trends in real long-term interest rates • World monetary policy has been extraordinarily relaxed since 2000, with interest rates falling to around 0% in Japan, 1% in the USA and 2% in Euroland. • But short-term interest rates have now rising in the UK, USA, Australia and Canada, with the markets predicting further monetary tightening over the next two years. • Meanwhile, in Japan and Europe, limited signs of economic recovery and some inflationary pressure have not yet led to any decisive moves in monetary policy. • Real long-term interest rates remain low: 10 year US Treasury securities yield 4. 57% in nominal terms, and 10 year indexed yields are 2%. • Possible explanations for continue low long-term rates: global saving glut; global investment shortfall; excess liquidity; heightened appetite for risk (qv. ‘the Greenspan put’).
a view from the top “Thus, this vast increase in the market value of asset claims is in part the indirect result of investors accepting lower compensation for risk. Such an increase in market value is too often viewed by market participants as structural and permanent. To some extent, those higher values may be reflecting the increased flexibility and resilience of our economy. But what they perceive as newly abundant liquidity can readily disappear. Any onset of increased investor caution elevates risk premiums and, as a consequence, lowers asset values and promotes the liquidation of the debt that supported higher asset prices. This is the reason that history has not dealt kindly with the aftermath of protracted periods of low risk premiums”, Alan Greenspan, August 2005.
bubbles in theory • Many theoretical arguments have been advanced to justify rational bubbles in asset markets, although this is not the focus of our paper. For example: • The survival of noise traders in financial markets: limits to arbitrage prevent rational traders from driving noise-traders out of the market (De Long, Shleifer, Summers, Waldmann, 1990). • Asset markets are micro-efficient but macro-inefficient (Samuelson, 1998). • Global games: dissatisfaction with common knowledge assumptions leads to ‘games of incomplete information whose type space is determined by the players each observing a noisy signal of the underlying state’. Differences between public and private information can drive selection between multiple equilibria (Morris and Shin, 2002).
features of good regional house price models • Regional house price models useful to address: • The ‘ripple effect’ from Greater London and the South East to the rest of Britain; • Housing affordability in different regions; • Policy issues relating to housing supply and migration. • Meen and Andrews (1998) suggest these features: • Data-consistency, economic interpretation; • Spatial lags, errors, coefficient heterogeneity; • Plausible estimates of income and price elasticities; • Clear implications for housing market efficiency; • Explanation of the ripple effect and demographics.
preview of the model • We estimate an annual econometric model of regional house prices for the 9 regions of Great Britain between 1972 and 2003. The model is a system of inverted demand equations with the predetermined housing stock as an explanatory variable along with regional income, real and nominal interest rates, demographics and other demand shifters. • Our approach contrasts somewhat with two other recent approaches (structural time-series modelling, focussing on unit roots and cointegration; and models of house price to rental ratios). • Our model can be viewed as an equilibrium-correction model with positive effects from recent rises in house prices (the so-called ‘bubble-builder’ effect) and negative effects from high levels of real house prices relative to their fundamentals (the so-called ‘bubbleburster’ effect).
the Nickell version • “This discussion leads us to conclude that there has probably been a substantial rise in the equilibrium house price to earnings ratio since the mid-1990 s. Of course, there is a good deal of uncertainty here, but it is clear that it may be legitimately argued that there has been no housing bubble whatever”, speech to the B. A. September 2005. • Equilibrium level of UK house prices has risen for four reasons: • Strong income growth (more two-earner households, more income inequality); • Low elasticity of housing supply response; • Strong population growth and net household formation; • Low real interest rates and the disappearance of front-end loading.
The ABC of house price determination • An inverted demand equation: suppose real house prices adjust to equate log demand with log of end of previous period supply, h(-1). • Let log housing demand be given by h = - rp + y + z , rp = log house price and y = log real income and z = other demand shifters. The own price elasticity of demand is - and we assume the income elasticity is 1. Solving yields • rp= (y – h(-1) + z)/ . • Note log (income per house) restriction. • Consensus that is approx 2. • z will include interest rates, demographics, expectations of rate of return or user cost, etc.
