Why Do People Move? New Data, Mysteries, and Agendas

November 24, 2019November 25, 2019 ~ Nathan Lauster ~ 4 Comments

How often do people move, and why? Canada has ok data on the first question, and as of yesterday (!) also some ok data on the second. The USA just released its most recent data, with even better answers for both questions. The big finding out of the USA data, attracting significant media coverage, is that Americans just aren’t moving as much as they used to… which is pretty interesting.

Let’s start by comparing the USA to Canada in broad terms. Here I’m looking only at moves over the course of a year (the one-year mover rate), and I’ll just pull from the USA data on movers for recent Canadian census years (2001, 2006, 2011, 2016), and add the most recent year available (for 2018-2019). I’ll also break the numbers down into their component types of moves: short-distance mobility (within county in the USA, within municipality in Canada), longer-distance migration (between counties and states, or within and across provincial lines), and immigration (from another country).*

Overall mobility for both Canadians and Americans dropped between 2006 and 2011, with the intervening Great Recession likely a big explanation for the decline (as well as its greater severity in the USA). But Canadian mobility rebounded, while the Americans continued to… well… stay at home. Just under 10% of Americans moved in the last year, compared to just over 11% in 2015-2016, when a comparable 13% of Canadians moved.

What’s apparent for both countries is that short-distance moves (within the same county or municipality) dominate moves overall, and correspondingly tend to drive broader trends in mobility and migration. Even though geographies of moving can be funky (and US counties are especially weird in this regard), this is a pretty stable pattern. Given the different geographies, it’s hard to read too much into the differences in longer-distance moves between Canada and the USA, but more long-distance moves cross state lines in the USA than provincial lines in Canada. And finally, while still small overall, immigrants (crossing international lines in the last year) make up a bigger proportion of movers in Canada than the United States, actually exceeding the proportion of movers crossing provincial lines.

But why do people move? The USA has good data on that! (Tables 17-18). Here we’ve got the main reason for a given move (often there are more than one), divided into a set of common categories. Let’s break it down by distance moved to show off some general patterns and how short-distance moves are different than longer distance and international moves.

Pretty neat! Short-distance moves (within counties) are dominated by those moving for housing reasons. Longer-distance moves (between counties) are much more heavily focused on work reasons, chief among these moving for a new job. International movers respond primarily to other concerns, with education being a big one! (Housing reasons drop away almost entirely). Strikingly, moves for family reasons are pretty constant across all distances. Thinking about immigration, the categories we get, including: Work, Family, Education, and Other (including refugees) map onto a variety of federal immigration programs, both in the USA and Canada.

Let’s also talk a little bit about the actual reasons given, starting with the work-related categories (in green), including moves because of a new job, moves because of looking for work or recently losing a job, moves to be closer to work (reducing a commute), moves because of retirement, and other job moves. Most work-related moves are for a new job or to be closer to work. Next come family-related categories (in yellow), including moves because of changes in marital status (e.g., moving in, getting a divorce), starting a new household (e.g. moving out of the parental home), and other family (e.g. moving to be closer to a parent, needing room for more kids, etc.). After that I’ve placed a variety of miscellaneous reasons for moving in shades of brown and red. The largest of these, separated out from a generalized “other,” are moving for school (e.g. university), moving for health reasons (e.g. closer to care), and change of climate (e.g. moving to Florida). But natural disasters also motivate a significant number of moves, especially for international movers, and in a world of climate change that’s definitely a category to keep an eye on. Finally let’s turn to housing-related categories (in blue). Here we see people moving because they wanted to own a home (usually after renting), because they wanted a better home, to live in a better neighbourhood, to live in cheaper housing, or because they were evicted or foreclosed upon, with a residual of other housing-related reasons bringing up the rear.

Let’s look at historical variation in reasons for move with handy data from the past twenty years.

Work-related and Family-related reasons for moving seem to have declined only slightly over time. The big decline in American mobility is strikingly concentrated in the decline in moving for Housing-related reasons. We might think of this as reflecting a real decline in housing opportunities, leaving younger people, in particular, “stuck in place,” as per this Brookings report. “Other” reasons for moving may have gone up slightly in recent years, though it’s difficult to fully compare given a variety of changes to survey instruments and coding (e.g., an instrument error may explain truncation in the 2012-2015 era, and new coding procedures for write-in reasons were adopted in 2016).

What’s the new Canadian data on reason for move look like? Unfortunately, it’s different and slightly less useful for some questions than the US data. But it’s something! (Hat tip to Jens, who told me it was out & already wrote up a blog post about it). What the Canadian Housing Survey has done is ask people about whether they’ve moved in the last FIVE years (rather than the last one year). If they’ve moved, the survey asked the reasons for their last move. Canadians could report more than one, which reflects the complexity behind peoples’ actual moves, but unfortunately also makes it difficult to distinguish and compare the main reason for peoples’ move. But let’s look at reasons overall. We don’t have quite the same set of reasons codified in Canada as in the USA, but there is significant overlap, and broad categories can be grouped in more or less similar fashion. Here (for selfish reasons) I also provide a cut-out for my province of British Columbia (BC).

