Identifying Booms and Busts in House Prices Under Heterogeneous Expectations

IT-Incidents Management & IT-Forensics, 2000

SSRN Electronic Journal, 2010

Using a vector-autoregression (VAR) model and data from the University of Michigan Survey of Consumers, we provide evidence on the importance of news and consumers' beliefs for housing-market dynamics and aggregate fluctuations. We document that innovations to News on Business Conditions generate hump-shaped responses in house prices and other macroeconomic variables. We also show that innovations to Expectations of Rising House Prices are particularly important in explaining the path of macroeconomic variables during housing booms. To disentangle the effects of News on Business Conditions from other sources of expectation-driven cycles, we estimate a VAR where the News variable is ordered first. Innovations to News on Business Conditions generate Expectations of Rising House Prices. However, during housing booms, innovations to Expectations of Rising House Prices unrelated to News on Business Conditions account for a large part of macroeconomic fluctuations. Shocks to News and Expectations account together for more than half of the forecast error variance of house prices, and other macroeconomic variables, during periods of booms in house prices.

This study looks at the characteristics and determinants of booms and busts in housing prices for a sample of eighteen industrialised countries over the period 1980-2007. From an historical perspective, we find that recent housing booms have been amongst the longest in the past four decades. Estimates of a Multinomial Probit model suggest that domestic credit and interest rates have a significant influence on the probability of booms and busts occurring. Moreover, international liquidity plays a significant role for the occurrence of housing booms and-in conjunction with banking crises-for busts. We also find that the deregulation of financial markets has strongly magnified the impact of the domestic financial sector on the occurrence of booms.

internationalconference.com.my

The sharp increase in house prices in several industrialized economies such as the US, UK and Spain has attracted much attention due to the distorted impact created by the bursting of bubbles in the economy (Gyntleberg & Remolona 2006). Governments and organizations such as the Bank of International Settlement (BIS), International Monetary Fund (IMF) and Organization for Economic Cooperation (OECD) have raised concern about the issues of asset price instability (overshooting the fundamental variables) in the housing market (Gyntleberg & Remolona, 2006). Housing bubbles are believed to be caused by the expectation of an increase in future house prices. The three price expectation theories: rational expectation hypothesis (REH), adaptive expectation hypothesis (AEH) and exogenous expectation hypothesis (EEH) are used to rationalize the behavior of people in creating housing bubbles. It is also believe that people who rejected the existence of housing bubbles follow the supply side economics theory whereas others who believe on the existence of housing price bubbles follow the Keynesians and Shiller theory and Austrian school of thought. Therefore, it is important to understand the theoretical and conceptual framework underlying housing bubbles before suggesting any monetary or fiscal policies that might prevent future housing bubbles crises.

Journal of Economic Behavior & Organization, 2012

International Journal of Economics and Finance

This paper addresses an empirical puzzle in the housing bubble literature: models of market fundamentals perform poorly in explaining investor exuberance in housing even though, individually, many fundamentals have strong ability to predict explosive growth in real house prices. We explore two plausible sources for the poor performance: missing fundamentals and missing bubble dynamics. To shed light on the relative importance of these sources, we conduct a detailed two-step investigation of the housing markets in ten rich countries using models, methodologies and datasets that are similar to those employed in the existing literature. Our findings consistently show that the predictive ability of models of market fundamentals can be dramatically enhanced once missing dynamics of housing bubbles are properly accounted for. GSADF denotes Generalised Sup Augmented Dickey–Fuller test and SADF denotes Sup Augmented Dickey–Fuller test.

