Leverage Housing Data to Lead the market by 30+ Days

 

Dave Wigginton: Hello everybody. We would like to welcome you to the presentation today: “Leveraging Housing Data to Lead the Market by 30+ Days”. We are pleased to have Mike Simonsen, CEO of Altos Research, with us today. The format of today’s webinar will be as follows: Following my opening remarks, Mike will give a brief intro to the Altos single family housing data and discuss current trends in the housing market. Following Mike’s remarks, I will discuss what the Altos single family housing data is telling us about the public homebuilders. We will then open it up to questions.

Before we get started, I wanted to review a few housekeeping items with you and let you know how you can participate in today’s session. You’ve joined today’s webinar listening through your computer’s speaker system by default. This means if you can hear me through your computer, you will be able to hear the presentation. If you’d like to call in using the phone, just locate your audio panel and select use telephone. The dial-in information and access code will then be displayed. You also have the ability to ask questions using your questions pane. Simply type in your question and click send. At the end of the presentation, we will have a Q&A session and take as many questions as we have time for. Now, as many of you participating today know, DISCERN is a web-based platform that provides unique insights from fundamental data. Altos Research is the premier provider of real estate market housing data and leading indicators and our foundational partner for our home builder application. Altos compiles listing data for over 90% of the country at the zip code level, providing viable insight into pricing, inventory, and volume data.

To illustrate the value of the Altos data, I wanted to share two charts with you before turning it over to Mike. The first chart shows how the Altos asking price data typically leaves the Case Shiller 20 index by 3 to 6 months, depending on where we are in a given housing cycle. If you look just going back to the trough of the most recent cycle, we can see that the Altos data bottomed at the end of 2010 or early 2011, while the Case Shiller didn't actually bottom until early 2012. And so there was a 12-month lead time there, but typically speaking it's about 3 to 6 months is what we found through our own testing. Now, naturally the Altos data will be more volatile as it is listing data and not actual final transaction data. However, important trends and inflection points can be identified and acted upon. Mike will dive deeper into the leading nature of the data during his remarks.

The next chart I wanted to share with you shows how the Altos listing absorption data lines up with existing home sales data as reported by the National Association of Realtors. Here you can see the two data sets are fairly coincident with one important difference; the Altos data is near real-time while the NAR data is reported on a lag. As you can see from these two simple charts, the Altos data provides viable context and insight into some of the most important trends (in the) US single family housing market.

With that, I’d like to turn the time over to Mike to discuss the data set in more detail, as well as current trends that he is seeing in the data at this point in time.

Mike Simonsen: Okay, thanks Dave. So let’s begin with a quick view of what the Altos data is looking at in particular. Specifically, we watch every active property listing for sale in the country, every week. This snapshot provides us...insight into the real estate market; the asking prices that are highly correlated with the ultimate transaction price that happens several months down the road. The cohort of properties gives a signal not only on pricing but also on things like supply and demand, the transaction levels as you saw with the pending home sales data from NAR, and leads that by, as Dave pointed out, three to six months.

We’re going to look at a few bits of data to get a feel for what the data’s showing right now for the US housing market. As we roll into 2016, we can see a lot of signals already in the first 6 weeks of the year and how that’s going to play out for the rest of the year.

The first chart we’re going to look at is, the year over year changes in asking home price. The blue line is single family homes. The black line is single family homes and condos. It’s a composite of both of those properties. You can see we’re starting the year with a 10% year over year price gap. We’ve had a pattern over the past several years that’s been low inventory and rise in demand, and that has continued to give us year over year price increases, strong housing markets for several years, with the absolute bottom being January 2011. You can see as we look at the Case Shiller data, that the transactions that filtered through throughout the year 2011 still had some price decreases to happen before the Case Shiller number started climbing but, you can see the bottom at January 2011, and the predicted nature at that point has been bull-ish in the subsequent 5 years, so we got a full strong cycle.

