I remember that the first serious job I had to do when I started as a consultant in the rental housing industry (in my first week!) was to review a rent roll for a high-rise rental building in central Toronto, an older concrete slab tower almost entirely composed of 1 bed and 2 bed apartments. The landlord had kept reasonably good records of tenant incomes, jobs, family size, etc. What I quickly realized was that the building’s residents were a complex mix of household types, despite the building’s simple unit mix and simple pricing scheme. Residents ranged from single persons to families with multiple children, from 20-somethings to senior citizens, from minimum-wage workers to doctors, and everything in between. That was eye-opening for me and a good way to start my career.
Although consultants and analysts who study demographics are careful with data, they almost always start with biases about which household types are most likely or least likely to be renters. Those biases should be cast aside. As I’ve noted before on this website, anybody and everybody can be a potential renter. I think that’s the best way to think about target renters, since separating households into categories artificially and unnecessarily shrinks the overall pool of prospective renters. In other words, when leasing new rentals, target renters don’t really matter.
In this post I want to make three important points about renters: first, renters are continually replaced; second, renters self-select when searching for rentals; and finally, renters come from a wide geographic area.
Never-Ending Flow of Renters
One thing is isn’t always recognized is that in Ontario there is a large pool of potential renters which never disappears, shrinks only temporarily and small amounts, and which is replenished every year. Let me explain.
Young adults starting their first or second jobs and forming households for the first time need small, affordable rental units, especially in Ontario’s large and medium sized cities. Although some of these young people will eventually get higher-paying jobs, save up money, and buy houses or condos, many will remain renters for years (some forever), while every year a new crop leaves high school or college or university. This means, for example, that even if 100% of young renters (first-time householders) stop renting after two or three years they will be 100% replaced by new young renters. And there is no future in which 100% of young renters never rent.
At the other end of the age range, every year a certain percentage of people will retire or be widowed or choose to downsize and will need rental housing. Although this group of aged renters will eventually die or move to care homes as they age, they are replaced every year by new retirees and widowers and downsizers.
And of course there is always a supply of renters who for various reasons remain renters their entire lives.
When you add these three groups of renters together you get a large pool of prospective renters available to developers seeking to lease newly built rentals. This pool fluctuates slightly in overall size due to demographic bulges and economic swings, but remains large and is replenished every year by a never-ending flow of replacements.
Renters filter and pre-qualify themselves through the choices they make about location, affordability, unit
types, and other factors long before they engage with your leasing staff or even your advertising and marketing. For a developer with a newly built rental building, additional filtering can be done during initial leasing, but, in my opinion, filtering out too many types of renters, or holding out for certain very specific types of renters, doesn’t make sense given the imperative to achieve full occupancy as fast as possible. Granted, there may be times when a developer may find it desirable to stretch out the initial leasing period to achieve the highest possible rents, but those rents had better be high enough to justify units sitting vacant and not earning revenues.
Ultimately, I think it’s best to let prospective renters filter themselves based on your building’s characteristics (unit types, amenities, etc.) and pricing (rents), and not impose too much filtering which only serves to artificially shrink the prospect pool at the exact time that pool needs to be as large as possible.
In my observation, renters are drawn from a much wider geographic catchment area than most developers realize, which means that the pool of potential renters for a new rental building is very large and heterogeneous and almost impossible to profile or draw a line around. Looking at the demographic profiles of households within 1 or 2 km or even 5 km distance from a new rental building, which is the typical analysis range used in most feasibility studies, captures data for a lot of households, but, of course, all of those households already have housing and may not have any intention of moving (or renting). In fact, it’s theoretically possible that all the renters who lease a unit in a newly constructed rental building will originate from outside the local area; some will be drawn from the wider region; some may be drawn from as far afield as other cities, encouraged by the opportunity to rent in a new building.
In other words, if your geographic catchment area is too small it may not capture and identify genuine prospective renters, so if you are relying on a demographic analysis to help design your building and create a marketing campaign you might not get it right.