SSRN Electronic Journal, 2013

observed property prices are assembled from an unobservable 'real' property price linked to macroeconomic conditions and the interest rate environment, and a noisy component given by market sentiment. Between January 1993 and July 2007 all IPD index logarithmic returns were positive. This long series of positive returns created an illusion among investors. It implied that they did not give proper consideration to macroeconomic evidence. That changed fundamentally in 2008. During the subprime crisis investor behavior changed from illusion to disillusion and the market prices occasionally fell well below the level indicated by fundamental economic considerations. IPD property derivatives can, according to the author, be used for risk management purposes. Investors have access to Eurex futures that can be utilized to hedge out property risk and avoid the consequences of price crashes. Giovanni Dell'Ariccia and Deniz Igan, IMF Research have authored chapter 3: "Dealing with real estate booms". Until the global financial crisis, the main policy tenet in dealing with a real estate boom was one of 'benign neglect'. It was considered better to wait for the bust and pick up the pieces than to attempt to prevent the boom. The crisis challenged this view. But preventive policy action is difficult to implement. The authors conclude that policy efforts should focus on booms that are financed through credit and where leveraged institutions are directly involved. Macroprudential tools (such as limits on loan-to-value ratios) are the best candidates to deal with real estate booms as they can be aimed directly at curbing leverage and strengthening the financial sector. Cycles are a common feature of real estate markets. Stylized facts suggest that the longer and higher prices go up, the more they will come down. Housing cycles are closely intertwined with credit and business cycles. Peaks and troughs are not far from each other. There are significant differences across countries. Legal and institutional structures matter. In order to improve policy options, the quality of empirical data should be heightened. Real estate is an important storage of wealth in the economy. Monetary policy is a blunt instrument for the task at hand. It is difficult to use fiscal tools. So, macroprudential regulation in the form of higher capital requirements, dynamic provisioning and limits on loan-to-value and debtto-income ratios are the most promising options.

SSRN Electronic Journal, 2013

To what extent do house price dynamics differ across market segments? And what determines this heterogeneity? We address these questions by analysing a data set of individual houses and mortgages, based on a survey of about 2,000 Dutch households over the period 2003-2011. We estimate a dynamic panel data model of house price dynamics by means of the Arellano-Bond estimator. Three main empirical results emerge. First, we generally find that house price dynamics imply a convergence towards their long-run equilibrium value, as indicated by a negative serial correlation coefficient and a positive estimated mean reversion coefficient. Second, there is evidence that the housing market in the Netherlands is inefficient. Third, there is important heterogeneity across different market segments. We document that the speed of convergence of house price dynamics and the efficiency of housing markets depends on the geographical location and degree of urbanization, the type and year of construction of a house, the type of mortgage financing and households' sentiment about the medium-term outlook for income.

This study examines some key aspect of ten Asian housing markets over the period from 1980 to 2014. Equilibrium or fundamental house price indices are determined by the interaction of supply and demand through panel ordinary least square (OLS) and individual country OLS techniques. On the demand side, the drivers of house price indices include interest rate, demography, and credit availability. On the supply side, domestic liquidity as well as global liquidity have large effects on housing supply. In the short run, rational expectations about changes in fundamentals including income growth, population and stock index explain much of the housing fluctuation. During the housing boom periods, investors seem to overreact to observable changes in fundamentals. Although fundamental factors leave a large share of changes in real estate prices unexplained, actual house price indices are driven largely by demand-side and supply-side fundamentals while the bubble components are driven by the irrational expectations of sustained price increases. The findings suggest changes in, especially for housing overvaluation in the Asian economies. The model of rational expectation explains around 60 percent of changes in house prices. During the last twenty years, some economies such as Hong Kong or Singapore have experienced more volatile than cycling components.

Journal of International Financial Markets, Institutions and Money, 2016

We conduct an econometric analysis of bubbles in housing markets in the OECD area, using quarterly OECD data for 18 countries from 1970 to 2013. We pay special attention to the explosive nature of bubbles and use econometric methods that explicitly allow for explosiveness. First, we apply the univariate right-tailed unit root test procedure of Phillips et al. (2012) on the individual countries price-rent ratio. Next, we use Engsted and Nielsen's (2012) co-explosive VAR framework to test for bubbles. We find evidence of explosiveness in many housing markets, thus supporting the bubble hypothesis. However, we also find interesting differences in the conclusions across the two test procedures. We attribute these differences to how the two test procedures control for cointegration between house prices and rent.