Andrew Farlow University of Oxford Department of Economics, and Oriel College Guest Lecture

1. Housing, consumption, and the economy: Why do house prices become misaligned, and what are the consequences? Andrew Farlow University of Oxford Department of Economics, and Oriel College Guest Lecture (with expanded post-lecture notes) The London Business School 2 June 2005 This lecture is based on papers to be found at: www.economics.ox.ac.uk/members/andrew.farlow

2. Today’s presentation draws off a five-part project • Part One: “UK House Prices: A Critical Assessment” • Part Two: “Bubbles and Buyers” • Part Three: “UK House Prices, Consumption and GDP in a Global Context” • Part Four: “Risk Premia and House Prices” • Part Five: “Mortgage Banks and House Prices” forthcoming • Book: Spring/Summer 2006, Publisher: Constable Robinson

3. Real house prices against trend

4. House prices against gross earnings

5. House price inflation Source: Halifax UK Real House Price Growth (annualized)

6. UK historically strong housing cycles • Strong housing market cycles, linked to volatile consumption, have been an overriding feature of the UK economy for over three decades. • OECD (2004): 1971-2002, UK the strongest correlation of housing wealth and GDP of any country surveyed. • IMF(2004): The UK (along with Finland, Ireland, and Switzerland) has had one of the most procyclical housing markets in the world.

7. Something more global this time? • Something extra this time: The “first global house price bubble” (The Economist)? • BUBBLE: “a situation in which temporarily high prices are sustained largely by investors’ enthusiasm rather than by consistent estimation of real value.” Shiller, R.J., ‘Irrational Exuberance’ 2000 page xi • Small-probability large-loss events – they matter. • Even if only a small chance, we should analyze the possibility. • IMF, 2003 World Economic Outlook, 40% of booms followed by busts, and 8% cumulative GDP loss (though important caveats about the data). • State of housing markets, part of a bigger picture of global imbalances?

8. UK House price falls and GDP falls • No fall in real house prices (blue line) not followed by major fall in UK GDP (gray bands). • Will the pattern repeat? Source: Bank of England Inflation Report, May 2004, p7, Chart 1.10.

9. Market efficiency • ‘Efficient markets’ view of asset prices: • Financial prices always reflect all information (defined at various levels of stricture) – current price is an unbiased predictor of the future value of the asset if information arrives randomly. • Explaining the way economists cling to ‘efficient markets’ thinking: “A peculiar psychological disorder known as ‘physics envy'...We would love to have three laws that explain 99% of economic behaviour; instead, we have about 99 laws that explain maybe 3% of economic behaviour. Nevertheless, we like to talk as if we are dealing with physical phenomena.” Andrew Lo, MIT, financial economist

10. Market efficiency • Following Fama, E.F., (1970), discrete case. • Let the expected return on an asset between t and t+1 be: r = Et[rt+1] = Et[Pt+1-Pt + dt+1)/Pt] • where E is the expectations operator • P is the per unit price of the asset • d is the dividend or rent • If Et[rt+1]-ρ = 0 is a fair game* where ρ is a constant term, then, by substitution, Pt = Et[(Pt+1+dt+1)]/(1+ρ) * Stochastic process xt is considered a fair game if Et[xt+1]=0. In the case here, Et[xt+1]=Et[rt+1]-rt.

11. Market efficiency • Solving for n periods recursively, using iterative expectations (something that economists easily slip in without thinking too much what it actually implies about those, such as house buyers, supposedly doing it): • If the second term converges to zero for large enough n, then the equation gives the long-run equilibrium price, and the value of the asset is the sum of expected future dividends or rents. • If the second term does not converge to zero, then the asset price is said to include a ‘bubble’ term.

12. ‘Efficient Markets Hypothesis’: Assumptions about investors • 1) Either all investors are rational and this is ‘common knowledge’* or… • 2) To the extent that some are not rational, their trades are random and cancel each other out so as not to affect price, or… • 3) To the extent that some are not fully rational and their trades are not random but are serially correlated, rational arbitrageurs eliminate their influence on price. Market forces work to eliminate the wealth of the irrational types, and they die out in the population. * Several recent interesting models of bubbles have all agents rational, but that this not common knowledge, with the rest of the framework essentially classical. See Abreu, D. and Brunnermeier, M. K. 2003.

