Categories
Commodity Prices Historical Prices Macroeconomics

Precious Metals

With the recent rally in gold, we’ve been hearing a lot about precious metals. Actually ever since the financial meltdown last year the gold bugs out there has been much more boisterous… so I’ve finally broke down and prepared a post on gold and silver. This is another one that’s not particularly about real estate prices, but I will throw in a tie-in towards the end.

Here is a graph of the historical gold price per ounce, nominal prices in US and Canadian dollars, and in inflation adjusted Canadian dollars. Many investors generally look at precious metals as an inflation hedge, but it is prone to bubbles of it’s own… obviously as was witnessed in the early 80′s and is evidenced in the inflation adjusted figures.

For those thinking about buying gold you should take note of the tail end of this graph and that while gold has just recently reached it’s all time (nominal) high in US dollars, in Canadian dollars it actually peaked in the winter here and is a fair bit below that price currently.

So, if you’re thinking of putting money into gold, don’t just trade blindly based on what it’s value is in US dollars, you need to figure in the exchange rate into your calculations.

Here we have the same graph for silver, charts similar though it’s spike in 1980 was even more severe. Ignoring that, we can see that like gold, overtime it tends to hold it’s value without much if any appreciation, but is trending up the last few years.

For those interested in such things, here is a graph of the relationship between gold and silver prices. We can see the ratio has gone as high as 97.3 and as low as 17.2. Since 1971 the average has been 55.7, with a standard deviation of 18.1.

Finally we’ll do that tie-in with real estate, just for shits and giggles. This is a graph of the number of ounces of gold it would take to buy the “Average Residence” in Edmonton. There has been a fair bit of fluctuation, particularly in the 70′s. Over the period presented, the average has been 272, median 261 and standard deviation 101. So our current situation is right around normal, but it got as high as into the 500′s in 2007.

Interesting to note, the huge gold spike in 1980 coincided with the prior real estate bubble. While housing prices had somewhat plateaued from ’77-’82, the ratio plunged from north of 550, to less then 100.

Lastly, the same graph for silver. Average was 14,660, median 14,721 and standard deviation 5,723. In this case we’re currently well above the long term mean, and it’s interesting to look at the differences in pattern/scale of some of the movements between gold and silver with this measure.

So, take it for what it’s worth. Like any of the other commodity analysis’ I did earlier, any relationship with real estate appears anecdotal at best, but I included it because it’s been requested often. What I take from looking at this data is that like other assets, beware of bubbles, and be sure to figure in the exchange rate should you start putting your money into any investments.

Categories
Historical Prices

Affordability update

Greetings freaks and geeks, hope you’re all ready for the long weekend. Before you go, here is the affordability update I promised.

As you may have noticed, suddenly this spring the word ‘affordability’ magically returned to the lexicon of the real estate pumper. A word nary whispered a year ago, but suddenly it’s convenient again. The improved affordability is obviously a result of the historically low interest rates… as the other two variables, incomes and prices, haven’t done anything to improve the equation.

To get a gauge of how interest rates stack up versus how they have in the past here is a graph of the average 5-year fixed rate taken out through May.

As we can see, this is the first time we’ve dipped below 5% (4.62% as of May). Obviously not as low as some of the advertised rates the were circulating at the time, but it should remembered that those were not available to everyone, mainly just prime new borrowers, those renewing/renegotiating were often shit outta luck.

Also interesting to note just how rare it is that rates are under 6%. Even since monetary policies started targeting inflation to 2% in 1992, rates didn’t hit that level until 2003 until after the big shift post-9/11 and many nations lowered their inflation targets to 1%.

In any case, when combining the lower rates with the stripping of mortgage qualifying standards by the federal government in 2006 (over the course of that year borrowers went from needed 10% down and limited to 25 year amortizations, to 0% down/40 year amortization and 10 years of interest only payments), it’s not hard to see that it created something of the perfect storm for the price explosion we witnessed.

So, should be no surprise that another plunge of rates could manifest itself into another credit orgy as we’ve witnessed the last couple months, particularly in our culture of conspicuous consumption and instant gratification. This is not just unique to Alberta, this is right across the country, and even in the US where judging from the reaction apparently up to last month it was thought no one would ever buy a house again.

Here is a graph of the income needed to qualify for financing to buy the median single-family home in Edmonton, charted with the median household income in the city. This is using the 32% gross debt service ratio, 10% down and using $170 per month in taxes and $100 in heating.

I took a sampling of 20 homes listed at/near $349,500 (the median SFH price), and they had an average and median annual property tax of $2050, which is roughly $170 per month, and the heating number at $100 may be a bit low, but for the pumpers sake we’ll use it just so I can’t be accused of skewing the numbers, maybe assume you have a ton of insulation and a helluva efficient furnace.

As we can see, there has been a major improvement in affordability, though it’s still a bit above the latest income figures (2007), which considering how 2008 ended and 2009 has played out, it’s probably safe to say they aren’t haven’t gotten much if any higher (and quite probably have started going down).

So, according to household income of $66,113 (in 2009 dollars), 10% down, 4.62% interest rate and $270 a month in taxes/heating a household should be able to qualify for about $300,000 with 25 year amortization. So, obviously as good as things got, prices are still about $50,000 above where they really should.

Of course, from what we’re hearing 10% down and 25 year amortizations are concepts rarely grasped in real estate now-a-days. Recent surveys have been telling us that the average downpayment is a mere 6%, even amoungst those already in the market, and the vast majority of amortizations are of the 30 or 35 year variety.

Recalculating assuming 35 year amortization and 10%, and only then do we truely near something resembling affordability… again, that’s assuming 10%, which evidently very few already in the market can even muster, much less the coveted first-time buyers.