In broad terms, we can see that the categories and their relative importance match up pretty well with what we get in the USA. Housing factors dominate reasons for move, and the largest reason people in Canada give for their moves is that they moved “to upgrade to a larger dwelling or better quality dwelling,” an explanation involved in over a quarter of all moves. Moving for family-related reasons comes next, followed by moving for work-related reasons, as in the data for the USA. Leftover “other reasons” in Canada is a little more inclusive in Canada than in the USA, but we can see that it’s still a residual category, without as much overall explanatory power as the others.

Looking at specific reasons, where they match up to reasons in the USA data, they tend to carry the same general explanatory power. Most moves are about finding housing, matching it to one’s family or household, and matching up to a job. But there’s one reason for move that really jumps out in the Canadian data, despite playing a much smaller role in the American data. So let’s talk more about evictions and foreclosures!

Being “forced to move by a landlord, a bank or other financial institution or the government” is a factor in over 6% of Canadian moves, jumping up to a staggering 10% of moves in British Columbia. One-in-ten moves involves a shove out the door! Those are big numbers. I’ve got ninety-nine reasons for why we might expect BC to see a higher proportion of moves involving these kinds of interactions than Canada as a whole (e.g., we don’t have enough homes, we’re dominated by Metro Vancouver‘s super-tight housing market, and we rely much too heavily on unstable secondary suites and condo rentals that can be reclaimed for use by their owners). But assuming this is mostly about eviction and foreclosure, I really don’t have any good explanation for why they would be playing such an outsized role in explaining moves in Canada relative to the USA. It’s a mystery!

To get a sense of how big of a difference we’re talking about, let’s go back to the data from the USA. In the most recent year, less than 1% of moves (an estimated 216,000 in total) were mainly the result of an eviction or foreclosure. We can go back further. The USA only began providing and recording evictions and foreclosures as a standard option in 2012, but they include a coding of write-in answers in 2011. Good timing, with respect to the aftermath of the Great Recession, as foreclosures piled up, weighing heavily on peoples’ lives as well as the post-Recession recovery more broadly. In the peak year of 2011-2012, an astonishing 792,000 Americans reported moving due to eviction or foreclosure. And yet… that number still represented just over 2% of all movers, with over 35 million moving in that year.

By contrast with the USA, Canada has low mortgage arrears and foreclosure rates and tends toward relatively strong tenant protections. It might simply come down to the survey options available for people to choose. “Forced to move” may be read as more inclusive than “eviction or foreclosure” in such a way that people more readily recognize their circumstances in the former (language of everyday life) than in the latter (legal language). Canadians may also be expanding the range of reasons they were forced to move to encapsulate more ambiguous situations like “my landlord kept trying to sell the place, with showings every week, so we had to get out of there.” So maybe the US and Canadian data just aren’t fully comparable here.

Returning to my ninety-nine reasons for BC’s high rate of forced moves relative to Canada as a whole, it’s worth noting that we do actually have some data on evictions, thanks to Nick Blomley’s team at SFU. Eviction proceedings mostly follow missed rent checks, just as foreclosures almost entirely follow borrowers missing their mortgage payments. Overall, even in Metro Vancouver, the proportion of evictions related to landlords reclaiming dwellings for their own use appears to be pretty small, involving less than 4% of tenant-landlord disputes between 2006-2017 (compared to nearly 40% involving missed rental payments, p. 9 & 12). That said, landlords reclaiming dwellings for their own use seems to be on the rise (p. 10). But overall, the informal ways people feel forced to move by their landlords, banks, or governments, may play a significantly larger role than formal eviction or foreclosures, perhaps even pointing to some shortcomings of the US data for missing a more expansive understanding of forced moves. Can you guess what I’m going to say next? We need more research on this topic!

Forced moves attract attention because they’re the kinds of outcomes we should be working hard to prevent, and it’s important to provide strong protections enabling and supporting people to stay put in their housing where possible. There are good reasons to support an anti-displacement agenda, especially providing for tenant protections. But bearing this in mind, it’s also important to recognize and normalize moving.

Most moves represent positive experiences for people: leaving home, getting married, making room for a child, getting a new job, moving closer to work, moving to better housing or a better neighbourhood. Sometimes such moves are vital, as when people need to escape from a bad family situation. The right to move is protected in some form or another in both the USA and Canada (Charter of Rights!). But it’s largely meaningless without the right to housing. We should be protecting the right to move, together with the right to housing in places people want to move.

To put the matter differently, an anti-displacement agenda is important to protect peoples’ existing housing arrangements, focused on those currently lacking legal standing to remain in place (i.e. most tenants). But anti-displacement efforts must be coupled with a broad pro-mobility, pro-housing agenda in order to fully enact, protect and expand peoples’ right to move and right to housing. Fortunately, evictions and foreclosures seem to be declining in the USA, but moving overall has also declined. Evidence suggests that the decline in moving in America may be most strongly related to a decline in housing opportunities (e.g. Glaeser & Gyourko). We know moving overall has rebounded in Canada, though we don’t yet know if people are increasingly feeling forced to move. The numbers out of BC are certainly disturbing. Pushing for an expansive right to housing means continuing to work toward strong protections for existing tenants, but also – and crucially – working to make sure people can move pretty close to the places they want and need to go.

