China, conservatism, econome-tricks, and land bubbles.

1.  China’s trade deficit.

Lots of people have linked to the most recent trade data from China, which show a deficit in March.  One month may not be that important, as China is expected to swing back into surplus.  But buried in the report is this almost mind-boggling statistic:

China’s exports totaled $112.11 billion in March, up 24.3 percent from a year earlier. Imports reached $119.35 billion, up 66 percent compared to the same period last year, the Customs Administration said in data posted on its Web site.

That’s right, those “mercantilist” Chinese just increased their imports by 66% in the midst of the worst worldwide recession since the 1930s.  China generally has little effect on the US business cycle.   But to the extent that it does have an influence, its impact since March 2009 has been strongly expansionary.  March 2009 was when the big worldwide stock market rally began.   And this tidbit from the FT suggests that China was under pressure to devalue the yuan back in the dark days of early 2009.  They didn’t devalue.

Renminbi

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
2.  Brink Lindsey on the decline of modern conservatism
 
I though this diavlog was exactly right.
      
    
3.  A minority of doubters
 
Here’s how physicists view econometric studies of the macroeconomy:
   
Suppose you have many years’ worth of figures on a large number of economic indices, including inflation, employment and stock market prices. You look for cause-and-effect relationships between them. Bouchaud and his colleagues have shown that even if these variables are all fluctuating randomly, the largest observed correlation will be large enough to seem significant.

This is known as the “curse of dimensionality”. It means that while a large amount of information makes it easy to study everything, it also makes it easy to find meaningless patterns. That’s where the random-matrix approach comes in, to separate what is meaningful from what is nonsense.

In the late 1960s, Ukrainian mathematicians Vladimir Marcenko and Leonid Pastur derived a fundamental mathematical result describing the key properties of very large, random matrices. Their result allows you to calculate how much correlation between data sets you should expect to find simply by chance. This makes it possible to distinguish truly special cases from chance accidents. The strengths of these correlations are the equivalent of the nuclear energy levels in Wigner’s original work.

Bouchaud’s team has now shown how this idea throws doubt on the trustworthiness of many economic predictions, especially those claiming to look many months ahead. Such predictions are, of course, the bread and butter of economic institutions. But can we believe them?

To find out, Bouchaud and his colleagues looked at how well US inflation rates could be explained by a wide range of economic indicators, such as industrial production, retail sales, consumer and producer confidence, interest rates and oil prices.

Using figures from 1983 to 2005, they first calculated all the possible correlations among the data. They found what seem to be significant results – apparent patterns showing how changes in economic indicators at one moment lead to changes in inflation the next. To the unwary observer, this makes it look as if inflation can be predicted with confidence.

But when Bouchaud’s team applied Marcenko’s and Pastur’s mathematics, they got a surprise. They found that only a few of these apparent correlations can be considered real, in the sense that they really stood out from what would be expected by chance alone. Their results show that inflation is predictable only one month in advance. Look ahead two months and the mathematics shows no predictability at all. “Adding more data just doesn’t lead to more predictability as some economists would hope,” says Bouchaud.

In recent years, some economists have begun to express doubts over predictions made from huge volumes of data, but they are in the minority. Most embrace the idea that more measurements mean better predictive abilities. That might be an illusion, and random matrix theory could be the tool to separate what is real and what is not.

I’m in that minority of doubters.

4.  Phoenix = Hong Kong

Ryan Avent discusses an interesting hypothesis (by Gupta and Miller) on why some sunbelt markets saw big bubbles, while others did not.

Thinking of the bubble as a Sunbelt phenomenon is a bad idea because it’s not correct, but also because it generates confusion over what characteristics were important in driving bubble inflation. So it’s important to note that outside of the Sunbelt, there were many other bubble markets, primarily on the east and west coasts—San Francisco, Portland, and Seattle, New York and Boston. What these markets all have in common, and have in common with Los Angeles and Washington, is that housing supply is relatively limited. So what emerged in these markets, initially, was a healthy price signal. This, incidentally, is how basically every bubble begins: with a healthy price signal. Demand for these coastal markets was high and rising, and housing supply was not keeping up. Therefore, prices rose. The bubble took shape thereafter, as rising prices combined with growing enthusiasm and rapid credit expansion, which fueled the growth of a bubble mentality.

