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Housing John Muellbauer, Nuffield College, Oxford. Presentation for FMG’s London Financial Regulation Conference, July 2, 2009. Presentation draws on “House Prices and Credit Constraints: Making Sense of the U.S. Experience” John V. Duca* (Federal Reserve Bank of Dallas, Southern Methodist University) John Muellbauer (Oxford University), Anthony Murphy (Oxford University). (*usual Fed disclaimer applies) US House Prices Fall Since 2006, Reversing Some of Earlier Gains 2008:q4 +23% +11% + 6% Source: Freddie Mac, Bureau of Economic Analysis, Federal Reserve Board, and authors’ calculations. Mortgage and Housing Crisis Lower Demand for Housing Less Home Construction Lower Capital of Financial Firms ↓Home Prices & Wealth, Slower Consumption ↑ Counter-Party Risk, Money & Bond Mkts Hit Slower GDP Growth Credit Standards Tightened on All Loans RHS of J. Duca graphic: defaults, bad loans & spreads feed back via banks and credit markets • Empirical literature on defaults necessarily implies non-linear or asymmetric responses to asset prices, e.g. Aron and Muellbauer (2009) on UK mortgage defaults. • Link defaults to asset base of banks, as in BOE RAMSI. • Bernanke (1983), Brunnermeier (2008), Von Peter (2009) and BIS 2009 papers by Duffie, Shin, Tirole, and Rajan all about aspects of this amplification. Late Mortgages More Prevalent, Reaching New Records Source: Mortgage Bankers Association. LHS of pic: sources of amplification/feed back via household sector • Extrapolative expectations or momentum trading. • Log-linear house price model implies a type of ‘frenzy’ effect. • Downside risk or fear induced by defaults can amplify downside. • Linearity of budget constraint implies elasticity of consumption w.r.t. asset prices is higher with higher asset prices. Housing Services Approach to Modeling Home Prices • D for housing services (≈hs≡housing stock)) log hst = a0 + a1 log yt + a2 log zt − a3 (log rhpt + log usert ) • Inverted demand => house price equation log rhpt = [a0 + a1 log yt + a2 log zt − log hst ] / a3 − log usert where housing stock, income, and housing variables are per capita. Add adjustment dynamics….. ‘Neoclassical’ demand for durables theory defines ‘user cost’ • Real user cost is (user)(real house price index). • ‘User’ is real interest rate + (rate of tax, transactions cost, risk premium) – expected rate of appreciation of real house prices, see J.S.Cramer (1957), Irving Fisher(1930) . Housing Price-Rent Approach to Modeling Home Prices • Arbitrage between owner and rental markets implies price-rent ratio akin to P/E ratio ( Rent / HP )t = usert Also obtains when agency costs make renting housing services more expensive vs. owning • Inverting and taking logs: log( HP / Rent )t = − log usert real user elasticity = -1; and ratio invariant to housing stock and swings in income Housing Price-Rent Approach to Modeling Home Prices • In Yong Kim’s (2007, USC manuscript) model binding (max LTV) credit constraints on marginal home buyer plus rental agency: ( HP / Rent )t = f (usert , ltv, ydeviations ) Where the magnitude of the negative user elasticity can be smaller than 1. Comparing the Two Approaches • Relative advantages of housing services: – Practical where rent markets regulated (UK) – Does not ignore that income shocks can drive rents and home unlike the price-to-rent or P/E approach – Grounded more in consumer demand • Relative advantages of P/E approach: – Good where rent markets flexible (US) – Does not require good housing stock data – Rents capture many factors special to housing absent from variables in canonical housing services approach – Grounded more in finance and arbitrage Financial Innovations Affecting US Mortgage Markets • US: rising home-ownership, higher home-price ratios, and increased leverage (lower home owner equity stake) • Yet, avg. LTV conventional prime loans flat. Why? – Innovations ease LTV constraint for 1st time buyers • Credit scoring sorts subprime & nonprime borrowers • New financial products enable selling of nonprime mortgages by offering new (later insufficient) credit protection – Innovations lower mortgage costs and easing ability to tap housing wealth for existing owners • US changes more evolutionary, less abrupt than in UK • Home equity lines of credit, 1986 Tax Act • Lower fixed costs of mortgage refinancing, deeper MBS mkt – Increases option