13. Market efficiency • Sharpe et al.* defines a perfectly efficient market as “one in which every security’s price equals its investment value at all times”. * Sharpe, W.F., G.L. Alexander and J.V. Bailey, 1995 • Strongly ‘efficient’ market: • Asset price changes should be unpredictable since they only respond to new information – which by definition is itself unpredictable. • Prices changes should follow a ‘random walk’. • Returns unforecastability – where the mathematical expectation of returns is based on all publicly available information at time t. • Efficiency tests essentially become tests of forecastability of price changes.

14. Problems testing efficient markets • Power of statistical tests in distinguishing efficient markets hypothesis from alternatives is weak. • Null hypothesis of market efficiency not well defined.  • This is the ‘joint hypothesis’ problem: Market efficiency is always tested jointly with some model of equilibrium – i.e. some asset pricing model. • Whatever the result, it can always be claimed that the asset pricing model was misspecified. • In particular, it can always be argued that a higher return could simply be compensation for accepting higher risk.

15. Tulipmania • To give some idea of how this problem bites: In testing the theory of 17th century tulipmania, Peter Garber concludes that we cannot clearly declare it a case of ‘irrational’ pricing; gyrations in price could have been based on information revelations at the time about which we know relatively little now, and we may misuse our benefit of hindsight. Garber concludes however that he is “hard pressed to find any market fundamentals explanation” (Garber, P. M., 2000, quoted from Garber, P.M., 1989). As John H. Cochrane puts it (in a review of “Famous First Bubbles”): “Garber suggests fundamental explanations, but he does not nail the case shut. If it were easy, the events would not have passed into legend.”

16. More on problems testing efficiency • Furthermore, all models of asset pricing used in tests require currentexpectations concerning future paths of all variables in the pricing formula – no mean feat. • And no model is any good at dealing with non-linear feedback loops (even chaos) since this makes the tests – based on linear models – impossible to use to prove lack of a bubble (never mind the fact that agents are supposed to form expectations over the future paths of such systems). • We never get definitive answers.

17. Efficient house prices? • If housing markets were efficient, housing prices would anticipate optimally the stream of real returns – including housing services – that housing will pay in the future. • Inefficiency = serial correlation of house price changes. • Or, another way to think of this, inefficiency = future house price movements can be predicted from information available now, namely deviations from the long-run trend and recent price increases. • The general conclusion is that housing markets are not efficient.

18. Specific Problems in Testing Housing Market Efficiency 1 • Similar problems to testing for equity price bubbles – but probably much worse. • We do not have many long or high-quality time series on prices or rents (the ‘dividends’ for real estate) for owner-occupied dwellings: • The implicit rent of owner occupiers is never directly observed – there is no market to derive an exact valuation. • We end up using proxies from rental indexes in government statistics. • But, if the measure is based on properties different from those that are owner-occupied – which it often is – the tests are biased.

19. Specific house test problems 2 • House price series are hard to construct: • Real estate is not a standard commodity (size, quality, depreciation, expenditure on improvements, etc.). • We would like repeat sales on the same properties where the kind and quality is known. • It is after-tax returns we want to know about: • But agents vary in tax liability. • So, after-tax returns are pretty impossible to measure. • We need decades of data for analysis of volatility. • CONSOLATION: While there may be little chance of proving definitively that a market is not efficient, this conclusion may not apply in periods of extraordinary price rises… • And these are usually the periods we are most worried about. 

20. Efficient Market • OBSERVE: The the same problems that economists have, will equally apply to ordinary investors. • YET we presume that these investors think about these issues in order to work out when and how to invest efficiently… including in housing! • CONCLUSION – It is a bit of a red-herring presuming that we can rely on tests of housing market efficiency for definitive answers. • Like a good doctor dealing with a difficult patient, we might want to treat tests as one of our possible diagnostic tools, and look at other analysis and at the logical reasons for why markets may find it hard to be efficient… for example, due to arbitrage failure.

21. The bottom line “The bottom line...is that theory by itself does not inevitably lead a researcher to a presumption of market efficiency. At the very least, theory leaves a researcher with an open mine on the crucial issues....market efficiency only emerges as an extreme special case, unlikely to hold under plausible assumptions.” Schleifer, A., 2000, p16.

