Boom and Bust in US and UK House Prices The Contribution of Changing Credit Conditions and Bubbles

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Boom and Bust in US and UK House PricesThe
Contribution of Changing Credit Conditions and
Bubbles

• Anthony Murphy
• Oxford University
• Institut DÉconomie Publique
• Housing Market Bubbles and Cycles Conference
• Marseille, Oct 22-23 2009
• Based on joint research with John Duca (Dallas
  Fed) and
• John Muellbauer (Oxford University)

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Outline

• Bubbles and changing credit conditions
• Explaining US house prices
• Inverted demand house price equations
• House price to rent equations
• Future US house prices a simulation
• Credit conditions and UK house prices
• Concluding remarks

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Bubbles

• It is difficult to detect bubbles in asset prices
  using formal econometric tests.
• The tests have low power and are based on a range
  of auxiliary assumptions.
• For example, Gurkaynak (J. Econ Perspectives
  2008) suggests that bubble tests of equity prices
  do not do a good job of differentiating between
  mis-specified fundamentals and bubbles. It is a
  matter of taste and personal preference that
  makes the econometrician choose between bubble
  and fundamentals-based explanations of stock
  price behaviour.
•  

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Bubbles (Contd)

• I believe the same caveats apply to detecting
  house price bubbles persistent deviations of
  house prices from fundamentals - where we have
  even less data and less agreement on
  fundamentals.
• In the US and the UK, many house price bubble
  episodes appear to be generated by changes in
  credit conditions, which were either long term
  changes or widely perceived to be long term
  changes at the time (e.g. the subprime
  revolution).

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In a Nutshell

• Standard econometric house price models for the
  US (and, to a lesser extent, the UK) do not work
  well in sample .so not very useful for policy
  analysis or simulation.
• Why?
• Econometric answer mainly structural break, due
  to classic omitted variable bias.
• Economic answer models do not take account of
  changing credit conditions / standards.

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In a Nutshell (Contd)

• Easier credit drives up the house price to income
  and house price to rent ratios, ceteris paribus.
• Proxy mortgage credit conditions for the US using
  the cyclically adjusted trend in the loan to
  value ratio (LTV) for first time buyers. Source
  American Housing Survey.
• The adjusted LTV data are consistent with
  standard accounts of lax credit conditions and
  the growth in sub-prime lending etc.

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Aside The US Housing Bust(A Scary Bedtime Story
for Central Bankers)

• Financial innovations in securitization and
  changes in procedures by rating agencies, inter
  alia, resulted in the sub-prime revolution.
• Loans were extended to borrowers with poor credit
  histories, previously denied loans.
• Many of the loans were for adjustable rate
  mortgages which particularly benefited from the
  lowest interest rates for decades in 20012003.
• The house-price rises, set in train by these
  credit-supply and interest-rate changes, fooled
  many people into thinking that such rises would
  be sustained.
• Fundamentals changed in 2003 as interest rates
  returned to more normal levels, and high rates
  of building expanded the housing stock, while
  house prices became increasingly overvalued.
• As the extent of bad loans gradually became
  clear, the fundamentals changed again, as the
  supply of credit for all types of mortgages
  contracted.

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In a Nutshell (Contd)

• Two basic (theoretical) approaches to modelling
  house prices the inverted housing demand and
  house price to rent approaches.
• Our LTV based measure of credit conditions work
  well in both econometric models
• LTV highly significant
• Obtain plausible and (more) stable long run
  equilibrium parameters and adjustment speeds
• Models track well in pseudo out of sample
  forecasts.
• Can use models to examine various interesting
  scenarios.

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What Do We Want To Explain? US House Prices
Gains 1999-2006
2006q4
45
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What Do We Want To Explain (Contd)? Recent US
House Prices Falls
2008q4
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Source Freddie Mac, Bureau of Economic Analysis,
Federal Reserve Board.
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Econ 101 What Determines House Prices?Two
Models.

• The most basic theory of what determines house
  prices is just a story of supply and demand,
  where the supply - the stock of houses - is given
  in the short run.
• Then house prices depend on the stock of housing
  and the factors driving demand.
• This is our inverted housing demand model.
• In the US, where rental markets are
  well-developed and rents are generally market
  determined, the most popular model of house
  prices is the house price to rent model.

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Model 1The Inverted Housing Demand Model

• The inverted demand equation is obtained by
  considering the demand for housing services
• where hp real house price, y real income,
  and z other demand shifters.
• Own-price and income elasticities are ? and ß.
• Inverting yields

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Demand Shifters (zs) in the Inverted Demand
House Price Model

• Housing is a durable good (with an investment
  component) ? expected or permanent income and
  user cost should be important drivers.
• User cost after-tax interest rate property
  tax rate depreciation rate - expected rate of
  house price appreciation (less transactions
  costs).
• Expected capital gains seldom observed so must
  be proxied by past capital gains or a reduced
  form model etc.  

