Last Downturn

[speedingpullet]

Probably because the Excel file is saved as an *.xls file.

If you have Word, import the file into a new document and save it as a *.doc file. Word and Excel are sister software packages, and so support each other’s file types.

[Anonymous]

Thanks for the tip, SP, but that didn’t work for me: I pasted the chart as a picture in Word, saved the document as a Word document, and attempted to upload it; no joy.

[ocbuyer]

Interesting discussion. I feel that being comfortable with a “buy range” or overall bottoming out or leveling off is what people should try to predict.

Here’s why – they need to know how much cash they should have on hand. I think that’s going to be the toughest part of this downturn. Getting cash.

Laugh if you want, say “duh, that’s the toughest part of ANY recession.” But i want you to think about this:

Credit Debt = all time high
Home Ownership = all time high
Cost of Living = all time high
Consumer Expectations = all time high
Graduate Debt = all time high

The institutional lenders are going to get their asses kicked by the international markets and the government regulators (while losing their tail on hard money RE investments too). At the same time the interest rates will begin to rise again to stave off inflation. All the while, people own more homes, have more debt and are less liquid than they were during the last recession.

(BTW If you want a real comparison to what we have now i.e. gas and economic climate, international relations, etc. Look to the 70’s and see what sort of mess we became when the gvmt. got it’s hands involved. which consumers will demand again)

CIF as my pops calls it Cash In Fist – will rule the day.

Will you be ready?

[PerryChase]

jp, you need to create a .jpg file out of graph. Do alt-PrtScn then paste onto an image application, save the file at .jpg then upload.

[no_such_reality]

The institutional lenders are going to get their asses kicked by the international markets and the government regulators

Really, right now, the international market can’t buy enough subprime mortgages.

[rocketman]

I prefer to use Snagit by Techsmith Link for transfering any image off your desktop to a .jpg file (charts, excel files, etc). I’ve used it here on several posts. You can even capture video. I recommend it very highly. There is a Trial copy you can download.

[powayseller]

The charts I referred to in Campbell’s book were for the San Diego market.

[Anonymous]

Very helpful, Perry and Rocketman; thanks!

[img_assist|nid=1478|
title=Volume vs. Price, San Diego Resale Homes, 1988-2003|desc=Very helpful, Perry and Rocketman! Here’s the belated graph. I look forward to your read of the data. Pink is annual sales volume of San Diego resale homes from Robert Campbell’s book. Blue is the OFHEO price index data for San Diego homes.|link=node|align=left|width=400|height=219]

[technovelist]

As the guy who was falling off a tall building said when he passed the 30th floor, “Everything’s fine so far.”

[powayseller]

This is what I think: Campbell is missing the inventory part of this. He’s got sales, he’s got price, but he doesn’t have inventory. So he generates a buy signal in January 1996, but then generates a false Sell signal in 12/01, which turns again into a Buy signal in January 03. However, I can’t criticize his approach too much, because it’s the only published buy-sell method I’ve seen, so he sure gets credit for coming up with a system.

I’m surprised the OFHEO price index is fairly flat. In Campbell’s book, he says prices rose 150% from 1982 to 1990, and fell 30-40% untkil 1996. But the blue line doesn’t show that.

Why do sales decrease from 1999 to 2001? This surprises me,because I bought my house during that time, and the market was HOT. People were making offers on homes without even having seen them. Realtors had to check the MLS every hour. Once, my realtor called me to tell me about a house that had just been listed a few minutes before. I rushed over to see it, and as I arrived, darn it, a couple was already there. I asked them if they were looking at it, and if I could join them and see it too “Oh no, we’ve already made an offer”, they said. That is how it was. Houses were sold within hours of being on the MLS. The house we ended up buying had just been put on the MLS a few hours prior to our offer, and there were several backup offers going late into a Friday evening. It was absolutely crazy back then.

A friend told me the night he listed his house, a couple came over at midnight and woke him, to make an offer on his house; they had not even seen it. He sent them away, and called his agent at 8am ruefully the next morning. “Don’t worry,” said the agent, “I’ve got 4 other people on their way over to look at it”. So how could sales be down in that time period?

[Anonymous]

I put a call into DataQuick to find out the price to purchase:

1. Monthly sales of resale homes and median prices of such for San Diego county.
2. Monthly sales of resale homes and median prices of such for La Jolla.

If it’s not too expensive, I’ll buy the data and rerun the graphs.

Then, I’m going to attempt to use my dated experience with times-series analysis to see if regressing price on Campbell’s dependent variables leads to a simpler model. Graphing some of Campbell’s data, it’s hard to believe that notices of disclosure and foreclosure sales are not colinear, and that you can use one or the other, instead of both.

Actually, the best approach would be to turn the data over to a UCSD Ph.D. (it’s a world-class econometrics school) for analysis. Anybody have any good contacts to a good UCSD econometrician?

[sdduuuude]

All this talk of timing and cyclical markets led my mind to this graph, posted in another link:

Historical Prices.

While I see two cyclical events in the 80s and 90s, then major wierdness in 2001, I’m not so sure I see a cyclical pattern here that I can assume will continue perpetually.

Trying to predict the bottom of something that isn’t consistently cyclical is a bit of a challenge, methinks.

Look at Japan for the last 13 years, for example, after their last crash. Maybe we will see more stability after the coming bubble deflation, and there won’t be a stark “bottom.”

Maybe SD data is more cyclical, if one looks back more than two cycles, but I have only seen two cycles plotted.

D

[Anonymous]

PS, the OFHEO data has its weaknesses, as you know: it only tracks the prices of resale homes, not new homes, and is limited to homes financed via conforming mortgages, i.e., relatively inexpensive homes (it does not track homes financed via jumbo mortgages). However, the OFHEO series is particularly valuable as it gives a clean index of price changes, and is less susceptible to reflecting price increases due solely to increases in sizes of homes in recent years.

I’m not sure what price data Campbell used, as he didn’t reveal such in his book, per memory; I assume that he used DataQuick median sales price, not the OFHEO price index. And, his data is monthly; I used annual data, which smooths the peaks and valleys (but eliminates the horrid seasonality of the monthly data). So, that could account for the differences.

[powayseller]

OFHEO data ignores the tremendous amount of dollars put into kitchen remodels, additions, pools and backyard barbeques, and myriad other contracting work on the house. A kitchen remodel typically costs $60K, so it could increase the value of a $600K house by 10%. Thus, the OFHEO data is somewhat overstating the changes in price.

Campbell used DataQuick for Existing Home Sales, Construction Research Industry Board for New Building Permits, the SD Cty Recorders Office for NODs and Foreclosure Sales. The appendix of his book lists this data from 1988 – 2004 in 4 tables. Table 5 is interest rates.

[Chris Johnston]

Chris Johnston
iamafuturestrader.com

JG – I asked him if he had done any regression analysis and he told he that he had considered it but did not have to time to do it. I was mostly interested in how many std dev’s some of these things in his model were from a regression line.

[Author]

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