Now, as prices rose, some housing demand shifted to other markets with strong local economies, including Phoenix, Atlanta, and Dallas. These markets tend to have very elastic housing supply, and so price increases translated into rapid construction, which prevented prices from rising and kept the bubble at bay.

Except that in Florida and the desert southwest, it didn’t. So has our housing supply model failed?

Not necessarily. As it turns out, you can “catch” a bubble from elsewhere. Migration to Las Vegas and Phoenix came overwhelmingly from Southern California. Residents of Los Angeles would cash out their homes and move east, buying one or two properties in cheaper markets, investing in those properties, and generally transmitting the bubble mentality that characterised the real estate markets of the California coast. Analysis of price movements has identified ripple effects from the Los Angeles property market to the Las Vegas property market, and thence on to the Phoenix property market. It seems likely that a similar phenomenon took place in Florida, which absorbed a great deal of migration from bubbly northeastern markets.

These “caught” bubbles were incredibly damaging, because they combined rapidly rising prices with rapidly rising inventory, leading to massive housing overhangs and price declines up to and greater than 50% from peak. But other Sunbelt metropolitan areas managed to avoid them, perhaps because they absorbed more workers from declining markets elsewhere in the south or northeast or midwest. Housing supply growth then prevented any big initial increase in prices which might have led to the enthusiastic growth in credit that triggered bubbles elsewhere.

I think this might be part of the story, but it doesn’t seem to fully explain the difference between Dallas and Houston on the one hand, and Phoenix and Vegas on the other.  Consider this article from Demographia:

The Phoenix metropolitan area is sometimes erroneously characterized as having a responsive (traditional or liberal) land use market. In fact, the Phoenix market is highly prescriptive, as a result of the combination of strong land use regulations (“growth management”) and the large share of developable fringe land by the state of Arizona, which has been restricting sales to maximize revenues.

The state of Arizona owns a large share of the developable urban fringe land in the Phoenix urban area. The state has been auctioning land at a rate well below what the market could accommodate. This is illustrated by the large increase in prices per acre and in a comparison with agricultural land values.

 In 2002, the average auction price of urban land was $32.600. By 2006, which was the peak of the Phoenix housing bubble, urban land sales reached an average auction price of $190,800.1 Rising land prices are the principal element of house price escalation in the Phoenix area over the period. As median house prices have declined in Phoenix (median house prices declined 39 percent in the year ended November 2008),2 average auction prices fell back to $68,600 in 2008.

Agricultural land in Maricopa County (the core county of the Phoenix metropolitan area) had a value per acre of approximately $8,500 according to the 2007 United States Census of Agriculture. Further, there was plenty of agricultural land, an amount in Maricopa County alone nearly equal to the entire urbanized land area of Phoenix in 2000. At the 2006 peak state auction prices, “raw”3 land was being sold at more than 20 times the value of agricultural land per acre. Moreover, the land ownership was highly decentralized, with nearly 1,800 farms. If “raw” agricultural land had been freely available for development, purchasers would not have paid such high prices for the land sold by the state.

.   .   . 

Prescriptive Land Use Regulation and Price Volatility:

Not only does prescriptive land use regulation artificially increase house prices, but it also makes prices more volatile. Prescriptive land use regulation brings more chaotic “boom and bust” cycles to housing markets. They convert what would have otherwise been modest price bubbles into extreme price bubbles.

And it seems like a similar pattern occured in Las Vegas.  Consider three types of housing markets. 

1.  Open markets like the central US, with lots of privately-owned, low price farmland that can be easily developed.

2.  Markets with very little available land, due to high population and geographic barriers.

3.  Markets with plenty of open land, but government development restrictions that cause the price of developable land to greatly exceed farmland prices.

Of course even in case 2 (say San Francisco and Manhattan) zoning rules prevent skyscapers from being built in many low-rise neighborhoods.  But Phoenix and Vegas are especially interesting cases.  It seems that zoning restrictions caused the bubble in land prices.  If there is any other explanation for why farmland sold for $8500 while developable land went for $191,000, I’d like to hear it.  If not, then it appears that Phoenix and Vegas have the same sort of housing market as Hong Kong, which also subject to unpredictable government land auctions and frequent real estate bubbles.