Let me end by proposing a simple motto for our governments to work toward: Freedom to move and freedom to stay, we’ll get you housing either way.

*- I use the data with the most recent base in the US dataset (e.g., 2010 census for 2010-2011 year in USA), and for the 2001 Census year in Canada I extrapolate the finer categories here from cruder categories available using the corresponding proportions in the 2006 Census year. Check original files for a variety of other cautions with the data.

Metrics and Bird Memes

November 19, 2019 ~ Nathan Lauster ~ Leave a comment

Working with Jens von Bermann, I gave a talk yesterday at #HousingCentral on housing metrics! Specifically, we talked through and expanded upon our earlier joint blog post on the same topic. Click the image below to visit our full slides.

Included in the slides are a variety of graphics, mostly from past posts of mine and Jens’. In case you’re curious, follow the links below to find out more about them:

Rent correlation with vacancy rates

Price correlation with inventory (borrowed from YVR Housing Analyst)

and Job Vacancies

As for the conference, Housing Central is an annual shindig put on by the BC Non-Profit Housing Association (BCNPHA), including a special set of panels on research from the fine folks at the Pacific Housing Research Network (PHRN). Check the PHRN Symposium website for calls if you’re interested in presenting!

Last but not least, I took some bird pictures down along the southern edge of the Fraser River delta, and I really, REALLY want to turn them into as many housing memes as I can. So here’s me summarizing our Housing Central talk with a bird-based housing meme.

Enjoy!

Simple Metrics for Deciding if you have enough Housing

June 12, 2019June 20, 2019 ~ Nathan Lauster ~ 1 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 ~ 1 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).

Checking in with Numbeo

October 25, 2018October 25, 2018 ~ Nathan Lauster ~ Leave a comment

For those interested in making international comparisons concerning rents and housing prices, Numbeo is a potential god-send. I say potential, because there are still some big data quality concerns. But the basic idea is sound: crowd-source estimates of rents and housing prices (as well as costs for all sorts of other things), both for the “centre” of cities and farther out. The end result is a real competitor to even iffier rankings for things like quality of life (looking at you Economist Intelligence Unit!) I’ve been playing around with crowd-sourced data again recently, so I was reminded of Numbeo and thought I’d take a look.

How is Numbeo holding up? And what can it tell us about current housing dynamics? First let’s see what Numbeo tells us about Vancouver, based upon 18 months of crowd-sourced data from 93 contributors (as of Oct 24, 2018):

RENT: Numbeo estimates that rent for a 1BR in the centre of Vancouver average about $1930.86 (CAD). This compares nicely to a listing informed estimate of $1950 for Vancouver 1BRs from Louie Dinh (confirmed as approximate for Downtown unfurnished apartments by a scraper who shall remain anonymous). This runs high compared to CMHC Rental Market estimates of rents for Downtown Vancouver ($1468), but that’s to be expected given that CMHC includes all renters, including long-timers protected by rent control. That said, the CMHC’s estimate for Downtown Condos rented out ($1900) is a lot closer (see p. 35 of report). All things considered, Numbeo estimates strike me as reasonable for current rents on offer given the vague parameters (Vancouver centre).

PRICE: Numbeo estimates price per square foot for an apartment in the centre of Vancouver at $1,091/sqft. Looking around, this compares pretty reasonably – if a little low – with recent RE/MAX estimates ($1,195/sqft) and even better with realtor Steve Saretsky‘s handy reporting for Sept 2018 ($1,026/sqft). Worth noting that some lag may be expected given the 18 month reporting period from Numbeo.

INCOME: Numbeo estimates an average monthly net salary (after-tax) for Vancouver of $3,170/month. Looking at the Canadian Income Survey (CANSIM 11-10-0238-01), the average monthly after-tax income in 2016 was estimated at $3,042/month, and it’s surely gone up since. Again, seems pretty reasonable as an estimate.

I think it’s worth continuing to check in on Numbeo estimates, which may also vary dramatically from place to place, especially since the number of observers doing the crowd-sourcing also varies a lot (only 18 in Albuquerque!). But on the whole, Numbeo seems to be doing ok for Vancouver, the city I know best.

COMPARISONS: So if Numbeo data seems to be doing ok where I know it best, let’s do some comparisons! Here I provide some basic data for selected North American cities from Numbeo on one-bedroom apartment rents and price per square foot of apartments centrally located in select cities. From here on out, everything is reported in US dollars (just because it made things a little easier).

Price-Comparison-Numbeo-Oct-2018-B1

Cities are ordered by 1 BR centre rents, and the extreme high rent American cities – San Francisco, New York – lead the pack. It takes awhile to get to a Canadian city, starting with Toronto (right after Nashville!) before hitting Vancouver. After that, I pick out a few more of the big Canadian cities. I also add places like Honolulu (expensive resort city) and Albuquerque (one of my home cities!), just for kicks, and low-rent Montreal rounds out the pack at the bottom. Rent and price are correlated (r=0.84), but not perfectly. Strikingly, compared with American cities, all of the Canadian cities have higher prices than one might predict based upon their rents. Of cities examined here, Vancouver ranks 4th highest in price, but 17th in rents.

