Dorkbot is a semi-monthly meeting of “people doing strange things with electricity.” They have been chugging along in several cities for a decade-or-so. Back in 2005 I presented at a Dorkbot in London, so I have an enduring soft spot for these quirky gatherings. At this month’s Dorkbot in San Francisco, a meteorologist named Tim Dye presented a brilliant visualization called WeatherDots. It summarizes the weather data he collects near his home in wine country.

Inspired by how much time-series information Dye was able to squeeze onto a few pretty circles, I spent the plane ride to ABS East in Miami throwing together a “dot” visualization of the Altos Research weekly active market data. Here is a visualization of a year’s worth of real estate data:

http://www.altosresearch.com/customer/labs/redots.html

My Redots updates every week, and can be pointed at any of the Altos Research local markets by entering a city, state, and zipcode. Your web browser needs to play nicely with the amazing Raphaël visualization library, or you will just get a blank screen. I recommend using Google Chrome.

The Legend, or What Is It?
Each dot of color represents a week in a local residential real estate market, so each column is a month. The main color of the dot shows the week-on-week change in the median price of single family homes in a particular zipcode. A red dot means house prices have decreased since the previous week (or dot), while green dots are increasing weeks. The summer seasonality effect is pretty clear in our Mountain View, CA example.

The “halo” of a dot is the ratio of new listings to listings in general. If the newest listings coming onto a market are priced higher than the typical listing, then the halo will be green. This suggests a seller’s market, when new listings are asking for a premium. The price of these new listings will be absorbed into the market the following week, so you might imagine a dot’s halo merging with the main color.

A dot’s angle is the year-on-year change in market prices. Aiming northeastward means prices have increased since the year before, while southeast is a decrease. These angles strip away seasonality from the market, and show how secular real estate trends. Our Silicon Valley example is a bit down year-on-year. The thickness of a weekly dot represents the week-on-week change in the number of listings, put more simply, the inventory. Thin dots that are more ellipsoid are a shrinking market, where fewer listings are available for sale at any price.

A Thousand Words
Information visualization is a buzzy field with smart people doing striking work. For me the line between the big data and infovis communities blurs when a pretty picture enables statistical inference without necessarily running the numbers.

The August 2011 Altos Research Mid-Cities Report is now available.

(Our Mid-Cities Report provides a glimpse into real estate market trends for mid-sized US markets not commonly reported in the media. Click here for more background on this composite.)

As we analyze mid-sized cities like Pittsburgh and Memphis, differences continue to emerge as compared to the S&P/Case-Shiller 20-city composite.

“Price of New Listings” and “Percent Price Decreased” are two of our most leading indicators of future transaction prices and volume.

Price of New Listings are up slightly while this indicator remains in negative territory for the Case-Shiller markets:

Year-over-Year Price of New Listings: Altos Research Mid-Cities Composite vs. Case-Shiller 20-City Composite

Price Reductions – measured as the number of active listings with at least one price reduction in the most recent 90-day period – are lower in the Mid-Cities Composite, inching into negative ground in the last two months (a good thing – fewer price reductions usually means stronger demand on a relative basis):

Year-over-Year Price Reductions: Altos Research Mid-Cities Composite vs. Case-Shiller 20-City Composite

The component metros of the Mid-Cities composite typically avoided the big sub-prime boom and bust – there’s been considerably lower volatility in these markets. While at an absolute level, the differences between these two composites are slight, they are distinctive and important to watch as the housing market continues its uneven recovery (See: Catfish Recovery: The Future of US Housing).

Much to the consternation of those around us, we first saw Detroit’s price strength back in April (See our National Housing Report).

From yesterday’s HousingWire article – “Home sales rise 9.6% in greater Detroit area”:

The Detroit area experienced a 9.6% surge in home sales in July… Home prices in the four-county metropolitan area climbed 8.8%…

Ask Prices: Detroit MSA since January 2010. Asking prices moved higher early in 2011 in response to early demand indicators.

Yep, I guess we were right on this one too… [self-aggrandizing tone intended]

We tackled several topics in my five-minute segment CNBC’s Street Signs on Wednesday  – “Housing: A Silver Lining” – so here’s some deeper analysis of the on-air arguments and assumptions.