Even if lots of rich Californians and New Yorkers had moved to Texas, it is hard to see how housing prices could have risen anywhere nearly as sharply as in Phoenix.  Unlike the western US, almost all the land in Texas is privately-owned.  And zoning rules are pro-development.  A big housing bubble can only occur if developable land soars in value.  But how could that happen in Texas, where there are many thousands of privately-owned farms in close proximity to its major metro areas?

My hunch is that the migration patterns cited by Ryan Avent were a necessary condition for the extreme bubbles in Phoenix and Vegas, but not a sufficient condition.  Development restrictions were also necessary.  One weakness in my argument is that I know little about Florida.  Obviously land near the ocean is somewhat limited, but less so in flat Florida than in mountainous California.  So I am not sure what category Florida belongs in.  Does anyone know what the Florida land market looked like during the bubble years?

Just thinking out loud, how much smaller would the housing,I mean land bubble have been if local officials had auctioned land fast enough to prevent prices from rising significantly above nearby farmland?  How much smaller would the 2007 subprime crisis have been?


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39 Responses to “China, conservatism, econome-tricks, and land bubbles.”

  1. Gravatar of Doc Merlin Doc Merlin
    10. April 2010 at 16:05

    Also, lots of areas in Texas are unincorporated and thus free of zoning restrictions. And our largest city is also mostly free of zoning restrictions.

    1. I need to learn some random matrix theory, or at least how to apply it.
    2. I agree with Hayek and think that prices are a fundamental signal. As such, I think they should be difficult to predict, but should have lots of predictive power.

  2. Gravatar of Mark A. Sadowski Mark A. Sadowski
    10. April 2010 at 16:51

    Here in Delaware we’ve run out of land. My father unknowingly conducted a natural experiment. He bought two lots of the same size and quality in 1967, except one had a house. The lot with the house cost $29,000, the lot without cost $4500. Both lots were reappraised in 2007. The lot with a house was $320,000 and the lot without was $195,000. Thus the house had quintupled in value whereas the land had gone up 23 fold. As my father always liked to say, “they’re not making any more land.”

  3. Gravatar of Lord Lord
    10. April 2010 at 17:26

    The big limitation in the west is water. Most of these areas are already overdeveloped for available water supplies. Desalinization is being developed in coastal areas due to these problems.

  4. Gravatar of Ian Dew-Becker Ian Dew-Becker
    10. April 2010 at 18:16

    As to number 3, the literature on forecasting inflation long ago figured out that they couldn’t do it. Atkeson and Ohanian made this point in 2001. People spent a long time trying to overturn that result, and I think they’ve mainly come around to the view that inflation is unpredictable. Stock and Watson, who are about as mainstream as you can get (Harvard and Princeton), have multiple surveys making that point. So I don’t really see what the physicists are teaching us.

    Furthermore, this quote struck me as odd: “Most embrace the idea that more measurements mean better predictive abilities. That might be an illusion.” Just because we can’t currently predict something, why would we assume more data wouldn’t help us predict it? Random matrix theory says nothing about whether better data would be informative.

  5. Gravatar of David Tomlin David Tomlin
    10. April 2010 at 18:47

    It’s interesting that even liberal economist Paul Krugman acknowledged that land-use restrictions contributed to the housing bubble, back in 2005 when people were still debating whether there was a bubble.

    http://www.nytimes.com/2005/08/08/opinion/08krugman.html

    Thomas Sowell emphasizes the impact of federal land sale policy on the Las Vegas housing market in his book The Housing Boom and Bust (pp. 16-17).

  6. Gravatar of scott sumner scott sumner
    10. April 2010 at 18:57

    Doc Merlin, I agree with all your points (and I also need to learn random matrix theory.)

    Mark, Yes, that’s true in many parts of the country. House prices rise roughly with the general level of inflation (cost of construction.) Land prices often rise much faster in densely populated and desirable areas. Thus house plus land prices can rise faster than house prices alone. Interesting that when people talk of higher house prices, they usually mean higher land prices. I do this too, but it is very misleading terminology.