What happens if we add incomes into the picture? Below I take the same cities and divide both rents and prices by incomes to get simple estimates of relative housing costs. Now the familiar (to Vancouverites) pattern emerges of Vancouver being the priciest real estate in North America, followed by New York and Toronto. Canadian cities look pricey in no small part because our after-tax incomes look relatively low compared to Americans. Rent-wise the story is a bit different. New York and Miami lead the continent, followed by Vancouver, Toronto, LA, San Francisco, and Boston, all hanging reasonably close together.

Price-Comparison-Numbeo-Oct-2018-B2

Relative to income, there’s no doubt both Vancouver and Toronto are expensive places to live in North America. But these are also places with a lot of international immigration. Immigrants make up nearly half the population of Toronto (46%), followed closely by Vancouver (40%). And immigration is increasingly Asian, especially in Vancouver. As I’ve pointed out before, it’s also useful to put Vancouver – in particular – in the context of the broader Pacific Rim.

Here’s base rents and prices (USD), drawn from Numbeo.

Price-Comparison-Numbeo-Oct-2018-B3

San Francisco still leads by rent, but it’s got nothing on Hong Kong when it comes to price. Notably, the price per square foot for apartments in Shanghai, Beijing, Seoul, and Shenzhen are also more expensive than in San Francisco. Add Tokyo and Taipei to the list of Pacific Rim cities with more expensive prices than Vancouver. This helps put Vancouver’s prices into context. Compared to most cities of the Pacific Rim, we’re still cheap. And lots of people are probably coming here with real estate money in their pockets from holdings they’ve sold (or in some cases held onto) back home.

Let’s run the same comparison checking in on income.

Price-Comparison-Numbeo-Oct-2018-B4

Compared to incomes, Vancouver stands out for its pricey real estate in North America. But again, in the broader context of the Pacific Rim from whence many of its immigrants arrive, Vancouver still looks cheap. Real estate is crazy expensive in Hong Kong and the major cities of Mainland China. It’s only slightly less expensive in Taipei and Seoul. Vancouver and Tokyo look quite similar.

The picture for rents is less dramatic than for purchase, and also holds different possible lessons. Average rents for available apartments are still crazy high in Hong Kong, Shanghai, Beijing, and Taipei, consuming over two-thirds the take-home pay of the average income earner. But rents aren’t far behind in Vancouver, San Francisco, and LA, which all hang close to ratios for Shenzhen and Guangzhou. There are a lot of high rent Pacific Rim cities. As I’ve argued before, rents are probably the most important thing to focus on in terms of insuring people can live in our cities. But it’s worth noting that available 1 BR rents take up under 40% of average incomes in Seoul and Tokyo. What might they be doing right in terms of taking care of renters that other Pacific Rim cities could emulate?

At any rate, as before, I’d love feedback on Numbeo numbers! They’re already showing up in academic papers. Do they look right to you? Way off? Better or worse than last time I checked in?

Hit me up with your thoughts!

Addendum: If you want to play around with my data download & the excel sheet I used for the above, here it is: Numbeo-Look-Oct-2018-B Note that the income data from Numbeo was hand entered, because I couldn’t find a central source for it, unlike the pricing data by City.

The Great Wait: Changes in Timing in BC’s Birth Rates

February 13, 2018 ~ Nathan Lauster ~ Leave a comment

While putting together slides for my life course class I returned to BC Stats data on age-specific birth rates. It’s really nice data, broken down by local health area. I’ve played with data on the Total Fertility Rate before. This time I wanted to highlight a far simpler transformation in birth rates that I’ll call the Great Wait!

What is the Great Wait? Basically, it’s the transformation in age-specific patterns of childbearing, whereby most women are having children later and later in the life course. When I was playing around with the BC Stats data I accidentally produced a chart illustrating the Great Wait, and I just thought it was too beautiful not to share.

Notice the gradual shift from peak childbearing in ages 25-29 (in 1989) to peak childbearing in ages 30-34 (in from 2003 onward). By 2005, more 35-39 year olds were having children than 20-24 year olds (so called “geriatric pregnancies” – which is like seriously a total FAIL in medical terminology). By 2010, the birth rates for 40-44 year olds began exceeding those of 15-19 year olds. We have fewer and fewer teen moms, and more and more new parents in their forties.

There are many interesting causes and implications of this shift. On average women are taking longer to develop their education and careers before having children than ever before, facilitated by improved contraception and assisted reproduction technology. It may also be that women just don’t feel as ready to settle down into motherhood as they used to – either because the alternatives remain too interesting or because they don’t feel prepared for the job of being a parent yet (I’ve explored this latter explanation with respect to the role of acquiring housing as a stage prop for the role of parenthood here in my academic work).

With respect to the implications, some of the childbearing delayed will inevitably be childbearing denied, as later-life pregnancies are biologically less certain for women, and some new risks are entailed. But on the whole, having children later means parents tend to be more committed and more prepared, with more resources at their disposal to help care for their children. Not a bad thing. On a technical note: the ongoing shifts in the timing of when women have children somewhat artificially inflate the magnitude of recent fertility declines. This is to suggest that 1.4 children (our estimate of the number of children women in BC have on average based on TFR measurement) is likely somewhat lower than the number of children the average of any given cohort of women will ultimately end up with. It’s kind of a demographer fixation.