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Bubble Builder and Burster Dynamics

• Housing market not efficient, so systematic
  mis-pricing (over or under shooting) can persist
  for quite a while.
• There is also a large extrapolative element in
  expectations.
• A series of positive shocks to fundamentals can
  lead to rising prices and the expectation of
  further appreciation, leading to greater and
  greater overvaluation. In due course, the
  increasing negative pull from fundamentals and
  increased supply reduce the rate of appreciation.
  
• Lagged house price capital gains or losses are an
  important determinant of current house prices,
  termed the bubble-builder by Abraham and
  Hendershott (1996).
• The deviation of prices from long-run
  fundamentals is then the bubble-burster. 

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Yet More Explanatory Variables!

• Demography e.g. the proportion of households in
  the under-35 age group where many first-time
  buyers are to be found.
• Many mortgage borrowers face limits on their
  borrowing and may be risk averse ? nominal
  interest rates and proxies for downside risk or
  mortgage default may be important. (Cameron et
  al., 2006).
• Finally, changes in credit conditions matter for
  house prices (and savings), particularly in the
  US and UK.

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Model 2 The Ratio of House Price to Rents

• Ceteris paribus, arbitrage between owner-occupied
  and rental housing markets implies the house
  rent-to-price ratio equals the real user cost of
  housing.
• Hence, the log house price-to-rent ratio equals
  the log of the inverse of real user cost
• Very simple and attractive model.
• But misleading if credit conditions vary etc.

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More Realistic Model of House Price / Rent Ratio

• Kim (2007) show that in a model with binding (max
  LTV) credit constraints for marginal home buyer,
  plus rental agency costs
• ydev deviation of income from trend
• The negative user cost elasticity can be smaller
  than one.

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The Inverted Demand Model - Data

• Home Prices Freddie Mac repeat sales of homes
  purchased with conforming mortgages. Deflated
  using PCE index.
• Income Real per capita labor plus transfer
  income.
• Real Mortgage Rate After tax and depreciation
  adjusted nominal mortgage rate, minus 16 quarter
  annualized appreciation (adjusted for home
  selling costs).
• Real Housing Stock Feds Flow of Funds estimate
  of the replacement cost of housing structures,
  deflated by housing construction price index.
• Monetary Target Regime Dummy for monetary
  targets that boosted interest rate volatility
  (1979 Q4 to 1982 Q3)
• Deposit Regulations Two quarter change in effect
  of Regulation Q (REGQ) dis-intermediation policy.
• Tax VariableTax advantage to housing capital
  gains since 1998 Q1

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Inverted Demand Model Results

• Our mortgage credit conditions measure is highly
  significant (both economically and
  statistically).
• Can treat it as exogenous.
• Models with credit conditions are more stable.
• In pseudo out-of-sample forecasts, models without
  the credit conditions measure do not track the
  sub-prime boom and bust.
• Models with the credit conditions measure do.
• Credit conditions effect identified in the data
  by (small) earlier relaxations in mortgage credit
  standards.

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VEC (Vector Error Correction) Inverted Demand
Results
Vectors allow trends in variables but not in the
cointegrating relationship. Controls include 0-1
dummies for monetary targeting regime and 1998
capital gains tax relief, depreciation rate on
rental properties, and consumer income/interest
rate expectations. Statistics from Tables 1 and
2 from the paper.
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Real House Prices Tracked Better by Long Run
Equilibrium of LTV vs. Non-LTV Models
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Long Run Equilibrium House Prices Similar for LTV
Models Estimated With Without 2002-2007
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House Price to Rent Model Results

• Our findings are qualitatively similar when we
  model the house price to rent ratio.
• Models with our LTV based measure of credit
  conditions perform better.
• One specification of the estimated long run rent
  and house price to rent equations for the US is
•  
• The estimated speeds of adjustment to the long
  run equilibrium are very slow, since rents are
  very sticky.

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March 2009 Hostage to Fortune - The Simulated
Path of Future US House Prices

• If our model is correct, the US house price bust
  may last quite a long time!
• Our March 2009 simulated path of future real US
  house prices, shown in next figure, assumed that
• The US economy recovers very slowly no real
  income growth and unemployment only returning to
  its long run average in 2014
• Mortgage credit condition revert to their end
  1999 value
• Actual house prices have not fallen as fast in
  the past two quarters.

