Category Archives: News

Christmas In Killarney

The holly green, the ivy green
The prettiest picture you’ve ever seen
Is Christmas in Killarney
With all of the folks at home

It’s nice, you know, to kiss your beau
While cuddling under the mistletoe
And Santa Claus you know, of course
Is one of the boys from home

The door is always open
The neighbors pay a call
And Father John before he’s gone
Will bless the house and all

How grand it feels to click your heels
And join in the fun of the jigs and reels
I’m handing you no blarney
The likes you’ve never known
Is Christmas in Killarney
With all of the folks at home

Christmas In Killarney — Bing Crosby

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Looking for something interesting to read over the holiday weekend? Try this:

Historic Turning Points in Real Estate

As you might imagine, I am a fan of Robert Shiller. The PDF link above is to his latest paper.

Excerpts:

Exerpts The real estate market changed its direction markedly around 1990, from a booming market to a market in the doldrums for the better part of a decade, and then the market started accelerating upwards at increasing rates. The national home price boom since the late 1990s appears unprecedented in US history, although the “baby boom” in housing of the late 1940s and early 1950s comes close, and there have been some very large local booms. The rate of US housing appreciation slowed after 2005, and, to some eyes at least, it would appear just sometime after mid 2006, we are entering a new regime of downward price changes.

It would seem that demand for housing services should be relatively inelastic in the short run, especially with regard to the number of units (rather than their size). Most families want just one house. The decision to own two or more houses, or the decision to break up the family to spread out over more houses, is not made very often—most commonly only at important life turning points or job changes. It is difficult for builders to transform two small housing units into one larger unit, or one large unit into two small housing units, without great costs. Hence, even small changes in the number of housing units might be expected to cause major short-run changes in home prices. However, home prices do seem to show enormous momentum, and sudden changes in the market seem rare. In a speculative market, a sudden change in some component of supply or demand may produce little price change if people think that the change is temporary, and so another component, a speculative component, offsets the sudden change. But the speculative component is inherently psychological, potentially unstable, and subject to contagion and herd behavior. People may change their mind about whether a change in price is only temporary or is the beginning of a new trend. They are especially likely to change their mind because we have professional marketers whose job is to get some kind of social response moving, and, when they do find some advertising pitch that resonates with investors, they will run it for all it is worth.

Analysis of past booms seems to indicate that investors in both the stock market and the housing market seem often not to understand the supply response to price increases. These are normal intelligent people, why would they repeatedly make the same mistake again and again? There seems to be what I will call a uniqueness bias, a tendency for investors to overestimate how unique an investment they favor is, failing to take account of the inevitable supply response to high prices. The uniqueness bias is reflected in quite a number of anomalies of human judgment that psychologists have documented, including the “representativeness heuristic,” “overconfidence,” “wishful-thinking bias,” “spotlight effect” and “self-esteem bias.” The uniqueness bias is related to failure to imagine how many possible competitors there are, a tendency to think highly of oneself and one’s associates and an association of investments with one’s sense of personal identity with an identified business model.

The uniqueness bias has its effect in the housing market when people imagine that the city they live in is unusually attractive, and increasingly so. They fail to understand that new such cities can be constructed in what are today cornfields or forests. In their 1990 paper, “The Baby Boom, The Baby Bust and the Housing Market,” N. Gregory Mankiw and David Weil argued that the housing market would soon crash as the baby boomers retired, neglecting to consider how supply would adjust to any such change in demand. In their 2004 paper “Superstar Cities,” Joseph Gyourko, Christopher Mayer and Todd Sinai argue for extrapolating some long-standing trends in major US cities, arguing that these superstars will only grow in status, assuming implicitly that there can be no new supply of the services those cities provide.

These narrative accounts do not prove anything, and we do not know that the change in thinking that appears to accompany ends of booms was in any sense the cause of the end of the boom. The change in thinking cannot be measured accurately, as we have only media accounts that suggest at it, that represent some journalists’ impressions that may not be replicable. Some economists would therefore be inclined to exclude any such effects from the economic model of the boom, and to try to explain the change in terms of some more well-measured economic effects.

But, if one considers that the prices paid for houses, as for any other speculative investments, surely reflects people’s willingness to pay, then the change in attitudes must have had an impact on prices. Just because we cannot precisely quantify and prove such an effect does not mean we should revert back to a null hypothesis that the changing psychology has no effect on home prices.

