information on housing starts is already easily available as open data, monthly stats by structural type and intended market, down to the census tract level. It is less helpful than the other parts above and risks directing staff resources away from other project just to replicate what’s already out there.

Bottom line

There’s no way around it. How many dwellings to allow in a city is ultimately a moral question rather than a technocratic one. Given the overwhelming evidence that people want to live in places like Vancouver, population forecasts necessarily reflect first and foremost how many new dwellings we’re willing to allow. In technical terms, it’s silly to imagine we’re meeting the needs of population growth when we’re in fact setting a hard cap on population growth. In moral terms, we come back to the central question: Are we planning for kicking poor people out? Or are we open to inviting more people in?

As usual, the code underlying the stats and graphs is available on GitHub for anyone to reproduce or appropriate for their own use. And if you want to read (much) more about how to know if you have enough housing, check our simple metrics post.


  1. For example the City of of Vancouver only allows at most one kitchen per dwelling unit and limits the number of unrelated individuals sharing a dwelling to 3 (+ 2 boarders or lodgers) to restrict sharing of homes. [return]

COVID deaths in context by weeks

co-authored with Jens von Bergmann & cross-posted over at MountainMath

In our previous post on COVID mortality in context, we tried to place COVID deaths, as recorded so far this year, in the context of expected deaths from previous years. There have been a lot more developments since that post. And unfortunately a lot more deaths too.

Here we’re providing an update to our previous post, but also expanding on that post by talking a bit more about new mortality analyses and the progression of outbreaks in terms of expected deaths on a weekly basis. First, an update! We previously placed COVID deaths in the context of expected deaths at the national level, starting after the 20th death was recorded. What does that look like now?

COVID-2-a

COVID-2-b

As visible in the mortality data, Belgium has moved to the forefront of the COVID outbreak in Europe in terms of COVID deaths relative to expected deaths from years prior. Ireland, the UK, and the US appear to continue to climb. By contrast, Spain and Italy, early centres of the outbreak in Europe, have largely leveled off. Though the USA “leads” in deaths from COVID-19, this doesn’t (yet) show up in the relationship between COVID deaths and expected deaths because the USA is enormous, with a lot of expected deaths every year, and the outbreaks of COVID deaths have been heavily concentrated in a few locales so far.

Overall, and as mentioned previously, there’s still a lot we don’t know in these comparisons. For instance, we don’t know if we’re actually counting all of the deaths due to COVID. Lots of people don’t get tested, and cause of death is always tricky to determine in the best of times, let alone with an overloaded medical system and coroners’ offices. As a result, revisions to the data can add dramatically to the death toll, as happened recently in New York City. In addition to good COVID death data, we’d also like updated data on mortality overall. We’ve seen recent – and very preliminary – data out of NYC and scattered other locales suggesting that all-cause mortality has risen dramatically in places with severe COVID outbreaks.

Where we have it, we can put updated all-cause mortality in conjunction with COVID mortality and expected mortality all together. Putting this on a weekly basis really provides a sense of the progression of outbreaks and how overloaded they leave medical systems in terms of the normal deaths they have to deal with. Given some of the data from NYC, here’s roughly what that looks like.

COVID-2-c

 

We notice a downturn in deaths as recorded by the CDC FluView for the last week they report data (the week ending on 2020-04-12). This is not a REAL downturn. Rather it illustrates the reporting lag for data on deaths. It can take several weeks for the numbers to fill in and stabilize. We added the reported Covid-19 related deaths as assembled by the JHU for reference. JHU data was aggregated up the week ending 2020-04-19, so it’s nominally a week ahead of the FluView data. However, these deaths are coded by date reported, unlike the CDC data that is coded by date of death, which causes the JHU data to lead a bit. Even accounting for a possible time shift in JHU data, it appears that JHU data does not account for the full increase in all-case mortality, hinting at likely under-reporting of Covid-19 deaths in the JHU data.

Unfortunately we still don’t have updated all-cause mortality on the country level. As suggested by the lag in NYC data, it takes awhile to compile in the best of times (here’s a look at efforts to gather some of the European data). So here we’ll provide a replication of our previous analysis, but breaking out COVID deaths against expected deaths on a weekly basis for countries instead of across the entire length of the outbreak.