value of fixed rate financing – Makes cash-out mortgage refinancing cheaper Average LTVs for First-Time HomeBuyers Using Non-Gov’t Mortgages Height of subprime boom Last point: 2007:q2 before August ’07 financial crisis Savings & Loan Crisis resolution passed 1989:q3, LTVs 3-4 % pts lower in 1989:q3 & 1989:q4 Source: American Housing Survey, calculations from Duca, Johnson, and Muellbauer (2008, in process). Non-gov’t mortgages exclude FHA & VA insured mortgages and other gov’t insured or direct mortgage loans that are generally limited in their individual size. 3-quarter moving average. Amplification via expectations • Much evidence for extrapolative element in expectations, or momentum trading, Piazzesi and Schneider (2008). • Hence a sequence of positive shocks, e.g. sub-prime explosion – fall in LTVs for first time buyers and lower interest rates, can cause appreciation, generating more demand and further appreciation. • Our ‘user’ incorporates 3-4 year memory of US hp appreciation. Error-Correction Results: LTV models Outperform non-LTV Models of House Price-to-Rent Ratio Log user costt-1 No LTV 81:3-2001 LTV 81:3-2001 No LTV 81:3-2007:2 LTV 1981:3-2007:2 -0.1864** (-4.19) -0.1951** (-7.95) -0.2673** (-11.22) -0.1698** (-21.97) 0.8873** (4.71) First-Time Buyer LTVt-1 0.8086** (6.19) No Yes* Yes* Yes** ECt-1 -0.0816* (-2.32) -0.1802** (-3.11) -0.0304+ (-1.67) -0.1985** (-4.31) Cut CapGainTaxt-1 raises home price 0.0064** (3.02) 0.0044** (2.94) 0.0057** (3.11) 0.0069** (4.31) R2 S.E. 0.541 .00506 0.662 .00447 0.639 .00500 0.748 .00432 Cointegration, unique vector? 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 3 and 4 in the paper. Amplification via intrinsic non-linearity • Log user cost amplifies falls at low levels, i.e. when recent years’ HP appreciation high relative to tax etc. adj. interest rate. • Similar fit and speed of adj. with LINEAR user cost and CUBIC in last year’s HP appreciation – like Hendry (1994), M&M (EJ 1997) ‘frenzy’ effect. • Similar results for inverse demand function approach. A Hostage to Fortune – The Simulated path of Future US House Prices • If our model is correct, the US house price bust may last to end 2010 (on Freddie Mac)! • The simulated path of future real US house prices, shown in next figure, assumes that: – The US economy recovers slowly; – Mortgage credit condition revert to their end 1999 value; – (Most importantly) our model is correct. (Incorporates rent equation with slow feedback from house prices.) Forecast simulation for log US HP from HP/rent model HP index is Freddie Mac repeat sales, excl. refis • Index is less volatile than Case-Shiller 10 or 20 cities index and lags behind. • Forecast scenario is consistent with CaseShiller correction a little over half way through. • Fairly consistent with ‘futures’ on CaseShiller reaching bottom in mid-late-2010 Don’t forget impact of residential construction on GDP %, Res. Const. Share of GDP Figure 9: Residential Construction Share of GDP Drops in the U.S. and Spain 8 7 Spain 6 Germany 5 EU 27 4 U.S. 3 UK Japan 2 1 0 98 00 02 04 06 08 Concluding Comments • Study addresses an important data issue. • Findings: important to measure credit constraints facing marginal 1st time buyers. • Results: U.S. experienced a credit bubble that fuelled a housing bubble. • Model builds in major over-shooting possibility of HP given lagged appreciation in user cost term – driven by sequence of +’ve shocks – interest rates, fin. innovation Conclusions cont’d • Also amplification from intrinsic nonlinearity. • Implication: the unwinding depends partly on credit conditions returning to norm, but overshooting on downside too. • Timing pattern of 1st time buyers disrupted, wave of foreclosure sales may alter s-run dynamics UK contrast • UK sub prime problem a lot less serious. • Low levels of construction in last decade imply smaller supply-demand imbalance. • Current City boomlet is short-term positive. • Fall in mortgage interest rates is sharper than in US, given floating rate market. • But bigger hp boom and worse credit supply contraction. • CML right to correct mortgage repossessions forecast for 2009 from 75k to 45k. • But my guess is worse to come in 2010. UK policy measures • • • • ISMI rules relaxation Code of practice for mortgage lenders Home owner mortgage support scheme CLG mortgage rescue for the most vulnerable households • Bank rescues and guarantees • QE