The best guess is that ends of housing booms have multiple causes, and cannot generally be interpreted as just an unraveling of boom psychology. Still a rising sense of enthusiasm and excitement for the investments, followed by a sense of betrayal and embarrassment at having fallen for the boom and underestimating the supply response to the boom, played a significant, if unquantifiable, role in the booms and their subsequent break.

America's Debtor Prisons

It’s the most wonderful time of the year

(Most wonderful time)

With the kids jingle-belling

And everyone telling you

Be of good cheer

It’s the most wonderful time of the year

(Wonderful time)

It’s the hap-happiest season of all (wonderful time)

With those holiday greetings

And great happy meetings

When friends come to call

It’s the hap-happiest season of all

There’ll be parties for hosting

Marshmallows for roasting

And caroling out in the snow (out in the snow)

There’ll be scary ghost stories

And tales of the glories

Of Christmases long, long ago

It’s the Most Wonderful Time of the Year — Andy Williams

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Debtor’s Prison 1 Debtor’s Prison 2

Asking Price: $630,000IrvineRenter

Income Requirement: $157,500

Downpayment Needed: $126,000

Purchase Price: $436,500

Purchase Date: 12/13/2002

Address: 219 Terra Cotta, Irvine, CA 92603

First Mortgage $629,600

Second Mortgage $157,400

Total Debt $787,000

Beds: 3

Baths: 2.5

Sq. Ft.: 1,510

$/Sq. Ft.: $417

Lot Size: –

Type: Condominium

Style: Modern, Other

Year Built: 2003

Stories: Two Levels

View(s): City Lights, Hills, Mountain

Area: Quail Hill

County: Orange

MLS#: S515205

Status: Active

On Redfin: 4 days

From Redfin, “Vaulted Ceiling In Living Room, Private Corner Lot With Open View Of Outdoor Sports Center. Spectacular City Light View From Master Bedroom Balcony & Other Area. Beautifully Upgraded In Spacious Private Courtyard. Wood Floor On Fist Level, Dining Room Open To Kitchen, Convenient 2nd Floor Laundry Room. Plantation Shutters, Granite Counters, Stainless Steel Appliances.”

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So how is it that someone can own a house for 5 years, sell it for $200,000 more than they paid, and still end up leaving the bank with a huge loss? Welcome to the Great Housing Bubble mania. Today’s seller refinanced this property in July of 2005 for $787,000 taking out a whopping $350,500 cash. Now that the day of reckoning is at hand, if they get their asking price and pay a 6% commission, the shortfall will be $194,800. This is a recourse loan as it is a refi. Do you think they have $194,800 in assets for the bank to go after?

Debtor’s Prison 4Should this seller be sent to debtor’s prison? I would like to think so, but in reality, they are escaping debtor’s prison — their house.

What is a debtor’s prison? A prison is any place you cannot leave until you have served your sentence, and these debtors will not be able to leave until they can pay off their mortgage. Most will not be able to do so until market values go back up. Hopefully, it is a gilded cage, but it is still a cage.

Houses are America’s new debtors prisons. By the end of 2008, anyone who purchased between 2004 and 2007 will be underwater. Let’s say for a moment that the government comes up with some substantive bailout program where homeowners can stay in their house and continue making payments of 50% or more of their gross income. House prices will still fall, albeit at a somewhat slower rate if there are fewer foreclosures. Everyone who is underwater and making crushing home payments will be stuck in their homes until values climb back above their purchase price. Mozilla JailSince there are a great many people in this circumstance, and since each of these people is in at a different price point, each one will have a different term in debtor’s prison, but when their sentence is up, most will opt to sell to get out from under the crushing payments. Each of these people selling their home keeps prices from rising. This is called overhead supply. It is also why the market will not see meaningful appreciation without capitulatory selling. A bailout will make for a slightly higher bottom and a much slower recovery.

Anyone who purchased in the late 80s or early 90s knows the feeling of being imprisoned in their house. This is not a new phenomenon. This time around the sentence will be much longer, and the debt service will be much larger.

Home, sweet home? We will see…

Debtor’s Prison 3

My question to you today is this: Who is better off, the homedebtor rotting in their debtor’s prison, or the family thrown to the curb in a foreclosure?