A Limerick (and a reply to a response to a critique of a study)

December 8, 2017December 15, 2017 ~ Nathan Lauster ~ 3 Comments

John Rose posted a response to my critique of his study this morning. Almost immediately after I was alerted to the posting (via Kerry Gold), we met up to chat at Sweet Obsession cafe. In appreciation of this turn of events, I offer this limerick:

John Rose is a very nice guy

Despite our dispute o’er supply

We just met for tea

And we mostly agree

Where not, please see my reply

He really is a nice guy. And in order to insure our back-and-forth doesn’t become too tiresome, I’ll offer just a quick reply.

Though I replicated John’s Census results from 2001-2016 for Vancouver, I apparently did not replicate his results for other metro areas. I admit, I didn’t catch this, since I was focused on Vancouver (and since I ran the replication of his 1.19 ratio of new dwellings to new households very quickly, before he’d provided his full report). I don’t know why his results and my own differ for metro areas beyond Vancouver, but it’s worth looking into! The data should be from the same source (Statistics Canada), but sometimes they report things differently in different documents, and it’s also entirely possible that errors were introduced in transcribing data (in which case, they were probably mine! My response was hastily assembled). Though it does not change the results for Vancouver, it’d be good to nail down overall dwelling count and occupancy changes.
As John notes, the Census does not offer guidance with respect to how their procedural changes affect underlying dwelling count data between 2001 and 2006. But in noting their newly inclusive criteria for expanding the count of secondary suites, they clearly point out how single-family dwellings changed to duplexes in their structure data. This implies that each of those dwellings formerly counted as one unit (but containing a secondary suite) would henceforth be counted as two or more. As we know, Vancouver has a LOT of secondary suites, and this shift in classification both could and should have boosted the count of dwelling units significantly, even without any new dwellings being built or added. Worth noting as well that new secondary suites are the LEAST likely to show up in permitting data (though the Metro Van databook for 2017 at least tries to capture them). It would be great to get more from the Census on the characteristics of “dwellings unoccupied by usual residents.” On a related note: I’d love it if someone could point me toward or carry out an intensive study of how the Census counts dwellings in Canada!
John acknowledges the awesomeness of the construction permitting data, but does not (yet) engage with how much better it fits new household formation than census counts of dwellings, indicating a shortage rather than a surplus of supply. I’ll look forward to seeing his comparison between construction and census data if he’s able to pull one together! (Both of us have time constraints involving stacks of grading and lots of other work on our plates).

Otherwise, as I said, John Rose is both a nice guy and clearly well-intentioned. He mentioned during our conversation that his study was motivated over concerns about new construction in the Agricultural Land Reserve (ALR) in Richmond. On this point, we clearly agree. The ALR is worth saving, and we don’t need to expand our housing supply any further out into Vancouver’s agricultural and wild lands, which is part of why I focus on densifying single-family residential neighbourhoods as the best path toward making Vancouver a more affordable, more inclusive, more lively, and more sustainable city.

[Postscript, Dec 15th: for more see Jens’ careful response with a detailed dive into the data over at MountainMath, and see the smart historical commentary on the Census in Vancouver in the comments below by the folks at Changing City (added bonus: see their lovely pictures of changing streetscapes around town!)

What we talk about when we talk about a “housing crisis”

August 1, 2017 ~ Nathan Lauster ~ Leave a comment

What do we talk about when we talk about a housing crisis? People doing without any kind of housing? People living in inadequate housing? Crowding together? Spending too much on rent? People struggling to get into home ownership? People not being able to afford ownership of the single-family detached house they always dreamed about?

To me, a “crisis” suggests fundamental needs unmet. But just what’s a “need”? How should this be separated from a “want” or “dream,” if at all? Addressing these questions and trying to figure out how they matter was the subject of my keynote talk at the PartnerLife conference last week in beautiful Cologne, Germany.

I illustrated my talk with my favorite case study: Vancouver. In the process, I ran some numbers to compare how Vancouver is doing relative to other metropolitan areas if we address some of the different things we mean when we talk about a housing crisis. In particular, I was interested less in the kinds of “dream” measures used by organizations like Demographia (oh, it’s painful to even link to them!), and more in the fundamental measures of need (note: I’ve compared rents elsewhere, though I need to update the comparison!).

How is Vancouver, long considered the most unaffordable housing market in North America using Demographia’s single-family detached house measure, doing when we look at homelessness? How about when we look at providing basic standards, avoiding crowding, and insuring affordability?

To answer the first question, we can look at homeless counts. I’ll work on building this measure further, but for now I’ll just compare Vancouver with our near neighbours to the south (Seattle and Portland) and to the east (Calgary). Is homelessness a major crisis here?

