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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
——————————————————————————
| 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
——————————————————————————
Aliso Viejo Analysis — Link to Word Document
IR,
Thanks for the easy to follow, early morning, Sunday analysis. I, like many others, enjoy your writing.
Meanwhile, we all watch as the carnage on the street gets worse by the day.
Next year will bring some real fireworks to the show.
—–
$123 a day. It’s cheaper to stay at the Motel 6 while you wait it out!
Oops, this is missing regresion #5’s table, I’ve gone ahead and summarized it here:
Dataset: last 9 months (Same as regression 3)
Daysago Coeficient: 149, Pvalue: .1%
Daysago^2 — not used
Baths: 22000, P value 5.7%
beds: 17300, p-value 5.9%
Sqft: 86.1, p -value .1%
Yearsagobuilt: -1640, p-value .1%
Fixed effects: no
Adjusted R^2: 43.51%
Overprice — Not used in this regression
Definitely looking forward to math comments — not sure exactly how to interpret “overprice”, I think I’m on to something, but wanted to include regression 5 without overprice in case someone had a convincing argument of why not to use.
oh right, regression 4’s R^2 was 66.88%, in case you are curious. Not listed in that PNG, but it is in the word doc 🙂
Thanks for the analysis. I’m right there with you in thinking the situation could be even worse in Aliso than the numbers say. Many, many properties have taken huge hits in value (it’s not too easy to find a property that sold in ’04 and later for sale now that’s not asking below what they paid) and things aren’t slowing down.
Interesting listing I saw in Aliso was for a 2 bed, 2.5 bath detached condo originally asking $595k – said “Perfect for roommates.” Yeah, 2 people who each make about $75k and each have lots of cash for a downpayment are going to be OK with picking up a roommate and moving in together to split a mortgage. Not at that price. The asking price is now down to $569,500.
I wish I could claim credit, but this is Zileas’s work.
Yeah, I dont know how expensive detached vs non-detached condos are, but that sounds way high.
I really enjoy this whole blog and I have sent the link to many people interested in RE issues. I find it thoughtful, informative and sharp.
I was happy to read about Aliso Viejo properties today for variety.
Santa Ana has got some real wacky RE dimensions right now. It is classified as the subprime meltdown epicenter – and you can see the effects on inner city streets like Camille where the majority of the homes were in some state of re possession/forclosure/bank owned just 6 months ago, per a OCRegister article. The city leaders are pushing for “more development!” even as yesterday’s OCRegister article explains that the expensive live/work lofts across from MainPlace Mall have only sold 70/185 units to people willing to plunk down a half a million bucks to live in SANTA ANA.
Santa Ana leaders just recently repainted the big water tower that claimed the city was committed to “Education First!” – which was a good thing, b/c in reality there are not too many districts in the state with lower test scores than Santa Ana. Now the sign claims that SA is “Downtown Orange County”. Good grief.
I love the Irvine focus of this blog and it is intersting to read about neighboring cities, too
Thank you very much for the analysis!
It is interesting to note how well the whole range of your data fit within the range of statistical analysis….. meaning that housing is basically behaving like a market, with market forces being the primary drivers of valuation.
When I was going to school to get my Real Estate license, I remember so many of the smug students talking about their “genius” real estate moves… pontificating about “curb appeal” and what have you as the reason for their success.
Even on television, everyone making any kind of money in Real Estate pretty much saw themselves as uber-intelligent financial geniuses. “I buy a house, I fix it up, and I sell it for more…… next I will proceed to cure cancer.”
Anyway, if housing is statistically demonstrating that housing prices are more affected by macro economic forces rather than individually, then I think it is a fair assumption that the same held true when the market was going up. These people were making money at a time when ANYONE with a name, a social security number and a pen could make money by signing on the dotted line.
So much for “Curb Appeal”.
Yep.