Modelling Regional House Prices • We model real house prices in eight regions of Great Britain – the North (NT), Yorkshire and Humberside (YH), East Midlands (EM), West Midlands (WM), Greater London (GL), the South (ST), the South West (SW), Wales (WW) and Scotland (SC). • The choice of regions is determined by the need for consistent regional boundaries since the government switched from Standard Statistical Regions (SSR’s) to Government Office Regions (GOR’s) in the mid 1990’s.
• The heterogeneity in house price inflation is more obvious. • The leading role of Greater London house prices and the tendency of house pr • So called “ripple effect”.
Modelling • We estimate a reasonably standard system of inverted housing demand equations. • We use SUR to take account of contemporaneous spatial correlations. • The equations are non-linear with many cross-equation restrictions, because of common parameters, and interaction terms. • Some spatial coefficient heterogeneity is allowed for. • We use annual data from 1972 to 2003. • Details of the model used and the SUR parameter estimates are set out in the Appendices to paper.
Long Run Effects • The long-run solution is for lrhpr, the real log level of house prices in region r. • The key element in the long-run solution is the log of real personal disposable non-property income per house, the same as in Geoff Meen’s work. • For region r, we call this lrynhsr defined as: log(real non-property income) - log(housing stock)-1 0. 7*log(rate of owner-occupation)-1 in region r. • The owner-occupation term implies a modest spill-over from non-owner occupied supply onto the owneroccupied housing market.
• lrynhsr is really a combination of three terms. • The log per capita (or household) income and housing stock terms have equal but opposite signs. • Population (or the number of households) is implicit in this formulation – it just cancels out on both sides. • All regions are influenced not just by the own region value of income per house lrynhsr but also by the GB value, lrynhs. GB, with weights 0. 3 and 0. 7 respectively • The long-run effect of log real income per house on the log real house price is 2 in line with previous studies. • The speed of adjustment is 0. 25, meaning that about three quarters of the adjustment to an income shock is completed within four years.
Other Long Run Levels Effects • Region specific intercepts and (small positive ) time trends. • The log price of house prices in London relative to GB (rlhp. GL) which we allow to vary by region. This has a positive effect in the regions adjoining Greater London, capturing some of the role of London as the driver of UK house prices. • An index of credit conditions (cci) which measures credit supply to UK households. • The interaction of cci with both the log nominal mortgage rate (labmr) and the real mortgage rate (rabmr). • The interest rate effect are consistent with findings for mortgage demand by Fernandez-Corugedo and Muellbauer (2005).
Other Long Run Levels Effects • A reduction of rates from 5% to 4% has a stronger effect on house prices than a reduction from 10% to 9%. • Nominal interest rate effects are also a little stronger in London and the South. • We proxy downside risk using rrhnegr the average value over the previous 4 years of the negative return in the region’s housing market. rrhnegr is significant which means that a history of negative returns depresses house prices for some time to come. • Possessions not significant given rrhnegr.
Some Short Run Effects • Short run effects include house price and income dynamics as well as changes in nominal interest rates, the housing stock, population structure inter alia. • There is persistence in house price inflation. The estimated coefficient on the previous year’s house price growth rate is about 0. 45. • We allow the relative weight attached to house price inflation in the own region ( lrhpr, -1), in contiguous regions ( clrhpr, -1) and in Greater London ( lrhp. GL, -1) to vary by region. • Generally speaking, regions closer to London have the largest weights on London house price growth, reflecting the ‘ripple effect’ emanating from London.
• Income dynamics are important. • Outside London and the South East, the estimated coefficient on current rate of growth of national disposable non-property income ( lrpdin) is about 0. 66. • In London and the South East the estimated income growth coefficients are higher. • As expected, the effect decline over time as credit conditions (proxied by cci) have improved. • The previous year’s income growth rate ( lrpdin-1) has an estimated coefficient of about 0. 56. • Region specific income growth rates have little explanatory power. • Surprising? Common trends. Regional A/C’s income data very poor.
• The question of stock and flow equilibrium effects on house price determination is important. • The stock equilibrium effect enters through the log income per house variables, lrynhsr and lrynhs. GB, discussed above. • A flow equilibrium can be examined through the effects of housing stock changes and population changes. • The idea is that short term increases in the housing stock relative to population lead to short-term local excess supply, with downward pressure on local prices. • We measure this effect by including log(wpopr/hsr, -1) in each region’s equation. • We find a significant effect, suggesting that a 1 percent rise in working age population relative to the housing stock has a short run effect of the order of 1. 5 to 2 percent on the region’s house price index.