COVID-2-d

COVID-2-e

 

Overall, weekly COVID deaths as a percentage of expected deaths looks broadly similar to our earlier figure, which charted the rise in COVID deaths as a percentage of expected deaths since outbreak deaths began. But there are a few significant differences. The weekly chart better highlights the evolving overload on hospitals and health systems, as well as coroners’ offices, and this is reflected in the y-axis, demonstrating that COVID deaths in Belgium have more than doubled the expected deaths in the last week for which we have data. The weekly chart also more quickly identifies declines in the relative impact of COVID deaths in places where the worst of the outbreak has passed, like Spain, Italy, and France. It will take a long time for the expected death toll to diminish the impact of the overall death toll of COVID in our figures at the top of the post. But on a weekly basis, we can already see the toll of COVID receding in many places.

As we’ve noted previously, it will still take a long time to sort out the overall effects of COVID on mortality. Why? Well, we’re still nowhere near done with the outbreak, and we can expect deaths to continue until we have a vaccine and have reached some level of “herd immunity.” But we’ll also be sorting through the mortality data for years to come. Also important: the toll at national levels, while helpful in assessing cross-national differences, masks the impact at local levels where outbreaks often occur. So it is that the estimate from Belgium, where most recent weekly COVID deaths appear to have more than doubled expected mortality, is dwarfed by the estimate from New York City, where the most recent weekly COVID deaths appear to be more than six times the expected (pre-COVID) mortality.

As usual, the code for the post is available on GitHub in case anyone wants to refine or adapt it for their own purposes.

How many owner-occupiers can already defer their Property Taxes in BC?

We’re rolling around to property tax time, and municipalities are about to feel the COVID-19 crunch. The Mayors of Metro Vancouver have been leading an ask of the province to backstop municipal finances given that many residents and businesses may fail to pay their property taxes. Indeed, the City of Vancouver recently commissioned a survey indicating that due to job and income losses, some 25% of home owners in the city would be paying less than half of their 2020 property tax bills.

One ask from the Metro Mayors is for the province to expand it’s property tax deferral program to cover those not currently included. As they advocate:

propertytaxdefer-2

This, of course, is a big ask! But just how big? Here I want to separate out the ask for businesses and non-profits (where the ask is very big indeed), and focus on homeowners. And after all, homeowners are where the City has focused its survey. So how many homeowners are not currently covered by provincial tax deferment options?

There are two programs covered under provincial tax deferment: the regular program and the program for families with children. The regular program is open to any property owners (of a primary residence) over the age of 55, as well as surviving spouses (of any age) and persons with disabilities. The province effectively puts a lien on your property to secure the debt and charges 1.95% interest on outstanding taxes owed. The families with children program is open to anyone living with or supporting children under age 18, or children enrolled in education (e.g. university), or children with disabilities of any age, and the interest charged under this program (3.95%) is higher.

Just focusing on the two main groups covered, homeowners age 55+ and families with children, we can draw upon census data from 2016 to roughly estimate how many owner-occupied households are likely covered by existing tax deferment options. The answer: the vast majority, over four-in-five. Why? Because home owners are especially likely to be old or have children. Here are owner-occupier households in BC by age of primary maintainer and presence of children*:

propertytaxdefer-3

Overall this is good news! Most resident homeowners in BC are already covered under property tax deferment options. And the province will likely see a big uptake in deferments this year through existing programs. But those who fail to qualify also deserve provincial attention. And, of course, renters deserve a lot more attention too. I’d argue that it’s also well worth supporting expansion of the property tax deferral program more broadly since this also supports municipal finances at a very trying time. Moreover, if the province expands the program at family program interest rates, it may also help support provincial coffers down the road.

 

*- Here I lump the relatively tiny set of multiple family households into those without children, following the general household type categorization. See StatCan Table 98-400-X2016226 to play around with your own operationalizations.

So are you two a couple now? Asking for the BC Government

BC has been lauded for rolling out an assistance program for renters, unlike basically every other province. At the same time, BC’s also been criticized for the perceived inadequacy of that rental assistance program, as well as the fact that it literally goes straight to landlords. In conjunction with the temporary eviction moratorium, it would appear that the BC Temporary Rental Supplement (BC-TRS) is really aimed at supporting landlord incomes and easing tenant-landlord relations to avoid a rash of evictions once the moratorium has been lifted.

Here I want to question another aspect of the program, at least as we’ve seen it so far: What’s it got against couples?

