Running on Empties

August 19, 2019August 19, 2019 ~ Nathan Lauster ~ Leave a comment

(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 numbers

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.

Code linked at GitHub!

Taxing Toxic Demand: Early Results

July 15, 2019July 15, 2019 ~ Nathan Lauster ~ Leave a comment

(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.

Checking in with CHSP

June 19, 2019June 20, 2019 ~ Nathan Lauster ~ Leave a comment

With some new data out from the Canadian Housing Statistics Program (CHSP), it’s time to check some of my past work! Let’s see how it holds up.

First off, I suggested all the way back in January of 2018 that we focus way too much on “foreign” investors as a housing problem in Vancouver. As I noted at the time, I’m not sure why we care if our housing investors are foreign or domestic. But even if we did care, the evidence at the time (compiled from combining data sources) suggested the vast majority of investors were domestic. How does that hold up?

Pretty well! According to CHSP data about four-fifths of real estate investment in Vancouver appears to be domestic, with the remaining fifth is foreign (a.k.a. “non-resident” in Canada*). While I explored this issue by total property value, this matches with Jens von Bergmann’s corresponding estimates by number of owners. I was looking at Metro Vancouver (CMA) as a whole, of course, rather than the City. But the estimates are pretty much the same between the proportion of investors that are non-residents in the City of Vancouver (21.3%) and the CMA (20.9%). The level of foreign investment is high for the parts of Canada studied here, speaking to Vancouver’s transnational connections as a Gateway to Canada. But the differences aren’t that large. Outside Vancouver the range of investment considered non-resident runs from 3.4% in London to 15.7% in the City of Toronto. But returning to my initial point, I’m still not sure why we should care too much which investors are foreign or domestic.

What about investment overall. Are there reasons to privilege owner-occupiers over investors? To the extent investors are offering up their properties for rent we might actually want to encourage investment. After all, renters are generally the most disadvantaged in Canadian housing markets, making up the vast bulk of those in core housing need. So long as investors are renting out their properties, that’s providing more options to renters, which is especially important insofar as Canada’s more or less stopped building purpose-built rental apartments in recent decades and Vancouver has especially low vacancy rates. If investors aren’t renting out their properties, then maybe consider putting in place an empty homes tax! They seem to be gaining in popularity!

But maybe you still want to give people interested in buying a home to live in a leg-up over people interested in buying a home to rent out? This is a common policy choice (see, e.g., principal residence tax exemptions), often justified as building the middle class, helping the young in their aspirations, etc. Theoretically it could also work to challenge landlord monopolies. But in practice, it tends to advantage households in one economic strata (aspiring middle class) over two others (tenants and small-time landlords), while the big-time landlords owning purpose-built rentals may actually benefit from the reduced competition for tenants. Setting aside the various justifications, is there much difference between places in terms of overall investment?

What really jumps out in the CHSP data is the non-metropolitan parts of NS, ON, and BC. Many of the properties in these locations are probably vacation homes. Next comes the City of Vancouver, where 34% of properties appear to be investor owned, followed by London, ON (CMA) at 30%. Yes, just to emphasize the point: the City of Vancouver (highest foreign investment) and London CMA (lowest foreign investment) have remarkably similar rates of investment overall. Huh. (GOTO: why should we care which investors are foreign or domestic?)

But it’s also worth noting that the City of Vancouver is unusual insofar as it’s at the heart of a much broader metropolitan area. We only have data on two other municipalities here: Halifax and Toronto. For Halifax, the City and the Metro Area are exactly the same thing. For Toronto, after amalgamation, the City includes a full 44% of the properties within its broader Metro Area. For Vancouver that figure is only 25%. In short, the City of Vancouver is a very particular slice of its broader Metro Area, while the City of Toronto includes more of its suburbs and Halifax offers the whole enchilada. Looking at broader Metro Areas (CMAs), Vancouver doesn’t stand out much. It’s still on the higher end of CMAs studied, but there’s greater investment overall in nearby Kelowna, not to mention London and Kingston.

Let’s look at another recent blog post, this time exploring condominium apartment use, and co-authored with Jens and UBC’s Douglas Harris. This was a lot of fun! We drew on census data from 2016 and contemporaneous rental vacancy data to estimate empty condos as well as those occupied by renters and owner-occupiers. One of our main findings was that for every ten condominium apartments in a place like Vancouver, we get about six owner-occupied, three rented out, and one left “empty” at any given point in time. Jens turned this into some beautiful “waffle” charts, demonstrating variation across some major metro areas.

Looking only at our estimates of owner-occupation for some of the metro areas covered, we see our estimates from 2016 line up pretty well (within a couple percentage points) with CHSP data for 2018 in the metro areas of Vancouver, Toronto, Hamilton, Victoria and Kelowna. ~~Greater Victoria~~ Ottawa-Gatineau** saw the biggest difference between our estimates from 2016 and CHSP estimates from 2018, with the latter showing higher owner-occupation. This might reflect either historical change or differences in methods, or a combination of the two. All and all I’m counting this as a win!