The first answer to this question is indisputably: YES. Homeless is a major crisis wherever it occurs, with large effects, for instance, on the risk of dying. But a more nuanced answer, of more use in thinking through solutions and sorting out what’s working, is to consider the relative size of the homelessness crisis. Though it’s far from a definitive comparison, I started looking into this question by comparing homeless count data by relevant population size, across the regions of Vancouver, Seattle, Portland, and Calgary.* Here’s what I get:

HomelessCountComparison-2

Is homelessness in Vancouver a crisis? Yes. But when compared to other nearby metro areas, Vancouver looks like it’s doing better. This is important in terms of judging the response so far and thinking through how to continue dealing with this crisis.

Let’s address the second question: How are we doing in terms of insuring people are living in adequate housing, not feeling too crowded, and not spending too much money on rent? In Canada, we have a nice comparative measure of “core housing need” that gets at these components of housing crisis. Importantly, these aspects of a “housing crisis” remain detachable, revealing, for instance, different sorts of crises between the North of Canada (where the issue at hand tends to be crowding) and the South (where it tends to be affordability). Overall core housing need is worst in the North and on reserves, where we can talk about some serious housing crises. But here let’s just look at how Vancouver is doing by comparison with other metro areas in Canada given the most recent data available.**

CoreHousingNeedComparison

How’s Vancouver doing by core housing needs? Not so great. We’ve got a lot of people feeling the pain of unmet housing need, as defined by Canadian standards. Mostly these are people spending more then 30% of their income on rent. I’ll be the first to suggest that this is a funny standard, but it still indicates a real problem, especially for those at the bottom of the income distribution. At the same time, by comparison Vancouver is not actually the worst Canadian metropolis. The worst is tiny Peterborough, Ontario! What’s going on there? I’ve no idea, though now I’m quite curious (and it might just be the small sample size of the income survey). After Peterborough, Toronto is also worse than Vancouver.

While both homeless counts and core housing needs remain open to critique in terms of their conceptualization and measurement, they’re also the best measures of need we’ve got. As such, I’d argue they remain the best measures of when we’re seeing a real housing crisis. Using these measures, we can see that there are indeed housing crises at play in Vancouver. At the same time, in comparative context we can recognize that Vancouver’s doing much better at addressing these real crises than it’s typically been given credit for.

Why doesn’t it get credit for what it’s doing right? I think the unaffordability of the single-family detached house in Vancouver sucks up a lot of attention. I’ll continue to argue that this is a very BAD measure of a housing crisis. After all, if we want to reduce the size of our ecological footprints, if we want to support our great cities, if we want to combat isolation and obesity, and arguably if we want to sustain our democracies, then we want to discourage everyone from living in single-family houses. This means not everyone can get what they want. But it’s not a housing crisis if everyone still gets what they need. And by that measure, Vancouver’s doing better than most people think, even if it’s still got a lot of work to do.

*- It’s worth noting that the administrative basis for count data here varies between metro region (Vancouver), county (Portland and Seattle), and city (Calgary). I’ve used the relevant administrative data from the 2010/2011 census year as the denominator in each case. This means population has been kept constant for comparison purposes, while the homeless population has been allowed to grow, resulting in a slight underestimate of homelessness per 10,000 people in early years and overestimate in later years. Also of note, King County and Multnomah County are smaller than the metro areas of Seattle and Portland (accordingly). The City of Calgary is relatively co-terminous with its metro area. This could bias overall estimates of relative counts for metro areas. But even if we were to just use central cities (where the populations of Vancouver, Seattle, and Portland are quite similar at @ 600,000) or metro areas, the overall results would still be about the same – there are a lot more homeless people showing up in other nearby cities and metro areas relative to Vancouver. A caution also remains in the possibility for different definitions and methods in each region, particularly with respect to the meaning and coverage of “transitional housing.” Also of note: the big drop observed in Portland between 2011 and 2015 might be worth following up on!

**- the data here come from the CMHC, and are based on Canada’s income survey rather than census data. It’s possible the sample size of the surveys explains some of the variation, and rankings here should be considered preliminary until we get something more definitive, like the 2016 Census data! In past census years, Vancouver and Toronto usually compete in the metropolitan title for greatest proportion in core housing need, and Peterborough tends to be more middle-of-the-road.

Surveying Realtors

June 2, 2017June 2, 2017 ~ Nathan Lauster ~ Leave a comment

I’m always both fascinated by and wary of the data produced by real estate associations. I initially had a whole chapter in my book devoted to taking apart survey data on consumer preferences put together by real estate organizations (sadly, but probably correctly, it got cut). Here’s one of my favourite such survey questions (see slide 8) based on what Vancouverites might want to buy if, inspired by the Bare Naked Ladies, they had a million dollars. (Nearly a quarter chose to keep the $1 million and rent!)

I notice that such data is back in the news again, this time based on surveys of realtors, from April 2016 to April 2017, who’ve recently represented buyers in sales. The write-up leaves a lot to be desired in terms of methods (what’s the sample size of realtors and buyers? what’s the response rate? are there warning flags in terms of representation of realtors and buyers?) It’s also unclear whether this represents entirely re-sale or also sales of new residential real estate. This makes it difficult to evaluate the quality of the data. But it’s still kind of fun to play around with it.

