Gateway Communities of Vancouver

Gated Communities are kind of awful, but the communities that form at GateWAYS are actually pretty cool. In a world of immigration, that’s where we tend to get a lot of our diversity.

As Canada’s Gateway to the Pacific Rim, Vancouver is fortunate to be full of Gateway Communities, both as a central City and as a broader Metropolitan Area. Nearly half of Vancouver’s residents were born outside of Canada. Where do they come from? All over, but we get especially large representation from across Asia. Media stories tend to focus on Chinese immigrants to the area, where Mainland immigrants have recently overtaken historical streams from Hong Kong and Taiwan. But the streams from China constitute only a minority of Asian immigrants overall. Large streams from the Philippines, India, Iran, South Korea, and Vietnam also pour into both the City and Metro region of Vancouver. Other streams from the UK and Europe, the USA and the Americas, and Oceania (especially Australia) build upon the proximity and colonial legacy of Canada. Relative to the descendants of settlers past, First Nations and other Aboriginal identified Canadians make up only a small proportion of the area’s residents, though their cultural impact is profound and local First Nation bands are emerging as a development powerhouse in the area.

Let’s draw upon Statistics Canada’s community profile data from the last Census (2016) to put this all up for comparison:


The City of Vancouver and the Metro Region have similar aboriginal identified populations. There are slightly more settler descendants in the surrounding municipalities than in the City of Vancouver proper. Non-permanent residents, including those on student and work visas, round out the population, and are slightly over represented in the City of Vancouver relative to the Metro area as a whole. In terms of immigrant Gateway Communities, the City of Vancouver and the Metro Region as a whole are relatively well-matched. The big exception is that the City of Vancouver has historically added more immigrants from China than the region as a whole, which has added more immigrants from India. For recent migrants, this trade-off has shifted. Now the City of Vancouver and the Metro Region add about the same proportion of immigrants from China, but where the City of Vancouver loses immigrants from India, it adds immigrants from Europe and the Americas (especially the UK and the USA).


We can break out a selection of suburbs by their recent immigrants to see where people are going. Immigrants from India tend to favour Surrey as a destination. Richmond received outsized attention from Chinese immigrants. North Vancouver selects for Iranian immigrants. Also, North Vancouver, like the City of Vancouver, seems to select for immigrants from Europe and the Americas. As pointed out recently by Kishone Roy and in the past by others, American immigration to Vancouver probably hasn’t received as much attention as it should! Especially since the USA’s Federal Voting Assistance Program believes Vancouver houses more American citizens abroad than any other world city! (Given that the Census only shows 26,445 immigrants to Vancouver born in the USA, many of these Americans abroad are undoubtedly dual citizens who were granted citizenship from past residency in the USA or from their parents).


As demonstrated by the difference between citizenship and place of birth in the case of ties to the USA, just looking at place of birth doesn’t fully represent the nature of Gateway Communities. The same issues certainly arise in comparing those born in Hong Kong to the much larger community claiming ties to Hong Kong. Aside from place of birth and citizenship, there are other ways to think about and chart the diversity gathered in Vancouver by virtue of its Gateway status. Immigration often produces linguistic communities, that are sometimes (but not always) passed between generations of immigrants. How many languages have 5,000 or more speakers in Metro Vancouver?


The wonderful thing about looking at languages is that it helps break down nations into their component parts. Cantonese, once the dominant Chinese linguistic community in Vancouver, must now share with Mandarin. But Wu and Min Nan linguistic communities also remain vibrant and point toward the diversity within China as well as the Chinese diaspora. Similarly, we get a lot of immigrants from India, but we get an especially large number from the Punjab. When they arrive, they pass on Punjabi between generations. Far fewer speak Hindi, despite its dominance in India. We also see multiple linguistic communities from the Philippines, with both Tagalog and Ilocano having over 5,000 speakers. The vast majority of residents speak English, but French is also relatively common and a diverse cast of other European languages find a home in Vancouver.

Let’s look at the diversity of Vancouver in one more way. Instead of thinking about how different communities measure up to one another in Vancouver, let’s see how they measure up to their sending countries. In some ways this is similar to the study of Americans abroad carried out by the USA’s FVAP above, but we’ll put it in context of the population of sending country. How many people immigrate to Metro Vancouver per million people living in their homeland (i.e. country of birth)? This gives us a rough sense of the “risk” of moving to Vancouver and/or its “pull” upon various places around the world. We can look at both total immigrants born elsewhere and just recent immigrants, having arrived in the five years prior to the 2016 Census (2011-2016).


In terms of “pull,” Hong Kong is the hands-down winner. There’s at least one Hong Kong transplant living in Metro Vancouver now for every one hundred residents of Hong Kong. The pattern is similar, if less pronounced, for Taiwan. But Hong Kong and Taiwan are also (kind of) cheating. They are both still considered part of China in many respects, though their historical patterns of connection to Vancouver are far more intense. For more recent immigrant streams, the connection is less pronounced, but still there. The recent pull from the Philippines contends with Hong Kong, and along with South Korea and Iran, conspires to beat the pull for recent immigrants from Taiwan.

In terms of “pull” for recent migrants, about twenty-six Chinese in a million immigrated to Metro Vancouver between 2011-2016. This means Mainland China lags far behind Hong Kong, Taiwan, the Philippines, South Korea, Iran, the United Kingdom, and all of Oceania in terms of risk of immigrating into the region. But, of course, there are a lot more people living in Mainland China than any of these other places.

There aren’t that many more people in China than in India. India’s patterns of immigration to Vancouver are somewhat deceiving. Most arrivals are from the Punjab region, and the numbers belie the importance of Vancouver to the Punjabi Sikh diaspora. Looking just at residents of Vancouver with knowledge of Punjabi who were born abroad, the estimate for how many Punjabis have moved to Vancouver sits around 3,000 per Million residents of the Indian Pubjab, placing the “pull” of Vancouver for this particular region inbetween the pull for Taiwan and Hong Kong.

Overall, this is a reminder that the Gateway Communities of Vancouver are strikingly diverse. Ideally media stories should strive to avoid erasing this diversity when talking about how immigration affects the City and the region. ALL of these collectives offer the possibility for meaningful communities to form, gathered together here in Vancouver, just inside the Gates to Canada. Considered all together, they help keep the Gates to Canada open.

Simple Metrics for Deciding if you have enough Housing

(co-authored with Jens von Bergmann & cross-posted over at mountainmath)

What are the best metrics for understanding if a given place has enough housing, just the right amount, or too much? Whether you’re a potential renter or buyer or an analyst or policymaker, the answer really depends on what you’re looking for.

For potential renters and buyers, if you can’t find what you’re looking for and/or it’s not in your price range, then there’s not enough housing. If you can find it, then there’s just the right amount. When is there too much housing? Mostly if you’re already comfortably housed, but concerned about changes to your neighbourhood and/or you’re looking to maximize the price you can get for selling your housing. So we can root a set of foundational answers to questions about housing supply in peoples’ direct experiences interacting with the housing market. We can also extend this to non-market housing. If there are people on the waitlist, there’s not enough non-market housing (note: there are ALWAYS people on the waitlist and we definitely need more non-market housing).

But decisions about whether we have enough housing aren’t actually left to people interacting directly with housing markets. Most people can’t add much to the supply of housing by themselves. Housing has become exceptionally technical, and a vast slew of regulations now prevent most self-building except in informal sectors (in Vancouver most notably the subdivision of existing dwellings into suites, only a minority of which comply with building codes and have a permit). Instead most decisions about how much housing we have are produced via a combination of developers working through their financial models in conjunction with planners, regulators, and politicians working with tight existing constraints on what can be built where. Interestingly, both the comfortably housed and those looking to maximize their prices for selling housing DO get a voice. Why? They tend to be the ones electing (and speaking directly to) local politicians. This group notably includes local developers, who are both actively engaged in maximizing the prices they can get for selling housing and actively engaged in local politics (if you think market developers are unambiguously pro-supply, think again).

So how do we know if we have enough housing in a given place? Or, since the answer always depends upon the perspective, how do we hear from potential residents (including renters and buyers) about whether THEY have enough housing? Their voices are the ones that tend to get left out of debates. Usually, to the extent their voices are heard at all, it’s through some set of metrics informing decision-makers. So let’s return to metrics, because different metrics tell us different things!

Ideally decision-makers consider metrics with specific goals in mind: do we have enough housing in a given place for what purpose? Are we interested in enough housing to meet demand, preserve affordability, or address need? Enough to promote the right kind of growth? Enough to support transit, reduce greenhouse gas emissions, promote urban vitality? Or perhaps we’re worried about too much housing to support our preferred sales price, keep out the wrong kind of people, preserve our favourite aesthetic, maintain green space, or just generally keep our neighbourhood the way we like it? Being clear about these goals is helpful, insofar as they set the criteria for which metrics can provide meaningful answers. If we can decide on our criteria, then we still have to figure out the right metric. Let’s start by looking at the four common elements that make up most metrics:

  • Dwellings
  • Money
  • People
  • Land

These are the things we tend to track with our metrics for whether or not we have enough housing, just the right amount, or too much. Dwellings are housing. If we want to figure out if we have enough, then we definitely need to keep track of dwellings. Of note, dwellings can also be differentiated by square footage, number of bedrooms, and related characteristics. Money is an expression of desire, weighted by wealth and/or income (and hence also inherently unequal). People are bodies, variously disposed to live together and share space. Both money and people move around, unlike most dwellings, which are fixed in place. Land is how we fix dwellings in place, and can support various numbers of dwellings. By virtue of fixing dwellings in place, land also defines various kinds of places we might be concerned about: e.g. neighbourhoods, cities, and metropolitan areas. Places are connected to one another: what happens Downtown has an impact on nearby neighbourhoods (e.g. Kitsilano), just as what happens in the City of Vancouver has effects on what happens in the City of Surrey. As a result, metrics should pay careful attention both to place of interest and interconnection between places. In the background, fitting these elements together, we also want to keep in mind that time matters to how we construct metrics.

The key metrics we tend to track often involve just two of the elements above, measured at varying scales of aggregation, places, and times. We can provide a quick and dirty guide to the different questions answered by the key metrics we use to measure if we have enough housing or too much as well as the underlying logistical mechanism guiding our inquiries.

Class of Metric Q. Do we have enough / too much housing to… Logistical Mechanism
Money per Dwelling Preserve Affordability Market Allocation / Inequality
People per Dwelling Fit people into dwelling units Rationing / Sharing Rules
Dwelling per Land Support Urbanism / Reduce Env. Impact Rationing / Sharing Rules (via zoning)

How we define elements matters to how the metrics work, as does how we incorporate time and the level of aggregation (individuals, households, census tracts, cities, metro areas). We’ll keep coming back to these throughout, often with reference to examples from Vancouver, the metro area we know best, but it’s helpful to start by keeping things simple.

Money per Dwelling (a.k.a. price)

Perhaps the most obvious way to bring these elements together is by asking how much dwellings cost. Given the persistence of market allocation for housing, there will always be enough housing to meet demand… at some price. That’s because the price mechanism sets prices at where demand curves and supply curves meet. Put differently, the demand for $1 dwellings is practically limitless. The demand for $100 million dwellings is practically zero (so far). In between, there’s a demand curve specifying how many dwellings would sell at what price. On the supply side, self-interested owners would rarely sell dwellings if they could only sell them for $1. But they’d probably sell as many as they could get away with if they could sell them for $100 million. In between there’s a supply curve specifying how many dwellings will be sold at what price. The market pricing mechanism moves prices toward equilibrium where demand and supply curves meet. This is the stuff of basic economic analysis (brought to you by a mathematician and a sociologist).

