Forecasting the US real house price index: Structural and non-structural models with and without fundamentals

We employ a 10-variable dynamic structural general equilibrium model to forecast the US real house price index as well as its downturn in 2006:Q2. We also examine various Bayesian and classical time-series models in our forecasting exercise to compare to the dynamic stochastic general equilibrium model, estimated using Bayesian methods. In addition to standard vector-autoregressive and Bayesian vector autoregressive models, we also include the information content of either 10 or 120 quarterly series in some models to capture the influence of fundamentals. We consider two approaches for including information from large data sets — extracting common factors (principle components) in factor-augmented vector autoregressive or Bayesian factor-augmented vector autoregressive models as well as Bayesian shrinkage in a large-scale Bayesian vector autoregressive model. We compare the out-of-sample forecast performance of the alternative models, using the average root mean squared error for the forecasts. We find that the small-scale Bayesian-shrinkage model (10 variables) outperforms the other models, including the large-scale Bayesian-shrinkage model (120 variables). In addition, when we use simple average forecast combinations, the combination forecast using the 10 best atheoretical models produces the minimum RMSEs compared to each of the individual models, followed closely by the combination forecast using the 10 atheoretical models and the DSGE model. Finally, we use each model to forecast the downturn point in 2006:Q2, using the estimated model through 2005:Q2. Only the dynamic stochastic general equilibrium model actually forecasts a downturn with any accuracy, suggesting that forward-looking microfounded dynamic stochastic general equilibrium models of the housing market may prove crucial in forecasting turning points.     

A national or local phenomenon? A comparison of the US and the Chinese housing markets

This article investigates the common movements of house prices across cities as well as the macroeconomic underpinnings of the comovements in the US and China. Our empirical results indicate more differences than similarities between the US and the Chinese housing markets. The results from a Bayesian dynamic latent factor model indicate that the fluctuations of house prices across cities in the US are more a national phenomenon, while the dynamics of house prices across cities in China are mainly driven by the city-specific component. We further use VAR models to compare the roles of the underlying determinants in these two housing markets. The results show that the roles of monetary policy shocks and aggregate fluctuations in driving the common movements of house prices across cities differ substantially between the US and China at both short and long horizons.     

Was the recent downturn in US real GDP predictable?

This article uses a small set of variables – real GDP, the inflation rate and the short-term interest rate – and a rich set of models – atheoretical (time series) and theoretical (structural), linear and nonlinear, as well as classical and Bayesian models – to consider whether we could have predicted the recent downturn of the US real GDP. Comparing the performance of the models to the benchmark random-walk model by root mean-square errors, the two structural (theoretical) models, especially the nonlinear model, perform well on average across all forecast horizons in our ex post, out-of-sample forecasts, although at specific forecast horizons certain nonlinear atheoretical models perform the best. The nonlinear theoretical model also dominates in our ex ante, out-of-sample forecast of the Great Recession, suggesting that developing forward-looking, microfounded, nonlinear, dynamic stochastic general equilibrium models of the economy may prove crucial in forecasting turning points.     

THE PUZZLING DIVERGENCE OF RENTS AND USER COSTS, 1980–2004

This paper demonstrates that, in the context of U.S. housing data, rents and ex ante user costs diverge markedly—in both growth rates and levels—for extended periods of time, a seeming failure of arbitrage and a puzzle from the perspective of standard capital theory. The tremendous volatility of even appropriately‐smoothed ex ante annual user cost measures implies that such measures are unsuitable for inclusion in official price statistics. The divergence holds not only at the aggregate level, but at the metropolitan‐market level as well, and is robust across different house price and rent measures. But transactions costs matter: the large persistent divergences did not imply the presence of unexploited profit opportunities. In particular, even though detached housing is readily moved between owner and renter markets, and the detached‐unit rental market is surprisingly thick, transactions costs would have prevented risk‐neutral investors from earning expected profits by buying a property to rent out for a year, and would have prevented risk‐neutral homeowners from earning expected profits by selling their homes and becoming renters for a year. Finally, computing implied appreciation as a residual yields a house price forecast with huge errors; but either longer‐horizon or no‐real‐capital‐gains forecasts—which turn out to have similar forecast errors—imply a far less divergent user cost measure which might ultimately be useful for official price statistics. Some conjectures are offered.     

