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Anonymous
Anonymous
16 years ago

I’d be very interested in
I’d be very interested in what you find out about Bay Park, 92110.

Bugs
16 years ago
Reply to  Anonymous

It might be cumbersome to
It might be cumbersome to do, but breaking the different zip areas into their 3 or 4 primary market segments would give you a detailed view of what they were doing.

Generally speaking, most of the development occurs during the boom periods with only some clean up construction occuring during the busts. Each successive boom period introduced larger homes and more features. By breaking all the data into the 3 or 4 different cycles during which they were built you’d be comparing roughly similar sizes, similar designs and floorplans, and similar overall appeal.

It would be a hassle to set up but once you got it going maintaining it would be a snap. It’s the kind of analysis that could be extended indefinitely, and would be real helpful in identifying emerging trends and future opportunities. You’d be able to watch a trend start on the west side and sweep eastward or vice versa, and pick an area for investment prior to the trend getting there.

Anonymous
Anonymous
16 years ago
Reply to  Rich Toscano

Rich, thanks for the reply.
Rich, thanks for the reply. I can understand not doing 92110. I recently had my house in Bay Park appraised and the appraiser’s computer program claimed the darn thing had appreciated 30% in the last 12 months! That seems like madness to me.

Mr. Drysdale
16 years ago

Anyone know more about
Anyone know more about this?

As of 11-15-2007, there will be a new tool for figuring out how much toxic waste is in investment banks’ balance sheets. The new US accounting rule SFAS157 requires banks to divide their tradable assets into three “levels” according to how easy it is to get a market price for them.

This kind of full disclosure will reveal just how bad things really are “/

sjk
sjk
16 years ago
Reply to  Mr. Drysdale

It’s my understanding
It’s my understanding there calling it “level three accounting” and it will be bad news for financial stocks. Most of them use the “mark to whatever we say it’s worth model” not the market price.

Gold well into four digits is coming……………..

trex
16 years ago

Statistics to the Rescue
Two

Statistics to the Rescue

Two suggestions. Try running a regression on medians or median/sf by zip code, something like:

MedianPrice2007 = a + b*MedianPrice2006 + c*MedianPrice2006^2

Or, if you think there is a specific cutoff, you can assume is is BLAH (maybe $417k), and write:

MedianPrice2007 = a + b*MedianPrice2006 + c*LessThan417 + d*LessThan417*MedianPrice2006

Or you can ask a computer to find that cutoff using a method called maximum likelihood.

Free software that does all of this is at http://cran.r-project.org/

Or I can run it for you.

robson
16 years ago
Reply to  trex

SANDAG has lots of detailed
SANDAG has lots of detailed current information on a ZIP code level including factors like median household income, population, available housing units (broken up between single and multi-family), occupied housing units (single and multi seperated), and # of people per household. This can be found at http://datawarehouse.sandag.org/
In addition to this, if you want even more detailed data on specific ZIP codes, http://profilewarehouse.sandag.org/ has data on median age, ethnicity, and more detailed household income.
It might be interesting to see if you could identify 2 ZIPs with similar median values but different incomes and see if this makes a difference as prices change (higher income ZIP holds up better?) You could also test other variables this way like vacancy rate or persons per household. This could essentially end up as a regression like trex mentioned but with more variables and greater predictive ability.
One last thing that’s nice about SANDAG’s data is they update it annually. The bad thing is it’s an estimate based on the census, which can lead to errors in smaller areas like ZIPs.

LostCat
16 years ago
Reply to  robson

SANDAG BLAAAAAAAAAAAAAAA

SANDAG BLAAAAAAAAAAAAAAA

LostCat
16 years ago
Reply to  robson

SANDAG BLAAAAAAAAAAAAAAA

SANDAG BLAAAAAAAAAAAAAAA

NotCranky
16 years ago
Reply to  LostCat

Hi Rich,
I wonder if it

Hi Rich,
I wonder if it would be useful to have historical charts for appreciation on a price per square foot basis to compare with the down cycle data for each of the zip codes you select? It seems like you are saying the higher end markets and lower end markets appreciated for different reasons? The lower end because of easy credit. Why did the higher end appreciate then? Maybe it will take a different catalysts to cause weakness in the higher end? A lagging catalyst. Perhaps more economy driven than consumer credit driven? I do know one theory says appreciation allowed for a chain of move ups shifting demand up the scale. You also imply an economy where money is really made instead or borrowed, or borrowed from one’s other assets, being a larger contributing factor in the higher price range. How and when does must sell inventory hit this “second market”, which is supported by wealth? What would make prospective buyers unwilling or unable to pay current prices for it?

greekfire
16 years ago

I am not sure if this will

I am not sure if this will help or add to the confusion, but evaluating data in a spatial context can really help you get to the lowest common denominator. The two main analysis levels that you will want to focus on, at least as I see it, are either zip code or parcel. Most of the broader economic analyses produced by other parties appear to be done on a zip code level.

The shortcoming of this is that there are often sub-areas within zip codes that perform better or worse than others (e.g., 92008 zip code is vastly different depending on whether the property is west-of or east-of Interstate 5). Focusing on the parcel level might initially appear to be more work than it's worth, but once the base structure is set, it's just a matter of updating and table-joining APN's. The best analogy I can think of for the parcel level analysis would be Zillow's Heat Maps. I also think using this methodology would be more consistent with a measurement such as Case-Shiller's Home Price Index, which tracks existing home sale price changes.

[img_assist|nid=5392|title=|desc=|link=node|align=middle|width=466|height=337]

Rather than tracking a zip code number, you are tracking geographical areas (individual parcels). What's even more interesting is that using spatial analysis you can overlay other layers of data, such as sales volume changes, and compare your results. I could go on and on. My main point (finally) is that you might be better off using maps to help get your ideas across to the public. Run this by Vlad and he might help give you some more insight on how to do this.

NotCranky
16 years ago
Reply to  Rich Toscano

Hello Rich,
The historical

Hello Rich,
The historical picture for price per square foot by zip codes can be derived from the MLS excepting those transactions that didn’t go through that system. It would be tedious project. I also don’t know the protocol for using that data to derive and publicly post graphs.I am sure you know that though.
I would think there is another source wherein price, size, zip code, and close of escrow date are accumulated in a more handy format. Perhaps there is another subscription data service or a publicly available compilation from the tax assessor which would have it?