Valuation methodology is the acute problem for most of today’s housing market problems – refinancing, demand stimulus, foreclosures.  The valuation and appraisal process is broken, creating the problems raised by Brian Sullivan and Mandy Drury about tax assessment and refinancing obstacles.

1. Tax assessments & property valuations

Dependence on lagging transaction data is the kiss of death in any housing valuation model. (See: Fungal Houses and Tails of Price Change)

Brian argued that municipalities are over-valuing homes for tax purposes – much higher than what the banks are willing to value for purposes of refinancing. We have record low interest rates and refinancing applications are higher, but underwater homeowners can’t refinance and they’re in turn stuck with inaccurately high tax bills.

Here’s a case example from my neighborhood in Davis, CA: 571 Jerome Street

$575,000 (Sold Price, March 2008)
$549,000 (Listed for Sale, June 2011)
$542,000 (Current List Price, August 2011)
$530,000 (Current Median Ask Price for all homes in this Price Quartile)
$447,000  (Current Zestimate; Range: $334-534k)

2010 Tax Assessment:     $585,000

The last assessment was completed for 2010, using transaction data from 2009 when prices where in the $600,000 range:

Ask & Sold Prices since 2009: Second Price Quartile for Davis, CA, zip code 95616 (Price Quartiles are 25% price bands in local markets, providing a more precise analysis of "like" homes to a subject property)

The $585,000 valuation is clearly too high because:

• There’s been volatility and a downward shift of home prices in this community
• Market trends changes in this price quartile
• The property is currently for sale at $542,000 and hasn’t sold

Keep in my that you can’t have it both ways – a lower valuation for tax purposes and higher valuation for selling purposes – and be prepared to accept the seasonal upswings too. Notice the short-run lift in home prices since the early Spring this year.  On a real-time basis, valuations have risen back to the $550-560,000 range with an inflection point coming as the spring and summer selling seasons unwind that will push the valuation back down in the second half of 2011.

Most importantly, the housing market’s real-time volatility is not captured using most tax assessor valuation methods.

(Brian – send me an email with your city and zip code.  I’ll shoot you over a market report you can use with your municipality to argue for a reduction in your tax assessment.)

2. Refinancing current, but underwater, mortgage borrowers

At the start of the segment, CNBC played a sound bit about $2 trillion of performing mortgages paying 1% or higher than the current mortgage rate available.  Mandy asked about running credit checks and income verification to validate refinancing opportunities for current, but underwater borrowers.  Sounds simple right? This is the great tragedy of the HAMP program.

Here’s the fundamental problem – If you do a refi with a house that’s underwater, where does the missing loan principle go?

If you’re current on your mortgage, the bank has no incentive to provide a modification, refinance, or write-down. You’re asking the banks take a lower interest rate on a smaller principle for a current borrower. Would you do this deal if you were a lender?  How would shareholders respond to that on an earnings call? (“You did what!?”…)

A newly-released FHFA Working Paper – “The HAMP NPV Model: Development and Early Performance” explains the HAMP methodology succinctly in this context:

Testing modifications for positive NPV generally eliminates borrowers who are very unlikely to be foreclosed upon or who have substantial positive equity, because in both cases the mortgagee or lien-holder is unlikely to suffer meaningful losses in the no-mod case. The NPV test also eliminates borrowers for whom a modification does not meaningfully reduce their prospects of foreclosure.

This exemplifies how the system is set up to encourage borrowers to strategically default so they can seek modification, if the borrower is willing to take a hit on their credit rating.  Brent White at the University of Arizona covers this topic extensively.  (See: “Underwater and Not Walking Away” and “Take this House and Shove It: The Emotional Drivers of Strategic Default“)

3. Back to the valuation problem

A transaction is an agreement on price and a disagreement on value. – Trader’s Proverb

The crisis yields an opportunity for the housing market to reset it’s valuation methodology based on real-time indicators – active market indicators, rent-to-own ratios, mortgage rate sensitivity analysis, demographic shifts – all of which are largely ignored by the lenders and servicers in their instructions to appraisers and BPO providers.