But compared to what we witnessed the prior three years, any semblance of affordability seems like a dream come true. The market was correcting price wise by itself though last fall, but since then the trouble is that it’s interest rates doing all the work, and few expect them to remain all these record low levels.

Recalculting affordability at current incomes and even 6% interest and affordability is again a thing of the past. Figuring in 25 year amortizations and 10% down, at 6% interest that would suggest the median home in Edmonton is overpriced by about $80,000… at 7% over $100,000… at 8% $120,000. You get the picture.

And do not think those interest rates are unrealistic, with governments running up massive deficits currently, they’re going to be flooding the bond market, forcing prices down and thus yields up… which we know, drives up borrowing rates. The ugly truth is rates even hitting the double digits isn’t out of the question.

For those hoping incomes will be able to make up the gap rather then prices, consider this. At current prices at May’s interest rates, to sustain these prices, incomes would need to be $76,500… at 6%, $86,000…. at 7%, $93,500… at 8%, $101,000.

Then compare that to the long term trend in incomes, which for 30 years hovered in the mid-50,000′s and only recently has broken well into the $60,000′s. Even if incomes managed to continue climbing, those gains don’t come overnight, and a return of even modest interest rates would still destroy any illusion of affordability and again leave prices to make up the difference.

Double disturbing in light of that people are coming in with increasing small down payments and longer amortizations. This just leaves them with negligible equity, and as a result even small decreases in prices leave increasing numbers of recent buyers underwater.

And for those thinking inflation might save you, take a look at that first graph and ponder the interest rates witnessed in the 70′s, 80′s and early 90′s… those are what comes with inflation, interest rates will kill you long before inflation will come riding in on its white horse.

Categories
Historical Prices Rental Market

Rents and Incomes

Again I apologize for the lack of updates, but as I’ve said, I am swamped with actual work, so blogging has taken a back seat… and unfortunately there is no end in sight just yet, so, it is what it is.

I seem to be getting a lot of e-mail from people who have recently bought for some reason, often accompanied by a series of calculations of why it was a good idea. They seem to be looking for some kind of validation, so I say I’m happy for them and wish them luck.

They then reply saying that they though I was against buying now… to which I reply, I am, the fundamentals of the market are extremely poor, and as such they are exposing themselves to a great deal of market risk all for very little potential upside, that their calculations are flawed (and how)… but that it’s their money, and if they’re happy with their purchase that’s all that should matter to them.

Most don’t respond again after that point, though the one that did, did so with a very succinct “Fuck you”… not terribly clever, but I do appreciate brevity. I guess this must be just a small taste of what Garth Turner gets on a daily basis over on his blog.

Anywho, enough of the self-aggrandizement. Today I’m going to take a look at the relationship between rents and incomes in Edmonton. Just recently Statcan came out with their most recent Survey of Labour and Income Dynamics. Notably because as far as I can tell it’s the only freely available source of historical market incomes in Canada. Unfortunately data is a year or two behind, but beggars can’t be choosers. Here is a look at the household median numbers for Edmonton through 2007, real (inflation adjusted to 2009 dollars) and nominal.

Not surprisingly it was up over 2006, but not as far as many figured it would be. In inflation adjusted dollars it was up 5.3% year-over-year (from $62,750 to $66,100)… which would normally be quite impressive, but many had been prophetized these would be in the 10% range year-over-year (and even then it wasn’t nearly keeping pace with home prices).

It should be noted, average household incomes actually were up by over 10%… jumping from $76,875 to $86,150 (a 12.1% increase). But, as most know, average isn’t a great measure (as it tend to skew significantly), especially when the median is available. So, as per usual, we’ll be sticking with the median whenever possible.

Getting back to the graph, it is worth noting that this is by far the highest real incomes ever seen in Edmonton. Historically they generally have resided in the $50,000-$60,000 range, and as of this measure they’ve eclipsed the $65,000 mark for the first time ever.

Going forward it will be interesting to what happens to incomes… depending on the methodology and timing of when the survey was done, and how it corresponded with that little economic hiccup that hit in the fall. So, if incomes don’t drop for the 2008 SLID, I’d imagine they’ll take a big hit by time the 2009 numbers come out… but we are a year and two away respectively from knowing those.

Here we have the nominal incomes and average 2 bedrooms rents charted out…and before you say, ‘but, but, but I thought you said averages suck?’ They do, but this is one of those cases where it’s the best measure we have, and for what it’s worth, in the case of rents it probably doesn’t tend to get skewed nearly as bad as averages for items like income would.

One would expect them to chart a similar pattern, and they appear to, but that could just be me cooking the scaling, so here’s a scatter plot.

Unfortunately with rents only going back to 1990, this isn’t a major sampling, but they do appear to follow a general pattern and the R2 value is fairly significant at 0.8955. There are a few plots that are off a bit, but they’re all in the general area.

I didn’t find either of these graphs to be overly relatable though, so I devised another measure to graph out.

This is rent as a percentage of gross (before tax) income. Whether you want to think of it as monthly or yearly, the number remains the same. From 1990-2007, the average was 16.20% and the median 16.17%. So, for every $100 a person was paid, they would pay about $16.20 in rent.

As we can see there are three quite noticeable spikes. In 1992, 2002 and 2007… ’92 being the highest, when it was over 18.5%. Going back to the first and second graphs, we can explain those spikes in ’92 and ’02. as a result of very noticeable and sharp drops in income… which looking at the general trend I would chalk up to aberrations in the survey results, thus incomes were probably reported a bit lower then they really were.

Incomes likely were dropping at those times, there were significant recessions those years, but I just suspect they didn’t drop to the degree witnessed in the data. There does appear to be a fair bit of fluctuation in the numbers over the life of the SLID, so the incomes have made a very jagged pattern as many of the spikes are probably largely due to over/under-reporting, and it’s really more the overall trend to pay attention to. So when the numbers on either side are relatively close, but the one in the middle is way high/low, it’s probably largely an aberration.