I’ve broken the data, as presented by REBGV, down into my own categories. Here’s type of sale:

REBGV-Data-TypeSale

According to recent surveys of REBGV realtors, investment purchases make up about one in five sales. The role of foreign investment (largely, but not entirely post-Foreign Buyer Tax) is relatively small. But survey quality, about which we know little, likely matters a lot for these estimates. Are some realtors and real estate companies more likely to respond than others (especially those, like New Coast, likely to especially target overseas buyers)? Other important details are also missing: Are sales of newly constructed properties included? How do realtors decide who counts as a foreign investor vs. a domestic one?

Setting investment purchases aside, first-time buyers, targeted by a much-derided recent BC Liberal finance assistance program, make up nearly a third of buyers. That’s a pretty big chunk of sales! But here it’s not clear quite what counts as “first-time.” First time in Vancouver, first time in Canada, first time at all anywhere? Other moves, making up nearly half of all purchases, tend to be from buyers moving around from one dwelling to another.

Finally, there’s really interesting data breaking down moves of owners moving from one property to another by type (condo apartment, townhouse, and detached house) at old home and new. I simplified this into lateral moves, moves to likely bigger units (upsizing), and moves to likely smaller units (downsizing). Many general life cycle models of housing assume households tend to upsize over time as they grow, the better to fit with children. Downsizing only (maybe) occurs after retirement or when children move out. But with Vancouver steadily moving away from single-detached houses, upsizing is the least likely type of move between owned units. Instead, most moves are either lateral (e.g. apartment to apartment) or downsizing. That’s pretty interesting, and likely reflects, in part, how people moving here from elsewhere in North America typically find a house out of reach.

And just where are people coming from?

REBGV-Data-TypeMove

Hmmm… returning to the data quality issue, it’s a little concerning to me that the “investors” category in this question is so much smaller (14%) than in the previous question about type of sale (20.8%). Where did the extra investors go? Did some of them move as they made investment purchases? Were others counted as living in the same community? Weird.

But we get some idea about what proportion of sales represent people moving here from beyond the Metro area, and it’s about 12%. That could account for many of the downsizers, as they reckon with the realities of Vancouver’s pricey market (esp. for single-family detached homes). Another healthy chunk might involve retirees (more on that in a second).

Setting aside investors, we can actually do a comparison of where moving buyers are coming from by looking to Census data (or more accurately, National Household Survey data). The 2016 data on mobility and migration aren’t out yet, but the 2011 data (limited access here, but also recently out in IPUMS) provides a breakdown for those who’ve moved in the past year. Limiting the sample to those in Metro Vancouver, I looked at household heads who’d moved in the past year and owned their own home. How did where came from match up to REBGV data in 2016-2017?

REBGV-Data-TypeMoveCompareCensus

That’s actually a pretty good match! There is some difference in terms of who the Census thinks is moving within their own community relative to who realtors think of as moving within their own community. This likely relates to shifting definitions of communities (again, not defined in the REBGV data). But looking at the proportion of new buyers moving within the metro area (in green) relative to those moving in from away (blue and pink), the figures are actually quite close, at about 86% of non-investment residential sales being to local buyers.

The Census from 2011 would suggest slightly more recent buyers moving to the area came from outside Canada than the REBGV data from 2016-2017, but not by a lot (7.4% to 5.8%), and the disparity could arise from either historical change (including the imposition of foreign-buyer tax) or from issues with data quality (see above). Still, a pretty good match.

It’s actually harder to match up the “demographic” categories used by REBGV data to census equivalents. But playing around with the community profile data from BC Stats, I did my best. Here’s how new buyer households in the REBGV surveys from 2016-2017 kinda, sorta stacked up against all households in Metro Vancouver by household types in 2011.

REBGV-Data-TypeHH-CompareCensus

Again, it’s tricky to make sense of REBGV categories and match them up to Census categories (the census, for instance, does not differentiate between “young couples without children” and “empty-nesters,” and I’ve no idea how these were defined for the realtor survey either). I also don’t know how demographics on investors were tabulated, or where they fall relative to households looking to buy a place to live. But the general match-up between all households (from 2011 Census) and new buyer households (from 2016-17 REBGV survey) looks plausible to me in terms of what I might expect. New household formation drives a lot of sales. So couples without children are disproportionately likely to buy a place while retirees (or those age 65+ in the Census) don’t actually move all that much (there’s a lot of aging-in-place).

I don’t know that I have a big takeaway from all of this data exploration. I think the REBGV data remains kind of sketchy for estimating investment purchases until we get some basic information about data quality and representativeness out of the way. But setting aside investors, the data on where new buyers are coming from when they move within or to Vancouver lines up well with what I’d expect from the census, which is reassuring and kind of cool.

Good Age-Specific Net Migration Estimates Come in Threes!

May 5, 2017May 5, 2017 ~ Nathan Lauster ~ 2 Comments

Recently I posted on how we’re still not seeing any big age-specific losses in net migration figures in Metro Vancouver following the release of 2016 Census data. To summarize, there is STILL no flight of the millennials, BUT maybe there’s a slow leak of the Baby Boomers, which might be seen as evidence of “cashing out” of the local real estate market.