The BC Temporary Rental Supplement, as announced today, provides $300 per single person or couple household, and $500 per household with dependents. But roommates can apply separately for benefits, and it would appear each roommate is eligible for a $300 or (if living with a dependent) $500 rent supplement. Here are relevant items from the FAQ:*

Rental_Assist_1This means the “household” definition being applied by the province – whereby roommates constitute separate households – best matches the “family” definition of the Census, whereby family is defined by a couple (married or common-law) or parent-child relationship. The Census considers roommates as members of the same household, but unrelated, and hence not members of a family.

Why does it matter? Well, what’s the distinction between roommates and a couple?** Because if you’re a COUPLE you max out at a $500 benefit with children or a $300 benefit without. But if you’re ROOMMATES, it appears you qualify for $300 each, or more if there are children involved, maxing you out at $600+. In effect, couples have their status turned against them in terms of government benefits.

Interestingly, this isn’t the first time the current BC government has zeroed in on couple status as a determinant of less than favorable policy treatment. The BC Speculation and Vacancy Tax hinges upon marital status in terms of whether overseas partner incomes get counted toward family incomes, distinguishing “satellite families” hit with higher property taxes from everyone else. In effect, if you own a home this is a huge disincentive for formalizing, declaring, or maintaining transnational relationships, at least if your partner potentially earns more than you. BC tax policy says it’s better for you to split up than stay coupled with anyone outside of Canada, just as BC renter support policies seem to tell us it’s better to be single (with a roommate) than part of a couple.

One way of looking at the government position on rentals is that couples might be considered more resilient than singles. So singles, including roommates as well as single parents (who get $500), need more help and more allowances. And as I wrote previously, with respect to rental supports this might well be correct. Singles and single parents make up the bulk of those in core housing need. I’m happy that the BC government is providing special help to those with dependents, even if I wish the amounts were higher.

HouseholdsRenting-fx2

It’s also the case, as in my past research auditing rental listings, that BC’s tipping of the scales against renting couples might actually counteract some of the beneficial treatment they usually receive in the rental market, where landlords tend to discriminate against single parents and some same-sex couples (who may, in some cases, have been taken for roommates). Finally, policy is being rolled out at a ridiculously fast speed, which is important and a success in its own right because people are in need of money now. But that speed is bound to come at a cost in terms of care in crafting policies. We’ll see plenty of mistakes and unintended consequences of fast policy roll-out in the days to come. We shouldn’t forget the urgency behind the roll-out, even as we offer up critiques and fixes.

That said, we’re left with a fun contrast. If Pierre Trudeau famously declared “there’s no place for the state in the bedrooms of the nation,” the government of BC still wants to know: are you two an item?

 

UPDATE (Apr 12, 2020):  Another interpretation (in this case my partner’s) is that the BC – TRS is geared entirely toward assumptions about how many bedrooms different kinds of households need and what the associated costs might be. The logic being that couples might only need 1BR, whereas parents with children need at least a 2BR, and roommates are (ironically) assumed to sleep in separate bedrooms, also requiring at least a 2BR. This interpretation actually mirrors the logic of the Canadian National Occupancy Standards defining the suitability aspect of the Core Housing Needs measure. Accordingly, BC-TRS payments could be designed simply to go up in response to anticipated bedroom need. I like this interpretation a lot, so I thought I’d share it too! (I hinted at the importance of considering bedroom need in my previous post on the renter benefit, only I didn’t think they’d adopt the couple assumption from the National Occupancy Standards, which I’ve also researched in the past! Kicking myself a little that I didn’t think of this interpretation first, but also patting myself on the back for settling down with someone more clever than me…)

 

*- Yeah, also your adult kids don’t qualify as roommates (item 18) and you don’t get any assistance if your landlord is also a family member (item 19).

**- As it happens, I asked just this question in my dissertation… though from a viewpoint embedded within demography (i.e. are people more likely to cohabit with an unmarried partner in response to housing shortages, making them like roommates, or less likely, making them act more like married couples?) In the context of Swedish demography, easier access to housing meant greater likelihood of cohabitation, providing evidence that cohabiting couples tended to be acting more like married couples than economizing roommates. BUT, there’s a lot of grey in there. Especially insofar as we usually leave it to people to define their own relationships.

 

 

 

Context for COVID-19 Mortality so far

co-authored with Jens von Bergmann & cross-posted over at MountainMath

Unfortunately, more and more people are dying due to COVID-19. We won’t know the full toll from COVID-19 for quite some time. But we can at least start to get a sense of its impact. One useful way of assessing the impact, of course, is just to plot deaths attributed to COVID-19. This highlights the real loss of human lives associated with outbreaks. But as any demographer can tell you, deaths are a normal part of life. Within a given population, we can reliably expect a certain number of deaths to occur over any given time period. So another way of visualizing COVID-19 deaths is also useful: How many deaths attribute to COVID-19 are occurring as compared to the deaths we would normally expect to occur?