• Stock market or financial wealth effects? We failed to find a positive levels effects unlike earlier national studies. • Reason? Probably because no regional wealth data. • The rate of growth of the FTSE index in real terms ( lrftse) has significant positive effects, especially in Greater London and the South. • Also looked at a simple measure of downside risk for the stock market. lrftseneg, which equals lrftse if this is negative and zero otherwise, is important in Greater London and the South only. • The two stock market effects together suggest that, for example, a 20% stock market downturn has a much smaller (absolute) effect on house price inflation in Great London and the South than a 20% upturn.
• We investigated whether the growth in the regional population proportion in the main ages for first time buyers (20 to 39) had any effect. The estimated effect of this pp 2039 variable is statistically significant and positive. • We include dummy variables for 1988, 1989 and 2001. • In 1988 it became clear that domestic rates would be abolished in England Wales and replaced by the Poll Tax. It also marked the March announcement that from August 1 st, tax relief for mortgage interest would be restricted to one per property. • The 2001 dummy reflects 9/11 and stock market turmoil effects, likely to have been more severe in London
Checks on Model Adequacy • Overall the model fits well, although there is some evidence of mild autocorrelation in a couple of equations. • The stability of the model was checked by estimating it on different sub-samples. • In particular, there is no evidence that we over fitted the recent house price boom. • The specification was checked against quarterly house prices equations for the UK and the North and South of Britain and consistent results were obtained.
Some Caveats • We checked for income distribution effects, since space is a luxury good, and property tax effects (domestic rates, council tax) since variations in tax rates over time and over regions should have effects on prices. • Income distribution changes are trend like and so are hard to detect. • Despite pain-staking work constructing regional tax data back to 1975, the estimated tax effects were insignificant. The use of 1988 and 1989 dummies probably picks up much of the effects of the tax switches of the time. • Further work on the issue desirable, although handling expectations of tax changes will always be difficult. • It seems likely that property taxes linked more closely to house prices could have damped the market and had some long run effects.
Caveats (Cont’d) • No frenzy effects of the kind used by Hendry (1984) and Muellbauer and Murphy (1997) in the model. • No income expectation terms, though expectations effects are likely to be reflected in the interest rate and income dynamics which are in the model.
• • • Figure 4 shows the effects of downside risk, clearly a lagged endogenous variable, mea It suggests that the depth of the early 1990 s housing market recession had much to do w This was so especially in Greater London, where the effect only began to lift after 1995
• • • Figure 5 shows the effect of changes in the proportion of the working age population a It plays a considerable role in explaining the out-performance of Greater London house It also helps explain why house prices were apparently slow to respond to the interest r
• • • Figure 6 suggests that, before 1997 or so, the rate of house building broadly matched r Since then, the latter have greatly outpaced the rate of house building, especially in Gr The composite effect explains most of the rise in real house prices since around 1997, .
• • • Figure 7 a shows one version of an error correction term including income per house, Great The change in age structure and the rate of change in population per house, two near I(1) v Figure 7 a suggests that, given interest rates, incomes, population and housing stock, Greate
Base scenario cont’d • Regional population projections from Gov’t Actuaries Dept. • Shows decline in growth of working age population over next 7 years. • Further decline of proportions aged 20 -39, with largest decline around 2006, then tailing off a little. • Rate of growth of regional housing stocks = average of last 7 years. • Relative per capita regional earnings, tax factors, and employment rates unchanged. • Growth of owner-occupation = average of previous 7 years.
Results • No house price bubble if this is plausible scenario. • Increase rate of growth of housing stock by 50%: house price growth only marginally lower, though level effect accumulates. • What could go wrong? Economy turning sour.
Conclusions • UK base scenario suggests there has been no bubble. • If REITS and SIPPS valuation effects are significant, could be upturn. • If economy turns sour and no REITS, SIPPS valuation effects, could see moderate nominal falls in 2006 -7, esp. in London and South. • System response is important for answering question: if consumption, income, exchange rate feedbacks are large, could be self-reinforcing, but temporary, downturn. • Exposure to debt, high house prices in Anglo-Saxon economies is high, so global interest rate environment will remain kind.