Lessons from Leamer: Part 2

Continuing from previous post.

1. Housing shows up in the GDP accounting via employment generation. Price appreciation of houses is not part of GDP; and more importantly, increased land prices today if booked as an asset, a liability has to be entered. Where? Liabilities for future buyers. This is transfer of wealth from future generations to present generations!
2. The stickiness of housing prices downwards means, most importantly, price cycles follow sales volume cycle. Also, the volatility of the housing volumes is much higher than the price.
3. Sellers develop their expectations of prices from a backward perspective (“what did I pay for it compared to the offer price?”). Buyers have forward looking price expectations (“what will I get for the house 5 years from now”). So, sales only happen when there is a high bid price by sellers.
4. In a housing boom, the fastest appreciation happens for small houses with low-income zip codes (and smaller square footage = condos and small homes). Predictably, during a bust – they get hit the most.
5. To avoid business cycle fluctuations – one must avoid housing cycle fluctuations and job-losses in consumer durables.
6. Monetary policy that acknowledges the two factors – must face up to the fact that if real interest rates fall temporarily, then for equilibrium level of housing stock will return to “normal”, only if production and sales fall and allow a return to the mean. In contrast, if real interest rates fall permanently, then equilibrium levels of housing stock will rise.
7. Monetary policy that accounts for housing investment is a difficult inter-temporal resource allocation issue.
8. Today, one observes the presence of weak housing starts (new houses being built) and increased inflation – resulting in a conflict for what the “right” policy prescription ought to be.
9. In case of policy choices to be made between housing starts and inflation – there is no real conflict.
10. The best predictor for Fed Funds rate is the 10-year Treasury bond yields.
11. Its the Housing Cycle!
    

Predicting the Fed

The problem with best estimates is two fold.
One, the 'best-ness' criterion is contingent on the information available. So, new information can cause a previous “best” estimate to be a second best or worst estimate. (An extreme example would be, in a non-financial context, American foreign policy on September 10’2001. The next day’s events – led to wildly different prescriptions than most academics/policy wonks had anticipated.) So, the quanta, quality and timing of information is the key.

Second, the estimate itself is a result of some estimating method – an algorithm, a formulae – that econometricians call “estimators”. In essence, it is not just information that is relevant – but the method in which you process the information. It is entirely feasible, and relatively easy to show, that even with the most recent information using a wrong estimator can result in (a) biased estimates (b) unbiased but highly inefficient (c) biased and highly inefficient estimates. By inefficient, one means estimators that contain within itself the possibility of generating widely variant results as the underlying sample increases in size.

As you can imagine, getting oneself tied up in some subtleties is something that many participants on Wall Street have little time for, or worse, just find it plain useless. One of the biggest games is undoubtedly, inspired no doubt by Whack-a-Mole journalism in American media, is watching the Fed’s decisions on rate cuts. (Things are sometimes so absurd that, as Alan Greenspan writes, there are guys dedicated to watching the size of Greenspan’s briefcase!)

One of the well observed predicting markets is the Fed Fund Futures (FFF) market. The FFF is a futures contract written on the “average of the daily effective FF rate”. The FF rate is the rate that the Federal Bank of New York charges to federal fund brokers. The “effective” part in the earlier definition arrives from the fact, for any given day, different brokers who deal with differing transactional sizes, might be charged differently. So, the “effective” is the same as ‘weighted average’. A heuristic reason is that the Fed Funds market participants are likely to take into account all the factors that might affect the path of the interest rates that the fed sets. The rates implied by this market can be thought of as a first-order prediction. There are various issues – as of yesterday, the one month rates are given as below.

7-Oct 95.255
7-Nov 95.495
7-Dec 96.61
8-Jan 95.665
8-Feb 95.785
8-Mar 95.82
8-Apr 95.895
8-May 95.925
8-Jun 95.92

The implied 1-Month rates are given above.

The critical aspect is that the generic expectation is that the Fed will continue to accommodate and allow for a 4% rate over the next six months. i.e., 3/4 of a decrease in rates. Implicit in this expectation is

• Inflation, core or otherwise, will not be a problem. This, despite the clear trends to a 100$ barrel.
  
• The housing conditions will worsen -- i.e., when over 800billion USD worth mortgages are to be reset over the next two months -- expect a lot more defaults.
  
• The weakening dollar is not really something the Fed worries about. (How to reconcile rising import costs with its inflation hawkishness is a tricky question!)

I think, this will be a very difficult time for Bernanke and Co., primarily because with rising inflation (via oil, via imports, via China, via protectionist lobbies) and worsening labor markets -- the short end of the curve will have substantive volatility.

Again, my thoughts are that the demand for non-USD denominated assets will continue.