    Lord, Yes and no. Obviously water is a problem. But it is not so much the physical shortage of water, but rather the low prices charged to farmers (although this is changing in recent years.) If there was a free market for water the shortage would go away.

    I recall that when Arizona had less than one third of its current population there were constant news stories about how it was about to run out of water. In places like California they buy water from farmers when they really need it to expand the cities.

    Ian, Thanks for that useful info. It is very difficult for someone outside a field to come in and offer a meaningful contribution. The fields are now so specialized, and so highly competitive that all the low hanging fruit (i.e. good ideas) have been taken. I should have been more suspicious of their claim to originality.

    I do agree with their observation that studies that seem to have great statistical significance are often much less meaningful than they appear. Here I am thinking of all the “anomalies” in financial asset pricing. That’s what came to mind when I read the first few paragraphs. Knowing nothing about random matrix theory (or non-random matrix theory for that matter) I was wondering whether they were saying anything more than “data mining leads to spurious claims of statistical significance.”

  7. Gravatar of scott sumner scott sumner
    10. April 2010 at 19:05

    David Tomlin, Thanks for that info. I should add that both Krugman and I were wrong in assuming that Phoenix and Vegas fit into the “flatland” category. I had heard that there were some zoning issues near Vegas, but has no idea they were such a problem.

    In one post Krugman used the price run-up in Phoenix and Vegas as prima facie evidence that the market was irrational. It’s now clear that things weren’t quite that simple (which is not to say the market didn’t make a pretty embarrassing misjudgment.)

  8. Gravatar of Doc Merlin Doc Merlin
    10. April 2010 at 19:15

    @Scott
    Nevada is a mess, in part because the privately and state held land is very very small compared to the actual area of the state. The federal government auctioning off some of its land would really help the area out.

  9. Gravatar of Jon Jon
    10. April 2010 at 20:05

    That’s interesting about the inflation horizon. Perhaps that’s evidence that the FOMC does not really act effectively. i.e., the stance of monetary policy relative to the prior period is a random walk.

  10. Gravatar of Lorenzo from Oz Lorenzo from Oz
    10. April 2010 at 21:20

    Mark: I can see land should be increasing in scarcity as population goes up, but there is also the issue of control of which land can be used for what purposes. As other comments have noted. This is an area where one has to be careful about what local conditions actually are, especially regulatory conditions.

    And Scott’s point about the real issue being land prices is spot on.

    A great comparison is the UK, which has very strict zoning regulations, and Germany, which effectively has none (due to a constitutional “right to build” to protect citizens from arbitrary official power). This study of housing markets in (pdf) OECD countries includes a graph which makes the difference quite clear in effects on housing prices.

    In fact, it would be fascinating if anyone can point to a case of discretionary official control over market entry having positive effects.

    Here in Melbourne, a taxi plate is about the same price as median house prices due to exactly the same reason, discretionary control over market entry. It is so terribly important to “protect” us from “too many” taxis. Or “too many” houses.

  11. Gravatar of Doc Merlin Doc Merlin
    11. April 2010 at 00:19

    @Lorenzo:
    That is a very good point. We need to work to remove discretionary control from politicians and bureaucrats. I propose fighting to make every single type of state license into a “shall issue” license. (‘Shall issue means that if basic requirements set in advance are met, that the state cannot deny the licence/permit’) We may be able to do this through the courts. It would be a good case for the equal protection clause in the 14th amendment. If not, it would be a good case for legislation through that same clause.

  12. Gravatar of Tim Worstall Tim Worstall
    11. April 2010 at 00:33

    The UK housing market might be a good place to look for more confirmation about it being planning permission (zoning to you) which causes the bubbles.

    In the SE of England a hectare of reasonable agrcultural land (not good arable land, pasture or light woodland say) is around £4,000/£5,000. Get planning permission on that and the price goes up, even now, to £500,000.

    I’m not alone in saying that we’ve had no housing bubble at all in the UK. What we’ve had is a bubble in the price of planning permission. The solution therefore is not with the financing of housing. It’s with the allocation of planning permission.

    I keep thinking about the similarities with Taxi medallions in NYC for some reason…..