How about if you don’t just want to meet demand, but you want to meet it at a particular price? Maybe you want the market to meet a certain affordability threshold for a certain kind of dwelling? Let’s define this better: do we have enough housing if we want the average two bedroom dwelling priced at $250,000? In some places (e.g. Edmonton), this isn’t far off the mark. There’s enough housing there relative to demand that two bedroom dwellings sell for about $250,000. In other places (e.g. Vancouver), there’s not enough two bedroom dwellings to go around to everyone who might want them at that price, so they’re bid up to a far higher price. It would take the addition of a lot more dwellings to bring prices down to $250,000. So if that’s where you want prices to go, then there is definitely not enough housing.

The same general dynamics apply to the market pricing mechanism for apartment rents. Landlords respond to their understanding of local supply and demand when setting their asking rents. The longer their apartments stay on the market without being rented, the more likely they are to lower their asking rents accordingly. Vacancy rates measure the supply of apartments for rent. Correspondingly, the negative correlation between vacancy rates and rent change is very strong. As vacancy rates go up, rents come down. Here’s a comparison by metropolitan area in Canada.



Say you want to ensure average rents for two bedroom apartments are affordable, at about $1,200/mo (again, around the rent level of Edmonton, vacancy rate around 5%). The takeaway from the above would appear to be that if you want to lower rents to this level in a market like Metro Vancouver (average rent @ $1,650, asking rents much higher, vacancy rate around 1%), then you need to ensure that a lot more two bedroom apartments come on the market to rent. In short, you don’t have enough housing.

Exactly how many two bedroom apartments would you need to add to bring average two bedroom rents down to $1,200/mo in Vancouver? This is a tricky (and worthy) question to answer. It would require knowing the shape of the demand curve (made up by knowing how many apartments would be rented at each rent from, say $1/mo to $1 million/mo). It would be difficult to figure this out, even if we could ask everyone in Vancouver what rent they’d be willing to pay for a two bedroom apartment. Why? Two reasons: 1) at lower rent points, some people might be willing to pay for multiple two bedroom apartments (rich people do all kinds of odd things, and when we use price as our metric, the whims of the wealthy matter more than the needs of the poor); 2) we should almost certainly assume that there are a lot of people living outside of Vancouver (including former residents) who would love to move here if they could find a two bedroom apartment for $1,200/mo. They only get a vote in how much housing gets built through their influence on the demand curve. Otherwise they don’t get heard at all. So it’s difficult to tell just how many two bedroom apartments we would need to add to bring Metro Vancouver rents down to $1,200/mo.

Another way to set a metric is to set an ideal vacancy rate instead of a specific rent. Vacancy rate targeting was explicitly mentioned by several candidates in the last City of Vancouver civic election. Inflation-adjusted rents tend to fall when vacancy rates rise above 3%. Setting a vacancy rate target of 4% or 5% will work to deflate rents.

In general, if your goal in asking if a place has enough housing is to preserve the affordability of market housing, then prices (or rental vacancy rates) should be your metric. If prices are higher (or lower) then you want them to be, then you should work to add to (or reduce) the supply of housing accordingly.

But how do we add to the supply of housing? Generally the most important way to add supply is to build more housing. It’s what builders do. But it’s worth noting that if they want to build more housing, builders get stuck in the middle of even more demand and supply curves. Labour, materials, and (most variably) land all influence the costs of constructing new housing. Just like buyers and sellers in the housing market, builders also watch price signals, and they tend to build when they think they can sell the housing they construct for a significantly higher price than they pay to purchase labour, materials, and land, with the difference equal to profit. The Minimum Profitable Production Cost (MPPC), or the minimum cost to bring a new unit to market, sets a hard cap on when builders have any incentive at all to try and add housing. As a result, it also provides a lower bound on the price of new market housing. And this minimum cost rises as density increases and construction becomes more involved and expensive (the minimum profitable production cost of new rental housing in Vancouver is currently too high for market developers to offer new two bedroom apartments at $1,200 market rents). Not surprisingly, holding other characteristics constant, new housing always tends to be more expensive than old housing. As a result, when you compare new housing to old housing, it might seem like new housing is doing nothing at all to bring down prices. But when you consider that building new housing is the primary way of adding more dwellings to the market overall then you get how new housing might “soak up” some of the demand in a given market, thereby lowering the prices of older housing from where they’d otherwise be and bringing down prices overall. Of course, building new housing only adds to the total housing market to the extent that you build more new housing than you demolish, a point to which we’ll return below.

Aside from demolitions, how would one reduce the supply of housing? Generally speaking, we seldom see demolitions exceed new construction, so this doesn’t happen much. But there are a few examples we can talk through, perhaps most prominently AirBnB. In response to new profit-making incentives of AirBnB, many property owners have removed dwellings from the long-term rental market into the short-term, hotel-style market (these markets once weren’t so distinct, but they have become so over time with the passage of laws like BC’s Residential Tenancy Act). As dwellings get removed from the long-term rental market, it drives down vacancy rates and correspondingly drives up asking rents for those units remaining.

What else matters? Location, location, location. Additions and subtractions from the supply of dwellings for sale or rent don’t just have local effects. Their effects spill over into places near and far, tied together by their fixture to land and to transportation networks. For instance, the effects of building and renting out a bunch of new housing in Downtown Vancouver may be felt in asking rents in suburban Surrey. The degree to which additions of housing in one place affect rents in another is heavily dependent upon how long it takes and how much it costs to travel between them as well as to job centres and amenities. That said, some observers suggest that hyper-local “induced demand” may come in to play, meaning that new construction in Downtown Vancouver could potentially drop asking rents in suburban Surrey more than asking rents Downtown. The evidence gathered to date suggests this likely doesn’t happen much, but certainly the scale of the metric matters when thinking about how the addition of new supply affects prices and rents.

So far we’re also talking strictly about dwelling characteristics like bedrooms and size, but not about the structural type of dwellings. We can’t add more single family homes in the inner municipalities in Vancouver, so market mechanisms are constrained in terms of reducing the rent or price when we restrict ourselves to single family homes in the inner municipalities. Being very picky on location can have similar effects. Adding condos or rental properties in the downtown peninsula is more expensive than adding them in e.g. Dunbar. Adding housing in downtown requires concrete high-rise, which is substantially more expensive than 4 or 6 storey low rise which can still add significant housing in Dunbar. Providing amenities like public spaces and libraries for a growing population is also more expensive in areas that are already denser. Given demand and various constraints, it’s quite possible that the market won’t ever be able to supply rental housing at a cost that can push rents down into the $1,200/month range (or push the sale price into the $250,000 range) for a 2 bedroom apartment in Downtown Vancouver. But Surrey seems possible. Regardless, if we want to try we have clear price signals that we’d need to add a lot more 2 bedroom apartments than we have now.

Considered as a class, metrics for Money per Dwelling, including prices (per dwelling, per sq ft, etc.), rents (per BR, etc.), rental vacancy rates, and sales listings, represent transactional data reflecting market pricing mechanisms. Inequality is built into these measures as a reflection of how market allocation weighs the whims of the wealthy of greater importance than the desperate desires of the poor. Correspondingly, reductions in inequality make for more egalitarian housing outcomes. Given market allocation of housing, this is the class of metrics people should turn to if they’re interested in achieving or preserving affordability. They provide the clearest path for identifying if there’s enough (or too much) housing when affordability is the criteria of interest. Of course, these metrics don’t resolve the debate between those who want prices and rents to rise (home sellers and landlords) and those who want them to come down (home buyers and renters), but at least they provide a common empirical grounding.

People per Dwelling (a.k.a. residential crowding)

People per dwelling provides a different class of metrics for thinking about whether there’s enough housing, focused on residential crowding. Fundamentally these metrics ask if there are there enough dwellings to “fit” the number of people we have in a given place. Of course, this is only a potential measure of fit when houses are mostly distributed by the market. Wealthy people probably take up way more room (and rooms) than they need, while poor people more often end up stuffed together. There are two solutions to this situation: one is to ration housing, so that extra rooms are shared around. We see this only for the small proportion of our housing stock that’s non-market housing. Market housing isn’t at all rationed according to need, but instead doled out by wealth-weighted desire (money). The other solution, far more common across North America, is to outlaw too much residential crowding via maximum occupancy codes and sharing rules. This is very common, and in the absence of rationing housing according to need this tends to lead to the exclusion of poor people altogether.

Across most of Canada residential crowding remains low. This is especially true of those places with strong municipal regulations against crowding (e.g. fire codes and occupancy standards) and market distribution of housing. Non-urban, non-market housing, especially on First Nations reserves and in Nunavut, where rationing is more common, tends to be where we see the greatest number of people per dwelling. Here we see a real failure of investment in non-market housing to match occupancy standards observed elsewhere, though differences in family sizes and cultural openness to different rules for living together also play a role.


While crude aggregate crowding metrics can help reveal the lack of housing across reservations and Northern territories, they don’t tell us much about differences between metropolitan areas, which stick together in a relatively narrow range between two to three people per dwelling. The narrow range reflects how crowding is both generally outlawed and also discouraged by market mechanisms distributing the vast majority of housing (above). We also know residential crowding is on the decline in most places, resulting from long-term declines in childbearing, family size, and tolerance for living together combined with the general rise of affluence, occupancy standards and enforcement. Correspondingly, crude aggregate crowding metrics should probably not be used to answer questions about whether metros or municipalities have enough housing. They don’t tell us much.



Despite their problematic nature, people per dwelling metrics are commonly used to answer questions for which they’re not suited. Several municipal planners and even a couple of academics have used new persons (or new households) per new dwelling as a metric for whether a place is adding enough housing. Given constraints on crowding and market mechanisms, this is equivalent to asking whether housing supply is meeting demand (as above). Of course it is! By definition, local housing is ALWAYS meeting demand (at some price). Similarly, by definition if you count all of the housed people added and all of the new housing added in a given location, there will always appear to be enough housing added to house everyone (at some level of crowding). After all, only housed people are counted, meaning only the net “winners” able to out-compete others for the dwellings being offered by the market. Net “losers” not provided housing by the market don’t get counted at all! Put differently, if price metrics weigh the whims of the wealthy too high relative to the needs of the poor (a valid critique), then crowding metrics ignore everyone without local housing entirely: all the people who want to live in a place but are prevented from finding housing there don’t get a vote.

Contributing to this fundamental problem, net housing additions are also often poorly counted, either because of changing census methods or failure to combine completions data with demolitions data. This has proven a particular problem for analyses that take for granted how people distribute themselves into households and simply compare new households to new dwellings, taking the leftover number of new dwellings as “empty” excess (in this case, the number of net new housed households can never exceed the number of net new dwellings except in cases where there were previous “empty” dwellings). Given the myriad of problems involved, crude aggregate measures of new persons or new household per new dwelling are especially poor metrics for determining if metro areas or municipalities are building enough. The answer they provide, by default, is practically always “yes.” For similar reasons, reinterpreting past census counts into population projections as the basis for how much housing development to allow is backwards. In high demand places, the availability of housing limits population growth rather than the other way around. Planners and academics should stop using metrics that count only local winners as answers to whether we’re building enough housing.

What about more refined measurements of crowding at different levels of analysis? These are often worthwhile to consider. Given a few strong assumptions about the privacy needs of people while they sleep (practically the least interesting activity they undertake), residential crowding can be measured in terms of bedrooms rather than simply dwellings. Measured at the household level, we can get a sense of how many households are living in dwellings that force more than two people to share a bedroom. We can come up with even more elaborate rules, as in the Canadian National Occupancy Standard, where we assume people need one bedroom per sleeper, but we allow couples to share with each other, and kids to share with other kids (below age 6) and other kids of the same gender (below age 18). Applying these rules more clearly demonstrates the residential crowding on First Nations and in Nunavut. But once again, the metric tells us little about most municipal and metropolitan variation.