Forecasting industrial production in the Euro area

The creation of the Euro area has increased the importance of obtaining timely information about short-term changes in the area's real activity. In this paper we propose a number of alternative short term forecasting models, ranging from simple ARIMA models to more complex cointegrated VAR and conditional models, to forecast the index of industrial production in the euro area. A conditional error-correction model in which the aggregate index of industrial production for the area is explained by the US industrial production index and the business confidence index from the European Commission harmonised survey on manufacturing firms achieves the best score in terms of forecasting capacity. First version received: Jan. 2000/Final version received: March 2000     

Educational attainment,growth and poverty reduction within the MDG framework: simulations and costing for the Peruvian case

Using quarterly data, 1999:Q2–2009:Q3, we empirically examine the key macro determinants of housing prices for China’s residential market. Employing Granger causality and Vector Auto-Regression (VAR) models, we find that there exists strong bivariate causality between house price increases and its determinants. The variance decomposition suggests that speculative factors reflected by past increases in real house price contribute a relatively larger proportion to house price rises relative to fundamental factors.     

Asset prices, exchange rates and the current account

This paper analyses the role of asset prices in comparison to other factors, in particular exchange rates, as a driver of the US trade balance. It employs a Bayesian structural VAR model that requires imposing only a minimum of economically meaningful sign restrictions. We find that equity market shocks and housing price shocks have been major determinants of the US current account in the past, accounting for up to 30% of the movements of the US trade balance at a horizon of 20 quarters. By contrast, shocks to the real exchange rate have been less relevant, explaining about 9% and exerting a more temporary effect on the US trade balance. Our findings suggest that large exchange rate movements may not necessarily be the key element of an adjustment of today's large current account imbalances, and that in particular relative global asset price changes could be a potent source of adjustment.     

Forecasting US real house price returns over 1831–2013: evidence from copula models

Given the existence of nonnormality and nonlinearity in the data generating process of real house price returns over the period of 1831–2013, this article compares the ability of various univariate copula models, relative to standard benchmarks (naive and autoregressive models) in forecasting real US house price over the annual out-of-sample period of 1874–2013, based on an in-sample of 1831–1873. Overall, our results provide overwhelming evidence in favour of the copula models (Normal, Student’s t, Clayton, Frank, Gumbel, Joe and Ali-Mikhail-Huq) relative to linear benchmarks, and especially for the Student’s t-copula, which outperforms all other models both in terms of in-sample and out-of-sample predictability results. Our results highlight the importance of accounting for nonnormality and nonlinearity in the data generating process of real house price returns for the US economy for nearly two centuries of data.     

Employment responses to aggregate and sectoral technology shocks

This paper investigates the responses of sectoral employment in US manufacturing to a technology shock by its type: aggregate or sectoral. In order to distinguish between aggregate and sectoral shocks, we construct independent VAR models for identifying each shock separately: a factor-augmented vector autoregression (FAVAR) for aggregate shocks and a sectoral SVAR for sectoral shocks. Our aggregate model in particular extends the conventional small-scale VAR to the FAVAR framework of Bernanke et al. (2005) so that it can address the potential bias from omitted variables. The main findings are as follows: most industries exhibit negative employment responses to an aggregate technology shock while exhibiting positive responses to a sectoral technology shock. By comparing our FAVAR framework with Chang and Hong’s (2006) small-scale VAR, we show that applying the FAVAR results in significant differences in the estimated responses to an aggregate technology shock. Real rigidities (such as slow diffusion of new technology or frictional labor reallocation), rather than nominal rigidities (such as sticky prices), are crucial in accounting for the cross-industry difference in employment responses. In particular, the slow diffusion of new technology is closely related to the sluggish response of sectoral employment.     

The risk of falling house prices in Italy

This paper shows that Italian house market is less exposed to price shocks than the American one. Variations in the house price index in real terms have been studied along with the affordability ratio and the relation between house prices and rent levels for the period 1995–2004 in Italian provinces. Comparison with US data reveals greater overpricing in the US during the expansion phase (2000–2004). Although a speculative bubble in all US metropolitan areas considered does not emerge, US financial and economic structural factors make the US real estate sector more exposed to price shocks. To test the compatibility of Italian house prices with fundamentals an econometric model is designed to analyze the provincial house prices from 1995 to 2003.     