  13. Gravatar of greg greg
    11. April 2010 at 00:42

    You will be forgiven if you believe the Chinese are cooking the number – again:

    China passenger car sales up 63 pct in Mar.

    At this pace, China will break the all-time record of 17.4 million car sales set by US in 2000 this year.

  14. Gravatar of Doc Merlin Doc Merlin
    11. April 2010 at 01:58

    @Tim:
    “In the SE of England a hectare of reasonable agrcultural land (not good arable land, pasture or light woodland say) is around £4,000/£5,000. Get planning permission on that and the price goes up, even now, to £500,000.”

    Sounds about right… this is the danger of giving the state licensing and regulatory powers. It /increases/ the stickiness in the system.

  15. Gravatar of StatsGuy StatsGuy
    11. April 2010 at 06:16

    Statisticians have been aware of the over-prediction problem (particularly in low degree of freedom macro models) for a very long time. They have a lovely pejorative term – data mining. In essence, if you throw 20 random variables into a model, one average one appears significant at a 95% level. The random matrix observation is just an extension of this scalar property.

    And indeed, many social scientists (economists too) build lots of models and test lots of variables before they get to the one they want. The better ones formulate a hypothesis and limit their testing.

    But even if _everyone_ in the field was being rigidly pure about hypothesis-formation and then testing, the FIELD AS A WHOLE is engaging in data mining. Imagine 20 economists all pick a single different variable to test. One finds a significant results and gets published. Even though no one single person is data mining, the process as a whole yields the same result.

    And if you think economics is bad, take a look at epidemiology.

  16. Gravatar of scott sumner scott sumner
    11. April 2010 at 08:40

    Doc Merlin. I agree.

    Jon, It depends on how big the fluctuations are. Even with optimal monetary policy there will be small random fluctuations in the price level. If they are big, then policy is probably inefficient.

    Lorenzo, That’s a fascinating comparison between Germany and the UK. I think it is quite possible that the greater instability of the UK banking system is at least partly due to their inefficient land use policies. I suppose they defend the restrictions as a way of keeping the countryside beautiful, but when I’ve traveled in Germany the countryside also seemed fairly attractive.

    Tim and Lorenzo, Those analogies with taxi medallions are quite accurate. There are worth many hundreds of thousands in cities like Boston and NYC.

    Thanks Greg, There is no doubt that the Chinese car market is exploding in size. In 15 years they’ve gone from having streets clogged with bicycles to clogged with cars.

    Statsguy, Yes I completely agree with all of your observations, and have made similar observations elsewhere. I had assumed there were saying something more profound than data mining–my mistake. Random matrix theory sounds so impressive.

    Memo to myself—never again quote a science journal discussing economics.

  17. Gravatar of Lord Lord
    11. April 2010 at 09:03

    Yes, water for farms is subsidized, but so is water for housing by being priced at the cost of existing production rather than the marginal cost of increased production. This produces the demand for unsustainable growth that land use restrictions counter.

  18. Gravatar of StatsGuy StatsGuy
    11. April 2010 at 10:28

    … didn’t mean to belittle the article. I enjoyed reading it. It was interesting to see how an idea moved through different fields. In a sense, random matrix theory is like putting a prior on how much correlation (or “predictability”) you would expect to see from any given set of random data. It’s like saying, if I throw a hundred random predictors at something, I should expect to be able to predict it with a certain accuracy (given the data set size) just by random chance. If I were then to use some sort of model trimming mechanism – Bayesian or iterative or whatever – I ought to expect to get a certain number of good looking variables. So does my actual model beat this random baseline by enough to suggest there’s some degree of real predictability here?

    I’m not belittling it – it may help make the field more disciplined (or maybe not) – but it’s not like these issues weren’t known. Nor is it like there aren’t other problems model-builders face that are just as bad if not worse (bad data, missing data, missing variables, regime/world change, endogeneity of outcome to predictors, etc.)

  19. Gravatar of Mike Mike
    11. April 2010 at 11:39

    If supply was more elastice (less restricted by government) isn’t it likely that the inflation of the bubble would just manifest itself more in terms of quantity and less in terms of price causing a greater inefficiency and more of a “negative bubble” (if I may be allowed to make up terms) when it burst and the resources used for housing got reallocated to more efficient uses.