We can also refine measures to explore residential sharing at particular ages. When do children leave home? It might be that adult children remaining living with their parents is a sign of need for more dwellings. This is tenuous as an indicator (some children want to stay home, others do not), but interesting!


We can also count individuals without dwellings. This is a form of mismatch. Given the current distribution of housing, how many people are going without? Homeless counts offer an important signal about whether there’s enough housing: if we can count people who are homeless, then there is not enough housing. But this is a broader problem with inequality. Bringing more housing to market may not solve the problem, especially since the demand for housing isn’t just local, and the whims of the wealthy will continue to outweigh the needs of the homeless. Homeless counts are an especially good signal of the need for more non-market housing. Of course, another good signal of the need for non-market housing are the waitlists for cooperativesubsidized and supportive housing. Effectively, both homeless counts and non-market housing waitlists register urgent local needs not being met by the market distribution of housing. That said, homeless counts and waitlists suffer some of the same problems as other crowding metrics insofar as they only tend to record housing need that’s already in a given locale. But people fall in and out of need and they also move. The dire needs of refugees in tent camps tens or thousands of miles away do not get considered, even if those refugees might eventually show up in a municipality. As a result, there remain difficulties in determining just how much need to meet: there are probably no ethically satisfactory stopping points. And even if there were, under rationing systems of all sorts, housing waitlists can grow to enormous lengths. As with attempts to preserve market affordability, we can know we need to build a lot more non-market housing without necessarily knowing when (or if) we should stop.

Finally, returning to the notion of “excess” dwellings, we can also count dwellings without people in them. This is ultimately a bad measure of whether there’s enough housing without a) greater knowledge of the reasons why units appear to be empty and without b) a corresponding will to expropriate “bad” empty units and ration them out according to need. Speaking to the first point, if dwellings register as “vacant” and available to the market (e.g. rental vacancies or unoccupied sales listings), then these dwellings will help reduce prices (see above). If they’re not on the market, they may reflect development processes (pre-demolition or recently constructed dwellings) working toward adding more housing. A variety of other procedural transitions (deaths, inheritances, etc.) may also account for dwellings without people in them before we get to second “vacation” residences (whims of the wealthy, etc.), and alternative uses (AirBnBs, etc.). To the extent these kinds of unoccupied dwellings are rising, they may result in reductions to the market supply of housing, pushing up prices for dwellings that remain. Finally, keeping housing empty and off the market may result from attempts to reduce transaction costs and/or speculatively manipulate market pricing. This is of greatest concern from the standpoint of maintaining market stability and affordability. The diversity of reasons that dwellings might show up as unoccupied means that, by itself, keeping track of unoccupied or empty dwellings is probably a bad measure of whether the market is building enough housing. After all, empty units may be adding to supply or detracting from supply, with varying affects on affordability, depending upon whether they’re on the market. That said, like homeless counts, “empty home” counts can be useful as an indicator of how the market is working to match people to dwellings (given underlying and unmeasured inequality). Moreover, empty homes can be bad in their own right, potentially deadening neighbourhoods. A Lincoln Institute report defines thresholds at which vacancy becomes a problem, with “low” vacancy (a problem for facilitating moves) below 4%, “reasonable” vacancy between 4%-8%, and high vacancies at 8%-20%. “Hypervacancy” (20% or more) poses special problems, especially in the case of declining cities. All major Canadian metro areas fit in the “reasonable range.”


But in high demand cities, lots of empty homes can point toward the desirability of higher property taxes, potentially including Empty Homes Taxes, which can distinguish between types of vacancies and induce owners of empty units and second homes to more quickly return them to market, boosting supply and lowering prices. This will reduce the profitability of any speculative market manipulation. But of course another response to that kind of manipulation is to add more dwellings and credibly promise to keep adding dwellings, placing pressure on prices and rents to lower over time and make speculation unprofitable.

Dwellings per Land (a.k.a. dwelling density)

Dwellings per unit of land as a class of metrics measures dwelling density, constituting yet a different aspect of whether there’s enough (or too much) housing in a given place. This class of metrics has important implications for urban dynamism and environmental impact. It also has potential effects on parking, noise, and the preferred aesthetics of many neighbourhood organizers. Dwellings per unit of land is often measured as dwellings per acre or hectare. Beyond definitional issues, there are tricky aspects to measuring this, insofar as both the areal unit (lot, block, neighbourhood, municipality, metro area) and what gets counted as potential land for dwellings (in the denominator) really matters. If we’re interested in housing density, should one count only land allowing dwellings? What about streets? Or other land uses, like industrial parks? What about recreational parks? Schools? Subtracting out streetscapes makes a big difference, and when other features fall within small areal units, like blocks, they can really affect measures of housing density, making a block with a park look much less than dense than the block next door, even if both are made up of entirely the same kind of housing. Counting only land allowing dwellings constitutes “net housing density” while counting all land and uses constitutes “gross housing density.”

Overall it’s worth noting that this class of metrics is also a bit of a dodge, since often what we’re really interested is people per unit of land, better known as population density. After all more people in a given place constitute more potential interactants in public spaces, more likely transit riders, more shares of infrastructure, and more possible “eyes on the street.” More people also constitute more potential competition for parking and services. People sharing space are also often understood to be poor and potentially dangerous, bringing down property values. So debates over housing density as a class of metrics are often really about how many people should be encouraged or tolerated in a given place. But the regulatory powers of cities are stronger over buildings than bodies, so the focus often ends up being on dwelling density rather than population density. Aside from population density, dwellings per unit of land can have independent effects on the aesthetic “character” of neighbourhoods, as expressed by many peoples’ aversions to high-rises. As noted above, we can, more or less, substitute between population density and housing density just by dividing population density by average household size. This doesn’t always work, insofar as denser housing tends to hold smaller households, but it still gives us a rough translation. We can even figure in unoccupied dwellings if we want, which would give us an overall standard of about 2.34 people per dwelling in Metro Vancover. Alternatively, instead of measuring dwellings per acre, we could measure bedrooms per acre. Bedrooms relate more closely to population than dwellings, and are often similarly regulated by cities.

In terms of impact, housing density (or dwellings per acre) has been linked to urban vitality. Jane Jacobs famously set a few thresholds for what she considered suburban (six or fewer dwellings per acre) and truly urban (one hundred or more dwellings per acre). She considered “in-between densities” as less conducive to the “lively diversity and public life” of the city. Needless to say, the vast majority of the landscape of North American cities fall in Jacobs’ “in-between” ranges, “fit, generally, for nothing but trouble.” Outside of Downtown and a few other scattered census tracts, the same is also true of Metro Vancouver. Where the best threshold for urban vitality might be located remains a matter for debate.


Similar thresholds have been suggested for what kind of densities can support urban transit. Commonly cited thresholds suggest about 12 dwellings per acre around a large central business district is enough to support a decent urban transit system. Guerra & Cervero provide more careful updates on this estimate, exploring capital costs in conjunction with what can be supported by population and jobs located near stations. Using their estimates, a project like Vancouver’s forthcoming skytrain extension along Broadway, at a capital cost of nearly $500/km2 CAD (nearly $600/sq mile USD), would require over 120 people per acre gross population density to support, or more than 50 dwellings per acre near skytrain stations.

Generally speaking, higher dwelling densities enable more transit viability, encourage people to get out of their cars (when coupled with jobs and commercial destinations), promote lower energy useage and support transitions to more sustainable cities. But higher dwelling densities also challenge some peoples’ conceptions of what they want their neighbourhoods to look like and how many people they want to compete with for parking. Moreover, higher dwelling densities tend to be forbidden on the vast majority of North America’s urban land base. Why? Zoning.

Most residential land, including in the City of Vancouver and surrounding suburbs, is zoned to support single-family residential character. At its strictest, single-family zoning insures only one dwelling can be built per lot, and in some cases minimum lot sizes can be enormous. Dwellings are often rationed out according to quite draconian land use rules. Even on the relatively modest 33’ x 122’ standard residential lots that make up a large part of Vancouver’s urban landscape, a single dwelling per lot standard nets only about 10 dwellings per residential acre. Initiatives to add and legalize secondary suites, laneway houses, and most recently duplexes (with secondary suites) means that the actual range of legal dwellings per lot on most single-family zoned land in the City of Vancouver can get all the way up to 40 dwellings per acre. Not bad, but nowhere near the densities supportive of urban vitality or skytrains.



On the other hand, a 33’ x 122’ lot located within a commercial zone in Vancouver is allowed greater dwelling density and the ability to build out to lot lines. Even under the same broad height restrictions applied to single-family zoning, twelve dwellings can easily be fit into a given lot while retaining a central courtyard, achieving a dwelling density of about 120 dwellings per residential acre, like this low-rise apartment building in a C-2 (where one of the co-authors of this post lived when he first moved to Vancouver). This moves solidly into Jane Jacobs & heavy transit supportive territory, though the difference between net density and gross density suggests we’re still not quite there yet.

Setting Rules to Metrics

A lot of the metrics we describe above are set into rules (e.g. by-laws, policies, etc.) for regulating cities. In particular: zoning by-laws often set hard limits to dwelling density (dwellings per land) and maximum square footage (Floor Space Ratios) for given lots. The metrics embedded in our zoning effectively mean that we’re rationing out how many dwellings we allow per land parcel. Through the sharing rules embedded in our occupancy standards, we’re also disallowing most residential crowding. But after we apply these rationing and sharing rules to structure housing production and occupancy, we switch to the market in terms of how we develop and distribute most housing. In high demand locations, the net result of these general policies is construction for rich people and the gradual exclusion of poor people. Their dire needs in the market weigh as less important than the whims of the wealthy. Since poor people are also prevented from sharing existing dwellings in high concentrations, they can’t even get a foot in the door, and don’t show up in crowding metrics at all.

While some rules set to metrics are built to be responsive and flexible, automatically adjusting to conditions (e.g. setting rent control to inflation, and setting below-market rates at a set discount from market rates), others require lengthy hearings and political debates to change (changing zoning). As presently configured, debates about dwelling density largely exclude everyone not currently living in our cities. Indeed, this is one reason legislators in places like California and Oregon have moved to erode the power of municipalities to exclude development near transit hubs. They want to give potential renters and buyers a bigger say in whether we have enough housing by allowing them to speak through the demand curve, encouraging developers to build more housing in these places. To date the political process hasn’t let them get away with much, which ironically insures that developers profit hansomely from the scarcity of new housing being added to the market. In a high demand place like Vancouver, this means that in the long term, rents and prices tend to just keep going higher (though as we’re learning, in the short term prices can still swing up and down in line with speculative booms and busts, just like anywhere else!)

If we’re concerned about the exclusionary effects of high prices, we could reform our zoning regulations to be responsive, automatically adjusting to both transit development and market conditions (just like with rent control or the setting of below-market rents). There seems to be a lot of potential in considering this possibility. One example would be to set affordability thresholds. We could, for instance, automatically enable a rise in the number of dwellings permitted on a lot equal to one for every $250,000 in its assessed value. Once a lot hits three million in value, we could automatically enable up to twelve dwellings, looking something like the building above. Thresholds for non-market housing could be set even lower, enabling non-market developers (including the City) a competitive advantage in securing lots. Cities could also take over the production of non-market dwellings themselves, purchasing low-density lots and using their power over zoning to upzone and redevelop for the higher densities needed to support a more economically diverse population.