Using this model, when you dial in a few variables like sqft, beds, baths, year built, and they happen to be near the data range, the ranges it allows are about +/-6 or 7%, a full 95% of the time… There are certainly extraordinary houses with incredible views, the best interior upgrades ever, and fantastic curb appeal will fall outside this range, but it is the exception to the rule.
If anyone wants analysis on another zip code or price band, here’s what you do:
1) Ask your agent for a BUNCH of data from MLS. Ask for like all sales in a certain band of interest to you in a specific zip code — so if you were curious in Irvine you might ask for 92656 zip code data.
2) When you ask for the data, be sure to specify a fairly narrow band — vary the square footage by +/- 25% or so (i.e. 1200-1900 sqft), keep the beds within 2 on either end (i.e. 2 to 4 beds), same with baths… then try to keep it all the same type of home, unless you are getting me like 200+ data points, then you could mix condo + SFH.
3) Try to get me one of those MLS CMA client pages, those are easy to deal with since I can just copy the table.
4) Last 9 months is good, so is last 6 months, but the most important thing is as many data points as possible. MLS caps at 250 per report, so that is a good #, though anything over 50 is going to be enough for me to do SOMETHING with.
oh and send ths to Irvine Renter (the CMA link or data table), then he/she will get it to me, and ill run analysis on it.
I wonder how much the data reflect the last 2 months since the “credit crunch” worsened in August. I know in a couple of the zip codes that I follow via Catalist’s website, NO homes in any price range have closed escrow in over 2 months. In one of the zip codes, asking prices have dropped nearly $70K in the last couple of months. However, since no homes have closed escrow (at least through the MLS), any average prices for the areas will not reflect the decline in asking prices.
That means the market prices are lower. Err, maybe with no sales, it means there is at the moment, no market at all.
You would think the Register or Times would pick up on these analyses and publish them!
Conclusions which impact locals, backed by FACTS – the duty of a newspaper to report. And I’m sure for a nominal fee, IHB would let them use it.
But the papers probably don’t want to offend the people that have been feeding them lots of advertising revenue cyclically through each boom. However, with the changing landscape of advertising, I’m sure the owners are pondering whether it would be better to offend their previous benefactors by not pussy-footing around the truth and gain a larger readership in the process.
A singularity!!
If your definition of downtown is the most run down place with the most homeless you can find then Santa Ana can indeed claim the dubious title of “the downtown of OC”.
I always thought that the OC Downtown was at Disneyland, between the Disneyland Hotel and the entrance to the Parks. I much prefer going to that downtown that SA. Of course, SA is not as bad as LA proper. You can actually walk around SA during the day time and it does have a bunch of intriguing restaurants IFF you speak some dialect of Spanish.
Of course, speaking of OC Downtowns, I much prefer the Circle at the City of Orange. Felix, the cuban restaurant, is a treat. And after dinner you can stroll to the park in the middle of the plaza and digest the empanadas and platains.
Statistical analysis works well when the type, age and shape of homes in the are of study are uniform. For example, condos, attached homes and SFH neighborhoods less than 10 years old ( I chose 10 as a WAG ).
This type of analysis starts to break down when you look into older SFH neighborhoods. Houses with the time and potential to have been significantly altered over the years, just maintained or been allowed to deteriorate.
In older SFH neighborhoods you will see some run down rentals, a lot of renovations, complex rebuilds and complete teardowns. At that point, statistical analysis starts to get flaky because there will at least four “focii of stability”: old homes, fixed up homes, rebuilt homes and tear downs.
So, instead of seeing a few outriggers as the with the former group, in older SFH areas you will have a problem coming up with a clean mean unless you allow for at least three ( or four ) groupings.
Yeah, definitely a concern, and why i constrained it to attached condos of 2-3 bed and 1000-1400 sqft. I was able to get the variables test at very high significance in the areas we care a lot about, and the standard errors of prediction on some samples ended up being pretty small (95% confidence intervals at +/- 5% or 6%). I would have the same concern if I was running on SFH not in a highly constrained HOA.
