Has There Been A British House Price Bubble

Has There Been A British House Price Bubble? Evidence from a Regional Panel Thursday 20 th April 2006 RES Annual Conference Gavin Cameron, John Muellbauer & Anthony Murphy Oxford University & Nuffield College

A Recent House Price Bubble? • Many theoretical arguments have been advanced to justify bubbles in asset markets, including the housing market. • Need to carefully examine and interpret the empirical evidence. • OECD, IMF and many UK commentators suggest that house prices are overvalued by 20% or more. • Evidence is weak – house price to income or rent ratios; non-structural models which are hard to interpret.

The Alternative View “…[T]here 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. ” Steve Nickell, MPC member. Keynes Lecture, British Academy, Sept 2005.

The Nickell View (Cont’d) Equilibrium level of UK house prices has risen for four reasons: • Strong income growth (and 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.

Source: Nickell (2005).

Source: Council of Mortgage Lenders (2005).

Need Good Models • To sort shift in fundamentals from short term bubbles • ‘Structural’ models better than twodimensional charts and black box models. Regional Data • More informative ‘cos more variation • Some data limitations.

Features of Good Regional House Price Models? Meen and Andrews (1998) suggest: • 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 • Model house prices for 8 regions of Great Britain between 1972 and 2003 with a system of inverted demand equations with the housing stock as an explanatory variable along with regional income, real and nominal interest rates, demographics and other demand shifters. • Our model has equilibrium-correction form with positive effects from recent rises in house prices (‘bubble-builder’ effect) and negative effects from high levels of real house prices relative to their fundamentals (‘bubble-burster’ effect).

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 ABC of House Price Determination • Derive inverted demand equation. • Suppose real house prices hp adjust to equate log demand with log of end of previous period supply hs-1. • Let log housing demand be given by ln hs-1 = - ln hp + ln y + z , ln hp = log real house price and ln y = log real income and z = other demand shifters. • The own price elasticity of demand is - and we assume the income elasticity is 1 (in line with the consensus in the literature).

The Inverted Demand Eqn (Cont’d) • Solving for ln hp yields: ln hp = (ln y – ln hs-1 + z)/ • Note log (income per house) restriction. • Consensus is that 1/ is approx 1. 5 to 2. • z will include real and nominal interest rates, demographics, expectations of rate of return or user cost, etc.

• The regions experienced broadly comparable long run movements. Greater Lo

• 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 • The equations are non-linear with many cross-equation restrictions, because of common parameters, and interaction terms. • Some spatial coefficient heterogeneity is allowed for.

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. • 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. • Modest spill-over from non-owner occupied supply onto the owner-occupied housing market.

• 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 ⅓ and ⅔ respectively • The long-run effect of log real income per house on the log real house price is 1. 6, in line with previous studies. • The speed of adjustment to long run equilibrium is allowed to vary with Stamp Duty rates.

Other Long Run Levels Effects • Region specific intercepts and (small positive ) time trends. • 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).

Other Long Run Levels Effects (Cont’d) • The interest rate effect are consistent with findings for mortgage demand by Fernandez-Corugedo and Muellbauer (2005). • We proxy downside risk using rrhnegr the average value over the previous 4 years of the negative return in the region’s housing market. Mortgage repossessions 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 ½. • 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. • In London and the South East the estimated income growth coefficients are higher. • As expected, the effect declines over time as credit conditions (proxied by cci) have improved. • Region specific income growth rates have little explanatory power- national income growth does the work.

• The question of stock and flow equilibrium effects on house price determination is important. • The flow 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 include 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.

We failed to find a positive financial wealth effect unlike earlier national studies, but…. • The rate of growth of the FTSE index in real terms ( lrftse) has signif. positive effects, in Greater London and the South. • Define simple measure of downside risk for the stock market. lrftseneg = lrftse if this is negative and zero otherwise, important in Greater London and the South only. • The two stock market effects together suggest that a 20% stock market fall has a much smaller absolute effect on house prices in Great London and the South than a 20% rise.

• Growth in the regional population proportion in the main ages for first time buyers (20 to 39) pp 2039 variable is significant and positive. • 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. Also 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.

Checks on Model Adequacy • Overall the model fits well, although there is some evidence of mild autocorrelation in one equation. • 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 1997 -2003.

• • Figure 3 shows the estimated long-run effect of the credit conditions index (cci) and r Relative to the 1970 s, the estimated effects of cci, in terms of its direct, positive effect

• • • Figure 4 shows the effects of downside risk, clearly a lagged endogenous variable, me 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 populatio 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 shows one version of an equilibrium correction term including income per hous The change in age structure and the rate of change in population per house, two near I(1) v Figure 7 suggests that, given interest rates, incomes, population and housing stock, Greater

Base Scenario • Forecast period 2004 to 2010 • Growth rate of real non property income: 2. 1%, 1. 5%, 2. 0%, 2. 3%, 2. 5% • Inflation rate: 1. 3%, 2. 1%, 2. 7%, 2. 8%, 2. 6%, 2. 4%, 2. 2% • Mortgage interest rate: 5%, 5½%, 5%, 5% • Growth rate of real FTSE index: 9%, 8%, 7%, 5%, 5% • CCI constant.

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 & growth of owner occupation = average of last 7 years. • Relative per capita regional earnings, tax factors, and employment rates unchanged.

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.

Scenario B – The Gloomy Scenario • Growth rate of real non property income: 2. 1%, 1. 2%, 0. 5%. 1. 0%, 1. 5%, 2. 0% • Inflation rate: 1. 3%, 2. 5%, 3%, 2. 8%, 2. 6%, 2. 4% • Mortgage interest rate: 5%, 5½%, 6%, 5½%, 5% • Growth rate of real FTSE index: 9%, 8%, 0%, 5%, 5% 5%

Some Conclusions • UK base scenario suggests there has been no bubble. • If REITS valuation effects are significant or stock market remains strong, small upturn is likely. • If economy turned sour and no REITS 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.

Conclusions (Cont’d) • Exposure to debt, high house prices in Anglo-Saxon economies is high, so global interest rate environment will remain kind? • Did ‘house price terrorists’ such as Andrew Oswald, The Economist and OECD help prevent a bubble? • A bubble in 1988 -9 not ruled out.