The next thing we’ll look at... so we’ve had our 4 years, or 5 years of pricing. Primarily, the main story of that time, has been a shortage of inventory. So if we look at inventory levels, this is the Altos 20 City Composite, these are the twenty cities that make up the Case Shiller 20, a really effective national proxy. You can see the declining rate of volume of properties for sale - over a million in January of 2011 to way down here at 250,000 in January of this year. This is the Altos 20 City. It’s a significant decrease in that time. You can also see the annual cycles of January trough, the June 30 peak, and the cycle happens again. We look at the factors that create the prices in the housing market; supply and demand. We can look at the story about supply, the shortage of properties for sale; supply is going to continue to be constricted this year. And so if you look at the signals of 2016, supply is, as we said, restricted and therefore that is a bullish signal for pricing. If we look at the other half of the equation, demand, it can be very difficult to measure for housing in a market like this one, a supply-constrained market. So traditionally, the only way to measure demand would be to measure transaction volume. How many houses are selling? I can you if there’s demand for that. But in a supply-constrained market, the demand is not measurable by transaction buying; you have to look for other signals. In the active market data, we have a really compelling signal that we identify as percentage of listings on the market that have price reductions. If you think about this number, a good rule of thumb is that about 35% of properties listed there are over-priced. Sometimes that’s strategic, other times it’s accidental. But about 35% of them are overpriced and take a price cut before they ultimately sell. In a cool market, as the bubble is bursting around, you can see that percentage of properties that have taken price cuts climb. They can climb over 40% even over 50%; half the stock has taken a price cut. And conversely when demand is higher than normal, you see price cuts at 20%, 25%. Even in some of the California hot markets, maybe 12%, low teens. So that means that 35% of properties listed were expected to be overpriced, but the demand is adequate as such that only 12% needed to take a price cut, and that’s really bullish for future pricing, 3,6,12 months out. You can measure demand and can see the organic levels of demand because properties get priced and they don’t need a price cut even when you’d expect them to do so. So you can see that in the price cut data all the way back to January of 2009 we’ve got the middle of the bubble burst, but we had the homebuyer tax credit. That pulled demand forward until April of 2010. In April 2010, the tax credit expired and price cuts shot way back up to 45%, so you can see that the demand had pulled forward and evaporated. However, by January of 2011,  as I mentioned, you’re already seeing that reset. And so price cuts peaked at 40% in 2011, by 2012 they’re in the low 30’s, and by 2013 we started the year at 25% for price cuts. So demand we can see for 2012-2013...it was going to turn into really strong home price increases, which it of course did. And then we’ve had...a very healthy three-year cycle where at the peak buying season, price cuts are about 30% or just under. At the end of the year, price cuts are a little higher as people are moving south for the holidays, and then that resets at the beginning of the year.

So you can see right now, as we pass into the first quarter, we can see we are going to trough out in the upper 20’s. Our peak at the end of last year was below 40, and that implies that in the next 3,6,12 months there’s enough demand out there that home prices are going to remain robust and growing, around the same pace as we’ve seen the last couple of cycles. So that’s a 10% year, that’s an 8% year. We’ve seen the story that supply is low, demand is the same as it’s been and so that is an easy way to identify where we’re going to have house price increases for the full year.

The other elements of forecasting on prices per year can be seen in a number that we use. It’s the asking prices of the newly-listed cohort of properties. We will look at this one at the absolute level of this number. This is across the Altos 20 City Composite. The blue line is a weekly number, the black line in this case is a 90-day rolling average, you can see the January trough and newly-listed cohort is a fabulous wisdom of the crowd’s example because realtors know exactly what’s happening in the market, they know where the demand is and price properties in aggregate accordingly. So we can use the trough of the newly-listed properties as a gauge for the price increases for the year. We can see about - again it’s a bullish signal for pricing for 2016 - if you think about the range of properties, the range of signals in pricing in this data, you get a new cohort of properties that are listed every week, and that shows up in the blue line here. After a few weeks that cohort, that inflection point is now the rest of the whole market. Another month after that, we’re looking at the properties start to be absorbed but they start to go into contract. A month after that we start seeing the pending data, and after that there’s number like that Case Shiller number.  So you can see a four-month cycle where the inflection points domino through. They are highly correlated, and it happens because this is the first place...the new listing cohorts is the first place that signal shows up. So the 2016 story is the supply is low, lower in fact than where it has been. Demand is just as good as where it has been, and for the other signals, like the newly listed cohorts, the pricing is strong so the forecast for the year is about 10%.

