# Running on Empties

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

A spectre haunts housing policy. The spectre of empty homes. So how many empty homes are out there?

Unfortunately, inept analyses of census data often leaves us with incomplete, or even worse, completely wrong answers to this question. When we get data on empty homes for a given city, they’re seldom put into comparative perspective. What’s worse, sometimes when they’re put into comparative perspective, they’re compared with the wrong data and picked up by credulous media, spreading misinformation. So let’s try to do it right!

Here we want to compare some big metro areas and cities in Canada with similar metro areas and cities in the US. As a bonus, this comparison sheds some light on our incomplete data in Canada, and why empty homes have managed to become so central to Canadian housing discussions.

## Empty homes

In Canada we only have one national measure of empty homes, the Census. It estimates the number of dwelling units that are not occupied on census day. It does not offer any insight to why those homes are not occupied. Nor is it part of the standard release data, for most censuses it is only available as a custom tabulation. However, the related number of homes not occupied by usual residents is part of the general census release data and available down to the census block level. It is given by the number of dwellings minus the number of households (aka “occupied dwelling units”), so it includes dwellings that are occupied by people who usually reside in a different “household.” To understand what that means we need to remind ourselves that the census counts people, and tries to count them only once. And each person belongs to exactly one household. This gets tricky for people that call several places their “home”, for example a student that rents an apartment near university but also lives with their parents during summer, or someone working in Fort McMurray for months at a time but lives with their family elsewhere during work breaks. These people may think of their family’s home as “home”, and the other place as “temporary”. In the census, the “temporary” home will be counted as “occupied by temporary residents” and not count as a “household,” as their main household is elsewhere.

Canadian data is pretty simple. To start off we look at Canada’s major census metropolitan areas by their share of unoccupied dwellings. For context we also show the temporarily occupied units. We get a range of unoccupied households somewhere between roughly 2% and 10%, with most bigger metros hanging toward the middle, between 4% and 8% (or what the Lincoln Land Institute considers the desirable range of “reasonable vacancy”).

We can also look at municipalities, keeping in mind that the comparison across municipalities is inherently difficult as different municipalities play different roles within (or outside of) metropolitan areas. Here’s a selection of municipalities, including the boundaries for the old (pre-amalgamation) City of Toronto, just for kicks. Note that municipalities still tend to hang between the 4% to 8% reasonable vacancy range, but the high share of temporarily occupied homes in Waterloo stands out, likely a function of students making up a large share of the town’s population.

## US Data

US data on unoccupied homes is available from multiple places. Here we use the American Community Survey as similar to the Canadian Census. (But see also the American Housing Survey for fun cross-referencing).

US data is great in that it adds important context to unoccupied units, specifying the reason the unit is unoccupied. This context is often completely absent from Canadian housing discussions. It clearly splits out the transactional vacancies, (units for rent or for sale), from moving vacancies (units sold or rented, but not yet occupied), from recreational vacancies (units for recreational, seasonal or occasional use), from other vacancies (not otherwise accounted for).

The range for US Metropoles is also much higher than for Canada, running 12% and higher in the seasonal vacation-oriented metros of Florida, Arizona, and Southern California. Just below these metros sit some of the rust belt metros (Pittsburgh, Detroit, St. Louis) that have lost population, resulting in higher “other vacancies” from homes left behind. Houston seems driven by a high proportion of dwellings available for rent. Overall the data show that many empty homes may be accounted for by these kind of transactional vacancies and moving vacancies, together comprising vacancies we might also think of as good vacancies insofar as they enable people to move between homes to find the best fit. Down toward at the bottom we see just under 5% in the Twin Cities of Minnesota.

Overall, vacancies tend to be higher in the US than in Canada. As unoccupied dwellings rise much above 5%, they seem to be increasingly explained by recreational vacancies and other vacancies. A baseline of other vacancies remains largely unavoidable (e.g. homes under major renovations, tied up in court cases, etc.), and also appears to include people showing up as temporary residents in Canada. We can use ACS data on Usual Residence Elsewhere to provide figures similar to what we get in Canada, comparing all North American metros on roughly the same basis. Here we’ll just show the 14 biggest US metros along with the 6 biggest in Canada.