Conclusion (and Preview)

Overall, there’s still lots to think through when asking if we have enough housing! But metrics can establish crucial common ground for providing answers. Stripping down our metrics to their basics helps demonstrate their utility in terms of what answers they can provide and who they give voice. Overall, price (and rent) metrics provide the best indicators of whether we have enough housing to preserve or achieve market affordability. Non-market waitlists and homeless counts provide the best indicators of local non-market housing need (though they still exclude need from elsewhere). By contrast, residential crowding metrics (people per dwelling) don’t generally tell us much in urbanized Canada, and tend to privilege the voices of those already living in a place (e.g. the “winners” in finding housing). Dwelling per land metrics point toward the limits often imposed upon getting to enough housing in a place, and potentially spell out the rewards for getting there in terms of sustainability and urban vitality.

In terms of underlying logics, the market distribution of housing tracked by price metrics is problematic insofar as the whims of the wealthy far outweigh the dire needs of the poor. But when we simply wave away price metrics, and pretend we’re rationing out housing by need instead (by only tracking persons per dwelling), then we’re saying we don’t care who wins for the limited amount of housing we’re willing to offer when we ration out dwellings to land. Really addressing housing need is a monumental and important task, and requires a much greater investment in non-market housing. But questions quickly arise as to how non-market housing should be rationed, and advocates should pay more attention to providing answers that don’t assume that no one ever moves.

In future posts on housing metrics, we’ll compare across specific measurements within the same class and dig further into more complicated metrics that combine multiple classes (e.g. price to income multiples, core housing needs, shelter & transportation cost to income rations, etc.) So consider this a preview. Rest assured we’ll keep playing around with metrics!


Comparing Homeless Counts, BC Edition

We most commonly hear about homelessness as a big city housing issue. But are big cities where people are most at risk of becoming homeless? Comparing homeless count data enables us to start answering this and related questions.

Homeless counts draw upon volunteers and service-providers to provide point-in-time (one night) estimates of people without regular access to long-term housing. People are typically defined to be experiencing homelessness, as in the BC Homeless Count from 2018, “if they do not have a place of their own where they pay rent and can expect to stay for at least 30 days” (p. 11). People counted as homeless include both those staying in shelters and transition houses (counted by service providers) as well as those sleeping in “…alleys, doorways, parkades, parks, and vehicles or people who were staying temporarily at someone else’s place (couch surfing)….” (p. 11-12). Suffice it to say, this is not an easy population to find or track on any given night, and people are often also asked about where they spent the prior night during visits to service providers the next day. As a result, the “hidden” homeless population is always going to be larger than the number of people counted through homeless counts, meaning counts are always underestimates.

Homeless counts are also a lot of work, and even with the generosity of volunteers, they require significant funding and coordination to carry out in a defensible manner. We tend to know a lot more about homelessness in big cities in part because they’ve got more resources to direct toward tracking the issue. So it’s great news that BC Housing has been working with partners to provide counts for smaller communities. The BC Homeless Report, delivered in December of 2018, summarized much of what’s been learned so far.

The report is worth a read, and the count data, all by itself, is useful in assessing where urgent need for more supportive housing can be found. Here’s a lovely summary map of the data for BC, bringing together new counts funded for smaller communities with the most recent (at the time) data from other counts, funded by the Federal Government or independently (often from larger communities like Metro Vancouver and the Fraser Valley).


We can clearly see from the numbers that more people are homeless in Metro Vancouver than anywhere else in BC. But Metro Vancouver is larger than anywhere else, so this doesn’t seem too surprising. What’s more, adding all of the communities covered, more people appear to be homeless outside of Metro Vancouver (3,904) than within (3,605), despite the fact that Metro Vancouver contains over half of the total provincial population within its boundaries. And we haven’t even got homeless counts here for several large communities in BC (e.g. Squamish, Whistler, Powell River, Trail). So already we know homelessness seems bigger outside of Metro Vancouver than within. Maybe not just a big city problem after all!

But we can try and do better than that. Let’s try and create a rough baseline risk of experiencing homelessness at a given point in time for each community covered by a homeless count. We can do this by dividing the number of people counted as homeless by the total number of residents in each community. This seems pretty simple, but there are actually a number of considerations that go into creating this baseline risk (which is perhaps why the report itself does not attempt it). First, are most people experiencing homelessness coming from the community where they’re being counted? In fact, we know that they are. Check table 3.9 (p. 37). In no community studied in the report do the majority of people counted as homeless report living there for less than a year. In most communities studied, the majority counted as homeless have lived in the area for five years or longer. Homelessness is mostly local. So we’re on sound footing assessing the risk of homelessness as local.

But what do we mean by local? Local can easily cross municipal boundaries to include broader catchment areas (e.g. metropolitan areas). And there may be clustering of homelessness within broader catchment areas, following services and shelters. We know, for instance, that while the City of Vancouver contains around a quarter of the Metro Area’s population, it includes well over half of the region’s homeless counted, (table 34, p. 39). So fitting local base population to local homeless count isn’t entirely straightforward. Still, outside of metro areas these problems are diminished.

In the chart below, I draw upon homeless count data while making a best guess as to what constitutes a local population to set a baseline risk of experiencing homelessness in each community where counts took place. I mostly use municipalities here, but switch to metro area or regional district where suggested by the Homeless Count Report. For BC communities, I order by population size. But I also include, for comparison purposes, baseline risks calculated from homeless counts for a few other big cities (Calgary, New York City) as well as King County (Seattle) and LA County (Los Angeles). Data for US cities come from a big report to Congress also released in 2018.


Pulling all the data together, it appears that homelessness is definitely not just a big city problem. Tiny little Merritt, BC, appears to have the same baseline risk of homelessness (1.4 in 1,000) as Metro Vancouver. More strikingly, the little communities of Nelson and Salt Spring Island seem to have nearly 8x the risk for people experiencing homelessness as Metro Vancouver. These estimates reveal greater prevalence of homelessness for these places than we get from population-adjusted counts in New York City, Los Angeles, or Seattle.



Let’s put some bands around these estimates and put them back on the map, where the southern half of BC (rightfully) takes its place as centre of the world. In comparative perspective, while Metro Vancouver contains a LOT of people experiencing homelessness, the overall risk of experiencing homelessness at any point in time seems strikingly low, putting the area on par with other communities like Cranbrook, Merritt, and Comox Valley. The risks of homelessness seem higher in other large communities, including Nanaimo, Greater Victoria, and Kelowna. But it’s the high risks in small communities; Smithers, Terrace, Prince Rupert, Port Alberni, Nelson, and Salt Spring Island, that really stand out. Each of these little communities looks like the big cities to our South in terms of the base risk of experiencing homelessness.

For comparison’s sake, let’s see what happens if we use the (more generous) base populations of Metro Areas (CMAs and CAs) from BC Stats in dividing count data to assess risks of homelessness. Does much change?



Not really. A few communities (e.g. Nanaimo, Williams Lake, Vernon) move down a category, but we don’t see major shifts, which is encouraging. Still lots of caveats remain with respect to the data: is the quality the same across communities? How do count methods differ? Check the reports for these and other details, and by all means have a look at the reports and play with the data yourself! I’ll park my little excel datasheet here in case anyone wants to check my work or use it.

(And yes, yes, in case you’re wondering I’m still hoping to transition to a nice transparent R system with GitHub support later this year, but I’m… slow… and sometimes excel with hand-entered data – it’s artisanal! – works ok too).

A visit to Union St.

The resumption of Spring in Vancouver found me biking down to Union St. to grab some delicious Portuguese sweet bread from Union Market (near Hawks Ave). Highly recommended! Union Market is one of these old store fronts that popped up along an otherwise residential street prior to the advent of zoning. It was grandparented into the neighbourhood’s current RT-3 zoning through allowing:

“Dwelling Units, up to a maximum of two, in conjunction with a neighbourhood grocery store existing as of July 29, 1980, subject to the provisions of section 11.16 of this By-law.”

For anyone in love with these little corner grocery stores (and there are a lot of us), it’s striking and bizarre that we don’t allow them in residential zones anymore. But Union Market isn’t actually a corner store. Why? Because this beautiful old townhouse complex right next to Union Market occupies the corner.


To those of us who love townhouses, it’s striking that this form of housing is also forbidden from the majority of residential zones in Vancouver. Here it’s grandparented in with RT-3 zoning to preserve the pre-1920s cityscape of Strathcona. It’s even got heritage designation. It’s clearly a valued streetscape. So why isn’t this form of housing allowed on other residential streets?

Just down the street I passed another old store front. This one is also heritage (if you squint, you can see the marker near the door). But it’s no longer being used as a store. It’s been turned over to residential use. The whole lot was redesigned to support three different residential units about twenty years ago (1999). One in the back (along the laneway), and two up front. (UPDATE: as noted by an observant twitter user, the storefront portion seems to be AirBnB‘d, so it retains an ironic (?) commercial use…)


The history of this lot is fascinating, as revealed in a staff recommendation to City Council from 1999 supporting variance from existing RT-3 zoning to enable its renovation:

Heritage Value: The site at 658 Union Street is listed in the “B” category on the Vancouver Heritage Register and is noted as being an “unique example of [an] early multiple structure”. Three distinct structures were built on the site between 1893 and 1913:

· middle dwelling: the earliest structure was a one-and-a-half storey dwelling built in 1893 and situated in the middle of the lot; only the foundations and wall fragments remain;
· rear dwelling: the next was a one-and-a-half storey end-gable frontier-style structure erected sometime between 1901 – 1912, along the rear property line; and
· grocery store: the final addition was made in 1913, after street levelling activities in Strathcona were complete; it is a two-storey clapboard-sided grocery store with tin cornice, which abuts the Union Street edge of the property.

The latter two extant buildings are representative of architectural and historical themes unique to Strathcona including: Strathcona’s working class heritage; urban change/street levelling activities and community response; and vitality of neighbourhood through grocers and other home-based business.

Of note: the valuable “working class heritage” of Strathcona is precisely what was zoned out of single-use residential neighbourhoods of all sorts (RS, RT, and RM) in subsequent years. And supporting the renovation of the old store front required all sorts of variances from the RT-3 zoning currently in place. The administrative staff helpfully catalogued all the variations proposed:

Screenshot_2019-03-18 656-660 Union St - Designation Heritage Revitalization Agreement

That’s a lot of variance! It also demonstrates nicely just how many restrictions around development are currently in place in zoning by-laws. And this, of course, is simply to restore the “working class heritage” of the lot. Were there objections?

Oh yes:

As part of the Development Application review process, a sign was placed on the site and 47 surrounding neighbouring property owners were notified. Eight neighbours responded. Four support the project in its entirely, including the immediate neighbours to the west. The four others support the conservation component of the proposal, but have the following principal concerns:

· the proposed site coverage leaves little useable outdoor space at grade;
· the building length next to the east property line is excessive;
· entries, decks and coach house configuration would significantly detract from the privacy of the property to the east;
· the extent of proposed changes to the existing rear structure is excessive;
· the configuration of the front unit lends itself to conversion to an illegal secondary suite;
· the current difficulty of finding parking on the street will be exacerbated; and

· the proposed development is too dense relative to the single family dwellings typical of this block.

Strikingly, the neighbourhood association supported the retention (with some caveats) and the half of respondents supported the project in its entirety, with the remainder supporting parts. The most pertinent objections came from the property to the East of the lot. A variety of alterations were made accordingly. But the basics of the renovation remained, and ultimately the lot provided room for three units, subsequently stratified, and now assessed as worth $832k, $868k, and $1,100k.