Below we follow the rise in deaths attributed to COVID-19 through time relative to the expected number of deaths that likely would have occurred without COVID-19 during the same time. [UPDATE April 20: our newest post plots this on a weekly basis]

 

COVID-mortality1

 

This visualization places deaths reported from COVID-19 in the context of expected deaths overall. This helps establish where we know the mortality toll has already been enormous. As of March 31, the end-point of the animation, Italy leads the overall count in deaths attributed to COVID-19. Here we can also report that in just over a month, Italy’s deaths so far attributed to COVID-19 already add more than 20% to its expected deaths. But Spain’s toll relative to its expected number of deaths is ever higher. In just over three weeks time, we can see that COVID-19 already accounts for more than a 30% rise over the deaths that would’ve been expected without COVID-19.

 

COVID-mortality2

 

Unfortunately, most curves are still rising. So far. Initially curves grow exponentially, until aggressive containment or mitigation strategies flatten them. Curves that stabilize and flatten, or even begin to turn downward, reflect countries where deaths attributed to COVID-19 are being overtaken by deaths that might’ve been expected to occur anyway. Hopefully this reflects an outbreak coming increasingly under control – GOOD NEWS – rather than a data gap.

But the possibility for data gaps is very real. It will be quite awhile before we can properly estimate the overall toll from COVID-19. We already have preliminary data on deaths attributed to COVID-19 rolling in. But this data will be messy, excluding cases where COVID-19 was missed as a cause, despite being present, and possibly over-including cases where the cause was actually not COVID-19 (e.g. instead common influenza), or COVID-19 was present but the death should be attributed primarily to a different underlying condition claiming the life. Cause of death data is never clean to begin with. As COVID-19 overwhelms medical systems and coroners’ offices, we should fully expect that data quality will suffer further.

More concretely, COVID-19 deaths will show up in the mortality databases with code U07.1 or U07.2 in the current ICD-10 classification system (or RA01.0 and RA01.1 once ICD-11 comes into effect). But many will likely also get classifed as J11, J18 or J22. When the dust settles, we will have to check how these cases have evolved over time and estimate how many cases in 2020 (or late 2019 in the case of China) are likely misclassified COVID-19 cases.

 

COVID-mortality3

 

We will also eventually get data about overall mortality. We will likely see deaths increase beyond those attributed directly to COVID-19. Deaths will rise both in response to complications introduced by COVID-19 in those with pre-existing conditions, and in response to people dying due to failure of overloaded medical systems to be able to respond to non COVID-19 cases they way they normally would. At the same time, some other non-COVID deaths may go down. This can happen when COVID-19 claims lives that otherwise might’ve been claimed by something else (e.g. an underlying condition). But it can also relate to deaths that don’t occur due to lockdown and the measures related to dealing with COVID-19. For instance, the regular toll of influenza may diminish in response to the lockdown targeted at Coronavirus (making it unclear what the “expected” baseline case count for 2020 should be). Similarly, fewer cars on the road will likely result in fewer deaths from car accidents. For references, see the most common causes of death in Canada in normal years here. A similar discussion of the eventual breakdown we’ll need in mortality data can be found in this demographer thread attempting to summarize some of this complexity via twitter feed.

The mortality data coming in bears watching, both in terms of COVID-19 attributed deaths and deaths overall. Some analysts (e.g. in Italy and Spain) as well as some China skeptics, are already drawing upon anecdotal mortality data to suggest that the toll from COVID-19 is far greater than revealed in the official data so far. These kinds of analyses are especially potent when applied to cities and regions as opposed to countries. But ultimately it will take years for demographers to sort this all out. In the meantime, we can at least get a rolling sense of COVID-19’s toll by looking at deaths attributed to Coronavirus relative to deaths otherwise expected based on past data from the same rough period of time.

As usual, the code for the post is available on GitHub in case anyone wants to refine or adapt it for their own purposes.