Housing is illiquid compared to other asset classes. The assets are idiosyncratic and trade infrequently, and when they do trade, buyer and seller biases and preferences exist – motivation, financing availability, price floors and ceilings, and property requirements.

Appraisers are instructed to solve this problem – establish market value without a willing buyer and seller of the subject asset.  The asset itself isn’t trading and the appraiser is required by outdated regulations to use a specified number of closed and active market comparables, both of which leave their analysis incomplete. Even attempts such as the 1004MC form introduced by Fannie Mae in 2009 include minimum and subjective analysis related to real-time market conditions.

Every appraisers would like access to more data to provide better analysis because they know they’re only as good as their last appraisal.

4. Effects of the upcoming FHA Conforming Loan Limit change

Brett Kornfield and David Bugajski at Discern Analytics (using housing data provided in part by Altos Research) researched this question in great detail back in June. In short, the effects will be minimal.  Additionally, these effects will diminish further as the active inventory from June sells off throughout the summer, leaving even fewer homes available for sale affected. Here’s an excerpt from their June 10 Applied Strategy report – “The Diminimis Impact of Conforming Loan Limit Changes”:

Source: Discern Analytics (www.discern.com)

My final thoughts

• Fix the valuation problem with more real-time analytics.
• Good luck solving the moral hazard problem and incentives for strategic walkaways.
• No, I don’t wear ties to work – we’re in Silicon Valley.   Heck, it was lucky that I found a sport coat to wear.
• Yes, I know.  Mom always said I had a face for radio.

[08/15/11: Author "told-you-so" update - We saw rising home prices in Phoenix from foreclosure activity much earlier this year.)

From HousingWire: "Rise in REO value cuts Freddie Mac holding expenses by 90%"

"The decrease in REO operations expense was primarily driven by an improvement in both REO holding period write-downs and disposition losses as REO fair values stabilized during the second quarter," Freddie said in its second-quarter earnings report.

There's demand out there leading to overall market price stability.

Using Phoenix - a REO-laden market - we've seen this story before with the tax credit in 2009-10:

"Percent Price Decreased" measures the percentage of on-market sellers that have taken at least one price reduction in the most recent 90-day period. Fewer price reductions result from sellers observing increased demand at their ask price

Phoenix is less seasonal and inventory is constrained compared to recent years, yielding short-term price support:

Ask Prices & Active Inventory since March 2008: Phoenix MSA

When the remaining shadow inventory hits the active market, the tide might change, but for now, there’s a clear indication that short-term demand is supporting prices across the board.

Bell curves and real estate price changes.

Histograms are a visual way to summarize a huge set of numbers. The horizontal axis covers the range of numbers, while the height of the vertical columns shows how many occurrences of that number turn up in the data. Even when the histogram looks mostly like the classic bell curve, the slight differences tell a story.

Here is a histogram of quarterly changes in Altos Research residential real estate prices:

(three month changes in the median ask price of single-family homes, across about 300 random zipcodes with significant inventory, across our entire history)

This chart gives good perspective into the recent behavior of the housing markets. The histogram has a long positive tail, so we know there were many quarters when property prices rallied. However the center of the distribution is slightly negative, where we find most of the histogram’s “weight.” This center is around negative one percent, and shows the credit crisis hitting a broad set of markets. There is also plenty of weight outside of the positive or negative ten percent range, which suggests how volatile real estate prices can be even over a whole quarter. The situation is even more extreme if we chart sold prices the same way:

(three month changes in the median sold price of single-family homes, across about 300 random zipcodes with significant inventory, across our entire history)

There is much less volatility in list prices, as we can see by comparing the width of these two histograms. This is because active asking prices are sampled across a much larger population. Sold prices vary drastically, since one or two quirky transactions have an over-sized impact on a median or average of a small sample. I talked about this sample size problem in my earlier Fungal Houses post.

These histograms visualize the consistency of home price movement. Common automated valuation models are subject to wild swings in valuation opinions, because they are built on lagging sold price data. This is why some AVMs can see a six-figure change in a property’s valuation in one day.