22. Simple test of housing efficiency • Treat housing as just another other asset (something that ultimately gives utility via consumption, including housing services) and compare the change in the theoretical price of housing with the actual price. The theoretical optimal price (P*) equals the sum of the discounted future income that it will generate. Here, R is the rent level, g the expected growth rate of rents, r the mortgage interest rate, τ the risk premium. Those who own are treated as renting from themselves – which is how their ownership is treated in the National Accounts (Blue Book) under ‘imputed rent’* * Or, since the alternative to owning is renting, the opportunity cost of owning (via payment of interest) is compared to the alternative of renting. So if the typical rent is falling as house prices are rising, then buying housing and renting it to yourself is getting more expensive than letting someone else buy it and you rent it from them. There are, however, some weighty problems in dealing with the tax advantage of housing and the different tax treatments of renting.

23. Housing market efficiency test • Hence • If P/P* falls, real estate prices fall below their initial point (not necessarily the optimal reference point itself). If P/P* rises significantly (exceeding 1 with caveats) this is described as a ‘bubble’. • To use this to work out the degree of overvaluation of a property market we need to know some moment at which the market was actually in equilibrium.

24. Bubble builder-bubble burster 1 • Another popular (empirically-based) method used for checking for mispricing/‘bubbles’ in property markets, and applied in various countries, is the methodology of Abraham and Hendershott 1996. • Methodology is also useful for locating the moment at which a property market is in ‘fundamentals equilibrium’. (The nomenclature here is identical to that of Abraham and Hendershott) • *Abraham, J.M., and P.H. Henderschott, 1996. • As an alternative, Hall, R.E., 1978, runs a stochastic switching regime model in real house prices. Booms in house prices are associated with an unstable regime. The probability of the system staying in the regime fall as deviation from equilibrium increases.

25. Bubble builder-bubble burster 2 • The equation for property prices contains a fundamentals long-run equilibrium term (based on the efficient markets hypothesis, using a standard asset pricing model), drppit*, and an error term: drppit=drppit*+ut • Concentrating on the fundamentals term for a moment. The growth rate of the fundamentals term is modelled as a function of the growth rate of rental prices, real interest rate, real construction costs and real effective exchange rate, real GDP, real GDP per capita, nominal wage, real wage, population, and various supply factors (with all prices, rent, etc. deflated by the CPI). All variables (except interest rates) are in logs, so that rppi = log real property price index, RINT = real interest rate:  • drppit*=a0+a1RINTt+a2drrisat+a3dpcrcgdpt+...+lagged variables 

26. Bubble builder-bubble burster 3 • The error term is further specified to capture the dynamic adjustment. The term is the sum of a bubble ‘builder’ (expected future appreciation), a bubble ‘burster’ (the possibility of a price drop if price exceeds the fundamental price by a certain limit) and a random variable: • ut= λ0 +λ1drppit-1+λ2(rppit-1*-rppit-1)+εt

27. Bubble builder-bubble burster 4 • This analysis of course requires some estimate of the parameter rppi*, which needs also to be consistent with drppi* and the estimates of parameters ai. This is done as follows (and is worth explaining since it indicates a method for working back to the moment when the UK property market was last in fundamentals equilibrium): • i) Estimate without λ2 (i.e.. ignore λ2 for now) • ii) Construct a first-pass estimate of rppi* by cumulating the drppi* over time using the parameter estimates derived from i);

28. Bubble builder-bubble burster 5 • iii) Calibrate (not always a sound idea) the rppi* series on the assumption that actual property prices were, at some particular point in time, in equilibrium; • iv) Re-estimate drppi, this time including the λ2; • v) Keep repeating steps i) to iv) until the ai estimates stabilise, at which point the drppi* and rppi* will also have stabilised. This also pins down the year when the market was approximately in fundamental equilibrium. In the UK this would be about 1994. In the original paper, in Hong Kong it was about 1990.

29. Bubble builder-bubble burster 6 • If λ1 > 0, the lagged growth of real property prices acts in a way to perpetuate the growth of real property prices, i.e. a bubble. λ2 > 0 captures the notion that the bubble bursts when the actual price level λ1drppit-1 exceeds the equilibrium price level rppit-1*. • Putting all this together we get real property price growth:  • drppit* = (a0+λ0)+a1RINTt+a2drrisat+a3dpcrcgdpt+... • ...+λ1drppit-1+λ2(rppit-1*-rppit-1)+εt

30. Some results – nominal interest rate • These studies often start with the nominal interest rate included as a variable, but find that it drops out as insignificant in the long run. • This is a rather striking revelation. It backs up the claim made and analysed in Part One, and found in many studies, that ultimately – and contrary to much of the mortgage bank emphasis and media coverage – it is real interest rates that matter.