I also enjoy validation with regard to another one of our key points: investment in condominium apartments is generally much higher than in other forms of housing. Compare the two figures above! Non-owner-occupied condos range from a low of 36% (Peterborough CMA) all the way up to 87% (London CMA). Wow! That’s much higher than the range for properties as a whole, maxing out at 42% in non-metro Nova Scotia. The heavy investment in condominium apartments probably has to do with the financing model, which is heavily dependent upon pre-sales. Usually people buying a place to live need it immediately. But investors can afford to wait and thereby take a stronger role in providing the capital to help develop new housing. Overall, this accords with the effective replacement of purpose-built rental apartments (held by big landlords) with condominium apartments as rental stock (distributed among small landlords). There may still be very good reasons to favor purpose-built rental apartments (security of tenure being a big one), but until financial models and incentives for them catch up to the attractiveness of condo developments, we’re kind of stuck with the latter as a big source of market rentals.

The shift to condominiums as rental stock also demonstrates an important compositional issue. Places with lots of condominiums can have much higher rates of investment overall, even if their condominium stock is more often lived in by owner-occupiers. Where do we see the most condominium stock? Hello Vancouver!

In terms of use of condominium apartments, both metropolitan Vancouver and Toronto are both at the low end for investor ownership, just a percentage point ahead of Peterborough (37%). The City of Vancouver is a little closer to the middle (46%), but still on the low side. But condominium apartments dominate the overall stock of properties in Vancouver, driving up the overall investment we observe. Just to round things out, let’s drop condominium apartments from the mix, and look at other residential properties that could potentially be occupied. Dropping condominium apartments (where Vancouver is on the low side of investor ownership), we see that the Vancouver CMA is decidedly in the middle of the pack on other kinds of property investment. The City of Vancouver still runs a little bit high (up there with the Kingston CMA), but nowhere near the non-metropolitan areas. The dominance of condominium apartments explains away a lot of the investment patterns we see in Vancouver.

Overall I’m feeling pretty good about my past work! (And even better about the work of my collaborators – they’re sharp!) But I’m not quite as excited about the way the CHSP data has been reported on in the media. Given a barrage of stories about foreign investors and “toxic demand” driving broader Vancouver housing crises, it might be time to look at the data in comparison and see that we don’t really stand out in most ways.

Where do we really stand out? Well, as the CMHC notes it’s not about toxic demand so much as toxic supply elasticity.

CHSP-checkup-5

Translating that finding: we’re a city and region where a lot of people want to live that hasn’t built enough housing, market and non-market alike, to maintain affordability and keep everyone who wants to live here well-housed. Guess there’s only so many times that’s going to make for good headlines.

Want to play around with the data yourself, but not ready for R? Go to the Statistics Canada’s portal to run your own cross-tabs, or see my handy excel spreadsheet here.

*- “Resident” vs. “Non-resident” status refers to where owners are located in CHSP data, rather than to their citizenship. Owners located abroad are considered non-resident, even if they’re Canadian citizens. I only look at “applicable” properties eligible to be occupied by their owners in this analysis, ignoring the minority of properties where occupancy is considered “not applicable.” For a look at corporate ownership structures, have a look over at Jens’ recent post!

**- Oops! Initially somehow mixed up Ottawa-Gatineau with Greater Victoria. Looking more carefully at the geography here, it appears that CHSP only tracked data on the part of Ottawa-Gatineau inside of Ontario, which may also account for the larger variation from our 2016 findings, where we included the Quebec part of the CMA as well.

Gateway Communities of Vancouver

June 17, 2019June 17, 2019 ~ Nathan Lauster ~ Leave a comment

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

June 12, 2019June 20, 2019 ~ Nathan Lauster ~ Leave a comment

(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 cooperative, subsidized 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

May 3, 2019May 3, 2019 ~ Nathan Lauster ~ Leave a comment

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).

UBC Talk: Planning, Building, Dwelling

April 18, 2019 ~ Nathan Lauster ~ Leave a comment

There’s a talk at UBC Sociology here in Vancouver this week that’s right up my alley! Honorary Research Associate Martin Fuller will be speaking to his research on Baugruppe (see here and here), with a lovely riff on Heidegger for the title of his talk. Come check it out!

Title: Planning Building Dwelling: On the Activities of Housing

Abstract: This talk will outline a sociological approach to inhabitation that treats housing as a verb, housing as an activity. It does so through building up some of the lessons learned from an in-depth case study of a new residential building in Berlin. This building is unique for two reasons, firstly it was produced by a so-called ‘Baugruppe’, a group of people who are involved in the participatory planning of the building that holds their future homes, and secondly this group includes both private owners and a co-operative housing association. This talk looks into some of the dynamics of participatory planning, building and dwelling.

Martin Fuller is a researcher at the Technische Universität Berlin (Berlin Institute of Technology) in the Department of Sociology. His main research interests are in cultural and urban sociology, often looking at the intersections of the two. His PhD was completed at the University of Cambridge and researched early career visual artists in New York and Berlin. Today’s talk draws upon his recent research on housing, architecture, space and home.

The talk will be in ANSO 2107, from 11:00-12:20 on April 25. Lunch will be provided.