Where do stock market winners buy houses?

Prices in certain local real estate markets are bolstered by a rally in the stock market. The recent murmurings of another IPO bubble suggest that newly minted paper millionaires will soon be shopping for homes in Los Altos Hills and Cupertino. We can put numbers behind this story by calculating real estate price sensitivity to a stock market benchmark. If we choose the S&P 500 as the benchmark, the sensitivity number will be a sort of real estate beta. Since real estate is far less liquid than most stocks, I regressed quarterly changes in our Altos Research median ask price against the previous quarter’s change in the S&P 500. Historically speaking, those real estate markets with a high beta have gotten a boost in prices after a good quarter in the stock market. Those markets with a low, negative beta are not “immune” to the stock market, but tend to be depressed by a stock market rally.

Below is a map of the Bay Area’s real estate betas. These numbers were calculated using prices from Altos Research and benchmark levels from Yahoo! Finance. The darker red a zipcode, the greater an increase in the market’s home prices after a quarterly stock market rally. As we might expect, the betas in Silicon Valley are above average. However there are also some surprises in Visalia and Wine Country.

Our hypothesis for positive real estate beta is easy: those IPO millionaires. But what could cause a real estate market to tank after a good run in the stocks? Perhaps negative real estate betas are in more mobile labor markets, where stock market wealth triggers a move away from home ownership. Or maybe negative real estate betas turn up in markets where the condo stock is higher quality than single-family homes, like in some college towns. Remember the betas mapped above are based on only single-family home prices.

Real estate remains a difficult asset to hedge, an asset almost impossible to short by non-institutions. This is unfortunate, because a short hedge would be a convenient way for people with their wealth tied up in real estate to ride out a depressed market cycle. However like long-only fund managers, real estate investors could benefit from thinking in terms of benchmark sensitivity. If we choose a benchmark that represents the broader real estate market, we could hedge real estate buy purchasing non-property assets that have negative real estate betas. You would want your value-weighted real estate beta to net out to about zero. Now there is a plethora of problems and assumptions around making investment decisions with a crude linear sensitivity number, but at least real estate beta gives us another tool for thinking about housing risk.

(A version of this post with more background details can found be at http://blog.someben.com/2011/07/diff-house-hedge/ on Ben’s blog)

Ever wondered why your flat’s Zestimate bounces around so much?

In high school economics class you might have learned about fungible goods. This strange word refers to things that could be swapped without the owners especially caring. A dollar is almost perfectly fungible, and so is an ounce of pure silver. Paintings and emotional knick knacks are not at all fungible. Fungible stuff is easy to trade on a centralized market, since a buyer should be happy to deal with any seller. This network effect is so important that markets “push back,” and invent protocols to force fungibility. Two arbitrary flatbeds of lumber at Home Depot are probably not worth the same amount of cash. However the CME’s random length lumber contract puts strict guidelines on how that lumber could be delivered to satisfy the obligation of the future contract’s short trader.

Real estate is seriously non-fungible. Even a sterile McMansion in the suburbs can have a leaky roof, quirky kitchen improvements, or emotional value for the house-hunting recent college grads. If we consider many similar homes as a basket, or a portfolio of the loans secured by the homes, then the idiosyncrasies of each home should net out to zero overall. Across those ten thousand McMansions, there should be a few people willing to pay extra for a man cave, but also a few people who would dock the price. This is the foundation of real estate “structured products,” such as the residential mortgage backed securities (RMBS) of recent infamy. Like flatbed trucks delivering a certain sort of wood for a lumber futures contract, a RMBS makes a non-fungible good more fungible.

The Usual Place
The combined idiosyncrasies of non-fungible things rarely net out to exactly zero, especially during a financial crisis. Nonetheless traders and real estate professionals want to think about a hypothetical, “typical” property. We define a local real estate market by city, neighborhood or even zipcode. How do we decide the value of a typical property? There is an entire industry built around answering this question. One simple, clean approach is to sample a bunch of real estate prices in a local market at a certain point in time, and then average the prices. Or maybe use a more robust descriptive statistic like the median price.