  20. Gravatar of StatsGuy StatsGuy
    11. April 2010 at 11:40

    Just a note – zoning is a factor in land prices even in places where you might least expect it… Like Hong Kong, where the government deliberately restricted land for development for decades. It’s one of the ways HK was able to keep labor taxes low. 20% of revenue was from land sales, and high/accelerating land values kept property taxes high.

    http://sunzi.lib.hku.hk/hkjo/view/12/1200111.pdf

    I would guess that government ownership of land doesn’t figure heavily into the various “liberty” indices that we had fun arguing over a few weeks back – but I can’t say I really know.

  21. Gravatar of Doc Merlin Doc Merlin
    11. April 2010 at 12:44

    ‘I would guess that government ownership of land doesn’t figure heavily into the various “liberty” indices that we had fun arguing over a few weeks back – but I can’t say I really know.’

    We should email them and ask them to include it.

  22. Gravatar of TVHE » Data and prediction TVHE » Data and prediction
    11. April 2010 at 13:16

    [...] Scott Sumner we saw the following article that mentions economic data and economic predictions.  The statements [...]

  23. Gravatar of Artturi Björk Artturi Björk
    12. April 2010 at 01:27

    Statsguy:

    I would guess that government ownership of land doesn’t figure heavily into the various “liberty” indices that we had fun arguing over a few weeks back – but I can’t say I really know.

    Under which post?

  24. Gravatar of scott sumner scott sumner
    12. April 2010 at 04:50

    Lord, I agree that water should be priced at a higher level, although if done correctly for farming the usage on farms would drop so much that lower prices might be appropriate for the cities. But if higher urban prices are appropriate, then I am all for them.

    If the Canadians were smart they’d sell water to California.

    Statsguy, Those are all good points, which is why it is essential that macroeconomists do what Friedman and Schwartz did, and what I am trying to do with the Great Depression. That is look for other evidence of exogeniety, to help reduce the number of possible models. I don’t see many taking the time to do this.

    Romer and Romer also did a study where they looked beyond the data for evidence of exogeniety.

    Mike, You said;

    “If supply was more elastice (less restricted by government) isn’t it likely that the inflation of the bubble would just manifest itself more in terms of quantity and less in terms of price causing a greater inefficiency and more of a “negative bubble” (if I may be allowed to make up terms) when it burst and the resources used for housing got reallocated to more efficient uses.”

    This raises all sorts of difficult issues. First, much of the supply of housing was driven by speculation. If prices had not risen rapidly, the speculators would not have bought as many houses.

    Second, there are two problems caused by the housing bubble. One is the drop in construction jobs after the bubble burst. That turned out to be a rather small problem, as the economy kept booming despite big drops in construction during 2007. The other problem is the damage to the financial system. This was mostly caused by the big run-up in prices. And this problem had a bigger effect on the macro economy, even if it worked by distorting monetary policy, as I argue.

    Statsguy, I agree about HK, and indeed that’s why I titled my post Hong Kong = Phoenix. I’ve always been very critical of Hong Kong’s government involvement in land. Of course mainland Chinese cities are following the HK example, and probably get an even greater share of their revenue from land sales. But at least they sell large amounts of land, not the trivial amounts that HK sells. (They also steal land from peasants w/o adequate compensation.)

    Hong Kong would have been a good example for my Adam Smith post, which argued that laissez-faire involves a lot of government involvement in the economy. HK is almost universally viewed as laissaz-faire (by both fans and critics) but still has plenty of government involvement. I believe that half of all housing is government-owned.

    Doc Merlin. I think it is included, it’s just that there are so many thousands of way that government gets involved in the economy, no one factor is all that important, even if it seems important in isolation. Some western states in the US are 80% owned by the federal government. But most Americans don’t view Nevada or Wyoming as being more “communist’ than New York.