Overall Canadian metros tend to have lower vacancy rates (combining unoccupied with temporarily occupied) than US metros. Seasonal destinations (Miami and Phoenix) – that also provide second homes for many Canadians – top the vacancy rates for large metro areas, followed by a diverse mix of large metros. Edmonton and Vancouver, though high for Canada, fit very comfortably in the low end for the US (running from Seattle to Boston), while Toronto, Calgary and Montreal occupy the bottom.

What of the bad kind of vacancies, often associated with second or higher order homes for the wealthy or holding properties off the market for speculative purposes? Empty Homes Taxes and Vacancy Taxes in Vancouver and BC attempt to target just these kinds of dwellings, and so far they indicate that just over 1 in 9 unoccupied units end up getting taxed as second homes or otherwise vacant without defensible cause. Vacancy data from the US suggests that were such taxes imposed in places like Miami, that figure would likely be a lot higher. But Miami markets itself as a seasonal or vacation destination.

Vancouver’s Empty Homes Tax covers the City. BC’s speculation tax covers a region larger than cities or even any given metropolitan area. Just for kicks, let’s peek in on counties, a unit of governance in the US with no firm equivalent in Canada. Weirdly, counties can contain portions of cities, like New York County, which contains only the island of Manhattan within NYC. Sometimes counties are the same as cities, as seems to be the case for San Francisco county. Other times counties are a little larger, as with King County (containing Seattle). Sometimes they’re much larger, as with LA County. How heavily would vacancy taxes likely fall in these various counties? In the counties acting like metropolitan areas, including King County and LA County, overall unoccupancy rates are similar to Metro Vancouver. Vacation homes would likely be hit unless deemed ineligible for year round use. Some, but not all, other vacancies would likely be taxed. The vast majority of empty units probably wouldn’t remain empty long enough to trigger taxation. Counties containing Manhattan and San Francisco, with much higher seasonal use, would probably be hit much harder.

Altogether, unoccupied dwellings are broadly similar between the US and Canada, with slightly more dwellings showing up as unoccupied in most metro areas to the south. Lots of municipalities, regions, and counties might profitably consider Empty Homes or Vacancy Taxes. But most unoccupied dwellings in most metros wouldn’t be much affected by them.

# Taxing Toxic Demand: Early Results

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

The province has released (via press release) the first data on its Speculation and Vacancy Tax (SVT)! Huzzah!

Previously, we’ve speculated on what this data would show. In particular, we estimated that around 8,800 dwellings would show up as empty in a way likely to be taxed by the speculation tax. How close were we? Well, the speculation tax has so far identified 8,738 owners of empty properties. Hot damn! We’re on a roll!

But wait! No celebrating yet. It’s early days, and two issues remain to be resolved:

1. The province seems to identify owners owing taxes in its press release rather than properties owing taxes. There can be more than one owner per property! (And more than one property per owner…). On average, CHSP data suggests that there appear to be about 1.58 owners per property in major metro areas covered by the tax (e.g. family members co-owning properties, investors, etc.). That may mean that the 8,800 dwellings we thought would appear empty should correspond to 13,904 owners – and so far we’ve only found 8,738, so we’re still short!

2. There are around 23,000 undeclared taxfilers out there, so figures for owners of empty properties may rise. We really don’t know anything about these undeclared filers. The province, so far, has not identified them as likely speculators. Instead the press release goes out of its way to reassure those who haven’t filed that they’ll be contacted by the Province about applying for an exemption. It’s possible that late declarations are late for reasons unrelated to the tax (e.g. forgetfulness, hospitalization, death), in which case undeclared filings will probably come in similar to declared filings, with maybe another 180 taxed owners added. Similarly, it’s possible that late declarations reflect overlapping ownership claims and property owners’ assumptions that someone else had already declared on their behalf. Or it could be that late filings disproportionately reflect owners with limited ties to the province, boosting the number of vacancies likely to be discovered. It would appear that over one-fifth of late filers of the City of Vancouver’s Empty Homes Tax, for instance, ultimately ended up paying the tax (a much higher proportion than for those who filed on time). This kind of ratio, of course, could add another 4,500+ owners of empty properties if applied to the late filers. In other words, we’d end up pretty close to the 13,904 owners we initially projected (based on 8,800 dwellings showing up empty).