Explaining House Prices in Australia: 1970–2003*

This paper aims to explain changes in real house prices in Australia from 1970 to 2003. We develop and estimate a long-run equilibrium model that shows the real long-run economic determinants of house prices and a short-run asymmetric error correction model to represent house price changes in the short run. We find that, in the long run, real house prices are determined significantly and positively by real disposable income and the consumer price index. They are also determined significantly and negatively by the unemployment rate, real mortgage rates, equity prices and the housing stock. Employing our short-run asymmetric error correction model, we find that there are significant lags in adjustment to equilibrium. When real house prices are rising at more than 2 per cent per annum, the housing market adjusts to equilibrium in approximately four quarters. When real house prices are static or falling, the adjustment process takes six quarters.     

Regime switching and the (in)stability of the price–rent relationship: evidence from the US

Present value models of house prices assert that in the absence of self-fulfilling bubbles, a house price is equal to the present discount value of all future rents, which implies a linear relationship between house price and rent, and hence a stable price-to-rent ratio. Using a Markov switching error correction model, we re-examine this relationship in the US housing market and find two distinctive regimes: one with a long-run relation between house price and rent predicted by the present value models and the other in which the relation is nonlinear. Furthermore, we find evidence that deviations of house prices from the present value models’ predictions are caused by the overreaction of house prices to movements in rents rather than speculative bubbles attributable to extraneous factors.     

A DSGE-VAR model for forecasting key South African macroeconomic variables

The paper develops a Small Open Economy New Keynesian DSGE-VAR (SOENKDSGE-VAR) model of the South African economy, characterised by incomplete pass-through of exchange rate changes, external habit formation, partial indexation of domestic prices and wages to past inflation, and staggered price and wage setting. The model is estimated using Bayesian techniques on data from the period 1980Q1 to 2003Q2, and then used to forecast output, inflation and nominal short-term interest rate for one-to eight-quarters-ahead over an out-of sample horizon of 2003Q3 to 2010Q4. When the forecast performance of the SOENKDSGE-VAR model is compared with an independently estimated DSGE model, the classical VAR and six alternative BVAR models, we find that, barring the BVAR model based on the SSVS prior on both VAR coefficients and the error covariance, the SOENKDSGE-VAR model is found to perform competitively, if not, better than all the other VAR models.     

A model of housing and credit cycles with imperfect market knowledge

The paper presents a model of housing and credit cycles featuring distorted beliefs and comovement and mutual reinforcement between house price expectations and price developments via credit expansion/contraction. Positive (negative) development in house prices fuels optimism (pessimism) and credit expansion (contraction), which in turn boost (dampen) housing demand and house prices and reinforce agents׳ optimism (pessimism). Bayesian learning about house prices can endogenously generate self-reinforcing booms and busts in house prices and significantly strengthen the role of collateral constraints in aggregate fluctuations. The model can quantitatively account for the 2001–2008 U.S. boom-bust cycle in house prices and associated household debt and consumption dynamics. It also demonstrates that allowing for imperfect knowledge of agents, a higher leveraged economy is more prone to self-reinforcing fluctuations.     

中国房地产价格波动区域差异的实证分析

本文首先定性地比较了各地房价的波动,发现其波动具有明显的地区不平衡性。进一步,本文基于误差修正模型形式的paneldata模型讨论了房价区域波动的差异,并分析了造成各地区房价波动差异的原因,尤其是货币政策效应的区域差异。结论如下:无论是房价的长期趋势还是短期波动,信贷规模对东、西部地区影响都比较大,中部地区较小,表明政府实施的信贷政策对调控东、西部地区的房价是有效的。实际利率对各区域影响差异不大,且影响较小。人均GDP无论长期还是短期对中部地区房价影响都比较大,表明中部地区房地产市场的发展更多地依赖于该地区的经济发展状况。房价的预期变量在东部地区对房价的短期波动有较大影响。     

The effect of monetary policy on real house price growth in South Africa: A factor-augmented vector autoregression (FAVAR) approach