31. Some results – US, Hong Kong • Hong Kong and US metropolitan cities*, tendencies towards bubbles • Estimated bubble builder and the bubble burster parameters were significant and comparable: • Bubble builder parameter: • Hong Kong: 0.3 • US metropolitan cities: 0.5 • US remainder: 0.2  • Bubble burster parameter: • Hong Kong: 0.05 • US metropolitan cities: 0.1 • US remainder: 0 * Based on 30 US Metropolitan cities for the period 1977-1992 (Abraham, and Hendershott, ibid).

32. Some results, cont. • Specifically, the coefficient on the one-period lag in ARIMA(1,1,0) was found to be large, positive and significant: in other words appreciations tend to be followed by further appreciations. • Over the period covered, in US metropolitan cities a 1% increase in real property prices in a quarter tended to be followed by a 0.6% increase in the next quarter. • In Hong Kong, the absence of volume changes in the number of apartments and of the real construction cost index as explanatory variables in the estimated equations was “striking” and confirmed the relative importance of demand side factors in explaining short-term property price movements.  

33. Some results, cont. • In the US metropolitan cities, changes in market fundamentals and adjustment dynamics (including the bubble builder and bubble burster components) together explained about 60% of the variation in price movements and, separately, about 40%. • On the assumption that Hong Kong prices were broadly at equilibrium in the early 90s, the upswing peaked at about 40% to 45% above fundamentals level: “This estimate is broadly consistent with market perceptions at the time.” In 1998, property prices declined by an average of 40%.

34. Arbitrage • “To make a parrot into a learned financial economist it needs to learn just one word – arbitrage.” Stephen Ross (a learned financial economist). • Arbitrage = “the simultaneous purchase and sale of the same, or essentially similar, securities in two different markets for advantageously different prices” Sharpe, W., and G. Alexander, “Investments.” 4th Edition, quoted in Shleifer, A., and R. W. Vishny, 1997. • Arbitrage is at the heart of the theory of financial market efficiency. • Its failure is at the heart of cases of market inefficiency and bubbles • Inability to arbitrage especially at times of excess.

35. Arbitrage • Arbitrage is a game of co-ordination. • The outcome presumed in classical finance is based on the common knowledge of all players that they are all synchronising their arbitrage strategies and that it is riskless. • In theory, the desired outcome involves no risk of capital loss. • Arbitrage: • Removes influence of idiosyncrasies • Removes importance of institutional detail • ties price to fundamental value • Hence mispricing does not last

36. Arbitrage in practical reality… • We cannot presume that agents converge on the optimal outcome in this game if this common knowledge is lacking. • Player needs to be sure that enough other players are also arbitraging • Otherwise individual acts of arbitrage are too individually risky and costly; • Most positions involve capital and risk of loss; • Arbitrage limited by risk-bearing capacity of arbitrageurs in the aggregate; • These risks vary across markets and institutional structures; • Arbitrageurs are using “other peoples’ money”…So face a principle-agent problem; • Arbitrage = very broad interpretation, to include, decision on house buy/sell, bank lending strategies, estate agent tactics.

37. Arbitrage Failure – Why no-one corrects a bubble • Behavioural finance • Idiosyncrasies matter, psychological motivations, thought processes about each others’ thought processes, etc. • Institutions matter… especially at times of excess. • Combining psychological with institutional… harder to rule out arbitrage failure… hence bubble-type behaviour.

38. Two-sided arbitrage failure • Customers don’t arbitrage • Mortgage banks don’t arbitrage each others’ behaviour either • Mortgage banks don’t arbitrage consumers’ behaviour • In times of excess it is better to exploit momentum of customers than to try to fight against it.

39. Risks to Arbitrageurs - general • Risk at various levels – the levels interact • Fundamentals risk • Financing Risk/Noise Trader Risk • Horizon Risk • Margin Risk • Short covering risk • No time to discuss most of this today • Fundamentals risk (next few slides) • Horizon Risk • Without fundamentals risk • E.g. Royal Dutch Shell, etc… • With fundamentals risk • Housing…

40. Fundamentals risk • Classical finance • The greater the deviation from fundamentals the more aggressive the arbitrage since potential returns are higher. • BUT arbitrage is a game of coordination: • The less that players collectively understand that they have deviated from fundamentals, the less aggressively they collectively arbitrage. • If an arbitrageur takes a position and then some good fundamentals news is released that justifies what had originally seemed a mispricing, the position must be closed at a loss.