It is interesting to note that for 2007 though, incomes were actually up a fair bit, yet rents increased even more. So this is very unlike the other two spikes, as this one was rooted in a disproportionate hike in rent. This will only become more evident when the 2008 numbers come out, as there was another big rent hike that year.

Using the long term average/median of 16.2%, and guesstimating a continued growth of incomes to $70,000 that would suggest rents in the $950 territory… while as we discussed last week, currently rents are around $1050 currently, though the rental market is becoming increasing soft here, and decreases have already been witnessed in Calgary.

Time will tell, but for now at least it appears that first real estate prices broke from incomes, then rents did, and they now both appear to be higher then market incomes would suggest they should be. It’ll be interesting to see how it all plays out.

Anyway, I don’t think there is anything ground breaking in this data, but it is good food for thought. If you have any questions or comments, fire away!

Categories
Historical Prices Rental Market

Price to Rent Ratios

Obviously there are no lack of indicators and ratios out there, we’ve looked at several here over the months. Some in my opinion are fairly useful, like absorption rate… others I feel are as useless as tits on a bull, like sales to new listings.

In any case, today we’ll look at another one… price to rent.

I briefly took a look a historical rents awhile back, but have since came across a better data set (well, same set actually but a longer horizon, and in cases like these, the more data the better). To set the groundwork, here are the rents for Edmonton and Calgary going back to 1990.

The two cities have tracked fairly similar patterns. Rents were relatively stagnant in the early 90′s, then in 1997 they started about a 5 year run up then plateaued again through 2005, after which they again took off. These figures are from October of each year for those curious.

As the name implies, the other part of the price-to-rent ratio is price. Creative name, I know. Anyway, here are the residential averages and condo averages for each city taken from October of each year.

Now our rent variable is that of 2-bedroom apartments, so obviously the condo average would be the better comparison other. So why did I include the residential average, well, it’s because I only had Edmonton condo averages going back to 1999, so I had to do some estimating to fill in the ’90-98 portion.

For the three years, ’99-01, the condo average was generally around 70% of the residential average (FWIW, in recent years that ratio has been closer to 75%). So going backwards, I used that proportionally. As prices were very stagnant over that period I felt that was an acceptable method… but if you disagree, I have also done these ratio’s using the residential average.

No such issue with the Calgary number as I had the full set. It is interesting to note that the Calgary condo numbers track extremely close to the Edmonton residential number. Also of note, the Calgary condo average is usually closer to their residential average (85%) then in Edmonton (as discussed in the prior paragraph). Why is that? I’m not sure, but perhaps condos are more prevalent in that city and thus make up a larger portion of the market.

Here we see the price-to-rent ratio for condo’s to two bedroom apartments in our respective cities (or, in case you didn’t like how I derived this as explained earlier, here is the graph using the residential average to two bedroom apartments, in any case, they yield very similar curves).

How this is calculated is to take the price, then divide it by the monthly rent multiplied by 12 (in other words the amount of rent paid in a year). Interestingly we can see that Calgary generally seems to track 2-3 points higher then Edmonton. So while Calgary prices and rents are both higher then those in Edmonton, their differences are not proportional.

Looking at the trend, through 2001 in both cities the ratios remained relatively consistent… but from ’01 through ’07 they steadily increased at a rate of around 2 points per year. A reflection of the big run up in prices, but interestingly this trend was very much evident long before prices really exploded in 2006.

We can also see the effects of the drop off in price, as the ratios for both cities fell off to the tune of about 4 points in 2008, reflecting the drop off in price while rents continued to go up as evident in the earlier graphs.

Both these upwards and downward movements display how rents are generally more ‘sticky’ then prices, in both directions. This should not be surprising though as sale prices concern only one-off transactions that take place that month, while rents are determined by ongoing agreements/relationships/leases that can be made months or even years earlier.

One of our regular commenters Chris, aka CM, mentioned a good article concerning the price to rent ratios witnessed in many bubble cities in the United States at their peaks. One passage reads.

Throughout the 1970s, ’80s and ’90s, the average rent ratio
nationwide hovered between 10 and 14. In the last few years, though,
it broke through that historical range and hit almost 19 by the time
the housing market peaked, in 2006.And while home prices — and rent ratios — have always been higher on
the coasts, they reached whole new levels recently. In the Washington
area, the ratio went above 20. In Boston, New York, Los Angeles and
south Florida, it topped 25. In Northern California, it approached 35,
higher than it had been in any city, at any point on record.

So it’s interesting to note some of those figures with our findings above. Firstly, that the nationwide ratio in the US was generally between 10-14. This would certainly describe Edmonton’s situation until the run up… and would largely also ring true for Calgary though they were closer to 15, so they were close.

In any case, Calgary would be at the high end, while Edmonton would be right in the mix. So it’s interesting to compare the peaks, Calgary at 25.4, and Edmonton at 22.9. These ratios are very close to those experienced in Boston, NY, LA and south Florida.

While all four of those US cities experienced bubbles, those experienced in LA and south Florida were far more extreme, and both have suffered very badly in the aftermath of the bubbles bursting. This could also indicate trouble for us, as the magnitude of the bubble experienced in Edmonton is much closer to those witnessed in LA and south Florida moreso then Boston and NY… the bubble in Calgary was somewhat in the middle ground, significantly lower then the former two, but much high then the latter.

Thus, our price to rent ratio is another indicator of just who severe a boom we experienced… the question is, how bad will the bust be?

Also just want to do a quick appendices on another measure I found. These are the rented accommodation indexes, and owned accommodation indexes for Edmonton and Calgary.

These are part of StatCan’s Consumer Price Index. Not entirely sure exactly what and how many factors are included in these measures, but all things being equal, I thought they were worth a quick look.