 

Update (2020-04-06)

It’s been a week since we posted this, and things are changing fast with covid-19 related deaths increasing exponentially and background mortality estimates only increasing linearly with time. The traces in the animated GIF already highlight this, but here is a quick update of what the graph looks like using data from a week later.

covid_mortality2

And for completeness, here is the static graph with the latest available numbers.

covid-19-mortality-final2-1

 

BC Renters by Household Type & Need

Yesterday BC unrolled a quick support package for tenants and landlords affected by COVID-19 related job and income losses. In addition to an effective moratorium on evictions (yay!) and a rent freeze for the duration of the crisis, the province offered $500 going directly to landlords to offset rents for those with lost income. The measure appears to be aimed at preserving landlord incomes and landlord-tenant relationships even as the eviction moratorium temporarily boosts the bargaining power of tenants. Lots of details remain to be determined, including, apparently, whether the benefit applies per tenant or per unit.

Here I wanted to quickly toss out relatively recent figures for what renter households look like in BC, broken out by Core Housing Need. Data come from a quick run with Census Analyser (CHASS) for 2016.

HouseholdsRenting-fx2

Many renting households contain more than one income earner, likely making them reliant upon multiple incomes that might have been affected by COVID-related disruptions. If BC goes with a $500 benefit per unit (as opposed to per tenant), this may diminish the ability of multi-income households to make rent. On the other hand, together with the federal CREB benefits of $2000 per month for up to four months, and BC’s $1000 one-time benefit, households that have lost multiple earners will (eventually) be bringing in replacement income. In the meantime, they’re left to negotiate with landlords – who cannot evict them for nearly any reason – for the duration of the crisis.

If we look at renting households in core housing need (before the crisis), most were likely single-income earning households. Single-person households will do the same in the present crisis regardless of whether the $500 rental benefit applies per tenant or per unit. But a lot of renter households contain children and these are also over-represented in core housing needs. Notably, this included over half of all single-parent households in BC even before the COVID crisis. If the benefit applied per tenant and actually included children, it might go a long way toward diminishing the immediate crisis besetting many single parents. It might also assist couples with children, whether they’re reliant upon a single income or not.

More broadly, BC should probably consider targeting some relief at parents, who can no longer rely upon schools or daycares for childcare. But renters with children also face an additional housing burden insofar as their rents tend to be higher. After all, they’re often paying for extra room without the benefit of an extra income. The federal benefits flowing to households with multiple lost incomes will only apply once (if that) to single-parent households. BC should consider extra rent benefits for these households.

Of course, this was true before the COVID outbreak. More broadly, COVID-related policy in BC, and Canada as a whole, so far seems to be working toward putting in place hasty new patches to its old social safety net. This is a good start, but Canada also needs to patch the rips that were already there, which are being torn even further apart under the strain of the present crisis. Raise supports for children. Raise the disability rates. Put policies in place to insure that Canada’s right to housing is more than just a vague promise. If we’re all in this together – as we should be – then now’s the time to prove it by renewing the social contract for everyone. Let’s get to it.

 

UPDATE: Single person households make up a larger portion of renter households (above) than they contain in terms of total renters (below). Both are useful figures, but I earlier posted a figure with numbers based on total renters within households, rather than renter households. I’ve corrected the above to remain consistent with the language of households and avoid confusion. The slide based on total renters within household is now posted below.

HouseholdsRenting-fx1

Knock Knock Anybody Home?

co-authored with Jens von Bergmann & cross-posted over at MountainMath

Empty homes are in the news again in West Vancouver after a West Vancouver council motion asking the province for the power to levy their own Speculation and Vacancy tax.

THEREFORE BE IT RESOLVED THAT the Provincial Government provide local governments with the power to levy their own Speculation and Vacancy Tax, so that they too can address housing affordability and other community effects of vacant homes.

West Vancouver seems interested in the empty homes and not the satellite family component of the SVT, which may well be a wise choice given how messy and problematic a law defined based on spousal relationship can get.

The motion is interesting for several reasons, not just because of the focus on vacancy vs satellite families. It sets the stage by naming housing affordability as a key challenge.