  25. Gravatar of StatsGuy StatsGuy
    12. April 2010 at 05:32

    Atturi –

    http://www.themoneyillusion.com/?p=4354
    http://www.themoneyillusion.com/?p=4618
    http://www.themoneyillusion.com/?p=4626

    Mostly pointless argument…

    I’d always known about HK’s land policy, btw, but thought to look up Singapore. It’s not quite on HK’s level it seems, but it appears that HK and Singapore not only use land policy to raise revenue and restrict development, but also to stabilize asset prices…

    http://www.aseanaffairs.com/singapore_govt_to_reduce_land_sales_to_help_property_market

    Judging from the 2009 property values, Singapore achieved its goals.

  26. Gravatar of David Tomlin David Tomlin
    12. April 2010 at 11:19

    I thought this diavlog was exactly right.

    Scott, I think this would be an interesting topic for you to expand on in a future post.

    Lindsey doesn’t give much evidence to support his assertions, or explain what he means by a ‘version of rightwingery’ that is ‘dumbed down’, ‘populist’, and ‘unappealing’. (I don’t watch Fox News with any regularity, so I have no opinion on that.)

    I think Lindsey is clearly wrong to suggest that Frum’s views on Miers and Palin have been out of the ‘main currents’. Polls have shown conservatives and Republicans evenly divided on Palin’s qualifications, with the majority going either way.

    What has taken Frum out of the ‘main currents of the Republican party and the conservative movement’ is his continued support for the kind of big government conservatism justly discredited by the failure of the Bush administration.

  27. Gravatar of Lorenzo from Oz Lorenzo from Oz
    13. April 2010 at 02:39

    I’m not alone in saying that we’ve had no housing bubble at all in the UK. What we’ve had is a bubble in the price of planning permission. The solution therefore is not with the financing of housing. It’s with the allocation of planning permission.
    Tim, that’s priceless.
    DocM, yes, agree entirely about “shall issue”.

    On Florida, I had the following comment emailed to me from someone very knowledgeable about land regulation in the US:
    … the entire state of Florida is under a growth management (urban consolidation) law, which creates scarcity and has been particularly effective in doing so in South Florida. There has been net domestic outmigration from South Florida this decade, yet even with that (as in LA and coastal California markets), the existing demand and speculative demand have overwhelmed the ability of suppliers to build sufficient new suburban housing… because land prices were driven so high and regulatory barriers were so great.

  28. Gravatar of scott sumner scott sumner
    13. April 2010 at 05:15

    Statsguy, I also knew about Singapore. Does anyone know the land use policies in non-Chinese countries like Korea and Japan? I would guess that Japan has more of a tradition of private property.

    David, You make some good points. I do plan to do a much longer post on this issue, when I can find the time. Obviously it is very complicated.

    Lorenzo, Thanks, I’ve seen enough examples where I am willing to go out on a limb and say that the entire housing bubble was actually a bubble in land use permits, and that countries that lack such requirements, did not have bubbles. Indeed this is even true of rich countries that are far more densely populated that the US, but lack restrictive zoning, such as Germany.

  29. Gravatar of D. Watson D. Watson
    13. April 2010 at 07:35

    By the by, Diamond and Rajan at the 2009 AEA meeting wrote, in support of several things you have said recently about China and its trade imbalances: “Clearly, the net financial savings in one part of the world have to be absorbed by deficits elsewhere.” (AER, 99:2, p. 606)

  30. Gravatar of Lorenzo from Oz Lorenzo from Oz
    14. April 2010 at 04:43

    Japan is the land use restriction nirvana. Farming plots in the middle of major cities, the whole thing. When the Japanese land bubble boomed, so that the land Japan sat on was apparently farm more valuable than the entire continental US, people noted that it was an incredibly “thin” market, with very few sales since the land use restrictions were so intense.

  31. Gravatar of Lorenzo from Oz Lorenzo from Oz
    14. April 2010 at 05:11

    Bernanke has spoken on housing bubbles and monetary policy. Some of his data is of interest.

    Also, that should be “far more valuable” in the previous comment.

  32. Gravatar of scott sumner scott sumner
    14. April 2010 at 05:13

    D. Watson. Yes, China’s critics are actually telling them to save less.

    Lorenzo, Yes, I seem to recall that the land under the Imperial Palace and gardens was worth more than the entire state of California

  33. Gravatar of scott sumner scott sumner
    14. April 2010 at 05:18

    Lorenzo, I agree with him that monetary policy probably played only a modest role in the bubble, but disagree that the Taylor Rule is the appropriate benchmark for policy.