It’s interesting to see how they’ve evolved since 1971. For both cities the two figures have tracked very closely up until 2006 when the rapid run up of real estate prices caused a significant decoupling. Rented actually have largely continued their traditional course, but Owned underwent a massive spike, to a magnitude never witnessed before.

It’s also interesting to note that in both cities in the early 70′s that the Owned index was actually significantly lower then the Rented index.

Categories
Historical Prices

What is a ‘bust’?

So, just what is a ‘Bust’?

Don’t google that.

The first hits you’ll get will probably be of the Russ Meyer-esque definition… and don’t get me wrong, big fan of that variety, but looking up that sort of thing while others are around can invite trouble from HR, or even worse, the MRS.

I’m talking about market busts, and in this case, housing market busts… and for the most part, these are safe for work.

There is no one universally accepted definition… which is also true for ‘booms’ (or ‘recessions’, or ‘depressions’, as we’ve seen of late), so that’s probably why there is such spirited debate about such things.

I did some googling of my own, and found all sorts of numbers thrown around, but it seemed most were in the 10-15% range peak-to-trough.I ended up stumbling upon a couple pretty good long term studies that I think I’ll go with.

One was from the IMF and published in 2003. It studied 20 busts from 1970-2002 in 14 industrialized nations. It’s findings were that to qualify as a ‘bust’ the peak-to-trough contraction must be at least 14% in real dollars (inflation adjusted).

Also perhaps of interest, on average they lasted about 4 years, and the average correction was 30% (again, in real dollars). There is actually a lot of really interesting findings in there as it compares equity to asset bubbles, and it’s quite prophetic when you compare it to what’s happened in the U.S. of late.

The other study was by the FDIC in the US, and was published in 2005. It studied US markets from 1978-2003 and they concluded that to be a ‘bust’ the peak-to-trough contraction must be at least 15% in nominal dollars.

This may not sound like a big difference from the IMF definition, but the distinction between real and nominal dollars is actually a significant one. Which I’ll display later when I compare our current situation with the bust in the 80′s. Personally I’d lean towards using real dollars, especially over the long term, but there is an argument to be made for the use of nominal dollars, particularly in the short term.

First we’re going to take a look at the IMF definition. Here we see the various price measures here in Edmonton and their percentage declines since peaking respectively when adjusted for inflation.

From this graph we can see that for all measures have satisfied the IMF definition of a ‘bust’ already… so, if you didn’t think the name of my blog was correct, how are them apples!

Now the FDIC definition. This graph yields an interesting observation… both condos and single-family homes have surpassed the threshold and are therefore busts… but somehow the overall residential average (which those two make up), has not…. how does that work?

Basically the answer is proportion. When prices were peaking, the ratio of condos/SFH’s selling was much higher for whatever reason *coughspeculationcough*… since then the ratio has dropped, thus, more SFH’s and fewer condos are counting towards the average… and when you have more higher priced units selling and fewer lower priced ones, it’s brings up the overall average.

So, even though the residential average hasn’t passed the threshold just yet, everything that makes up the average actually has. So, by the FDIC measure too, Edmonton qualifies as a bust.

Clears as mud? Good. And we wonder why so many people hate statistics!

Now, as promised, a comparison with the 80′s bust. Since we’re were just talking about nominal prices, we’ll stay there and do them first. To compare apples-to-apples, we’ll stick with talking the residential average as I don’t have the other price breakdowns going back that far.

Here we see the 80′s bust took about four years to go from peak-to-trough, and the total contraction was 29%. We can also see that our current bust is tracking very closely thus far.

Also interesting to note the volatility in the prices. The overall trend is down, but there are a ton of spikes along the way. Evidently there is a lot of bouncing left in a dead cat. So, for those eager to call bottom don’t get too excited… even a couple or three consecutive strong months are not unusual, and can be quickly erased. Bottoms are only revealed long after the point.

Now the comparison using real prices. As we can see, by this measure the 80′s bust was much longer and much deeper.

Took 100 months before finally hitting bottom, over 8 years (though I suppose one could argue that it effectively hit about two years earlier, the absolute bottom wasn’t struck until month 100), and the total contraction came in at a whopping 48%.

We can also see that our current bust appears to be tracking faster, passing the 14% threshold two years earlier.

An interesting note is that for the 80′s bust, the real peak actually occurred two and a half years before the nominal peak. Real prices had already dropped about 13% before nominal prices peaked. This could happen because at the time there was very high inflation, and also something of an extended plateau in price.

This is in stark contrast to our current peak, where inflation was relatively low and there was no plateau to speak of… thus real and nominal peaks were simultaneous.

In conclusion, it appears that by most measures we are not just heading towards a bust, but have already arrived. And it’s not one of those things that bouncing back above the threshold undoes, once you cross the line, it’s done.

Like virginity in many ways, you can argue about the criteria, like it should be nominal dollars not real… or I was drunk, and it was Mexico… or it should be 14% not 15%…. or it was just the tip, just for a sec, just to see how it feels… but wherever you draw that line, once it’s crossed, it cannot be uncrossed.

Categories
Historical Prices Macroeconomics

Population and Migration

I’ve been meaning to take a look at this for a while and finally got off my fat ass and did it… or I guess technically, sat on my fat ass and did it… um… I digress.

So, today I’m going do another reader request and to take a quick look at population/migration and it’s effect on real estate prices. Many speculate that it was high migration and growth that not only triggered the run up in prices, but also argue that it should sustain that level of prices. We’ll see if that theory stands up and earns my unpatented stamp of approval, or not.

Lets start with population. Here is a look at Edmonton’s reported population from 1962 to present, charted against the inflation adjusted residential average price in Edmonton.

As we can see there, the population curve is quite gentle and consistent. Contrast that to the price curve, which the late 70′s bubble is quite pronounced, as is the spike in prices witnessed of late.