That’s a lot for twenty year old dwellings, and probably well beyond “working class” territory. But most of the value, as always in Vancouver, is in the land. What do we get in allowing three households to split the cost of the land rather than one? Well, the single structure beat-up house next door (to the West, partially obscured by the tree above)  is assessed at $1,568k. Even taking into account a bit of land lift, each of the three twenty year old units created remains far cheaper than the nearly hundred-and-twenty year old unit (likely in need of some repairs?) next door.

What about that persnickety neighbour to the East? Well… about that… some dozen years after the old store front building was re-done, the lot to the East was entirely re-developed through a lot assembly with the adjoining house. Now the redevelopment of the two lots support and serve as their own heritage infill case study.

Screenshot_2019-03-18 11 03 2010-Union-st -pdf pdf

It’s pretty fancy! What’s striking is that the two lots together now support SEVEN different dwelling units, centred around an interior courtyard.

Screenshot_2019-03-18 11 03 2010-Union-st -pdf pdf(1)

And how much are these new (2013) units? They’re assessed from $523k all the way up to $1,259k (I’m assuming for the big laneway house at the back). In other words, none of these practically brand new units reach the price of the run-down old house on the lot two down. Why? Because they’re sharing land costs. Here’s what the four lots in question look like from the back, via Google Maps satellite view:

Screenshot_2019-03-18 Google Maps

Even though there’s been uplift in the land value with the permission of extra density on the two redeveloped lots, the uplift still doesn’t come anywhere near cancelling out the benefits of sharing. From left to right for the lots centred above, beginning with the partially shaded lot containing the old car covered in vegetation, here are the assessed land values for the lots:

  • one lot with one dwelling*: $1.523 million = $1,523k/unit
  • one lot with three dwellings: $2.291 million = $764k/unit
  • two lots with seven dwellings: $3.878 million = $554k/unit

Despite the benefits of sharing land, none of the ultimate unit prices (ranging from $523k to $1,259k) seem likely to provide stable and affordable housing for the working class households of today. For that, we’ll need more purpose-built rental and social housing. But both of these things become more viable when land costs can be shared across units. Maybe the best way to insure that the working class heritage of Vancouver continues on into the future is to enable and support purpose-built rental and social housing everywhere – especially in the places this kind of housing has historically been excluded. Vancouver’s re-legalization of duplexes on RS zones and moves forward on Making Room are probably good steps along the way.


*- It’s actually unclear how many dwellings are contained in the dwelling with the car in the backyard because we don’t know whether it’s been subdivided to contain one (or more) suites. Legally there is only one dwelling available to be owned.






Tax Speculations

co-authored by Jens von Bergmann & cross-posted at MountainMath


BC has introduced the Speculation and Vacancy Tax and instructions for filling out the declarations are in the mail. The tax targets homes in major urban centres that are left empty, or that are owned by “foreign and domestic speculators” that “don’t pay [income] taxes” in BC. The tax rate is 0.5% of the assessed value in 2018. From 2019 onward rates increase to 2% for foreigners (not permanent residents nor Canadian citizens) as well as citizens or permanent residents that are deemed members of “satellite families.” A “satellite family” is defined as a family – combining spousal incomes – where less than 50% of total worldwide income is declared (and taxed) in Canada.The portion targeting empty homes follows along similar lines as the City of Vancouver Empty Homes tax, with similar exemptions. Homes are generally exempt from the tax when owner-occupied or rented out for at least half of the year. Importantly, foreign and satellite family owners face additional burdens in renting out homes. Tenants must either be arm’s length, meaning they have no special relationship with the landlord, or, if non-arm’s length, they must be permanent residents or Canadian citizens with Canadian income at last three times the annual fair market value of the rent for the entire residential property.

The tax has been reported to affect about 32,000 homes, about 20,000 of which will be British Columbians with the remaining 12,000 foreigners or residents of other provinces, and generate around $200M in revenue. Unfortunately the province has not shared a more detailed breakdown of how many homes are in each of the category the tax targets, the empty homes, the foreign owners, or the satellite families.

Like everyone, we’re curious how it’s all going to work! Here we want to try and put out some preliminary guesses as to how many a) empty homes and b) foreign owners might get taxed. We also want to think a bit more about satellite families and imagine how possible consumption audits might work. This enables us to make some educated guesses about c) the population at risk of being audited. Surely some of those audited will either have to pay the speculation tax or end up referred for income tax avoidance. Others will have ready explanations for why their property holdings fail to match their reported incomes, likely explaining their lifestyles as products of income volatility or legal gifts (falling beyond combined spousal income). Finally, we want to address the possibility of better rental income reporting as a result of the Speculation and Vacancy Tax. Might there be even more d) revenue gained from better reporting rental income relative to direct Speculation and Vacancy tax revenue? Let’s find out!


We’re guessing from various sources detailed below that the BC Speculation and Vacancy Tax will identify about 8,800 properties as vacant and subject to the tax (i.e. not exempt). An overlapping 46,000 properties owned by “foreign” owners may be subject to the tax if they don’t secure qualifying tenants for their properties. Another overlapping 45,000 households may be at risk of being identified (or audited) as satellite families, mostly living in pricey single-family detached (or suited) dwellings. Around a third of these households will be headed by Canadian-born residents, and investor class immigrants will likely end up overrepresented within immigrant households at risk of being ID’d as satellite families. Metro Vancouver will be most affected by the tax. The collection of income and property value data together with the registration of tenants in rented properties will potentially bring in more revenue indirectly, by increasing compliance with reporting of rental income and reducing tax avoidance/evasion more broadly, than by direct payment of the Speculation and Vacancy Tax.

Empty homes

How many empty homes will the tax effect? Empty homes are hard to estimate. The City of Vancouver commissioned a study based on BC Hydro data to estimate the number of empty homes in the city in a similar manner to how the tax applies, coming up with 10,800 to 13,500 empty homes in the city. In the first year, 2,538 properties were subject to the tax (roughly half declared themselves so with the rest failing an audit or failing to file or appeal). Another 5,385 were declared exempt (some of the exempt properties were not in the universe of the Ecotagious study). It is unclear how much of the difference is due to previously empty homes getting occupied or evasion. It is difficult to use this to estimate the total number of empty homes affected by the speculation tax, but one very rough estimate would be to take the number homes unoccupied on census day and scale the numbers down by a factor 8.6, roughly the ratio of the 21,820 homes unoccupied on census day in the City of Vancouver to 2,538 empty homes paying the empty homes tax.


Overall there were 75,870 dwelling units that were unoccupied on census day in the regions where the Speculation and Vacancy Tax applies. If we use the Ecotageous study for the City of Vancouver as a guide, we would expect 41,725 empty properties using the definitions from the Speculation Tax, and 8,825 properties that will pay the tax. This might be a low-ball, given that the province has more effective means in checking for evaders entering into “fake rental” agreements and that the tax rate is lower (for the first year, and for permanent residents and Canadian citizens that make up the bulk of the affected owners in the years after) than in the City of Vancouver, and that the tax can be offset against BC income taxes, potentially inducing fewer people to sell or rent out their property in response to the tax.

Foreign owners

How many foreign owners will the tax affect? Foreign owners are defined as those owning property without being a citizen or permanent resident in Canada. Keep in mind that foreign owners won’t face any speculation tax so long as they rent out their properties to an arm’s length tenant or so long as the deal seems plausible for a non-arm’s length tenant (right now 37.7% of all secondary market renters in the regions affected wouldn’t meet the specified plausibility requirements, but that’s mostly due to their income being too low and that doesn’t matter for arm’s length tenants). Only if the foreign owner themselves occupies the property, leaves it empty, or keeps family members (like children) housed upon the property will they face the tax.

Ever since instituting the Foreign Buyer Tax in 2016, BC has been tracking data on how many purchases are made by foreign buyers. But for a variety of reasons, this kind of transaction data is a poor reflection of the number of foreign owners at any given point in time. Statistics Canada has sought to better collect data on foreign property ownership through its CHSP program, but the definitions differ from tax policy definitions. For CHSP purposes, it’s the primary residence of owners that matters rather than citizenship or permanent residence status – in other words, do owners live at foreign addresses or Canadian addresses? Some people with overseas primary residences will have Canadian permanent residence or citizenship. Some people with primary residence in Canada will not have Canadian permanent residence or citizenship (this status, for example, covered both authors of this blog post when they first moved to Canada). While imperfect, the measure of primary residence probably isn’t a terrible proxy for who will face taxation.

Extrapolating from the CHSP data for the region covered by the Speculation and Vacancy Tax suggests that 46,110 “foreign” owned properties might face the tax, in addition to the relatively small number of foreign individuals likely registering their properties through corporate ownership. It’s important to remember that there may be a significant overlap between the empty properties we looked at above and the properties of non-resident owners likely to face the tax, so these are non-exclusive categories. But we don’t yet have any good data on the degree of overlap.


Of note, so far we can report that the impact from the Speculation and Vacancy Tax will vary widely by geography. Many municipalities have very few empty properties or foreign owners. Others, as near UBC (Metro Vancouver A) have a lot. Of course it’s worth noting that the housing around UBC is unusual for many reasons, including its student population (often boosting census unoccupied counts and highly transnational). Moreover, Electoral Area A weirdly extends into the mountains of the North Shore, where a small number of empty cabins complicate the picture, but there aren’t too many, so we don’t show that part on the map.


Satellite families

What about satellite families? Brace yourself for a much longer discussion, necessarily delving into the definition of satellite families, the methods BC may attempt to use to audit those they suspect of being satellite families, and the limits of the information we can gather about satellite families.

In common parlance, satellite families refer to families where income earners live and work in one place while children and spouses live in another. Within the family sociology literature, this includes a variety of spousal and spouse-like relationships grouped as Living-Apart-Together (LAT). It also includes adult children being supported by parents who live elsewhere, and minor children who might be living with other caregivers (like grandparents) while receiving parental support. Within the immigration literature, satellite families might also be understood to include a wide variety of ways families work around and across borders, often sending different family members to places where they’re likely to see the most economic opportunities, but involving remittances sent back across borders for the good of the family as a whole. The Philippines and Mexico are perhaps the places most studied where families send workers abroad who return remittances back home, usually with a long term goal of reuniting the family. But other countries, including China and even Canada, have similar traditions. Satellite families create transnational ties, constituted in part through the flow of resources across borders but between family members. Vancouver likely has a lot of satellite family members engaged on both sides of transnational income sharing, both as wage earners supporting those abroad, and family members supported by those abroad.

Vancouver also has a lot of wealthy residents, including both immigrants and non-immigrants. And if there’s one thing we know about wealthy residents, it’s that they often don’t pay their fair share of taxes. In the specific context of BC’s Speculation and Vacancy Tax, the debate over satellite families has often emphasized tax avoidance. Satellite families are frequently suspected of gaming tax systems for their own advantage by deriving their income from another country, leaving it untaxed by Canadian income tax. Income tax, of course, helps to fund many services (e.g. education, healthcare) enjoyed by Canadian permanent residents and citizens. Property tax also funds many services, though as many observers have noted, BC has very low property tax rates. So it’s possible to game tax systems – entirely legally – by one family member working in a location (outside of Canada) where income tax rates are lower than BC’s and paying income taxes there, while other family members buy property and enjoy many of the services of BC, where property taxes are often lower than elsewhere. This is the situation the Speculation and Vacancy Tax is meant to correct, though of course it also potentially creates problems for transnational families who aren’t attempting to game tax systems. It also has no impact on satellite families who rent rather than own. Aside from identifying satellite families, the joining of property data with income data also has the potential to identify tax evasion. As revealed by recent CRA audits, tax evasion among wealthy Vancouverites is probably pretty common.