Knife Catcher Wanted

By request…

(all right you Chipmunks! Ready to sing your song?

-I’ll say we are!

-Yeah!

-Let’s sing it now!

Okay, Simon?

-Okay!

Okay, Theodore?

-Okay!

Okay, Alvin? Alvin? ALVIN!

-OKAY!!!)

Christmas, Christmas time is near

Time for toys and time for cheer

We’ve been good, but we can’t last

Hurry Christmas, hurry fast

Want a plane that loops the loop

Me, I want a hula hoop

We can hardly stand the wait

Please Christmas, don’t be late.

The Christmas Song — Alvin and the Chipmunks

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139 Treehouse Front 139 Treehouse Kitchen

Asking Price: $1,324,998IrvineRenter

Income Requirement: $331,248

Downpayment Needed: $264,999

Purchase Price: $962,000

Purchase Date: 12/17/2003

Address: 139 Treehouse, Irvine, CA 92603

First Mortgage $769,342

HELOC $500,000

Total Debt $1,269,342 + Neg AmGourmet Kitchen Award

Beds: 5

Baths: 4.5

Sq. Ft.: 3,049

$/Sq. Ft.: $435

Lot Size: –

Type: Single Family Residence

Style: Contemporary

Year Built: 2003

Stories: Two Levels

View(s): City Lights, Hills, Has View

Area: Quail Hill

County: Orange

MLS#: S511977

Status: Active

On Redfin: 38 days

From Redfin, “Back on the market. Buyer take over existing loans. .view side. .Gourmet Gas (5 burner cooktop) Granite kitchen. Stainless steel appliances. .2 convection ovens+microwave. .Enormous walk in pantry. Giantfamily rm/cozy fireplace. Downstairs bedroom and full bath. Wood floors thru out downstairs. .Extra lg. lgrm & DR. 2nd floor plush carpet. Fabulous master/retreat(or 5th bedrm) Separate oval tub & big stall shower. 3/4 bds. up+loft. The builder states plan 3 is 3049 Sq ft plan 2 is 2841 sq. ft. $275 mo. mello”

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Buyer take over existing loans? Whoa! Think about what is going on here… This seller has taken out a negative amortization loan with a 3.6% teaser rate that is about to recast. She has also taken out a $500,000 HELOC to cash out her downpayment and likely fuel some consumer spending. Now that the bills are coming due, she wants someone else to come in and pay them. Unbelievable!!!

(I feel like such a realtor 🙁 )

Any of you want to overpay for her depreciating asset so she can pay off her bills? I will pass.

Mortgage Equity Extraction

Colors

The video links below were disabled for direct embedding on Youtube. Just click on the links and enjoy.

Snow Version

Colors / Dance — George Winston

Water version

Colors / Dance — George Winston

Our contributing arithmetician, Zileas, has provided an analysis of Aliso Viejo’s market for your study.

Conclusions:

– Over the last 9 months, the typical 2 or 3 bedroom condo of 1000-1400 sqft in Aliso Viejo dropped roughly $123 in value per day. I personally think that this number has gotten worse, but I can only prove $123 a day with the data I have. This comes out to about a 1% drop in value per month for the condos I looked at. This is less than half the rate of decrease in Ladera, which you would expect since Aliso is closer to the beach, is more developed, and anecdotally has fewer toxic mortgages and what not.

– For every $1 you overprice your home, you LOSE $0.81 off the final sales price… So if your home is worth $400k, two choices you might consider are (based upon the data): price at $400, sell at $400, or… Price at $425, sell at $380K. This is because buyers tend to ignore overpriced houses in this market (which has so many choices!), and move on to someone who is showing they are willing to sell. Again, sellers: Price to sell!

– If you are going to buy a 2 bedroom condo, you may as well go high on the square feet and get a bigger living room or bedroom or whatever, in my opinion. The cost of buying a condo with a bigger living space (but the same # of beds/baths/basic features) is only $88 per extra square foot in Aliso, which makes those 1400 square foot 2 bedrooms seem a lot more attractive than the 1000 sqft ones… I’d sure pay 35K for 40% more living space!