The last thing that I would like to point out for the year is...it’s not rosy everywhere. It’s the first time in a couple of years where it has not been up totally across the board, and you can see it in places like high-end Houston. One way to look at the data is by price range quartiles. So the high end of the market may be behaving very differently from the low end of the market. This is just a quick chart of the data for Houston. Using the same price reductions model as I’ve mentioned, you can see the red line as the low end of Houston’s market. Interest rates are low, employment’s reasonably good, the economy is growing. However, starting at the end of 2014, precisely correlated with the implosion of the oil markets, high-end Houston has had price cuts and is significantly cooler. So you can see that those guys are over 40%, 45% at the high end of the market. So you can see exactly where demand is happening in Houston, and so while in general the US market continues to be strong, there are pockets like this where we can see a little weakness. And what I’ll do right now is turn it back over to Dave so he can show you how that correlates into the homebuilder equities and their geographies and price ranges.

Dave Wigginton: Thanks, Mike. So in addition to evaluating the current trends of leading indicators for the broader housing market the data can be applied to the public homebuilders providing important context and insight. Some of the high-level insights we’ve gleaned from the national trends over the past couple of years are as follows: First, it appears homebuilders in general are gaining share within the overall home sales market as fewer and fewer existing homes come to market as reflected by the inventory of listings, which has been in a downtrend since the last recession, as Mike showed earlier in the presentation.

Second, the housing cycle has been primarily driven by a lack of supply to date as he discussed as well.

And third, barring an access of new supply, it would appear homebuilders are in a fairly decent sweet spot given the lack of new and existing supply relative to demand.

And finally, all signs seem to be pointing to a very healthy spring season, as Mike pointed out in his remarks.

Now, digging into the data at the ticker level, we can see how individual tickers rank versus peers on price and volume trends. So if we go pick a random ticker that’s in the homebuilding universe, we can go ahead and see what some of those trends are looking like at this point. So looking at year over year price changes and year over year changes in listing price, we can get a sense of which companies’ markets are experiencing the most robust versus the least robust growth in the peer group. And as we can see from here, Hovnanian, Tri-Point, and William Lyon as of the most recent data point are experiencing the most robust year over year growth while on the bottom side we can see Cal Atlantic, UCP, and NVR are experiencing the least robust amount of growth in the homebuilder universe.

Now if we wanted to take it a step further and look at volumes, we can go ahead and select absorptions and we can get a sense of what’s going on on a year over year basis for growth in volumes or expected volumes within the markets where the homebuilders operate. And so again what we can see here in our rank of the homebuilders is that absorptions are growing at their highest rates in Taylor Morrison’s markets, M/I’s markets and Toll’s markets and are the least robust and UCP’s, William Lyon’s, and Tri-Point’s. And so we get a sense of what the hierarchy is for fundamental housing data within these companies’ markets.

Now we have the ability to dig a little deeper and we can confirm some of the price action that we have been seeing. For example, Hovnanian was experiencing the most robust year over year growth in listing prices in its markets. And what we can do now is dig into Hovnanian’s data and get a sense of, well, is this a one-off thing or is it something where we are seeing a lot more action? As you can see here, the line is basically straight up for Hovnanian, confirming that it looks like pricing trends in Hovnanian’s markets are very strong at this point.

Now NVR was on the lower end of those price increases and so if we jump into NVR’s specific data, we can take a look at and see what year over year growth and housing prices in NVR’s markets look like at this point. As we can see from here, it’s been flat but definitely there was a little dropoff in early February and it’s marginally working its way back up, but it’s still weak compared to peers at this point based on the overall rank, and the chart confirms that for us.

Now, taking it a step further we can look at absorptions as well to confirm the trend for absorptions. So Taylor Morrison was at the top of the absorption list at this point. And if we go into here and select our absorptions we can see exactly where the trendline is for Taylor Morrison on a year over year basis and get a sense of whether the most recent data point was an anomaly or if it’s actually a positive trend. And as you can see here, it looks like absorptions are picking up on the year over year basis pretty nicely for Taylor Morrison at this point so that would lead us to believe that there is a strong improving trend in their markets and we will be monitoring additional data points here in the future to confirm that trend.

Now at the other end of that example for the absorptions was William Lyon, and again just taking a quick look at William Lyon’s absorptions to get a quick sense of whether the most recent data point is a one-off data point or if it’s actually confirming a broader trend at this point, we can see that in William Lyon’s markets that year over year absorptions have been in a clear downtrend since the middle of the 4th quarter and have leveled off here in February. And so we will be keeping an eye on that to see if there’s any sort of meaningful change in that trend that would indicate weaker or stronger than expected volumes within William Lyon’s operations.