How many satellite families are there? We really don’t know yet. There’s no good data on the issue, especially since families filling out census forms may, or may not, choose to list members regularly working overseas as resident in BC (and census residence is different from tax residence). That said, we have a better sense of who might be at risk for either listing themselves as satellite families or being audited under suspicion of tax avoidance or evasion. But we have to make some guesses about what might trigger audits.

First, let’s remind ourselves of what data is being collected. The declaration form for the Speculation and Vacancy Tax asks about worldwide income for property owners, including the combined worldwide incomes of spouses. This is attached to property tax data from the assessment rolls. So the tax authorities should have declared data on worldwide income, income taxed in Canada, and property values. Recall that owners with less than half of their worldwide income declared in Canada are considered satellite families. Some people will identify themselves as satellite families. But in other cases, they may provide false declarations regarding their worldwide income. This opens up a variety of auditing opportunities for BC and the CRA. How will they decide who to audit for compliance?

We already know the CRA has identified lifestyle audits as a lucrative means of tracking tax evasion. We also know they’ve got a rule in place regarding rents deemed legitimate for non-arm’s length tenants. We can build on this to explore cases likely to trigger audits if undeclared as satellite families. Keep in mind there may be many explanations for discrepancies between property value and income, including family income volatility; dramatic appreciation of housing purchased long ago; or living off savings, inheritance, or gifts. But other explanations will identify home owners as “satellite families.” Of note, still other explanations may be referred to the CRA or police authorities when they suggest tax avoidance, tax evasion and/or work in illegal economies. So how many people are at risk of being audited as satellite families?

Wait, just a few more methodological caveats! We will try to estimate the number of households at risk of being audited or labeled as satellite families using the recently released 2016 PUMF data. 2016 data is, of course, now somewhat dated, being collected prior to a number of policy changes of interest to what we’re exploring, including (but not limited to) the imposition of the Foreign Buyer’s Tax in 2016, the imposition of the Empty Homes Tax in the City of Vancouver, and the slow roll-out of the Speculation and Vacancy Tax itself. We also won’t be able to achieve a perfect match with the regions the speculation tax applies in, having to make due with using Census Metropolitan Areas. The largest discrepancy is that this drops the Nanaimo area region. PUMF data is based on a weighted subsample, so estimates based on PUMF data are never counts as when using the census, but ranges based on different weightings. In most cases, actual census counts will be contained in these ranges, so PUMF data adds a conceptual nuance we usually don’t see when using census data. At this point it is good to remind ourselves that what we are really interested in is not the census counts but the actual numbers on the ground that the census is trying to estimate, but as usual we will gloss over this last step and be satisfied with estimating census counts. Here we will use the primary household maintainer as a proxy for the owner, and we will ignore dual or multiple ownership scenarios where owners fall into different categories. The speculation tax puts heavy emphasis on spousal income, which is different from family income or household income. That makes it a bit difficult to use census data to compare, we would need another custom tabulation to extract the income of spouses only. For this post we will gloss over this issue and just use household income instead. While family income may be closer to spousal income, we simply felt that household income is a more appropriate measure in the context of the Speculation Tax. People that favour different preferences are welcome to grab the code and make the appropriate adjustments. A related issue is what counts as a “satellite family”, in particular it is not clear if it applies to individuals who are not married or living common law. While only married (including common-law) tax residents in BC would appear to be at risk of declaring themselves part of satellite families, single individuals could also be flagged for lifestyle audits to determine tax compliance, so we include both, but we separate them. Throughout we will exclude immigrants that came in 2015 or 2016, as their 2015 Canadian income may not correctly reflect their subsequent Canadian income. Moreover, we exclude households that have moved within the preceding year, as well as properties worth less than $150,000, as these are exempt from the tax. Generally we don’t report if a category contained fewer than 30 (unweighted) cases.

Home-value-to-income based triggers

Assessed home value to income ratios could serve as a trigger for consumption based audits. But what’s a good ratio to use? For a foreigner renting out their property to a non-arm’s length tenant, the tax requires the income of a tenant to be at least three times the (fair market value of the) rent in order for a foreign owner to be exempt from the tax. We take this as a hint we can use this as an implicit definition of a satellite family. A satellite family may be identified as a household with declared income taxed in Canada that is less than 50% of three times the imputed rent. Why? There’s the expectation encoded in the non-arm’s length definition that housing costs will take up no more than one third of income. And if more than 50% of the household’s combined spousal worldwide income is declared outside of Canada, one is considered a satellite family. To estimate imputed rent we use a gross cap rate of 3%. This test is effectively asking that owner households spend at most two-thirds of their total Canadian income on shelter cost based on imputed rent.

However, this will catch quite a few “house-rich but income-poor” people. Take for example a senior that bought their house a long time ago for a lot less money than it would take today. If their house is now worth, say, $2M, then the imputed rent comes out to be $5k a month, or $60k a year, requiring an total annual income of at least $90k to pass our test. Given the fairly large appreciation of property, especially in the years before the census, it seems reasonable to adjust the trigger by how long the property has been held. The province will have the exact time the property was purchased to fine-tune this, but using census data we can only check if the person lived in the same residence one and five years prior. As we are exempting people that moved within the year before the census (analogous to the Speculation Tax exempting properties in the year they transacted), this leaves us with the five year timeframe.

Given the explosive rise in property values in the year before the census, we discount the imputed rent by a factor of 0.8 if the household maintainer moved into the property between one and five years before the census, and by a factor of 0.5 if the maintainer moved in more than 5 years before – reflecting the roughly doubling of property values within the five years before the census. We call this the adjusted imputed rent test.

Of note: our data is top-coded for dwelling-values above $2M, which can lead to some mis-classification for some properties with very high dwelling values, but ultimately different ways of adjusting for this have little big impact on the high-level numbers. We added an additional filter excluding households with household income above $90k, which softens potential issues around top-coded dwelling values.


Combined spousal income determines satellite family status under the Speculation and Vacancy Tax, so we separate out our estimate of those failing our adjusted imputed rent test (and hence at risk of being audited) by marital status. This yields an almost identical number of single vs married or common law households failing the test, combining for around 45,000 in total. While only married (or common-law) people would seem to be at risk of being labeled satellite families given the focus on combined spousal incomes (“gifts” to children and other family members don’t count the same), it’s possible that auditors will still include single people in the pool of those at risk of being audited for tax evasion and failing to accurately report worldwide income. So we’ll keep both singles and marrieds in the analysis, but treat them separately.

Household Status

We also want to look at other statuses that might matter. Students and seniors come to mind as being particularly vulnerable to audit because of their lower incomes. In addition, seniors may be especially likely to have purchased their homes long in the past, meaning their homes may have done much more than double in value since they’ve lived in them. So let’s see what happens when we separate out these groups.


We see that in particular single seniors make up a good portion of households at risk of being audited, but the bulk is taken up by working age population that is not attending school. Household type gives a different way to understand the makeup. If satellite families mostly involve an overseas wage earner supporting a spouse and children, do we see a lot of these types of households?


As it turns out, there are relatively few people who report being married but living as a lone parent who fail our adjusted imputed rent to income test. There are only around 2,400 married or common law household maintainers that show up in lone parent households, making up a small proportion of those failing our test overall. But it’s possible that many respondents filling out census forms still report their spouses as belonging to the household, even if they spend a significant amount of time working overseas, so we shouldn’t count out other married and common-law categories, split between those with and without children, from being considered satellite families.


What kinds of dwellings are people who fail our test living in? First let’s talk about dwelling values. By our metric, the disjuncture between dwelling value and reported income triggers possible audits. Higher dwelling values have a mechanical effect on increasing the income needed to avoid an audit, so we’d expect households with higher dwelling values to be more likely to fail our test. Is this what we actually see?


As one would expect, relatively few lower dwelling value homes are impacted. But each half a million dollar value bracket between $500k and $2M seems fairly evenly filled by about 4,000 households, jumping to higher number for homes above $2M, especially those occupied by married or common law household maintainers. Those most at risk of being audited would appear to be those living in the most expensive homes.

What structural sorts of dwellings are the people who fail our adjusted imputed rent to income test living in? Condo apartmentsSingle detached homes? Both dwelling type and condominium status will be available to government auditors. Our data only has three dwelling types: single detached, apartment and other. In our focus on owner-occupied dwellings and taken together with the condominium variable, we’re mostly separating condominium apartments from single-detached houses, with the latter showing up either as a single-detached house or a non-condominium apartment (i.e., house with a secondary suite or “duplex”). But there will be some other types of non-condo apartments and other types of structure (e.g. rowhouses) showing up as both condos and non-condos.


Most of those at risk of being audited would appear to live in single-detached houses, with or without suites, with condominium apartments taking a distant second. It would appear that not too many other kinds of housing will be targeted, or at least we don’t see enough of them to provide reliable estimates of their frequency. But this analysis by itself is interesting in policy terms. As a reminder, some condominium apartments will be temporarily exempted from the tax if they have restrictions on rentals – an out not available to other dwelling types (and also not available for long!) Detached houses with secondary suites have another potential loophole. Regardless of their status, property owners might be able to avoid paying the Speculation and Vacancy Tax on their house as a whole so long as they rent out one of the suites on the property to an arm’s length tenant, pointing toward the categorical flexibility houses with suites repeatedly demonstrate in policy terms.


In the context of satellite families we often think of immigrant households. These are the households expected to maintain transnational connections, though overseas income earning may diminish with time (and generation). Of course, non-immigrants can also find themselves earning incomes (or partnered to those earning incomes) outside of Canada. Moreover, we know Canadians of many stripes and backgrounds attempt to evade taxes, just as they also have “bad years” where their incomes may drop out of the normal. So let’s look into immigration by period, including non-immigrants in the mix. How does immigration relate to risk of being audited as a satellite family using our adjusted imputed rent test?



Higher numbers of non-immigrants (i.e. Canadian born) fail our test than any ten-year immigration arrival bracket. Non-immigrants especially dominate the set of single people with lower incomes than expected by housing values, but they also appear in great numbers for married people. This is a striking finding, but also reflects the greater overall size of the non-immigrant population. Looking at immigrants by period, we tend to see what we expect: recent immigrants fail the test more often than more established immigrants. Recent immigrants failing our test also tend to be dominated by married couples, unlike what we see for non-immigrants, but this gap diminishes over time as immigrant patterns come to look increasingly like Canadian born patterns.

Looking at the share of owners failing our test in each immigrant category, as opposed to their total numbers, helps clarify these patterns further.



Here we see that higher proportions of recent immigrant owners fail the adjusted imputed rent test than for non-immigrant owners or more established immigrant owners. Reading shares by period of arrival sideways, the evidence would suggest that more recent arrivals owning homes will likely move toward non-immigrant patterns for home owners the longer they remain in Canada. But culture and wealth of immigrants may vary with period, so there may be other explanations at play as well.

Where are those who fail our test coming from? Let’s take a look, using place of birth! Of note, sending countries vary from period to period, meaning the period analysis (above) influences the place of birth analysis (below) and vice-versa. Arrivals from China, in particular, tend to be more recent. We should remind ourselves that place of birth is not necessarily the same as the place people immigrated from. In particular in the case of China, sizable portions of immigrants arriving from Taiwan and Hong Kong were actually born in China.



