Demand doesn’t create units; units create demand

5 min read

That man invented tools is a misleading half-truth; it would be more accurate to say that tools invented man. – Arthur C. Clarke, 1962


In nearly four decades in this business, I have never believed in the logic structure underlying traditional market studies, because they have their causality backwards. And (confession time) I have never used one to make a decision, because a much better means of testing the market is readily available.

The classical market survey treats changes in demand as an input variable independent of changes in supply. That problem-definition is what the market surveyor’s client (the developer) wants – a detailed analysis reaching the conclusion that the proposed new LIHTC apartments will be “absorbed” (interesting word) in the marketplace. The developer wants this conclusion not to make its own decision, but rather to convince other people with money that it’s safe to fund the project.

Thus a market survey tracks population growth, household formation, and immigration to and emigration from the market area, to derive a forward projection of total (renter) households. This is then compared against available supply to derive the implied need for new rental housing. While all of this is fascinating, quantitative, and valuable reading – one learns a lot about a place – it is all predicated on the implicit assumption that the presence of demand summons forth new supply. Yet if you take half a step back, you’ll question that very premise.

Apartments may be rented to people but they are occupied by households, and a household may be any number of people. Market surveys count population, estimate total households, and then tend to assume that household size (people-per-household) is unchanged regardless of external demand or supply pressures. Yet we know that household size and household composition can always change at the micro level, and that at the macro level both are changing throughout America, with the trend being toward smaller households. In 1890, the average American household was 4.9 people; in 1930, 4.1; in 1940, 3.7; in 1970, 3.1; and in 2000, 2.6. Meanwhile, though many changes in average household size are gradual, some are abrupt. Five years ago, roughly 3,000,000 homes were empty, not because America’s population had shrunk, but because the number of households declined as singles combined into roommates or boomeranged back to the family couch. When the economy get better and a housing market becomes more “affordable” (as measured by rent-to-income ratios), households undergo “family fission” as one household splits into two. When the economy booms and rents jump, people double and triple up.

The central verity is this: Housing demand is elastic1. There’s neither intrinsic household size nor any intrinsic apartment size. The number of people in a household and the amount of space that each person “demands” vary all the time, based on the quantity, quality, and price of accommodations. Take the same hundred people and move them from Boston to Boise, or New London to New York, and you’ll see vast changes in the rate of formation of new households and in the average number of bedrooms that they’ll rent.

The swings are even more dramatic globally. Via my non-profit (the Affordable Housing Institute), I’ve worked where a typical “affordable household” is six people living in 250 square feet in one place – and nine people occupying 4,000 square feet someplace else.

Elasticity of housing demand makes it absurd to project future demand independent of future supply (both amount/type and price point) because demand goes up and down like a hyperactive elevator.

The Proper Approach

All right, you say, if you don’t use demand-side market surveys, what do you use?

Imagine that the subject property already exists. Compare its rent and amenity package with those of comparable properties in the same market area (market surveys do an excellent job of defining these). Finally, derive an estimated market rent for our property, by making linear adjustments (add or subtract so many dollars per month) for differences in location, condition, amenities, size, and so on: a carport is worth $20, a walk-in closet $15, and so on. (HUD’s Estimate of Market Rent by Comparison, Form 92273, and is a nifty, easy-to-use, readily customizable example.)

As compared with a demand-side projection, this supply-side approach uses a simpler and more credible logic: The market knows, and the market adjusts. As long as the city is large enough, new people will pay a price consistent with what current people are paying. If new supply is added at an appealing market price, new households will form, so what is absorbed is in fact not new supply but rather is new demand.

Demand doesn’t create units; units create demand.

David A. Smith is Chairman of Recap Real Estate Advisors, a Boston-based real estate services firm that optimizes the value of clients’ financial assets in multifamily residential properties, particularly affordable housing. He also writes Recap’s free monthly essay State of the Market, available by emailing [email protected].

[1] I know of only one category of counterexample to demand-elasticity: when the target population is highly specialized (e.g. elderly, formerly homeless veterans) that there simply are not enough of such people. This can be a risk in special-population affordable housing, where the ribbon of eligibility is sliced too thin.


David A. Smith is founder and CEO of the Affordable Housing Institute, a Boston-based global nonprofit consultancy that works around the world (60 countries so far) accelerating affordable housing impact via program design, entity development and financial product innovations. Write him at [email protected].