The most readily available residential home prices in the U.S. market are “closed” transactions, the price a home buyer actually paid for their new place. Using a closed transaction price is tricky, because it is published several months after a property is sold. Can a typical home price really be representative if it is so stale?

Sampling
Even if we ignore the time lag problem, there is another serious challenge in using transactions to calculate a typical home price. Within any local real estate market worth thinking about, there are very few actual transactions compared with overall listing activity and buzz. Your town may have a hundred single-family-homes listed for sale last week, but only four or five closed purchases. A surprise during the buyer’s final walkthrough could wildly swing the average, “typical” home price. For the statistically inclined, this is a classic sample size problem.

There are plenty of ways to address the sample size problem, such as rolling averages and dropping outliers. Or you could just include transactions from a wider area like the county or state. However the wider the net you cast, the less “typical” the price!

Another approach is to sample from the active real estate market, those properties currently listed for sale. You get an order of magnitude more data and the sample size problem goes away. However everyone knows that listing prices do not have a clear cut relationship with closing price. Some sellers are unrealistic and ask too much, and some ask for too little to start a bidding war. What is the premium or discount between listing price and actual value? We spend a lot of time thinking about this question. Even closed transaction prices are not necessarily the perfect measure of typical “value” since taxes and mortgage specifics can distort the final price. Our solution is to assume that proportional changes in listing prices over time will roughly match proportional changes in the value of a typical house, especially given a larger sample from the active market.

A Picture
Below is a chart of Altos Research‘s real estate prices back through 2009, across about 730 zipcodes. For each week on the horizontal axis, and for each zipcode, I calculate the proportional change in listing price (blue) and in sold price (red) since the previous week. Then I average the absolute value of these proportional changes, for a rough estimate of volatility. The volatility of sold prices is extreme.

Questions galore about “The Catfish Recovery” in last week’s webcast.   The good people at Business Insider covered the event and posted our favorite image:

Here are the main takeaways and responses to a few comments.

1. If you’re expecting a smooth, consistent 3-5% annual growth rate in housing, you’ll be waiting a while. It just ain’t happening.
2. No, we didn’t proclaim – “It’s a great time to buy or sell real estate.”  What we said was that periods of volatility and market inflections offer opportunities for traders to profit with the right information.  (See John Paulson circa 2007)
3. Expect a triple dip, a quadruple dip, and a quintuple dip.  Heck, we saw back in July that 2011 home prices would fall below 2009 levels.  We confirmed this in November. Expect volatility. Repeat: Expect volatility.
4. History shows that volatility is normal. And we all agree that the mid-90′s through 2007 was just a bit abnormal. (a.k.a “The Constant Growth Myth”):

"History of Home Values" from Robert Shiller's "Irrational Exuberance"

Additional comments, criticisms, and scathing remarks welcome.

NAR’s Pending Home Sales Index for March moved higher, counter-balancing the discouraging Case-Shiller numbers reported yesterday for February and reflecting our early-year observations (see: “Whew! Seasonal uptick in housing prices” from February 4 and “Is Spring Springing?” from March 4).

Shadow inventory, defaults, demand, and economic growth remain huge challenges over the next few years. But so far this spring, the data indicates we’re heading more for stability more than a big double-dip.

Expanding on my quick thoughts from yesterday’s Case-Shiller report, here are five (5) reasons to be optimistic this Spring:

1. Sellers are optimistic. The price of new listings moved higher starting in February with aggregate market ask prices following soon thereafter:

Active market prices are one of most vital leading indicators to future transaction prices. When aggregate asking prices move higher, transaction prices eventually catch up. You’ll just have to wait until the June or July reports from S&P to see price trends change in the Case-Shiller.

2. Year-over-year active market prices in TX, FL, AZ, and CA have all inflected this Spring:

While they’re still in negative territory, less negative is better than more negative.

3. Active market inventory is moving lower:

Housing supply matters – CA prices are the least inflective so far (where year-over-year inventory is the highest comparatively) and market prices have been resilient to increasing supply this year.