41. Fundamentals risk • Inability to define a fundamental value = two problems. • More difficult to test market efficiency • One can never reject the null hypothesis of market efficiency when it may be that the model was misspecified. • Hard-to-define fundamentals make it easier for mispricing to develop in the first place. • With no unique fundamental value from which players can know (in the common knowledge sense of the word) that they have deviated, it is impossible to achieve common knowledge of deviation from fundamental value. • This increases the uncertainty of players regarding the acts of other players and, hence, the uncertainty of gains from arbitrage; • Which reduces the expected payoff from any given position; which reduces the incentive to take those positions in the first place

42. Fundamentals risk…housing • Housing added problems: • Very heterogeneous asset. • Highly segmented market. There is no central exchange and information about the value of the underlying asset is much more imperfect. • Behaviour of intermediaries. Very few (do any?) estate agents price according to macroeconomic or other fundamental factors – instead they price almost always in comparison to recent local sales. • Consumers - better at pricing relative to fundamentals. • Only very imperfect substitutes, since the risk will have been poorly, if at all, hedged. • Small transaction and information costs become serious impediments to arbitrage.

43. Greenspan 1 • "I don't think we have a bubble in house prices. First, let's remember it's very difficult to get one. Unlike stocks, where you have a single market, low transaction costs and an ability of people to pile on nationally and cumulatively, residential housing markets are all local." Federal Reserve Chairman Alan Greenspan, 17 April 2002, Testimony before the Joint Economic Committee.

44. Greenspan 2 • Greenspan emphasises entry of speculators – perhaps day-traders with low transactions costs, able to ‘pile on’, as Greenspan puts it, and generate herd behaviour and bubbles. • But entry is not the only issue in a bubble phase; correction, via acts of arbitrage, is important too. • Day traders can also easily ‘pile out’, correcting the market. We can’t immediately presume that the power of the first dominates the latter (the very knowledge of the ability to ‘pile out’ may itself act as a corrective influence). • Arbitrage is a double-edged sword; just as rational agents arbitrage away inefficient pricing, irrational traders arbitrage away efficient pricing. • If each group has significant risk-bearing capacity, both will influence the price. • The ability of the first group to offset the second group is very weak in housing markets.

45. Substitutes, Short-Run Supply, and Short Sales • Arbitrage needs close substitutes • Arbitrageur goes short the expensive security and long very similar but cheaper securities • Works even if some agents are not fully rational • Even if demands are correlated • Price determined relative to close substitute and NOT the absolute supply of the asset • So, may achieve “Law of One Price” in shares to one company even if not of whole market.

46. Equities substitutability • Equities weak substitutability • Roll, 1988 • Amount of variation in return on large stocks explained by aggregate economic factors, returns on other stocks in the same sector, and firm-specific publicly-known news. • 35% on monthly data. • 25% on daily data. • In other words, there is very little substitutability between even equities.

47. Substitutability… • Even if individual securities have better substitutes than the whole market, many broad categories do not have substitute portfolios. • Risky to get in and out of shares as a class given given high historical rate of return. • Housing – only strategy if there is suspected overpriced is to get out altogether, with intent to get back in later when price is lower: • But very risky. • Housing also has no (or low) ‘release valve’ of international trade, unlike equity.

48. Inability to go short in housing • Creates an asymmetry • Can’t exploit profit opportunities if market expected to decline (c.f. can in principle in equity markets). • Buy-and-hold in periods of excess positive return • Asymmetry feeds bank expectations of how to exploit volatility. • Feeds bubbles.

49. Replicability of asset • The more easy it is to create substitutes to current assets, the less likely it is to get a bubble. • Affects interpretation of slow supply response of housing … as not just a determinant of ‘fundamentals’ … but as a ‘bubble’-generating feature too. • Normally the ex ante knowledge that excess price relative to fundamentals will draw forth extra supply of underlying asset, acts to numb price pressure • Slow supply response feeds episodes of mispricing • Supply lags – long planning and build process - exaggerate boom-bust cycle. • Option based component to when high uncertainty about fundamentals… resist supplying till some of the uncertainty is resolved

50. Diversification, Liquidity and Arbitrage • Portfolio theory • Should be well-diversified. • In the limit (though taking into consideration transactions costs) should participate in all securities markets • Should not over-invest in assets that correlate with own income • Plenty of equity evidence that workers do oveinvest in own firm stock) • Standard arbitrage notion is that small number of marginal positions, with limited extra exposure