It could have likely been deduced from the first graph, but here is some further derivation to back up that overall population growth doesn’t appear to have much impact on prices. Prices are very volatile, and largely appear to move independent of population.

Included in this graph is the percent change of both Edmonton and Alberta populations, I didn’t include Alberta’s population in the first for scaling purposes. Alberta’s, like Edmonton’s population, generally fluctuates between 0%-5% growth year-over-year (FYI, the spike in Edmonton’s population in 1964 was due to their annexation of Jasper Place).

Thus we will conclude that overall population growth has no direct relationship with residential real estate prices. Now, onto migration.

That is a graph of net migration into Alberta since 1962, and it’s two primary components, interprovincial and international migration. As we can see, interprovincial appears to be the primary driver of net migration. From here on out we’ll just deal with net migration, I just wanted to give you all an idea of what how international and interprovincial figures stacked up.

Now here is a graph of net migration and year-over-year price change (bear in mind those migration figures are for the entire province, not just Edmonton). If there was a relationship between the two, one would expect that migration would be the leader, but as can be seen in 70′s through the 80′s it was actually price that lead through the first bubble. We can also see that through the 90′s, even with fairly good migration, prices really didn’t seem to respond.

There are some periods that may appear to support a relationship, like in the late 80′s and in ’05 and ’06, but on a whole it doesn’t appear there is any direct relationship. It’s also interesting to note that the recent spike, which reached record level in 2006, still doesn’t appear to be anything exceptional, and migration retreated quickly in 2007 and 2008 back to historical norms.

This leads me to conclude while there is likely some short term effect of migration on price, but in our recent boom its effect was probably over blown. I would hypothesize that the shortage of inventory and rental units experienced was more rooted in speculative buying then actual increased demand.

As we’ve seen an explosion of listings since the peak, particularly of condos and smaller homes, largely of which have been sitting empty. This appears to support the hypothesis that we saw a great increase in properties bought, and then held to be flipped at a later date.

This sort of behaviour was also evident in the rental market, where the number of total units dropped from 75,267 in 2005 to 67,907 in 2008. These were largely due to condo conversions. That kind of contraction will of course increase demand, even if the population increases. The number of occupied units actually only increased about 775 from ’03 to ’06, then dropped by over 4,000 in ’07 and another 3,000+ further in ’08.

Beyond that, our current situation where we find the city seriously overbuilt and with another 10,000 units in construction. We can’t get to that point without demand being perceived as much higher then it is in actuality.

So, while a rapid increase in migration could likely pinch supply short term, the escalation of prices we experienced here was more rooted in speculation and good old irrational exuberance.

And as far as long term price support goes, not a chance. As I’ve said before, Edmonton isn’t Manhattan, we can build for as far as the eye can see in every direction and generally it’s not any problem getting materials or labour… any short term situations where demand outweighs supply, will quickly be corrected… or as we’ve witnessed, over-corrected.

At least that’s my opinion.

EDIT: Had a request for the graphs of change in net migration vs change in price, so here they are. I had already prepared them, but they got cut from the original post to keep the length down.

Categories
Historical Prices

The ‘Burbs

I was doing a quick search of Google News local real estate stories, and stumbled upon this story from Sherwood Park talking about how prices had dropped $120,000 since their peak in July 2007.

They appear to be privy to some figures that are not publicly available, so I can’t speak about that claim. I’ve been trying to piece together as much data as I could on the prices in the suburbs, and while I’ve gotten a fairly complete collection, it’s still a bit patchy at this point. So, won’t be any graphs in this post, but hopefully by this summer I’ll be able to at least do some good ones for decline from peak.

While I don’t have the complete data, I do have enough to offer up some interesting examples.. and it gives me an excuse to reference an flick from Tom Hanks bad comedy career phase. So, in some cases the peaks I note may not be the absolute peaks reached by the market, but they are close as I have all the second half ’07 figures, and the Edmonton market peaked in mid-07.

So I’m going to take a quick look at the respective bubbles, first the prices five years ago (March 2004), the highest value I have for them in 2007, then their values as of March 2009.

St. Albert
March 2004 Average – 226,074
March 2004 Median – 209,900

Sept 2007 Average – 497,380
Sept 2007 Median – 477,500

March 2009 Average – 376,944
March 2009 Median – 354,500

St. Albert saw prices more then double during the bubble, and since then have seen prices both average and median prices drop over $120,000 respectively, roughly 25%.

Sherwood Park
March 2004 Average – 235,700
March 2004 Median – 227,725

August 2007 Average – 468,400
March 2007 Median – 439,000

March 2009 Average – 400,074
March 2009 Median – 390,000

Sherwood Park has actually had a surprisingly good March, as in January the average and median prices were 376,545 and 353,500 respectively. Even with that they have seen average price drop almost 15%, and median 11% since peaking.

Spruce Grove
March 2004 Average – 185,417
March 2004 Median – 173,899

Sept 2007 Average – 420,990
Sept 2007 Median – 416,000

March 2009 Average – 328,509
March 2009 Median – 314,500

Spruce Grove is another entrant in the six-figure club, seeing their median price already fall $101,500 since the peak. Their average had also passed that mark in December, but has since rebounded a bit. Spruce Grove was also the bubbliest of the larger ‘burbs (populations over 20,000), seeing their median price go up 139% and average 127% during the bubble.

I also have notes on some of the smaller suburbs, so instead of typing all that out, I’ll just give you some of the overall highlights.