So in conclusion, we at DISCERN have found the Altos data to be incredibly helpful in identifying trends and inflection points within the single family housing market. In addition, we have been able to extract viable insight as it relates to the public homebuilders as a group and individually.

So that concludes our remarks at this time. We would like to open it up to questions and we are going to pause for a minute just to poll the audience and aggregate our questions together here and we will be back in a quick moment.

Okay our first question is for Mike. Mike, you said the Altos data leads the market. The question is...how far a leading is the data?


Mike Simonsen: There’s a couple of ways to look at how the active market data leads the traditional housing data. There’s a direct measure of home prices by looking at the asking price versus the eventual sales price. You can see on this chart we have the Case Shiller, which is measuring the closed transactions, and the blue line is the Altos asking data. So if you think about it, the properties that are on the market now are at their asking price. In a month or so, they get an offer and they go into contract, and then generally sometime in the next one to four weeks or so, or six weeks, they close so call it another month, and then the Case Shiller has another month lag time for reporting after that. So, you can see a one, two, three, four-month window between the direct measure of asking prices and the ultimate point where they close. And it makes sense right? They’re highly correlated, the asking prices in aggregate to where they’re ultimately going to close.

There are times when that compresses, so when, for example the text credit was expiring, it expired April 1 of 2010. When that was expiring there was forcing properties to close quickly, and so offer to close was happening very quickly, and so that would compress that lead time. There are other times where that extends a little further, but in general you can think of it as a 90 to 120 day lead time.

Now you can get signal further leading when you look at the package of signals that are supply and demand indicators, so you can add another month onto that by looking at the cohort of newly listed properties because that’s going to lead the full market. You can look at the price reductions, the days on market, the inventory and a handful of others that are leading even further. Really what you’re looking at is year over year inventory changes are going to be predictive of pricing twelve months out, so if we see a big surge in inventory then you’ll see that impact a whole year ahead of time. You can also see that really in things like the Houston price data, where it really took a full year post oil price changes to see it in the housing market. So the supply and demand indicators can be as far as twelve months out. Beyond twelve months there’s a lot of variables that aren’t measured in the active data yet.

Dave Wigginton: Great, thanks Mike. Next question actually kind of goes back to the Houston data that you showed and really was more asking on a broad basis if you’re seeing any more noticeable or noteworthy trends similar to what you highlighted in the Houston data.

Mike Simonsen: So, at the price quartile level in general it’s a healthy market across the board in the US which means across a high end and lower end of properties. The inventory shortage is across the board so we don’t see real strong demand changes happening. In general it takes an extraordinary event to notice real different changes at the high end of the market versus the low end. So right now, with the exception of places like Houston, it’s indicating to be a pretty broad year across the board for price increases.

Dave Wigginton: Great. And then our next question is related to coverage. If you could just give us a sense of what the coverages of the Altos data nationally and maybe what the margin of error might be based on that coverage.

Mike Simonsen: Yeah so the Altos data would cover essentially 100% of the US, all 50 states, almost every county. This is the asking data so properties get listed for sale and then we start tracking. There are a few things that are outside of that data, some very rural stuff. There’s things like there are properties that are being sold that aren’t actually listed publicly so there will be transactions that will happen that don’t go through normal channels, so those may be outside of our set. There are some unusual things like that. It tends to be a small percentage in any given market, but those do exist.

We’ve found over time that the factors that impact the asking prices you do see are also impacting the stuff that you’re not seeing so if you’ve got properties that are selling without being listed, those properties are experiencing the same levels of demand as the stuff that is being listed and therefore the price changes are broadly represented.

We use the Altos 20 here very commonly as the proxy for them rather than tallying everything up for the one view. We use the 20 city composite and we use that because it matches the same MSA’s as the Case Shiller 20 and so it’s a good view for how the nation looks as a whole. But then you know in the DISCERN platform of course they dive into wherever those builders are building locally by zip code and so all of that covers essentially 100%.

Dave Wigginton: Great, thanks Mike. I’m just going to give a couple of more seconds to see if there are any other questions. Okay, it doesn’t look like there’s any further questions at this time so again we would like to thank you for joining us on today’s webinar looking at how the Altos single family housing data can lead the market and provide valuable context and insight. If you have any further questions, please feel free to reach out to me directly or your DISCERN sales person and we look forward to working with you in the future.

Thanks again for your time.