Totally valid question. Because I have fewer data points in sales recently, those sales are “underweighted” relative to the abundance in the summer — no market at all doesnt get measured. This data proves a certain severity level, I think theres a case, with approaches like yours, to be made for things being even worse.
When i get home ill give you specific # of sales in the data i used from the past 3 months.
Zileas and House of Cards, I am looking for a 2 bedroom condo in Aliso Viejo or a neighboring city. This afternoon, I viewed a decent 2 + 2 + garage + carport REO condo in Laguna Niguel offered by the bank for $299K. The (intelligent and articulate) agent explained that even recent comps are nearly irrelevant, because prices have dropped so dramatically recently. She said prices appear to be continuing to drop right now at a rate of about 3% per month.
I have noticed that there are small pockets of older (pre-1980) condos in Aliso Viejo, Lake Forest, Mission Viejo, San Juan Capistrano and maybe other cities in South Orange County that are very distinctly Latino and appear to be stand-out poorer areas within a sea of middle class residences. When you search for the cheapest prices for a given number of bedrooms, bathrooms, sq ft or other feature, condos in these little pockets always show up first, usually for much less than anything in the surrounding areas. None of the databases allows you to include micro-area crime rates, income, or ethnic backgrounds as part of a search, but these do make a big difference in price at the bottom end.
I don’t like the idea of using ethnic backgrounds, but I wish the search engines would allow you to easily incoporate some micro-level income or education or crime stats directly in the search criteria. Anyone close enough to ZipRealty to put a bug in their ear?
Instead of using ethnicity, I think the filter should be economic.
That is, measuring the median INCOME at a micro level will make the measurement more meaningful and removes any possibility of “ethnic prejudice” from the equation.
Just make sure though to keep track of the gypsies though. As I noted earlier, fringe groups like “Reformed Zoroastrians” are fine with me, but not gypsies. No way, sorry. They’ll steal the toilet, look at you with their evil eye, curse your first born for seven generations and damn your property values for two generations.
The Times writes to a 9th grade reading level, and the Register, I don’t think really has standards, so this stuff would be way over people’s heads.
Tonye, off-topic, but as it happens I grew up in a part of the world with a small high-profile Romani population with an old and distinctive tradition. I wouldn’t mind if that tradition, with the legal protections it had, were present here in Southern California, but I know it would drive 99% of the homeowners here nuts.
As a person who lives in a foreign country to wit: Miami, I fail to understand why one would want to live away from Hispanics. In Miami, it isn’t possible to do so, and Cubans are nice and most of them are middle class and lots of them are rich. The other Hispanics came here to get rich or at least better off and are doing so at a reasonable rate.
What’s the deal?
To the answer about how many RECENT sales were used, only 14 of the 77 were in the past 90 days.
I went ahead and re-ran this analysis with a weighted least squares method, where weights per month were [average # of sales per month] / [# of sales in that particular month].
What this does is gives equal weighting to “slow” months as “fast” months, and gives slightly different results.
I’m pretty sure this is technically valid to do, so thank you for the suggestion. I went ahead and ran it by one of my old profs who is an expert on this specific type of issue as it turns out since he regresses markets a lot… we will see what he says.