Bubbliest Suburb (Percentage) – Morinville – Average +166%, Median +183%

Bubbliest Suburb (Dollars) – St. Albert – Average +271,306, Median +267,600

Biggest Percentage Decline – Stony Plain – Average -25%, Median -35%

Biggest Dollar Decline – (Average) St. Albert -120,436 (Median) Stony Plain -149,000

So while the city/region as a whole hasn’t eclipsed the 100K mark in decline from peak, at least four of it’s suburbs have the dubious distinction, St. Albert, Spruce Grove, Stony Plain, and Morinville. There may already be more in fact, but as I said, my dataset is not complete just yet.

In any case, I figured this information might be interesting to some out there. It’s also very relevant since the suburbs are so intertwined with the city and make up such a large part of the CMA population.

Categories
Historical Prices Inventory Sales

Seasonality

Hope you all enjoyed your long weekend. If it was anything like mine, it was likely full of sun and way too much refined sugar.

If you’ve been reading the releases coming from the various real estate associations, you may have noticed they are now talking up the month-over-month sales and price figures more so then they have in the past.

Traditionally they usually focused on year-over-year figures… but as those are coming in as declines as of late, they are referencing those less and less. Something of a marketing move, as they’re in the business of stimulating sales, and hearing about prices and sales going down doesn’t make their message very receptive to buyers or sellers.

As a result of this, it’s also increasingly being pointed out that month-over-month gains are actually normal in the spring, as real estate for whatever reason has a lot of seasonality. So today I’m going to take a quick look at the traditional seasonal movements of prices, sales and inventory.

The 100 value is the relative value in January… and from there it behaves like percentages. So 110 = 110%… so if the price was 200,000 in January, that value would be equal to 100… so 110 would be 110% of the January price, so 220,00.

Here we see how prices typically move through the year. The blue line is the historical trend, and as you can see it usually shoots up about 7% in the first half of the year before leveling off, then drops off a couple points in the summer, and ends the year up about 5%.

We can also see that over the last year we’ve severely deviated from the norm. In 2008 it tracked normally through March, then fell off slightly through spring and then really dove throughout the second half… and 2009 thus far has been an extension of that.

Here’s a look at how prices behaved during the boom relative to historical norms. In this case normal seasonality barely registers as a speed bump, and while the boom was on through mid 2007, normal seasonality was no predictor what-so-ever. Which has also been true thus far in the bust.

Basically, seasonality is no indicator of prices during a bubble. That said, it is noticable that during the downturn, spring still does appear to be the strongest period, and falls off from there. Which seems to indicate the second half of 2009 could witness a very big decline.

Here we look at sales. Similar pattern, but in much larger proportions. Sales continually improve through May (typically more then doubling the January tallies), then going back down the rest of the year.

In 2008 we can see that it tracked a closer pattern in sales then prices. This also holds true though the boom, so while the scales may change, the general seasonal pattern remains.

It’s also notable that 2009 appears to be gaining very strongly, but this may be more of a result of an overly poor January. Again though, it is tracking quite closely to the historical seasonality.

Finally we’ll take a quick look at inventory levels. Again we’re seeing levels track fairly normal curves, 2008 went a bit higher, and 2009 is shaping up to be lower.

On the other hand, during the the boom, and the onset of the bust it looked quite different. In 2006 inventory actually tracked the opposite pattern, and declined through to the summer. This obviously caused a inbalance of supply and demand, and really fed the skyrocketing of prices.

Then in 2007 it was a very different story when inventory tracked normally, then picked up, then did some skyrocketing of its own. Which then caused a glut of inventory, and halted then reversed prices. Obviously a very volatile period there in 2006 and 2007.

So, as we can see, we should not be surprised by sales increasing in the spring, and the real estate boards out there should enjoy trumpting month-over-month gains while they can, cause come summer they’re going to have to find something else as both month-over-month and year-over-year figures will be poor.

As for some April predictions, I’ve been working on some different models but they’re hardly perfect… but I’m a glutton for punishment, so lets throw out some numbers and see how I do.

As mentioned before during a bubble prices are kind of anyones guess, but I imagine they’ll hold their prices in April. Active Inventory I’m going to say will end April around 7,900. Sales I’m going to peg at about 1,700 for April.

Though that could be a ways off, on one of the local agent blogs they are citing the EREB claiming 574 sales already this month, which by my calculations would put us on pace for almost 1,800 on the month (there were 1,830 last April)… though in the paragraph prior they talk about how much things have slowed sales wise and gave quite a low tally for the week.

So, in conclusion… who knows?! Time will tell.

Categories
Alberta Canada Commodity Prices Historical Prices

Oilberta Redux

Earlier this week I did an entry on the relationship between oil prices and housing prices in Edmonton, and there were some interesting findings which led to new hypothesis’. There was also some good ideas that came up in the resulting discussion.

So tonight I’m going to take a bit of a deeper look at possible relationships with housing prices… or maybe I’m just doing it because that last one got lots of comments and I’m an attention whore… it’s really hard to say.

In any case, beyond just oil we’re going to look at natural gas, stock markets, and several other possible indicators. Be forewarned, there is going to be a ton of charts and graphs in this one, so if that isn’t your thing, well, you’re probably on the wrong blog. Anyway, this one will be a big’uns.

Like the last entry, I’m going to use Toronto as my control sample. Partially because Toronto isn’t exactly known for oil and gas (well, other than hot air… I kid, I kid), and it’s also the city I have the next best data set on.

This time we’re going to be using yearly averages rather than monthly, so you’ll notice the graphs look a bit different. As real estate tends to be a bit of a lagging indicator and prone to a fair bit of seasonality, yearly may actually be the better measure. I also have the yearly averages for Toronto going much further back, so that should improve the findings.

And here we have a plot of oil (West Texas Intermediate) and natural gas (Henry Hub) over the same period, also yearly. You’ll notice they chart fairly similar paths. You may be thinking to yourself that this graph doesn’t show prices getting as high as you recall, or diving last fall, but remember, these are yearly averages and/or spot indexes. These won’t look nearly as volatile as daily, weekly or monthly figures… for example here is the the monthly figures semi-transparent over the yearly ones.