Today I wanted to provide both some metropolitan comparisons to note how Vancouver’s patterns fit with a couple of similar places, and also some municipal comparisons within the Metro Vancouver area. I also wanted to make some technical adjustments in how I modeled mortality* as I aged people through the past five years to estimate net migration, which really matters for older adults (not so much for the young). Again, I’m using 2011 and 2016 age distributions drawn from census profiles to get at age-specific net migration estimates for each of the metro areas and municipalities below.

First let’s compare Vancouver as a metropolitan area to two other metro areas: Edmonton and Toronto. I like this comparison primarily because Vancouver is nestled nicely between these two areas in terms of size, and they’re all big university towns.

ThreeMetroNetMig-2016

For Vancouver, you may notice that the figure looks very similar to what I posted two days ago, up until you get to folks in the 70s and above. That’s where mortality effects really start to matter! I think the above is a better approximation of those effects, but it’s tricky to get them right.

Comparing Vancouver to Toronto and Edmonton, what stands out most for me is just how similar these three metropolitan areas look! Metro Edmonton has grown faster over the last five years in % growth terms, but age-wise, the basic pattern of growth is the same as in Metro Vancouver or Metro Toronto. Young people (including Millennials) pour into all three of these areas, and then mostly stick around.

I noted in Vancouver there was new evidence (at least new to me) of a slow leak of Baby Boomers over the last five years. It appears this leak is also showing up in Metro Toronto, with a very similar pattern. It appears there are fewer folks in their late fifties and sixties than might be expected, suggesting they’re leaving town (cashing out?). Then people in their seventies and above start returning (probably for the good health care & related facilities).

There is also a later-life leak of Metro Edmontonians, but it starts later and never quite stops until the latest age. This could reflect more of a straightforward retirement and return home effect for the many folks drawn to the region, but it’s hard to say. At any rate, all later life migration patterns are dwarfed by the influx of younger adults (and their children) into these growing regions. I don’t see a lot of cause for concern about any particular age-groups shying away from our rapidly growing metro areas.

What about within Vancouver’s metro area? I’m somewhat ambivalent about emphasizing municipal differences in age-specific net migration patterns insofar as metropolitan areas tend to be tightly integrated. When a group disproportionately moves over the border from one municipality to another, it doesn’t have a big impact on the vitality of the region as a whole. Nevertheless, it’s worth tracking, and it certainly can have big implications for quite local livability, diversity, development, and transportation questions.

Here I’m just going to compare Vancouver and Surrey, the Lower Mainland’s biggest two municipalities, with Maple Ridge, a smaller suburb further out.

ThreeMunisNetMig-2016

Here you really get a sense of how tightly connected central cities and their suburbs can be. As the region’s central city (and biggest university town), Vancouver receives an ENORMOUS influx of young people. Then, as they move into their thirties (and often start having children of their own), they tend to move out again, slowly leaking out of the City thereafter. Nevertheless, so many young adults move to the City of Vancouver that they overwhelm the later leavers. In net terms, the majority of young adult arrivals stick around in the City of Vancouver all through their later lives.

But back to the leavers – where do they go when they leave? Mostly to the suburbs. Maple Ridge is the City of Vancouver’s mirror image in this regard. People in their thirties and beyond account for most of this suburban municipality’s growth. By contrast, young adults, especially of university age, but extending into the twenties, flee Maple Ridge. Where are they going? (see above).

What about Surrey? It’s still a suburb, but also increasingly a centre of action in its own right within a multi-polar metropolis. At the moment it’s hit a sort of demographic sweet spot where it’s gaining people at all ages. Nevertheless, it’s worth noting that while young adults aren’t exactly fleeing Surrey, their contribution to its growth isn’t as strong as for older adults or their children, and it remains nowhere near as strong as what we see in the City of Vancouver.

On the whole, these net migration patterns are not too surprising for a relatively large metropolitan area. Young people tend to leave home and move toward the vibrant city centre. Later they tend to move back to the suburbs as they settle down and start families of their own. If anything, what’s striking here is just how many young people remain in the City of Vancouver as they age, living on their own or in diverse families across a wide array of the different housing options the City is working to provide – if still, typically, at too great an expense!

*- my mortality modeling from my earlier post was really crude – simply applying five years of the expected death rate to the starting (2011) population. Bad demographer, bad! Now I’m using BC Deaths data to apply a survival rate and age the population from 2011 year by year, for each of the past five years, allowing one-fifth of the population in any given age group to age to move to the next mortality risk with each year and then applying the survival rates to the surviving population in sequence. This still doesn’t account for the mortality of recent migrants (in other words, recent arrivals could die and never be counted by the census, and I don’t take into account their mortality in any separate fashion – if I did it would boost the net migration estimates, especially for older adults). I’m also twiddling a bit with my estimates for 0-4 year olds and 85+ year olds, as needed by modeling (infant mortality is much higher than any year afterward until quite late in life, and after 85 I’m dividing the population into about half experiencing 85-89 vs. 90+ mortality). But I think I’ve got most of the technical details now closer to realistic for estimation purposes. As noted previously, none of this really matters much for younger population groups.

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