4. Days-on-Market is moving lower:

5. Housing starts were higher in March.

Maybe it ain’t all that bad out there this Spring …

Couple of highlights from this month’s update:

In March 2011, while the majority of markets tracked posted decreases in home prices, this is a trend that seems to be reaching its conclusion. Quietly, increases have been seen in several markets across the country, in markets along the East Coast, in particular.

As is expected at the time of year, inventory is steadily on the rise, with sellers hoping to tap into a deeper pool of home buyers.

If you’re more of a visual person, here’s what we’re talking about:

Altos 20-City composite: Price & Active Supply since November 2011

If you’re not signed up to receive our monthly report via email, register here.  (Yep – it’s free because we’re like that…  :–)

Okay, not really roar, but I am a panelist on the “Distressed Residential Overview” session on Thursday afternoon.  Click for the full conference agenda .

Here are last year’s presentation slides in which I showed how our active marketing housing indicators lead home price indices such as the S&P/Case-Shiller.

Predicting US Housing Prices: Opal Real Estate Deal Makers Summit (April 2010)

In particular, check out Slide #7:

Turns out that our data and projections back in April 2010 for this year were exactly correct – showing both inflection points and magnitude of housing price movements through early 2011.  More simply – we nailed it.  [patting oneself happily on the back].

Want to know what 2012 will bring?  Guess you’ll have to attend. Register here.

(Note: Neither Altos Research nor I receive any compensation for promoting this conference.)

I don’t practice Santeria / I ain’t got no crystal ball / Well I had a million dollars but I / I’d spend it all…

- Santeria, Sublime

If you’ve followed Altos Research for any length of time, you’ll have noticed that we’re a bit pedantic about the orientation of housing market data. For years we’ve been talking about the power of the insights in the active market. Understanding real estate is not just about which transactions closed, it’s about how much the sellers are asking, changes in those  ask prices; it’s about inventory and demand metrics. The active market of homes for sale is rich with information and insight about future transaction prices, home values, mortgage defaults.

Today’s Real Estate Market Yields Tomorrow’s Home Prices

The at-a-glance view of a mortgage portfolio or RMBS security in AltosEvaluate Forward Valuation Model.

We’ve noticed more and more lately that our institutional clients understand this active market informational wealth, and they keep asking us the same questions, “How much should we adjust our bids? How much are home values going to change in [random zip code]?” You only have to smack me in the face so many times before I wake up and realize the world has a need. That brings us to today.

Announcing AltosEvaluate: Forward Valuation Model

Today, Altos Research launches a technology we call Forward Valuation Modeling (FVM). Part of our AltosEvaluate Mortgage Analytics Suite, the FVM looks at everything thing we know about the current, local housing market–all the pricing and price change trends, all the supply and demand metrics, plus everything we know from years of tracking this data– and investigates how those characteristics have impacted local transaction prices previously. The FVM uses that insight to forecast price changes in each local market over the next 3, 6, 12 months.

In the capital markets, investors buy residential mortgage bonds or whole loan pools to trade or keep as investments. When you make these investments, you have to investigate every asset in the deal, maybe 1000 or 5000 loans in random neighborhoods around the country. You look at the loan, LTV, the borrower, FICO score, payment history, everything you can find to apply a value to that loan. AltosEvaluate FVM brings the ability to look at the local housing markets underlying every mortgage in the deal and make decisions based on the trend in housing values.

For example: You might realize that the 85% average LTV in the pool today is looking more like 95% LTV as the FVM illustrates how property prices are likely to fall in most of the markets over the next twelve months. These loans are MUCH more likely to default and you’re much less likely to get a return on your investment. Bid accordingly.

There’s so much to tell about this new technology, I’ve struggled to keep this post at a manageable length. If you’re interested in more, just contact us. Here’s a quick FAQ to wrap up. AltosEvaluate Mortgage Analytics Suite brochure is embedded below.

AltosEvaluate FVM FAQ:

What does the FVM do? The FVM forecasts the home price changes coming in local housing markets in the next 3, 6, and 12 months.