As discussed in the prior entry, oil and home monthly prices in Edmonton had a high correlation… but they also had a very high correlation with Toronto prices. Which led me to conclude it was a spurious relationship.

These correlations remain using the yearly figures over a longer term… and are also present with natural gas. Though that shouldn’t be surprising, as noted earlier, oil and gas followed quite similar paths and actually have a correlation of 0.89 from ’72-’08.

Over the full term, ’72-’08, Edmonton had a correlation of 0.90 with oil, and 0.86 with gas. Toronto came in at 0.71 with oil, and 0.82 with gas. While the gas values came in quite close, there was a fair difference in oil.

This appears to be a timing issue though, as over the last 10 years both cities came in at 0.89, and over the last 20 years Toronto came in at 0.91 and Edmonton at 0.92.

On the natural gas front Toronto actually came in higher over the shorter terms, 0.84 to 0.79 over the last 20, and 0.80 to 0.63 over the last 10. Quite a large difference there on the latter.

As a result, I would again hypothesize that the correlations are due to a spurious relationship, due to a common lurking variable.

Here are a couple scatter points to illustrate the what we’re looking at.

You’ll notice the R2 value is lower than the correlation. For those unfamiliar, R2 is the coefficient of determination, and is the correlation squared (so, obviously its symbol would just be R).

In those graphs you can see the general trend formed over time. The higher the correlation, the tighter the plot points correspond with the trendline.

You may be wondering if there is a relationship between year-over-year gains… and to answer that in a word… no. Regardless of the city and the commodity (or any of the multitude of other indicators discussed later for that matter) any relationships are negligible at best.

To give you an idea of that, lets contrast the above graph of Edmonton vs natural gas prices to Edmonton’s year-over-year price change plotted against those of natural gas. You’ll note in the earlier graph that the points are often grouped and you can see an overall trend.

Now note in this year-over-year graph that the points are all over the place with no apparent rhyme or reason. The R2 is also almost 0, which just reinforces that there is little or no relationship.

As I have said before, I think the relationship between oil/gas and real estate is a spurious relationship due to a common lurking variable… and I’ve theorized that variable could be the greater economy and/or financial markets as a whole.

To further explore that line of thinking, I compiled a couple spot indexes for the NYSE Composite and S&P/TSX Composite. Here is a look at how those have performed over time.

These being composite indices they give you a good idea of the overall stock markets. With the NYSE being the largest stock exchange (by dollar value) in the world, and TSX being the dominant one in Canada they should be pretty good indicators for our situation.

The findings also may support my earlier musing, with both having significant correlations with real estate prices in both Edmonton (NYSE-0.87 TSX-0.92) and Toronto (NYSE-0.88 TSX-0.89).

To go a little deeper even, I consulted a Statcan report of the leading business indicators. These include ten different measures (including the S&P/TSX index) from various sectors of the economy.

Here is a list of the others, average work week-manufacturing, housing index, United States composite leading index, money supply, new orders-durable goods, retail trade-furniture and appliances, durable goods sales excluding furniture and appliances, shipment to inventory ratio, finished products, business and personal services employment.

Here is a look at just a few of those and how they’ve charted out.

I’m not going to look any deeper into these specifically, I just wanted to give you guys an idea of what they graphed out like. All the measures listed above appear to have a decent correlation on one level or another with real estate prices, except the average work week one which didn’t appear to have any correlation.

What I found most interesting was the composite of those ten different figures. As it covers many different sectors of the economy, it gives you a measure of the economy as a whole. As such, I’m going to focus primarily on that single measure, and here is how it graphs out.

And here is a scatter plot of the composite vs Edmonton real estate prices.

You’ll note that the points all plot fairly close to the trendline. Also interesting to see the snakelike pattern, which is present in the the natural gas plots earlier, but are not as clear as in this one.

Here is the time series for the leading indicators and Edmonton’s real estate prices. We can see there is far less variability in the composite than in housing, but they both are clearly trending up. This also reveals itself in a high correlation between the two, 0.92. The relationship also remains very strong regardless of the term, something that cannot be said for oil or gas which vary from very strong to moderate depending on the period.

Here we see the same composite graphed with Toronto prices. Again they look quite similar, and real estate remains the more volatile. The correlation for these two come in at 0.90.

FINALLY, we’ve arrived at the last graph. Here is the Edmonton prices plotted out with the composite, oil and natural gas prices for comparison. Yes it’s a bit of a mess, but I tried to make it as understandable as possible considering the multiple axis’ values. But I think it’s a good visual anyway.

I’m sure some will take exception with the scaling, but I tried to be as fair as possible and match up the various lines with housing prices long-term as best as possible. So just as a disclaimer, scaling can be misleading, so take it for what it’s worth and remember these can be made to appear to back up any conclusion.

Alright, here we can see the relative volatility of the four indicators. Oil and gas being the most so, then real estate, and finally the composite which is very smooth and relatively straight. It’s this smoothness is probably what yields the consistently high correlations, as all the prices increase over time.

I suppose one can see any number of things from the graphs, but I feel that these findings largely back up my earlier hypothesis that housing prices are probably more closely tied to the overall health of the economy, than to individual commodities.

While positive correlations exist between home prices and oil/gas in Edmonton, the same correlations exist in Toronto, a market without such resources close at hand… therefore implying a spurious relationship between the two factors.

While oil and gas certainly effect financial markets and the economy as a whole, throughout Alberta and Canada, it is still but one factor, granted one of the bigger.

That said, I’m not sure a direct causal relationship between be proved between the economy and real estate either. The economy is too complex for even an army of economists with lifetimes to study it to truly understand… much less one half-assed blogger.