So we’ve learned that our estimates are at least going to end up in the ballpark in terms of vacant properties declared to the province. In addition, we’ve got data on where owners of vacant properties appear to reside as citizens (within BC, elsewhere in Canada, or outside of Canada) and we get our first look at declared satellite families. To date, we’ve had very few ways of estimating the size of the latter population. The tax defines satellite families as those earning more than half of their combined spousal incomes outside of Canada (hence undeclared on Canadian income taxes). Rather than attempting to estimate this directly, we mostly played around with the kinds of situations (mismatches between incomes and property values) likely to trigger audits in case people didn’t file as satellite families. As we discuss in our earlier post, there are many reasons why people may end up in satellite family arrangements. It is probably more productive to think of the component targeting satellite families as complementing federal tax law that is quite ineffective in taxing worldwide income of residents, although the SVT can only capture homeowners and determining residency for transnational families is inherently complicated.

The data that we’ve got so far may change, of course, both as remaining undeclared owners file and as audit systems begin to look through cases. But to put the data in context, let’s plot our preliminary declarations data against what we know about properties overall in the areas covered by the tax. Here we compare CHSP data on property ownership, residency of owner, and owner-occupation with the declarations from the Speculation Tax so far. Note that CHSP data and SVT data have different bases, the former is based on properties, while the latter is based on declarations, so property cross owners. This means we can look at all of the properties that are owner-occupied in the taxable region and compare them to the number of owner-occupiers declaring themselves part of satellite families, as in the first two columns below. You have to squint to see that second column, because compared to all owner-occupied properties in the region (800,000+), the number of declared satellite family owner-occupiers is very small (3,241).

We can make the same basic comparison for the number of properties that are investor owned (which we use as a catch-all for any non-owner-occupied property). Through the CHSP data, the non-resident (“overseas”) investor-owners can be distinguished from those residing in Canada. They’re much smaller in number, but they’re definitely part of the mix. We can compare the number of investor-owned properties to the number of owners declaring a vacant property subject to the Speculation and Vacancy Tax. Comparing, it would appear that the vast majority of investor-owned properties are not left vacant for the length of time needed to trigger the tax. Instead almost all appear to be rented out, making up a sizeable proportion of rental stock.

As we also discussed in our Speculation post, we didn’t know the overlap between properties left “empty” and those deemed “foreign-owned.” Now we do! It’s hard to see it in the figure above because the declaration numbers are so tiny, but owners declaring vacancies that show up in the Speculation Tax data look like they’re just over half foreign (see also figure below). Some owners may be holding for purely speculative reasons, some may be running short-term rentals, some affluent investors may have second (or third) vacation homes in the area. Other owners may be stuck in transitions of various kinds not covered under the Speculation Tax exemptions. What’s clear from the figures is that owners of vacant properties are few in number compared to investor-owned properties overall. Of course, some properties may have been rented out as a means of avoiding the tax, and as with the City of Vancouver’s Empty Homes Tax, we’d suggest that this aspect of the tax is worth supporting, even if the overall numbers of people paying remain small. As a bonus, the proceeds from the tax are earmarked for affordable housing!

Let’s return to our fudge factor to put properties and owners are the same footing. If we assume, based on estimates from CHSP data (detailed below) that there are 1.58 owners per property, then we get a total of 1,655,243 owners overall required to make declarations, using 1,048,290 residential properties from the CHSP data as a base. That probably over-estimates to total number of declarations required, as not all residential properties are required to declare their SVT status. Still, this matches reasonably well with the “1.6 million” letters on how to apply for exemptions it appears the government expected to send out back in January. Using this figure as our base, we can estimate the percentage of all owners who’ve so far declared themselves as members of satellite families or owners of a vacant property. What’s that look like?