– Older condos sell for less… About $1700-$2000 less for every year older they are. Note that homes get older as you own them…

– Compared to typical AV properties similar to them, Windflower condos are worth a bit more (about 20k, which is roughly 2%). Other developments may be worth more or less than the average development, but I couldn’t prove it with the data I had.

– I’d really love wider and better data. If any Realtors want to hook me up for my benefit, and for helping you persuade your sellers to price reasonably, look me up!

What Properties Did I Look At?

I used 77 condos sold in the last 9 months in Aliso Viejo, with 2-3 beds, and 1000-1400 square feet. The properties ranged in age from 6 to 18 years old. I performed this analysis on Nov 22nd, on data that is a week old.

How Did I Value Them?

I used statistical regression, which is a fancy way of drawing a line through a bunch of points, but instead of lines which only allow you to study one thing in relation to another, it can study a lot of things in relation to one thing all together. This is the same type of technique that they use in science, pharmaceuticals, and economic models. There are math notes on the specifics for the curious or mathematical inclined at the bottom.

I valued homes using several slightly different methods and selections of properties to make sure my conclusions were robust. The key factors that went into housing value were: when it sold, when it was built, # of square feet, # of bathrooms, # of bedrooms, and how much the seller overpriced/underpriced the property (final asking price – sold price).

How do you Interpret This?

– Use it to look at properties very similar to the condos I used – it would be lousy for looking at a 5 bedroom house in Aliso Viejo, or a condo in Newport Beach.

It is most accurate right now. This sort of analysis can get stale, things change!

– DON’T use it as investment advice, I’m not a certified professional, just a guy with advanced statistics training, an MBA, and some spare time.

– I have included some valuations of “typical” properties below. A “typical” property has average view, has average upgrades/condition, has average selling circumstances, and so on EXCEPT for the specific details I lay out. You would have to judge if a property would command an “above average” price (e.g. diamond-inlaid bathroom mirror) or a “below average” price (mold growing on walls, neighbor owns meth lab, etc) and adjust accordingly.

2 Bed, 2 Bath, 1000 sqft, built 1990, closing dec 31: $379,000 +/- 25K for relative quality of property.

2 bed, 2.5 Bath, 1400 sqft, built 1996, closing dec 31: $433,000 +/- 20K for relative quality of property

(the +/- are a “95% confidence interval” – if 20 houses sold of each of the above types, I predict only one each would fall outside that range on average)

Where are the Graphs?

Because I am plotting several things vs price, I can’t make a graph. You can graph sqft vs price, but you can’t graph beds AND baths AND year built AND sqft vs price all at once.

How NOT to Interpret this:

– Do not read too much into the $/sqft number. When you see this figure on real estate sites, you are valuing the entire property by it’s square footage. My model considers sqft to be only a part of the overall valuation (instead of 100% of it), so the number you see is smaller.

– You may be tempted to compare this to my Ladera Ranch analysis. They use slightly different methodology and data ranges. Therefore, direct comparison requires some pretty serious knowledge of how these sorts of models work, and even then you are doing a lot of hand-waving. Treat them as stand-alone unless you have deep knowledge of this stuff.

Details of Research for the Curious or Mathematical:

Basic Method;

Linear Ordinary Least Squares regression (which is indeed a BLUE regression) of various predictors on soldprice. Boxcox proved linearity with a theta of 7 (!!). VIFs, except where noted, were generally below 2 (beds and yearsagobuilt sometimes crested over it to like 2.35ish). No crazy weighting of data points or questionable pruning, no resampling my own error, no smoking crack, this is a pretty vanilla set of regressions.

All regressions were whitewashed of heteroskedasticity. And yes, there was significant heteroskedasticity because I had no 1 bedrooms and not that many 3 bedrooms in the sample.

Dataset:

This is in the basic “layman’s” posting, and trimming is described in the chart (to either 11 months (all), 9 months, or 6 months).

Predictors and Justifications Behind Using Them:

Daysago — # of days ago the property was built, captures gradual constant pricing decreases which we know exist!

Daysagosquared – Same as above, but captures accelerating trends to a degree.

Beds – More bedrooms usually means more value, and it has regressed well in other housing studies.

Baths – Same logic as above.

Sqft – Larger houses sell for more, duh.

Yearsagobuilt – Newer homes sell for more – inherently they look better, have higher tech construction, less deferred maintenance problems, etc.