So again, I conclude that while oil and gas certainly has a big effect on the local and national economy, I feel they do not have a direct causal relationship with real estate prices. But I don’t think any single indicator can claim a direct causal relationship. Real estate prices are effected by hundreds, if not thousands of variables… earnings, supply, demand, seasonality, land, labour, materials, just to name a few.

Oil and gas prices would certainly effect several of these, but there is a limitless interplay amongst these variables and countless others, and the effects of all these just cannot be quantified in an acceptable fashion.

That the same correlations exist between oil and gas prices and cities in both producing and non-producing regions indicate that real estate prices drill a lot deeper. Pardon the pun.

Categories
Canada Commodity Prices Historical Prices

Oilberta?!

One of our frequent commenters, Two-Thirds, offered up a couple local real estate folklore that he’d like to see tackled… his wish being my command, here is the first, the relationship between oil prices and real estate prices in Edmonton.

Those that have been reading this blog for awhile have already seen graphs of Edmonton’s historical prices several times, so we’ll start with a look at historical oil prices instead. For this post I’ll be using a spot index of West Texas Intermediate Crude… that seems to be the most commonly cited oil price, so should be a good standard.

To give you a better idea of the prices, here is the inflation adjusted price, and in Canadian dollars. As you can see from the graph, oil prices can be pretty volatile and undergo some very big swings, quickly.

It only goes back to 1971, this is because that is as far back as I could obtain exchange rates for… but that’s okay, since as you can see from the prior graph, prices were pretty much stagnant before the ’73 Oil Crisis, and Edmonton house prices were also pretty stable up to that point anyway.

It’s also good because creating these graphs over such long periods makes my year old iMac behave like a 486 trying to run Quake.

Now here is a look at how oil prices chart against Edmonton’s residential average price going back to 1971. We can see they have somewhat similar patterns, but it doesn’t appear that Edmonton’s real estate prices are nearly as reactive to oil prices as some may think. It is hard to say though, as real estate is something of a lagging market, not nearly as reactive as the oil market.

Realistically it takes time for the benefits of higher oil prices to makes its way through the economy. It takes months, if not years, for new projects to get off the ground, and the money from that to circulate.

So, when looking at the first boom in the 70′s, it could be argued that the rise in prices was, at least in part, due to the big spike in oil prices in January 1974. That delayed reaction could also explain why there was no apparent effect on real estate prices after the further spike in ’80 when prices briefly eclipsed $120/barrel (2009 dollars) then started shooting back down.

Also as oil prices started to move up in the late 90′s, real estate prices again started to creep up by the early 00′s… but real estate also started to decline while oil prices were still rocketing up too.

To counter that though, we can also see that real estate prices were declining during a period when oil prices, while dropping, were still well above what they were in the mid 70′s when they may have triggered the boom. Of course there were external factors at play at that time, like the NEP, which effect would be extremely difficult to quantify.

Then there is the big drop around 1986 when the price of oil plunged over 60%, and stayed there… while real estate prices seemed to have no effect, delayed or otherwise.

So, what’s it all mean? Hard to say, I guess one can see in those graphs what they wish.

To take a more statistical approach, we can take a look at the correlation between the two… this actually yields a seemingly remarkable result… a positive correlation of 0.68 since 1971. Anyone familiar with the measure knows that actually indicates a significant relationship.

But there could be many different factors at play, so to get an idea of what kind of correlation is normal I decided to also run the numbers against a control city that isn’t generally associated with oil and therefore one would not expect to find such a correlation… in this case, Toronto.

I only have the full numbers for Toronto going back to 1995, so to compare apples-to-apples as best as possible, I re-ran the number for Edmonton over the same period. Here are the graphs of those.

If you were shocked by the high correlation between Edmonton prices and oil prices earlier, you haven’t seen anything yet. Edmonton from 1995 to now is an astounding 0.87. Seems like that would make the relationship a slam dunk!

Not quite it seems. Sit down for this one. Toronto during the same period had an even higher correlation with oil prices… 0.89.

The two are very close, and this stays true (though in lower correlations) for the periods of the last 10 years and the last 5 years, inflation adjusted and nominal. Even figuring in moving averages with terms as long at 3 years to account for lagging reactions, there just doesn’t appear to be substantial differences between the two cities.

So, while a high positive correlation remains, I think that this finding of a non-oil and gas market having as high or higher correlations would pretty much refutes an actual relationship between oil prices and housing prices in Edmonton. Home prices here appear no more linked to oil prices then other cities in Canada.

To look at it from another angle, there is an equal correlation between real estate prices in Edmonton and Toronto, as their with between Toronto and oil prices. Though this should not be surprising since both also had similar correlations with oil.

In any case, I would have to conclude any kind of relationship between real estate prices and oil prices is anecdotal at best. It looks to be a spurious relationship caused by some lurking variable(s), and likely present in most if not all Canadian markets.

As is often said in statistics, correlation does not imply causation.

It seems that the real driver of real estate prices has probably more to do with the overall financial markets of which oil is a part of, or perhaps the economy as a whole… which would at least in part explain Toronto having just as high a correlation.

And just for shits and giggles, here is a little measure I derived… basically it’s how many barrels of oil it would take to buy an “average residence” in Edmonton at the market rates.

As we can see, this can be very volatile, with values anywhere from 2,500 all the way to 6,500 not being unusual over the last two decades. The median since 1971 has been 3,835 barrels, with a standard deviation of 1,270 barrels. Such a large range again would make me question any kind of hard relationship between oil price and house price, even figuring in real estate being a lagging indicator.

So in conclusion, while I’m sure oil prices have an overall economic impact on our fair city, of which housing prices would be a part of, from the data I’ve ran I see no tangible evidence of a direct causal relationship between oil and home prices. Any implication of such a relationship appears to be spurious. So, in the absence of any otherwise compelling evidence, this one is…