WHEREAS housing affordability is a key challenge in many municipalities but particularly in the District of West Vancouver with a median house price of $2.5 million, and a rental vacancy rate of 1.2%;

As evidence the motion rightly points at the low rental vacancy rate. The ownership metric is curious though as it explicitly focuses on “houses”, excluding more affordable multi-family units from consideration. This is likely no accident, as West Vancouver has a solid track record of focusing their energy on the most expensive type of housing by permitting fewer multi-family homes than more expensive single-detached houses to be built, the latter of which often just replace older single-detached homes and do not add to the dwelling stock.

west-van-completions-1

 

The next part reads:

AND WHEREAS according to the 2016 Census, approximately 1700 homes, or almost 10% of dwellings in West Vancouver, were identified as “unoccupied”;

This is incorrect, the 2016 census enumerated 1,525 unoccupied dwelling units in West Vancouver, comprising 8.2% of the total dwelling stock. Council is only partially to blame for this misstatement, reporting on this census metric has generally been sub-optimal, to say it politely. The problem is not just about getting the number right, but more importantly understanding what the numbers mean. The census enumerates homes that are empty on census day, and homes can be empty for several reasons. Some of which are mundane and even desirable, just one “whereas” ago it looked like council wanted more unoccupied homes – that are available for rent. There are other categories of unoccupied homes that are important in enabling residential mobility, homes that are rented but not moved in yet, homes that are for sale and unoccupied or bought and not moved in yet. The US ACS tries to track down reasons why homes are unoccupied, it can be instructional to use that as base of comparison when looking at Canadian data as in the following graph based on some of our past joint work.

West_Van_2

 

Being unoccupied on a particular day, for example Census day, does not give direct information about homes that might be targeted by an empty homes tax. The list of exemptions in Vancouver’s Empty Homes Tax or the provincial Speculation and Vacancy Tax opens another window into reasons why homes may be empty.

We can further break down the unoccupied homes the census found in West Vancouver by structural type.

west-van-unoccupied-3

 

In West Vancouver, most homes registering as unoccupied are single family homes, followed by units in suited single family homes that the census refers to as “Apartment or flat in a duplex”. This is to a large degree due to the building stock that leans heavily on single-detached homes. The two dwelling types have also been responsible for most of the growth in homes classified as unoccupied in the census.

It is helpful to also look at shares of homes in each type that registered as unoccupied, and put in context with the Metro Vancouver shares.

west-van-unoccupied-share-4

 

The shares of unoccupied homes are generally higher in West Vancouver, with the exception of row houses and highrise apartments. The shift in row houses is fairly recent, and should probably not be over-interepreted because of the small overall number of row homes. The difference in rates of unoccupied highrises likely stems from a relatively high share of rental highrises in West Vancouver.

The high share of unoccupied “duplex” units stands out. Recall that in Metro Vancouver units classified as “duplex” by the census are mostly suited single family homes. These register with the highest share of unoccupied homes throughout Vancouver, which is driven by empty secondary suites in such houses. Incidentally, secondary suites are exempt from both the City of Vancouver Empty Homes Tax and the provincial SVT.

In all of this it is important to remember that census unoccupied counts were taken back in 2016, before these taxes came into effect, and some owners will likely have changed their behaviour because of the tax and rented out or sold their previously empty home. Indeed, we now have a much more recent and much better defined dataset predicting how many problem empties are likely to be taxed by an Empty Homes Tax in West Vancouver. That dataset comes from the Speculation and Vacancy Tax itself. Worth noting: we are still in the pre-audit phase for the SVT and it is not clear how many owners are trying to dodge the tax by declaring incorrectly. But setting aside Satellite Families (where homes aren’t empty), the SVT numbers for the City of Vancouver aren’t very different from the City of Vancouver Empty Homes Tax numbers, where we are now in the third year and already have two years of complete declarations and audit cycles. So far so good.

Bottom line is that a much more reasonable expectation of the number of homes that may be targeted by a West Vancouver empty homes tax at this point is around 221, the number of vacant homes paying the SVT.

west-van-SVT-5

The next two whereas speak to revenue expectations.

AND WHEREAS the Province reported that in 2018, $58 million was collected under the Speculation and Vacancy Tax program, and that $6.6 million of that was collected from West Vancouver homeowners;

AND WHEREAS the Province of British Columbia gave the City of Vancouver the power to impose its own vacancy tax which has provided Vancouver with approximately $40 million in additional revenue;

The $6.6 million cited as being collected from West Vancouver covers both, vacant homes and homes occupied by satellite families. Only $4.1 million was collected for vacant homes in West Vancouver. The comparison the the City of Vancouver tax is somewhat irrelevant to this discussion, other than stressing again that revenue expectations is an important driver of this motion. One should note here too that the tax rate West Vancouver could charge for vacant homes is limited by a very simple calculus. Once the combined tax rate of municipal and SVT vacancy taxes exceeds the property transfer tax, owners can trigger a sale to e.g. a relative in order to pay the lower property transfer tax and be exempted from the vacancy taxes, with all the revenue accruing to the province. The City of Vancouver has hiked their Empty Homes Tax rate and is slowly approaching this limit.