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Real House Price Simulation Based on House
Price-to-Rent Model (LTVs Revert to 1999 Q4
Level in 2008 Q4)
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A Run Thru Some UK Results

• Cameron, Muellbauer Murphy (2006) Mmodel house
  prices for 8 regions of Great Britain between
  1972 and 2003 with a system of inverted demand,
  equilibrium correction equations.
• The housing stock is an explanatory variable
  along with regional income, real and nominal
  interest rates, demographics and other demand
  shifters, especially credit conditions.
• Identify significant direct and indirect (via
  real and nominal interest rates) effects of more
  liberal credit conditions.

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ABC of Regional House Price Models

• At regional level, demand in a region depends on
  own-price and prices in other regions.
• If prices are simultaneously determined, we have
  to invert system of demand equations.
• This is why (log) house price in a region depends
  on log income/house etc. in own region and in
  other regions.
• US studies of house prices at Metropolitan Area
  level all use Lucas islands paradigm neglect
  substitution between nearby locations.

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Long Run Effects Income Per House

• The long-run solution is for 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, this is defined as
• log(real non-property income) - log(housing
  stock)-1 - 0.7log(rate of owner-occupation)-1 in
  region r.
• Modest spill-over from non-owner occupied supply
  onto the owner-occupied housing market.

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• All regions are influenced not just by the own
  region value of income per house but also by the
  GB value, with weights of ? and ? 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.

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Figure 7 Equilibrium Correction Terms
0.5
0.4
0.3
0.2
0.1
0.0
-0.1
-0.2
-0.3
Greater London
West Midlands
North
-0.4
1975
1980
1985
1990
1995
2000
2005

• Figure 7 shows one version of an equilibrium
  correction term including income per house,
  Greater London catch up, credit and interest rate
  effects.
• The figure suggests that, given interest rates,
  incomes, population and housing stock, Greater
  London was only moderately overvalued in 2003,
  while the West Midlands and the North were
  substantially undervalued

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Long Run Effects of Credit Conditions

• Used an index of credit conditions (CCI) which
  measures credit supply to UK households.
• We find a significant CCI levels effect, as well
  as interaction effects of CCI with both the
  nominal mortgage rate and the real mortgage rate.
• Ceteris paribus, easier credit conditions raises
  house prices by relaxing downpayment constraints.
  
• In addition, nominal rates matter less and real
  rates more (so intertemporal effects are more
  important) when credit availability improves.
• Consistent with findings for mortgage demand by
  Fernandez-Corugedo Muellbauer (2005) and
  consumer spending by Aron, Muellbauer Murphy
  (2006).

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Figure 3 Long Run Effects of Credit Conditions
and Interest Rates
0.3
0.2
Credit Conditions CCI
(Excd Interactions)
0.1
0.0
Interest Rates
(Incld CCI Interaction)
-0.1
-0.2
1975
1980
1985
1990
1995
2000
2005

• Figure 3 shows the estimated long-run effect of
  the credit conditions index (cci) and real and
  nominal mortgage rates interacted with cci.
• Relative to the 1970s, the estimated effects of
  cci, in terms of its direct, positive effect on
  real house prices, is roughly canceled out by the
  effect of the rise in real interest rates.

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Some Short Run Effects

• Short run effects include
• house price and income dynamics
• as well as
• changes in nominal interest rates,
• new housing supply,
• population structure
• inter alia.

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Model Adequacy and Recent Bubble

• 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 house price boom at the end of the
  sample (1997-2003).
• Conclusion No evidence of bubble?

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Difficulties in Detecting Bubbles

• Does a bubble imply substantial positive
  residuals (i.e. under-predictions) when
  forecasting house prices for recent years, given
  parameter estimates for earlier years?
• No, regularities in the dynamic behaviour of
  house prices may be well captured by a model with
  extrapolative expectations, so that bubbles can
  arise without such residuals being found.
• A more decisive test? Using plausible scenarios
  for the drivers of house prices (e.g. long run
  values) for the next 5 to 10 years, is a major
  decline likely?
• Difficulty - There is a range of uncertainty
  about long-run values!

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Detecting Bubbles and Prospects for UK House
Prices

• At the time, many regarded the expansion in
  credit supply in the US from 2000 to 2005 as a
  permanent shift! We now know that it was
  unsustainable.
• Thus, what may have looked like a permanent shift
  in US and UK equilibrium house prices now looks
  like a bubble.
• In the UK, credit conditions have worsened
  substantially since the start of 2008.
• Over time, credit conditions will improve
  somewhat and new housing supply is severely
  constrained so house prices will recover in the
  long run, albeit to a lower level than before the
  current bust,

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To Do

• Mainly data issues!
• Update credit conditions index CCI
• New survey of mortgage lenders with extended
  coverage sort out discontinuities
• Data not published any longer need access to
  data.
• Revisions in historical national and regional
  employment data.
• New regional personal income data.