This is one of those blogs where the readers get very passionate with their responses.
There are a lot of sharp minds who opine on matters such as the one being discussed today, and whether they agree or disagree, the passion doesn’t waver.
I’ve often said that real estate “fundamentals” are no longer, well, fundamental, since they are somewhat flawed.
For example, the idea that “price growth vs. wage growth” is relevant in 2019, in the Toronto market, is foolish.
I know, I know; I’m a salesman. I have a vested interest in the real estate market going up, and I’m going to shape my narrative to my desires. I’ve heard it before.
It’s also been close to two decades since I studied economics in university. But it’s not like “fundamentals” change over time, right?
The idea behind “price growth vs. wage growth” is basic, and in theory, iron clad.
If wages are increasing by 5%, and the price of real estate is increasing by 10%, then prices will rise way too fast for buyers to keep up. And either debt piles up, or the market will eventually reset. Or both.
Basic, and iron-clad. In theory.
But as I’ve mentioned so many times before, and as market bears hate to admit happens, much of Toronto’s house-buying activity is propped up by this wonderful thing we have called “redistribution of income.”
Yes, this happens, with as much certainty as the cycle of life continuing to spin on.
People work, make money, get older, have more money, then redistribute. And repeat.
And the biggest redistribution of income ever happened when the Baby Boomers approached retirement, and decided to help out their kids. Now fast-forward a decade, and the money continues to flow, whether it’s from the Boomers as they glide into their 70’s, or whether it’s from the 50 and 60-somethings who realize that their kids will never own real estate in Toronto without a helping hand.
I can’t tell you how many of my clients, even those into their early-40’s, are getting financial assistance from their family. I would estimate more than half.
Argue against the act itself, but don’t deny its existence, or its effect on the market.
And then tell me once again why “price growth vs. wage growth” should continue to be the fundamental that we use to evaluate market strength and weakness.
I would make the same argument when it comes to another very well-known, and much relied-upon “fundamental,” which is debt-to-income ratio.
I know, I know; it’s happening again. The shill salesperson is getting ready to sell snake-oil.
But simply put: poor people don’t buy houses.
There aren’t a lot of folks making $120,000 per year who are carrying balances on their VISA at 29% interest. Give me an anecdote, or copy and paste a link, and I’ll tell you that it happens. But for the most part, this 171% debt-to-income ratio is not applicable to a person making $120,000 per year who has $205,200 in consumer debt, and is more likely applicable to the folks on the lower rung of the ladder who will not be looking for a detached house in the Beaches any time soon.
I’ve always had a problem with those two “economic fundamentals” for the two reasons which I just described, and having just read an article in Better Dwelling about the International Monetary Fund calling our real estate market, “over-priced,” I can’t help but think similar thoughts about their “fundamentals” as well.
Here’s the article in Better Dwelling, which is worth bookmarking if you’re not a regular reader:
On Sunday alone, three readers sent me this article, so it’s making the rounds.
As the article explains, the IMF looked to compare average home prices with “attainable” prices, which the IMF defined based on a static borrowing capacity model that takes income, interest rate, and debt into consideration.
There is subjectivity in any analysis, and this one is no different. You can use different averages or indexes for the “observed” price, as the IMF titles it, as well as a mix of many different fundamentals for the “attainable” price. But for now, let’s give the IMF a little credit and assume they know what they’re doing.
According to the IMF, the “observed” price is 54.7% higher than the attainable price in the city of Toronto:
Now if you showed me this chart, and didn’t tell me what the black and red lines were, I would have probably figured out that this had something to do with the real estate market.
And once we see the labels on each axis, and the definition of the lines, this starts to make sense. Especially as you see the decline in the black line in 2017.
But my gut-feel on the market and affordability wouldn’t have shown a disconnect starting in 2009. It’s really only been in the past two years that my buyers have started to feel the pinch, and again, I think this is because of how statistics can tell a different story than in-the-trenches experiences.
According to the IMF study, Toronto had the second-highest “disconnect” between observed housing prices and attainable housing prices.
Hamilton was #1 on the list with a 57.1% gap.
Vancouver finished a distant third with a mere 51.3%:
Now it’s not so much the charts, the stats, or the opinions therein that interest me.
For example, the author of the Better Dwelling article refers to the SBC model as “the price the broad market can support,” but I might argue with that conclusion. The author mocks the age-old real estate theory and saying, “the price someone pays, is the price of a home.” I get it, I do. But for everybody that had predicted a market crash in 2003, or 2007, or 2012, or 2016, I would ask, “How are the fundamentals stacking up against what’s actually going on in the market?”