Here Chinese born and Canadian born household maintainers contribute the most to owners failing our adjusted imputed rent test. But other sizable contributors to possible audits include those from Hong Kong, other East Asian countries, and the United Kingdom. The United Kingdom may seem unexpected as a group likely to face audits, but we have already seen some of the relevant cases documented in the news. Let’s look at share of owners failing our adjusted imputed rent test by place of birth.



Diving into the share of owners likely to trigger audits, we see in all cases that it’s a minority of owners at risk from each country. The uncertainty ranges are too large to sensibly rank the data by place of birth. We grouped immigrants from birth places with fewer than 30 (unweighted) combined cases into larger groups. Nevertheless there are sizable proportions of owners arriving from China, Hong Kong, and Other Eastern Asian countries at risk of being audited. This likely reflects Canadian immigration programs selecting for wealth, like investor class programs, popular in these countries. Comparing investor immigrants living in the speculation tax regions to all immigrants by place of birth, we notice how the investor program leans heavily toward Pacific Rim countries.


We know just over 22,000 property owners in Metro Vancouver were identified as investor class immigrants in 2018 CHSP data. We also know that the incomes of the investor class immigrants reported in Canada have tended to be lower than for other streams, as confirmed in the 2016 census data below.


Looking at the adjusted family income deciles, the bottom decile is very strongly represented, with incomes slowly rising the longer the immigrants have been here. While we don’t know how these roughly 62,000 investor immigrants group into households and household types and break up into renter and the 22k owner households, this does provide more circumstantial evidence that a fair number of investor class immigrants will get caught by the adjusted imputed rent audit trigger.

East Asian ownership patterns may also reflect price discrepancies that make Vancouver real estate seem especially cheap to immigrants arriving from across the Pacific Rim. Arrival with wealth, whether from the sale of a pricey residence overseas or other sources, enables movers to quickly purchase housing in Vancouver. Once arrived, they could become satellite families by returning income earners to countries of origin where they see stronger job prospects (and less discrimination), or they could simply be living off of their savings as they adjust to life in Canada as homemaking migrants (ungated version). In this, immigrants may constitute a special case of income volatility in the years after their arrival. And of course let’s not forget that where there is wealth, no matter the source, there are likely to be attempts at tax avoidance and evasion!

Regional Variation

Lastly we quickly check on how properties likely to be declared as satellite families or audited for lifestyle discrepancies are distributed over the CMAs that we consider. Not surprisingly, in terms of sheer numbers, the Speculation and Vacancy Tax is overwhelmingly going to target Metro Vancouver. Almost all of the properties failing our test are in Metro Vancouver. Which isn’t too surprising since it’s where all the people live.


But what about in terms of share? Metro Vancouver is also where the highest value homes are located and the area with the most transnational ties. So perhaps it’s not surprising that the share of households likely to declare as satellite families or be audited as such looks highest in Metro Vancouver. But by share it’s more clear that Victoria, Kelowna, and Abbotsford pull at least some weight.


Comparing to Shelter-cost-to-income triggers

Another measure that has been in the public discussion regarding satellite families is the shelter-cost-to-income ratio. Instead of (adjusted) imputed rent, we can take the actual shelter cost from census data. This won’t be directly available to the government for audit purposes, but the government could try to approximate this using the mortgage registered against the property from their land title database.

Replacing our adjusted imputed rent with shelter cost we now can ask that owners have enough income to cover three times their shelter cost. Folding in the Speculation Tax definition of spouses having to declare at least half their joint spousal income in Canada we arrive at a shelter-cost-to-income cutoff of 66.7%. That’s something that’s reasonably easy to check in Census data, just like we did for renters near the top.

The total numbers of owner households failing the shelter-cost-to-income test is very similar to those failing our adjusted imputed rent test, but the populations don’t fully overlap as the following graph demonstrates.


This shows that the tests are quite sensitive to the definitions, and using these kind of tests for audits will not be entirely straight-forward. Provincial auditors will likely be busy, and will require a robust data-driven audit system in order to be effective.

Rental Income Tax Reporting Compliance

Reading through the requirements one can’t help but think that the BC government will make use of detailed individual tax return data to enforce the regulation. They may be able to use rental income on tax returns to verify the that arm’s length tenancies were correctly declared. At the same time, this should prove a very effective measure to ensure rental income is properly declared by landlords, which in turn forces proper declaration of capital gains taxes in case of a sale of a secondary residence, both of which are suspected to have low compliance. To get a rough idea of the impact, we use census data to estimate the total rent being paid by tenants. The aggregate shelter cost of tenants not in purpose-built, social housing or basement suites in our regions is $3.09B. Here we exclude basement suites because they are affected differently by the speculation tax. Rent is generally a bit lower than shelter costs, because rent may not include utilities. Combined with this, as well as tax write-offs, we assume an effectively 15% of this total is due as tax on rental income. If we take the current compliance rate to be 50%, the compliance rate that was recently estimated for artisanal landlords in London, and assume that the speculation tax increases compliance to 100%, this would generate an additional $232M of tax revenue at the federal and provincial level, which is the same order of magnitude as the projected direct tax revenue from the Speculation Tax. On top of this, declaring rental income makes it harder to evade capital gains tax at the time of sale of as secondary property.


The BC Speculation and Vacancy Tax has been reported to affect about 32,000 homes, about 20,000 of which will be British Columbians with the remaining 12,000 foreigners or residents of other provinces, and generate around $200M in revenue. While we’re not certain where these figures come from, given our estimates above they actually seem pretty reasonable. We’re guessing about 8,800 properties will be considered vacant and non-exempt from the tax, overlapping with 46,000 properties owned by “foreign” owners and subject to the tax if left unattached to a decent rental contract. A sizable 45,000 households may be at risk of being identified (or audited) as satellite families, mostly living in pricey single-family detached (or suited) dwellings. As we note, around a third of these households will be headed by Canadian-born residents, but it’s likely many investor class immigrants will also be hit, and the vast majority affected will be in Metro Vancouver. Finally, the tax will probably generate a lot of revenue indirectly by increasing income tax compliance, quite possibly topping its direct revenue. We’ll be watching to see how it unfolds!

For those interested in more details on our methods, or people that would like to make different assumptions and continue to investigate along these lines, the code for the analysis is available on GitHub.

There is no Brain Drain, but there might be Zombies

co-authored by Jens von Bergmann & cross-posted at MountainMath & (as of Feb 8th) updated with slightly better mortality estimation


Zombie attack! Zombies fleeing Vancouver want to eat your brain… drain… or something.

A couple of weeks ago The Canadian Press reported a story asserting that young professionals were leaving Vancouver because of the high cost of housing. This fits in with a common zombie refrain that we hear from the media. It’s a story that just won’t die, no matter how many times it’s proven wrong: Millennials, or young people, or boomers, or people important for some other reason are leaving Vancouver because of housing. Usually there are supporting anecdotes, and indeed, it’s not too hard to find people leaving Vancouver who will tell you about their frustrations with housing. But here’s the thing: there is almost never supporting data that actually indicates a decline in people worth caring about. Why? Two reasons. First, in growing cities, like Vancouver, when some people leave, even more people come in to replace them. Second, ALL people are worth caring about.

If we set aside that ALL people are worth caring about – just for a moment – we can take up some important questions about differences in in-flows and out-flows of people in Vancouver. Maybe there are aspects of in-flows and out-flows that should trouble us. In The Canadian Press story, we’re led to believe Vancouver is experiencing a brain drain, so that all the smartest and best people are somehow leaving and they’re either being replaced with people who are not so smart OR they’re not being replaced at all. As noted above, Vancouver is growing. So we know whoever leaves is being replaced, and then some, by new people coming in. But are the people arriving in Vancouver somehow less brainy than those leaving? We’re both immigrants to Vancouver, and quite frankly we find that a little offensive. Everyone arriving in Vancouver has a brain, so population growth cannot result in a brain drain. But we set aside, for a moment that idea that ALL people were worth caring about. So let’s try putting differences in in-flows and out-flows in slightly less offensive terms by returning to the “young professional” framework. Are people arriving in Vancouver unable to do the same kind of professional work as those who leave? Are we losing out on educational credentials?

Ideally we could easily access direct information on in-flows and out-flows to Vancouver (and in some places with population registry data, this is easily accomplished). In Canada we work mostly with census data, and the out-flow data, in particular, isn’t generally made public. But as we’ve demonstrated previously, we can compare across censuses to get net migration data broken down by age group. We just age people forward from one census to the next and compare how many we see in the next census to get a sense of how many people – in net terms – must’ve moved in or out over the years in between.

Now if we’re interested in education then it complicates age-based net migration models. After all, people can and do acquire new educational credentials as they age forward in time. That said, we can probably assume that most people who acquire university degrees and more advanced credentials do so by age 25. We’ll leave out some late achievers, for sure, but if we assume we have a pretty stable division into those with a completed Bachelor’s degree or more, and those without by age 25, then we can get a sense of how those populations change as they age forward in time. So, with apologies to late achievers, that’s what we’re going to do.

We’ve got ten year age groupings by education to work with in 2016 data. So let’s go back to 2006 data for comparison. Is it plausible that we lost a bunch of “young professionals,” defined as people with university degrees, who weren’t replaced as they aged forward and left Metro Vancouver between 2006 and 2016? Data says… nope.


As a matter of fact, Vancouver added a lot more young university graduates than left. Young people with university degrees continued to arrive in greater numbers than they left well through their thirties and on into their forties (we like to think of forties as young). The age labels here refer to people’s “in between” age, that is the ages they mostly passed through between 2006 and 2016 (i.e., the age range each group was in 2011). It’s only once those with university degrees hit their fifties that we start to see a roughly even net flow out of in Vancouver. What’s more, this pattern looks very similar in other major Canadian metro areas. The only exception is Montreal, where people with university degrees really do stop arriving in their forties. But it’s probably not a housing crisis driving them out.

Strikingly, across the board, young people with university degrees are far more likely, on net, to move into our major metro areas than people without university degrees. In many respects, we should expect this. Professionals, in particular, are often drawn by their economic opportunities. Once they arrive anywhere, they’re often paid well enough that they have an easier time navigating local housing markets than non-professionals. Yes, professionals may also have higher expectations about what kinds of housing they deem acceptable than others, but people adapt. One of us has written a book with that theme. In the same way that professionals may drive gentrification, professionals are actually at LESS risk of displacement out of expensive places, like Vancouver, than are non-professionals.

Let’s double-check the results for Vancouver by looking at in-flow data. The Census provides information about where people lived five years before arriving at their current destination. Do we really see a lot of professionals moving into Vancouver through their thirties and forties? Yes. In fact, for “Skill Level A Professionals” this is exactly what we see. We don’t know how many are leaving from this data, but we know a lot of professionals are arriving – more so than in other occupational skill-level categories.


For mobility data the age group labels refer to people’s age in 2016. For an alternative view we can group non-movers and non-migrants (people that did move but not to a different city) together and show the makeup of each skill level by mobility and age group. Again we see that professionals tend to have higher shares of migrants than other skill levels, especially in our lower two age brackets. Those in occupations requiring only a high school degree or on-the-job-training are actually the least likely to come from afar.


Takeaway: we do not have to worry about a “brain drain” in growing cities like Vancouver. Moreover, we don’t have to worry about professionals leaving. Due to better pay, professionals are better equipped to deal with a tight housing market than most others. Building more housing would certainly give professionals more options to choose from, and we might want to relax our millionaire zoning to direct professionals toward competing with the independently wealthy rather than the poor and working class. But it’s the poor and working class we should really be worried about losing. More housing can lead to a more equitable city with room for people who aren’t well-paid professionals or independently wealthy. And if we want to prevent displacement, we should focus more on those actually at risk. That suggests both building more and promoting a LOT more non-market and rental housing.