Upshot

An Empty Homes Tax can be useful. It incentivizes better use of property by returning some unproductive properties back into the rental or ownership market. It generates revenue in case people are unwilling to rent out their mostly unoccupied home.

But it also comes at a cost, it can be intrusive and there are always edge cases. And it takes a sustained effort to administer fairly.

We believe that in the case of the Vancouver region the benefits generally outweigh the costs at this time. We can imagine that we might come to a different conclusion if e.g. the rental vacancy rate climbed up above 3%, but we don’t see a medium-term path leading to that.

Looking back at the City of Vancouver’s experience it seems prudent to approach an Empty Homes Tax with realistic expectations. In the City of Vancouver our Former Mayor said that the tax could free up as many as 25,000 empty units for rent, an unfortunate statement that raised expectations unreasonably high and is still being brought up when people criticize City staff for their EHT numbers not measuring up to lofty promises

The bottom line is that clear and realistic expectations are an important part of a successful implementation. It is good politics, and City staff will thank their politicians for this.

As usual, the code for the analysis is available on GitHub.

Overnight Visitors and Crude Travel Vectors

co-authored with Jens von Bergmann & cross-posted over at MountainMath

The spread of Coronavirus is reminding us of just how often people travel around, especially as various locations become quarantined and international travel corridors get shut down. So let’s take a look at some basic data on travel patterns here of relevance to us here in Vancouver. Then we’ll put them back in the context of Coronavirus.

TLDR: travel data is really interesting, don’t be frightened of travelers, and there’s still a lot we don’t know about coronavirus.

We’ve looked at the movement of people before in terms of migration, immigration and commuting patterns. But these are movements that are either regularized, everyday, and routine (e.g. commuting) or shuffle people between one settled set of routines and another (e.g. migration). Travel data gives us something different, representing something more like the unsettled movement of people. People travel for work, to visit family, and of course, for tourism. The Tourism Industry is interested enough in travel data that they ask Statistics Canada to compile data for them. Stats Canada combines Canadian travel surveys and border crossing administrative data to get us a decent look at overnight stays. So it is that we get overnight stayer data for Vancouver!

Let’s look at where people are visiting Metro Vancouver from. The Tourism Vancouver data has an interesting selection of countries available, with special breakdowns for Canada and the USA. More than a quarter of all overnight stays in Metro Vancouver are trips from elsewhere in British Columbia. Another quarter plus of trips arrive from elsewhere in Canada, with Ontario and Alberta leading the way. The USA accounts for just under a quarter of overnight visits. Altogether, Canada and the USA account for over 8 million of the roughly 10 million visits. Most American visitors to Metro Vancouver arrive from nearby neighbours down the Pacific Coast (WA, OR, CA), which together account for over half of travel from the USA. About as many people visit from all of Mexico as from nearby Oregon (140k).

Overnight1

Of the slightly less than two million international visitors from beyond NAFTA borders, a little over half arrive from Asian/Pacific countries, with most of the remainder from Europe. China, the UK, and Australia, Japan, India, and Germany each accounted for more than 100k visitors in 2019, South Korea, Hong Kong, and Taiwan not far behind. Let’s put all these flows together on a map (click for interactive access).

Overnight1a

Of some concern, lots of the places identified above have had recent outbreaks of Coronavirus. We’re still in early days of tracking the virus. And we know it’s already having major effects on travel. But can we look at current prevalence estimates and recent travel patterns to give some insights into crude vector risks for Metro Vancouver? Maybe. It’s worth keeping in mind that everything is still pretty much up in the air in terms of what we know!

First let’s look at up-to-date active confirmed Coronavirus cases drawing on data collected at Johns Hopkins.

Overnight2

Wuhan, of course, appears as the centre of the outbreak, and Hubei Province in China contains most of the active confirmed cases to date (as of March 03, 2020!) The number of cases is important to track, obviously, and the starting point for healthcare workers and epidemiologists alike. But focusing on these numbers can provide a misleading impression of how widespread the Coronavirus has become. So let’s come up with a crude estimate of prevalence instead of case numbers. Here we’re going to use active confirmed cases as our starting point. Another option is to track all confirmed cases, including those who have recovered (no longer testing positive) or died from coronavirus. But active confirmed cases might arguably give us a better sense of current spread.