Overprice – In a buyers market, overpricing means you get less offers, which should reduce the price you get for the property… Asking too much means you artificially decrease demand away from yoru actual “willing to sell” point.

Fixed Effects – some communities are gated or have great views or better layouts or have a slightly better location or were more upgraded at initial construction by the builder. It is hard to know the specifics, but a general fixed effects model can capture some of these unobservable differences. I put condos that had 7 or more sales in the same development into a group, and all others into the omitted category. 35 out of 99 condos were in this omitted category.

Regression 1, 99 condos: In this regression, I noticed that the regression was mediocre (borderline p-values on good predictors, though same signs and all that) unless I added in daysagosquared, which crudely captured an accelerating pricing trend with time. With it, the predictor coefficients became VERY good (daysago t-stat went from 1.98 to 4.48 for example).

Unfortunately, this regression was also highly multi-collinear between daysago and daysagosquared, though they tested for P<.01% for joint significance. Nonetheless, we don’t really care what houses went for in feb and march that much, so I decided to trim the data to get around having to use daysagosquared in future regressions.

Regression 2, 99 condos: This is pretty much the same regression, but I added in fixed effects as a robustness check and also to fish and see if any developments were obviously better or worse. Not useful for answering the question we all want answered, which is, what are houses worth today, but interesting nonetheless! It seems that windflower is worth 19k more (p <.1%). Nothing else could pass the null hypothesis, so I decided that, especially since I’m cutting data which would further strain it, I may as well dump fixed effects for this regression. Alas.

Regresion 3, 77 condos (Suggested): This is the regression I’m basing the majority of my conclusions on, and I think it is the best one in terms of predicting what you’d pay RIGHT NOW. The one variable that had weaker significance, baths, is one that we know does in fact have real-world significance, so I felt comfortable leaving it in with p=10.2%. Besides, the purpose of this analysis is to track price decrease more than anything, so its not doing much harm sitting in there adding a little bit of predictive power.

Regression 4, 37 condos: I trimmed the data to the last 6 months in this regression. My model started to get unstable from lack of data at this point. Among other things, beds and baths went deep into insignificance, and their predictive power appears to have gotten sucked up by other variables, especially sqft (the strongest predictor). It’s hard to make a good comparison of this to regression 3, though the general trends predicted in 3 are also predicted here.

Regression 5, 77 condos: this is for those of you who are skeptical about the overprice variable for a variety of reasons. I encourage you to think those through carefully and consider what it would mean for the variable to have different strengths, but I included this in case you consider it invalid. The regression is reasonably useful without the variable, you just drop R^2 a lot, and reach the same conclusions on the price trend.

How ‘bout those Stata Logs

Fine fine… Here is regression #3

Linear regression Number of obs = 77

F( 6, 70) = 8.52

Prob > F = 0.0000

R-squared = 0.5250

Root MSE = 18832

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| Robust

soldprice | Coef. Std. Err. t P>|t| [95% Conf. Interval]

————-+—————————————————————-

daysago | 123.4795 35.79553 3.45 0.001 52.08754 194.8714

bed | 15255.4 7203.995 2.12 0.038 887.4886 29623.32

bth | 17246.57 9697.281 1.78 0.080 -2094.052 36587.18

sqft | 88.41503 25.2406 3.50 0.001 38.07424 138.7558

yearsagobu~t | -1690.508 430.1378 -3.93 0.000 -2548.391 -832.6255

overprice | -.8145235 .2480114 -3.28 0.002 -1.309167 -.3198804

_cons | 259806.1 33764.72 7.69 0.000 192464.4 327147.7

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Aliso Viejo Analysis — Link to Word Document


PNG file regression analysis

Home Sales Data thru 11-5-2007

Median sale price

Sales volume

ZIP

code

Prev. 4 weeks

change

from ’06

Prev. 4 weeks

change

from ’06

92602

$717,500

-6.2%

15

-42.3%

92603

$1,620,000

101.2%

12

-57.1%

92604

$550,000

-14.1%

14

-53.3%

92606

$567,000

-26.7%

11

-50.0%

92612

$575,000

9.0%

22

69.2%

92614

$685,000

-0.7%

16

-30.4%

92618

$615,000

9.8%

9

-35.7%

92620

$650,500

-15.6%

30

-38.8%