So I went to the IMF website and found one of their “working papers” called:
In the paper, the authors describe how they come up with the Static Borrowing Capacity, and here’s where I might lose some of you, but I also might make some new friends!
This is from the paper:
The borrowing capacity approach should sound familiar. Inspired by the behavior of prospective home buyers, it indicates how much housing households can attain. It derives the maximum size of the mortgage loan that a household can safely borrow, given their income level, market interest rates, and a share of household income to be allocated for the mortgage payments. We assume the average household is credit constrained. Together with their available down payment, the mortgage loan indicates how much housing the households can afford.
This simple calculation is the way many households determine the size of an attainable mortgage, using a simple calculator on the internet. We assume that each household will reach for the maximum housing they can afford given their income level, the market interest rates and their down payment. All that while respecting the limits on loan-to-value (LTV) and debt service-to-income (DSTI) ratios set by the regulators. Available data for many countries, including the Czech Republic, show the distribution of LTV and DSTI ratios bunching around the prudential limits set by the authorities.
Borrowing capacity analysis is not a valuation approach but a prudential, liquidity-constrained approach. Its application can be particularly useful to macro-prudential authorities for deeper exploration of house price dynamics, or as a tool for counterfactual scenario analysis, and a communication vehicle. Within a macro-prudential context, an analogy can be made to road speed limits. Surely, a small violation of the speed limit rarely results in an accident, similarly as driving below the limit by no means guarantees full safety. Nevertheless, setting road speed limits represents a generally accepted prudential measure. Similarly, borrowing capacity approach provides a reasonable macro-prudential level of house prices that are consistent with sustainable household borrowing, low mortgage delinquency, and resilient financial sector. Regular communication of such a metric and its transparency may help anchor expectations of relevant market participants (banks and households) about the future course of macro-prudential policy.
It’s interesting that in this explanation of Static Borrowing Capacity, they as much as admit that it’s not necessarily accurate.
The “speed limit” analogy is a perfect one. They’re essentially saying that what will eventually become the “attainable house price” doesn’t serve as a point at which extending further will automatically harm the borrower.
Later in the paper, the authors put their tools to use in analyzing the real estate market in Prague in the early 2000’s, but ultimately conclude that what their research shows should have happened does not add up with what actually happened in that market.
Here’s a note from the paper:
In our experience, applying simple time-series regression model to Czech housing market does not lead to satisfactory answers. The estimated coefficients are often at odds with theory
(wrong signs) and they are unstable, which causes frequent revisions of view about overvaluation.
I smirked when I read this, to be quite honest, because it makes me think of every single market bear in Toronto in the past ten years.
“Does not lead to satisfactory answers.”
Yes, said by every market bear in the last decade.
It must be frustrating, right?
To be so smart, and know so much, and have so many statistics and fundamentals that point to this catastrophic real estate market collapse, only to see it continue to rise?
The end of the paper gives us this piece of insight into why prices and fundamentals might diverge:
Reasons frequently associated with the observed prices moving away from fundamentals are the factors causing severe demand-supply mismatch. Factors ranging from demographics to local zoning rules, not-in-my-back-yardism, and supply constraints. Although a closer inspection of insufficient supply and supply-side constraints goes beyond the scope of this paper, we note that such analysis warrants some caution. In particular, the comparison of change in the number dwelling completions and households will not necessarily provide a clear proof of supply imbalance. This is because the observed data can suffer from reverse causality. Only the households who find and purchase a dwelling settle in and register in the city, thus entering the population statistics. This can make the completed dwellings and change in households co-move together, while the market tightness remains. This has clear implications for the modelling framework and input variables used therein.
The IMF publishes all kinds of working papers and notes that these papers aren’t the opinion of the IMF, but rather their publication is to promote discussion.
I find all of this fascinating, maybe because I’m surprised that the way the world is going in 2019, the authors of this paper weren’t busy watching The Kardashians, but also because it sort of felt like two guys trying to reach a conclusion based on their own assumptions, only to have the data tell them something different.
Like I said above, it’s similar to the story of the Toronto real estate market.
And while the Better Dwelling article refers to Canadian real estate as “overpriced,” and the IMF provided the data to come to this conclusion, I really, truly believe that “overpriced” is a term based on opinion, and that the market pulling away from so-called “fundamentals” does not necessarily mean that market is overpriced.
Can fundamentals change over time?
Can some fundamentals become obsolete?
The Toronto real estate market might suggest as much…