The results I got are quite similar though, interestingly. What I saw was most of the variables being about the same — rate of decrease went incrementally weaker, the reliability of the beds/baths variables got bad (no surprise there), and the effect of people overcharging for their house and losing money for it got worse (also no surprise). I was a bit surprised though that the rate of decrease was largely unchanged…
[data — final 9 months]
Source | SS df MS Number of obs = 77
————-+—————————— F( 6, 70) = 12.16
Model | 2.6830e+10 6 4.4717e+09 Prob > F = 0.0000
Residual | 2.5751e+10 70 367865174 R-squared = 0.5103
————-+—————————— Adj R-squared = 0.4683
Total | 5.2581e+10 76 691853609 Root MSE = 19180
——————————————————————————
soldprice | Coef. Std. Err. t P>|t| [95% Conf. Interval]
————-+—————————————————————-
yearsagobu~t | -1703.167 414.7109 -4.11 0.000 -2530.282 -876.052
overprice | -.9800076 .2397532 -4.09 0.000 -1.45818 -.5018349
daysago | 118.8113 34.83972 3.41 0.001 49.32567 188.2969
bed | 7663.9 8551.082 0.90 0.373 -9390.695 24718.5
bth | 13067.5 8383.948 1.56 0.124 -3653.754 29788.76
sqft | 94.65238 29.81042 3.18 0.002 35.19737 154.1074
_cons | 280084.7 30154.41 9.29 0.000 219943.6 340225.8
——————————————————————————
I repeated the same thing without overprice and got:
[data — final 9 months]
(sum of wgt is 7.7000e+01)
Source | SS df MS Number of obs = 77
————-+—————————— F( 5, 71) = 9.21
Model | 2.0684e+10 5 4.1368e+09 Prob > F = 0.0000
Residual | 3.1897e+10 71 449252573 R-squared = 0.3934
————-+—————————— Adj R-squared = 0.3507
Total | 5.2581e+10 76 691853609 Root MSE = 21196
——————————————————————————
soldprice | Coef. Std. Err. t P>|t| [95% Conf. Interval]
————-+—————————————————————-
yearsagobu~t | -1530.838 455.922 -3.36 0.001 -2439.92 -621.7549
daysago | 142.326 37.97283 3.75 0.000 66.61029 218.0416
bed | 7177.312 9448.871 0.76 0.450 -11663.2 26017.83
bth | 18313.92 9155.872 2.00 0.049 57.62677 36570.21
sqft | 89.16838 32.91007 2.71 0.008 23.54756 154.7892
_cons | 261333 32935.71 7.93 0.000 195661.1 327004.9
——————————————————————————
So pretty much the same, weighting didnt impact the results much, so I guess it is somewhat steady in decrease. But yeah, selling at all IS harder now 🙂
The pecking order is: Catalans, Castilians, Basques, the other Spaniards, Cubans, Argentinians and the rest. ( OK that was in jest…. ).
In SoCal we are stuck with the “rest”. And many of them are not particularly rich. Also, we have a subculture of “chicanos”. These are not first gen, but from families that have been in the US for several generations.
The “Chicanos” have fallen to the racial and ethnic political dependency that many african americans have.
Now then, the vast majority of mexican-americans, mexicans and other central americans are very hard working and moving their way up.
But there’s that small group of “chicanos” that gives all the “latinos” a bad name.
I think the other thing people worry about is a nearby 2 or 3 bedroom apartment housing 10+ illegal aliens, which transforms the neighborhood into “low income housing”… or worse yet, someone renting out their condo in this way and doing the same thing.
The issue there is that low income means low income — you see worse cars in the parking lot reducing the perception of affluence of the neighborhood, and hence property values, you get more people using the community resources (also not good, no one likes crowding), and you get more people sending their kids to the local schools who aren’t necessarily invested in the community.
Add to that a group of people who often are not interested in becoming Americans. Plus, no one really wants a next door neighbor that they cant communicate with, or who has dramatically different cultural protocol of how to handle various situations.
Plus, where there is poverty, there is crime regardless of the race involved. A lot more people are uncomfortable living by an overcrowded house that has become low income housing than by , and that’s what drives the property value damage.
I think that’s what people worry about anyhow…
I should also add that the vast majority of african americans are also very hard working.
As usual, it’s the loud minority and the Hollywood machine that screw things around.
In the meantime, I’m just waiting for Raul to die off too so that we can get legal havanos in Nevada smoke shops.
The day that happens I’ll drink a nice Cuba Libre… just as we drank bottles of Catalan Cava (Codorniu) the day El Caudillo passed away.