There are some details to be explained when computing net migration data for professionals. We already noted that professionals might get degrees at some later stage in life, but that tends to bias our estimates toward lower professional in-migration. Furthermore, when computing net migration one needs to kill off an appropriate number of professionals to account for mortality as Nathan has explained in details before. We use BC mortality rates for the appropriate years and age groups for this, but that probably over-estimates mortality as educated people tend to have lower mortality rates. This would bias our estimates toward higher professional in-migration. We could adjust for that by reading into the literature to figure out the appropriate fudge factor, but the effect is so small that we just ignored this. We made some adjustement to how we compute mortality rates and now assume a 20% reduced mortality rate for people with bachelor or above, and according higher mortality rates for people below a bachelor. This is a very rough approximation of the impact of educational attainment on mortality.

Those interested in even more details we direct to the code for the analysis, where Jens is teaching Nathan how to code with R.

How are condos used?

Comparing How Condos are Used Across Canada

Co-authored by Jens von Bergmann; Nathanael Lauster; Douglas Harris (Cross-posted at

Condominium apartments are fascinating! At their heart lies a relatively recent legal innovation enabling individual ownership of units in multi-unit developments. Since their arrival, condominium apartments have become places to build homes, sources of rental income, sites of speculative real estate investment, and experiments in private democratic government. They’re also in the middle of many on-going debates about housing and the future of cities in Canada and around the world. In 2018, we formed a team to study condominium apartments and how they were being used in order to better inform public and academic debates. Team members include data analyst and mathematician Jens von Bergmann, sociologist Nathanael Lauster, and law professor Douglas Harris. We recently presented some preliminary findings at the National Housing Conference in Ottawa and we’re looking forward to continued research collaboration.

Here we make public some basic information about the development and use of condominium apartments across different metropolitan areas in Canada.


The first thing to note is that the legal architecture of condominium is deployed across a broad range of structure types. In addition to apartments, developers commonly use the condominium form to subdivide row houses, and occasionally single-detached houses (as in some gated communities). Nevertheless, condominium is used most commonly to subdivide ownership in low-rise and high-rise apartment buildings, and that’s what we focus on here.

The next thing worth noticing is that condominium is much more common in some metro areas than others. Vancouver jumps out for the proportion of its apartments – and housing stock overall – owned within condominium. Calgary and Edmonton also rely heavily on condominium to subdivide apartment buildings, although these sprawling metro areas are dominated by single-detached houses, much more so than Vancouver, reducing the overall prevalence of condominium.

We know that condominium apartments are exceptionally flexible forms of housing, but how are they being used across different metro areas? What proportions are owner-occupied? Rented? Occupied temporarily? Unoccupied?

We couldn’t extract data to answer the last two questions from the census because condominium status is recorded by respondents. However, using a variety of datasets, we figured out a transparent and replicable (if somewhat complicated) method for estimating temporarily occupied and unoccupied condominium units.

The answers to these questions about how condominium apartments are used speak to important elements in popular discourse and public debate. Since provincial governments introduced a statutory form of condominium in the late 1960s, developers have built condominium buildings rather than purpose-built rental apartments across much of Canada. Does this also mean that the proportion of owner-occupiers increases while that of renters decreases in cities where condominium developments proliferate? Or do owner-investors rent out their condominium units, augmenting the existing rental stock?


Our findings on how condominium apartments are used are really interesting! In all the metro areas we analyzed, the modal use of condominium apartments is owner-occupation. As a result, it appears that condominium apartments are enabling more homeowners to live in increasingly dense cities.

However, condominium apartments also make up a substantial proportion of the rental stock in many metro areas. While many condominium apartments are rented, relatively few show up as vacant (i.e. empty but listed as “for rent”) at any given point in time. Here we distinguish these rare vacancies, which are good for renters, from unoccupied condominiums. In tight markets such as Vancouver and Toronto we see effectively non-existent condominium apartment vacancy rates, comparable to purpose-built rental vacancy rates.

The least common use of condominium apartments is as a temporary residence (where owners declare their principal residence as somewhere else in the census, but occupy the unit occasionally).

Finally we get to the “empty condos,” or those that show up as unoccupied in the census. Overall, we estimate that between 10% to 23% of condominium apartments were unoccupied in 2016, depending upon the metropolitan area. We don’t know why so many condominium apartments appear to be unoccupied, but it likely relates to their newness and to their inherent flexibility as property. Flexibility can show up in the census as “unoccupied” directly, as when owners use condominiums as second homes, and indirectly, as when condominium apartments are left empty in order to facilitate transactions between uses. We suspect that condominium apartments may cycle more frequently than other forms of property between different uses and occupants, thus creating transition periods without occupants and inflating the proportion of unoccupied units. For instance, condominium apartments can more plausibly be re-claimed for owner’s use than purpose-built rental apartments, cycling in an out of rental supply and potentially creating less stable rental housing.

Strikingly, Vancouver and Toronto stand out as having the lowest proportion of unoccupied condominium apartments, a finding that may be somewhat counter-intuitive given the public attention that vacant units have received, rightly or wrongly, in both cities. When metropolitan areas rely upon condominium apartments as a key form of new housing supply, they should take the flexibility of the form into account. However, it appears that the proportion of unoccupied units in the housing stock will rise as the proportion of condominium apartments in the housing stock increases because condominium apartments are more likely to be unoccupied than purpose-built rentals, a pattern also noted with respect to other flexible housing forms, such as secondary suites (especially basement suites, which show up as units in a “duplex” in the census). This means that even though a smaller proportion of condominium apartments are unoccupied in Vancouver than elsewhere in Canada, a larger proportion of Vancouver’s housing stock shows up in the census as unoccupied.

In Canada’s three largest metropolitan areas, a pretty simple rubric applies: for every ten condominium apartments built, six are owner-occupied, three are occupied by renters, and one is unoccupied. In Calgary and Edmonton, add a renter and take away an owner-occupier. The data for the other cities we surveyed is available in the graphic above. As a bonus, we also provide a comparison with estimations from 2011 data to show changes over time in the graphic below.


In Vancouver, where condominium apartments have been an established part of the housing market for longer than in the rest of the country, there is very little change in the occupancy pattern between 2011 and 2016. In other big metropolitan areas, it appears that condominium apartments are increasingly used as rental stock. In most cases, the proportion of empty condominium apartments appears to be decreasing, something that may reflect the lingering effects of the 2008-09 property market crash. However, this is all very preliminary. But we’ll keep looking at the details as we proceed!


We mixed two data sources to arrive at these estimates–the Census and the CMHC Rental Market Survey–and that made coming up with the estimates a little more complicated. There are several assumptions that go into the estimates, and there are several issues with mixing the data that we set out below.


We cut the condominium stock into five different categories. The numbers of units occupied by owners and renters are straight-up census estimates from 98-400-X2016219 and 99-014-X2011026. To estimate the unoccupied units and the units occupied by temporary residents we used a custom tabulation of Structural type by Document type. We received this cross tabulation from Urban Futures, which one of use has worked with before on secondary suites. Both of those variables–the categorization of the dwelling type as well as the decision to label a unit without a census response as empty or occupied by someone who did not respond–is made by the enumerator. This allows us to ascertain the structural type of unoccupied units, and we can also get that information for units that are temporarily occupied.

So, we know how many apartment units were classified as unoccupied or temporarily occupied. To estimate how many condominium units fall into that category we need to make some assumptions. First, we assume that the apartment stock consists of three distinct type of units: condominium units, purpose-built rental units and non-market housing units. That’s not quite accurate. For example a single-family home with two secondary suites will be classified as an Apartment, fewer than five storeys if the census found the suites. These do exist in Vancouver, and elsewhere, but their numbers are small.

Given those three types of apartment units, we need to understand how many of the unoccupied and temporarily occupied units fall into each category. The CMCH Rental Market Survey has annual estimates of vacancy rates and universe size for the purpose-built rental stock. We take those estimates, only counting apartment units, to attribute unoccupied units to the purpose-built rental stock. In Vancouver, with its extremely low vacancy rates, this is a fairly small number. In Halifax, that number is comparatively larger. Further, we assume that the non-market units have a vacancy rate of zero, so that there are no empty non-market units. What’s left over we assign as empty condominium apartments.

Finally, we use the estimate of vacant condominium apartments and those on the rental market from the CMHC Secondary Market Rental Survey, using their estimates of the condominium vacancy rate and the condominium rental universe. The vacancy rate is not available for all years and all CMAs. We have marked the CMAs with an asterisk in case the data was not available and back-filled it with our estimate of the condo rental universe and the Rms vacancy rate. We have seen previously that the Rms vacancy rate tracks the secondary market vacancy rate reasonably well.

Attributing the temporarily occupied units gets even harder, but the numbers are smaller so getting things a little wrong has less impact. Here we again assume that no temporary residents live in non-market housing, and we assume they are equally likely to live in a condominium apartment (as owner or renter) or rent in purpose-built. That is a bit of a judgement call, but the details of these assumptions don’t make much of a difference to the numbers, and we invite people to grab the code if they would like to adjust the assumptions.

There are several issues when mixing CMHC Rms data with census data. For one, both are point-in-time estimates for slightly different times. The census is pegged in early May, the Rms for October. There may be fluctuations in temporary and unoccupied units, in particular in areas dominated by universities such as Waterloo, with the census being outside of the regular semester and the CMHC survey within.

Next comes the geographic problem, with CMHC switching to new census geographies at the end of the year, so the rental universe still reflects the previous census geography. Montreal is one such example where the CMA changed 2011 to 2016 as we have explained before. That leads to problems when estimating the rental universe, but the effect is moderated when focusing on the empty units.

Another issue is that the definition of apartment that CMHC uses differs slightly from the census.

Finally, for estimating the vacant condominium apartments that were on the rental market we used the CMHC rental condo universe estimate and not the one we derived from the census. There appear to be some differences in how CMHC and the census estimate rented condo units, with CMHC relying on surveys of property managers. In BC that likely involves tallying up units for which Form K was filed, likely leading to CMHC under-estimating strata rentals.

It is instructional to compare the two different estimates.


With the exception of Hamilton, the census condominium rental estimates are higher, in some cases substantially so. To shed more light on this we also compared the estimates of overall condominium apartments.


We looked at two separate census estimates: the occupied (by permanent residents) units that come straight from the census by filtering occupied units for apartments that are stratified, and the overall condo estimate that we derived by adding in vacant and temporary units. With the exception of Montréal the census estimate of occupied units only comes quite close to the CMHC condominium universe estimate. The differences are worth looking into in more detail at some point.

Waffle graphs

To communicate the makeup of condominium apartments we settled on a custom version of a waffle graph. Displaying proportions on a square grid makes it easier to read them compared to pie charts or tree graphs. The 10×10 layout rounds numbers to percentage points, which is the appropriate level of accuracy given the uncertainty in the data and is intuitive to understand. When rounding to the nearest percentage, the numbers don’t always add up to 100. So we don’t do traditional rounding but round with the constraint that the total adds up to 100 while minimizing the \(l_\infty\) error.

This does introduce potential problems when comparing across time or across geographies, where theoretically we could see an increase in the number of squares in one category although the actual estimated share dropped. This will only happen under very specific circumstances, and we checked that this did not occur in our graphs.


The code underlying this post is available on GitHub, as are the parts of the custom tabulation for 2016 and 2011 used in this post. Part of the Statistics Canada data we used requires conversion from XML into more manageable data format which, for performance reasons, requires python to be installed next to R that runs the rest of the code.