We can plot the evolving nature of active confirmed cases in terms of prevalence estimates across places, effectively dividing total number of active confirmed cases by population for our data reported so far. Setting this to motion, we can track outbreaks by prevalence across time. Even just looking at active confirmed cases, we get a sense that recorded prevalence has recently stopped climbing for Hubei province. Meanwhile, outbreaks in South Korea, Iran, Hong Kong, and the nearby state of Washington continue to grow. Also worth noting, some countries (e.g. South Korea) seem to have a better handle on testing the virus, providing better confidence in their numbers. The numbers coming out of other locales (Iran and the USA) seem far less reliable, either because of inconsistent testing, untrustworthy reporting by officials, or both. This sets a real limit on what we can know so far.

overnight3

Overall it needs to be stressed that – given the numbers we have so far – the prevalence of coronavirus is still very low. Even in Hubei province, the centre of the outbreak, not much more than a single active confirmed case per thousand people has been confirmed. Comparing locations of cases to surrounding populations, most places around with the world with outbreaks still see only about one active confirmed case per hundred thousand people. Even setting aside the hyper-cautious mood around the world and its effects on travel, if you met a visitor from one of these places in Metro Vancouver, fairly unlikely that they would be a carrier. There’s little reason to be scared of individual travellers!

But what about travel patterns writ large? Surely even if any individual presents a very low risk as a vector, by sheer number, the masses of people travelling through Vancouver from places with coronavirus outbreaks represent a risk. Indeed, that’s how the coronavirus has spread so far. We can very crudely estimate this risk by setting a base likelihood that each individual traveller from a given outbreak location is coronavirus-free (1 – cases / population). In other words, we might use currently active confirmed cases as our measure of prevalence, estimating we can be 99.99975% certain that a given traveller from Washington State will not be a carrier for coronavirus. But what if a LOT of people travel from Washington? Then we exponentiate 99.99975% by the number of visitors (126,493 for the first three months of 2019 as a proxy) to come up with an estimate that none of these travellers carry the virus (we really should be drawing without replacement here, but this is a good approximation), with the complement giving a rough estimate of at least one visitor being a carrier. This comes out at 27% using our current estimates. This only considers Washington residents travelling to Vancouver and still neglects Vancouver residents travelling to Washington and getting infected there. And it relies on current active confirmed cases, it does not include active but not yet confirmed cases. And it assumes travel patterns similar to a year ago. Still, it provides us with a measure of vector risk to Metro Vancouver that combines risk of coronavirus with travel volumes.

Let’s run with this for recent coronavirus outbreak data based on travel volumes similar to past years – EXCEPT excluding cases from Hubei province in China after January 23rd (when the quarantine went in place). What does our crude evolving overnight travel vector risk look like?

overnight4

Here we can see rapidly changing vector possibilities. Conditions are changing fast! Still, it’s hard to know how much to trust these numbers. Given what we understand about testing at the moment, it’s likely we’re still overstating the risk from high quality testing locales (South Korea), as well as understating the risk from places where testing has been poor (Washington) and places where we don’t have any visitor data at all (Iran). We’re also missing current data on how travel is changing as well as data on where people from Metro Vancouver are traveling, which is a big deal given that most of our cases so far represent returned travelers from abroad.

Here is a still of the most recent snapshot as of the writing of this.

Overnight5

Upshot

So here are the big takeaways from our exercise: 1) Visitor data to Metro Vancouver is actually really interesting, even for those outside of the tourism business. 2) Don’t shun travelers from abroad! The likelihood of anyone you meet, even coming from an outbreak centre, being a carrier of coronavirus is very, very low. 3) The combination of travel patterns plus coronavirus prevalence gives us some interesting ways to model evolving vector risks in Metro Vancouver. 4) But it’s not clear how much we should trust our data. Travel patterns have surely altered, and we need better coronavirus testing fast, especially in places like Washington State.

Overall, integrating travel data with coronavirus data may, if nothing else, help people and agencies prepare and plan better. Practically any planning is better than some of the ad hoc decisions being made out there, as when American Airlines suspended its flights to Milan only after pilots refused to fly there. For most people, the important thing is to listen to local health agencies, like the BC Centre for Disease Control, wash your hands, and be kind to those around you, wherever they come from.

As usual, the code for the post is available on GitHub in case anyone wants to refine or adapt it for their own purposes.

 

UPDATE: For a look at how the professionals are joining international travel data to coronavirus data, see Gardner (et al) here (now unfortunately outdated!)