This paper assesses the impact of monetary policy on real house price growth in South Africa using a factor-augmented vector autoregression (FAVAR), estimated using a large data set comprising of 246 quarterly series over the period 1980:01 to 2006:04. The results based on the impulse response functions indicate that, in general, house price inflation responds negatively to monetary policy shock, but the responses are heterogeneous across the middle-, luxury- and affordable-segments of the housing market. The luxury-, large-middle- and medium-middle-segments are found to respond much more than the small-middle- and the affordable-segments of the housing market. More importantly, we find no evidence of the home price puzzle, observed previously by other studies that analyzed house prices using small-scale models. We put this down to the benefit gained from using a large information set.     

On the Joint Test of the Uncovered Interest Parity and the Ex‐ante Purchasing Power Parity

This study revisits the relation between the uncovered interest parity (UIP), the ex‐ante purchasing power parity (EXPPP) and the real interest parity (RIP) for the UK and Japanese vs US data. The original contribution is on developing some joint coefficient‐based tests, obtained by rewriting the UIP, the EXPPP and the RIP as a set of cross‐equation restrictions in a vector autoregression (VAR) framework. Test results point to a “forward premium” bias in both the UIP and the EXPPP. The latter result is novel in the literature and stems from testing the PPP in expectational terms. Moreover, the results suggest a currency‐dependent pattern for the UIP, contrarily to the EXPPP equation. Finally, it is shown that conditioning the VAR on M3 growth differential has important explanatory power in resolving the aforementioned biases in both the UIP and EXPPP equations for the UK vs US data. At the same time, variables having a strong forward‐looking component (i.e. share prices) help recover a unitary coefficient in the UIP equation.     

The impact of monetary policy on local housing markets: Do regulations matter?

This paper shows that monetary policy has uneven impacts on local housing markets, and that the magnitude of the impacts are correlated with housing supply regulations. We apply the linearized present value model, which allows the log rent–price ratio to be decomposed into the expected present values of all future real interests rates, real housing premia, and real rent growth, to the housing markets in 23 US metropolitan statistical areas. Based on the indirect inference bias-corrected VAR estimates, we find that MSAs that are more regulated have (i) a higher variance in the log rent–price ratio, (ii) a larger share of the variance explained by real interest rate, and (iii) a stronger impulse response of house price to the real interest rate shock.     

Forecasting house prices using dynamic model averaging approach: Evidence from China

Forecasting house price has been of great interests for macroeconomists, policy makers and investors in recent years. To improve the forecasting accuracy, this paper introduces a dynamic model averaging (DMA) method to forecast the growth rate of house prices in 30 major Chinese cities. The advantage of DMA is that this method allows both the sets of predictors (forecasting models) as well as their coefficients to change over time. Both recursive and rolling forecasting modes are applied to compare the performance of DMA with other traditional forecasting models. Furthermore, a model confidence set (MCS) test is used to statistically evaluate the forecasting efficiency of different models. The empirical results reveal that DMA generally outperforms other models, such as Bayesian model averaging (BMA), information-theoretic model averaging (ITMA) and equal-weighted averaging (EW), in both recursive and rolling forecasting modes. In addition, in recent years it is found that the Google search index, instead of fundamental macroeconomic or monetary indicators, has developed greater predictive power for house price in China.     

Bubbles in UK house prices: evidence from ESTR models

Recent movements in stock and house prices have led to an examination of the presence of bubbles. Whilst, there is extensive research on stock price data, there is relatively less for house prices. This paper uses a present‐value model for house prices to test for the presence of bubbles. The results support the presence of a non‐fundamental component within UK national and regional house prices. In particular, for the majority of series considered, evidence is presented of linear non‐stationarity within the fundamental present‐value relationship, and of non‐linear stationarity, implying the presence of a non‐fundamental, or bubble, component. Furthermore, evidence is presented that prices adjust quicker when they are below fundamental equilibrium, than when they are above fundamental equilibrium, i.e. there is downward price stickiness. These results support the hypothesis that house price dynamics can be characterised by price‐to‐price momentum. Finally, forecast evidence suggests that real prices are likely to adjust downwards and converge with fundamental value.