  34. Gravatar of StatsGuy StatsGuy
    15. April 2010 at 13:21

    Andrew Gelman has a post on random matrix theory.

    http://www.stat.columbia.edu/~gelman/blog/

    He compares the distribution of eigenvalues from a random matrix to the distribution of a mean from a random vector (aka, the central limit theorem). Interesting, and makes sense.

    But he is similarly nonplussed about the relevance to the social science literature, though I disagree with his conclusions. He argues that most social scientists aren’t blindly data mining – but engaging in structured inquiry, but I think he misses the point that the discovery/publishing process as a whole can be considered a data mining endeavor, which is why it seems that we find so many “significant” results that later prove to be noise.

    I disagree with him that more data is always better in the sense of adding more variables to the model, or even more variables to select from. Any user of neural nets knows this. The more variables one throws into the model, the higher one should set the “significance” threshhold for retaining a variable in the model. If, for example, there are Three variables that really matter and one searches for those variables and a hundred others than don’t, one might pull 5 variables that don’t matter into the model (maybe miss one that does). Adding those 5 wrong variables not only inflates our sense of accuracy (bad, if we’re in the business of estimating risk as in finance), but also actually WORSENS the accuracy of the model by adding noise into the predictor.

    Theoretically, however, if one were to apply appropriate priors on each variable for inclusion in the model (prior to analysis) AND a prior covariance/conditional probability structure (heh, good luck there), and set appropriately higher standards for including a variable in the model, then expanding the range of variables to search for model inclusion should always be beneficial.

    This is why data miners judge themselves against holdout data… Maybe econometics journals should be run like the Netflix competition.

  35. Gravatar of rob rob
    15. April 2010 at 19:40

    re: econometrics. of course all us here believe the market is smarter than any individual participant, but if NO participant can make good long term predictions about, say, long term inflation, then are we to believe the market can do better? surely the market cant distill wisdom from a collective of fools. so my question: i understand there are plenty of studies showing equity markets are good predictors of the economy six months out, bu do many studies claim bond markets have been good predictors of inflation ten years out?

  36. Gravatar of ssumner ssumner
    16. April 2010 at 04:59

    Statsguy, I pretty much agree with all of your comments here, although I am not well-qualified in stat, for what it’s worth. (What does the term “holdout data” mean?)

    Gelman has a good blog, and does argue in several posts that we expect too much from statistics. But it may be even worse than he thinks, especially in macro. In my view macro doesn’t need better statistical techniques applied to the same old data set, rather it needs clearer thinking about the issues and different data sets.

    rob, You said;

    “surely the market cant distill wisdom from a collective of fools.”

    Don’t be too sure about this.

    1. Forecasts by crowds of the number of jellybeans in a jar tend to be highly accurate, even though each individual forecast is highly inaccurate.
    2. Einstein was quite smart, even though his wisdom came from nothing more than a collection of individually quite stupid neurons.

    In my view the real issue is not how well markets can make unconditional forecasts of macro variables (I agree they aren’t very good at that) but rather how well they make conditional forecasts, i.e. conditional on policy settings, and here I think they do much better.

  37. Gravatar of StatsGuy StatsGuy
    19. April 2010 at 17:22

    Holdout data – split the dataset into two samples. The first (usually larger sample) you use for estimation. Do whatever you want to build the model. The second (which is “held out” and kept blinded), you use to measure predictive accuracy. Theoretically, you should only do this once per holdout set.

  38. Gravatar of Reblogger Memo Enlightment » Data and prediction Reblogger Memo Enlightment » Data and prediction
    20. April 2010 at 08:16

    [...] Scott Sumner we saw the following article that mentions economic data and economic predictions.  The statements [...]

  39. Gravatar of ssumner ssumner
    28. April 2010 at 13:48

    Statsguy, Yes I agree. But the incentives are all to cheat. I still say we need to go beyond data, and try to persuade on a number of levels (logical arguments about the lessons of theory, about exogeniety, about likely omitted variable bias, etc, etc.)

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