How does it work? The FVM platform is what’s known as a machine learning technology, a system where we have a vast amount of data, some of which is useful for forecasting the future, the system “trains” itself on the data to build models on how to process the current market. In the case of real estate, this is critical because you have to have local models (what’s interesting in the San Jose housing market is not useful in the Miami Beach condo market, for example). Altos Research tracks 20,000 zip codes, so the AltosEvaluate FVM literally trains tens of thousands of local forecasting models.

Who uses it? Mortgage and RMBS bond investors, companies with exposure to real estate and who need to understand where the local housing values are heading so they can adjust their bids, or make buying and selling decisions in their portfolio.

Is it an AVM for the future? No. AVMs (automated valuation models) take the recent closed transactions and estimate a price for a given property.  The FVM says, based on everything known about this local market, real estate prices are headed down (or up).

How do I get more info? Email our housing market analytics team for more info, white papers, etc.

Forward Valuation Model (FVM) Brochure

Inventory (orange line, right axis) is showing an up-tick in this week’s data. Sellers on the market are subsequently responding with a flattening in the number of homes on the market with price reductions in the last 90-day period (black line, left axis). History tells us that the inventory count hits a trough in January each year, so we may have hit that trough this week. (See our January 2010 webcast.)

Altos 20-City Composite: Active Market Inventory & Seller Price Reductions since 12/15/2009 (weekly observations)

Higher inventory means increased competition among sellers which can lead to downward price pressure. (Remember what we saw in our January 2011 National Housing report with the Price of New Listings falling off a cliff?)

Demand is the mitigating factor here.  If there are enough buyers in the market to absorb the increased inventory, price reductions will be muted.  We saw this in real time during the 2009-10 tax credit years.  More buyers means less pressure on sellers to take price reductions:

Altos 20-City Composite: % of Active Sellers taking Price Reductions in most recent 90-day period (weekly observations)

Here buyer, buyer, buyer… [waving arms furiously]

It’s easy to get fixated on today’s housing prices and today’s active inventory.  But, it’s a healthy exercise going into the new year to do some year-over-year comparisons of our most leading indicators to get some longer term perspective.  A look at both the Price of New Listings (black line, left axis) and Active Inventory (orange line, right axis) in this light:

Altos 20-City Composite: Year-over-year Price of New Listings and Active Inventory as of 1/12/11

Thoughts? Comments?

Highlights from this month’s report (available for download here):

• The Altos 10-City Composite is now at $448,996, off 1.63% from last November 2010.
• All 27 of the major markets tracked by the report showed seasonal price decreases. Most were relatively minor, with the steepest declines seen in San Francisco (down 4.77%), San Diego (down 3.71%), and Minneapolis (down 3.16%), respectively.
• Housing inventory is off by 5.89% nationwide, with dramatic decreases in several major markets, including Boston, San Francisco, and Seattle.

This month’s featured chart highlights the difference in pricing of newly listed properties vs. the prices of the market as a whole. Note two trends in the following chart: First, the major January 1 dip implies a weaker Q1 than in 2009 or 2010 when the federal tax credit artificially boosted buyers. Next notice that the first inklings of the stability for the full year will be evident before January completes.

On Thursday, we hosted our Q4 Webcast – “When Does the Housing Recovery Start?” (slides | recording) – looking ahead to 2011 price and some other big questions on people’s minds:

• Have we hit bottom, or is there really more downside on the horizon?
• What are the implications of Robo-signing, QE2, Dodd-Frank, and the foreclosure pipeline?
• The supply problem – both of them.
• What are the opportunities for distressed mortgages, whole loans, and RMBS?

Watch the recording by clicking here and following the registration prompts.

And if you’d like to view and download the presentation slides, see below.

Highlights from our November 2010 Real-time Housing Report:

• The Altos 10-City Composite is now at $458,518, off 1.60% from last month.
• Home prices fell in 25 of the 26 markets covered in the report.
• Cities that felt decreases most sharply were San Diego (-3.28%), Salt Lake City (-3.27%), and Phoenix (-3.11%).
• Three of the 26 markets covered in this report showed increases in inventory, and nationally, inventory was down 4.64%.

Download a copy of this month’s report here.  (Yep – it’s free. Registration required.)