Declared satellite family members make up less than a quarter percentage point of owners overall. Owners of vacant properties make up just over half a percentage point. Together, taxed owners are less than one percent of owners overall. While these numbers might still change, depending on the late declarations (<1.5% of owners) and possible audits, as well as variations in number of owners per property in each sub-category, the findings so far demonstrate a much broader point: The situations subject to the Speculation and Vacancy Tax probably are rare, and probably aren’t contributing a great deal to BC’s housing crises.

It would appear that “toxic demand” in the form of Satellite-Family-Foreign-Owned-Empty-Dwellings just aren’t all that big a thing, and we should probably stop blaming foreigners and transnational families for our housing woes (especially given the toxicity such blame spreads to discussions of race and immigration in Vancouver). As always, there remain caveats to our assessment. The data isn’t final yet. And there may be some geographic clustering, or clustering by property types, so the impact may be somewhat bigger in very specific sub-markets. Single family homes on the west side of Vancouver, or in West Vancouver, have been identified as especially subject to “toxic demand” before. Once we get better numbers we will have a clearer picture of this, but these sub-markets that soak up most of the attention aren’t the main battle grounds of our affordability crisis, but rather speak to a crisis of certain professionals’ sense of entitlement. Until we learn more, let’s keep our vacancy tax. But let’s also keep our eyes on the prize of achieving broad regional affordability across a diverse housing stock, moving forward to provide serious answers to the questions of how we should make room, meet housing needs, and build enough housing to promote a more inclusive BC for everyone.

# Appendix

To get our fudge factor we look at the differences between CHSP data on owners and data on properties. On average across our CMAs there are about 1.58 owners per residential property, which may be a slight under-estimate as the data does not provide details for properties with more than three owners on title.

A quick check across metro areas affected by the SVT confirms that there is little geographic bias. In summary, there are no significant differences in how many owners are on title across CMAs or residency participation.

There also seems to be little variation across residency types, except that properties owned purely by non-resident owners have fewer owners on title, while properties owned by mixed resident and non-resident owners have more. But that’s expect. The share for non-resident participation properties confirms that the differences from the average are almost entirely due to conditional bias. Thus there should be little issue with applying the same fudge factor across the board.

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

# 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.

## 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!

..

# 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:

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.

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

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:

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.

### 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.

#### Dwellings

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. #### Immigration 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. ## Conclusion 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.

# Ottawa Talks Housing

Back in November of 2018, I visited Ottawa for the CMHC’s National Housing Conference and presented preliminary results from my working paper (co-authored with Jens von Bergmann and Douglas Harris) on Who lives in Condos? In my last blog post (also up at MountainMath), we detailed how condos were used for metro areas across Canada, as well as how we arrived at our estimates. Here I’m following up with video of our full panel at the conference, “Building an Affordable Future for Rental Housing,” as well as our full powerpoint, both made available via CMHC.

My talk runs from the 10.00 minute mark till about 19.15. Other panelists include Marika Albert, new policy director for the BC Non-Profit Housing Association; Catherine Leviten-Reid from Cape Breton University; and Jacob Cosman from Johns Hopkins. The panel was moderated by Zahra Ibrahim. It was a great panel, even though I had a nasty cold and I wish we’d had representation all across Canada.

In case my talking head doesn’t do it for you (with a wicked cold, no less), here are the slides I refer to during my talk (or tap image below).

Here’s the full program with links to all of the other great panels.

And for good measure, here’s a picture I took in Ottawa outside of the Arts Centre hosting the conference. Ottawa, you’re beautiful! Though you are also very, very cold in November.

# How are condos used?

Comparing How Condos are Used Across Canada

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

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!

## Methods

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.

### Overview

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.

## Reproducibility

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.