نوسانات قیمت مسکن و واکنش آن به تغییرات اقتصادی کلان

This article applies a three-regime Markov switching model to investigate the impact of the macroeconomy on the dynamics of the residential real estate market in the US. Focusing on the period between 1960 and 2011, the methodology implemented allows for a clearer understanding of the drivers of the real estate market in “boom”, “steady-state” and “crash” regimes. Our results show that the sensitivity of the real estate market to economic changes is regime-dependent. The paper then proceeds to examine whether policymakers are able to influence a regime switch away from the crash regime. We find that a decrease in interest rate spreads could be an effective catalyst to precipitate such a change of state.

Over the last few decades, residential property markets in many industrialised nations across the globe have witnessed large cyclical variations in prices and volumes. Real estate cycles are often characterised by a surge in prices followed by a fall or crash. An example of this was seen in the UK housing market in the late 1980s. Financial liberalisation in the UK led to a price boom, but following an increase in interest rates, residential prices experienced a sharp decline in the early 1990s. More recently in the US, nationwide property prices grew by over 61% between 2000 and 2005 but fell sharply by 38% in the four years that followed. There are similar examples in other countries including Japan, Ireland and Spain. These cycles are often linked with changes in macroeconomic drivers such as interest rates and economic growth. This cyclical nature of the residential real estate market has been a major topic of discussion over the years mainly because a large proportion of the average household's wealth is invested in property. The housing market in the US accounts for more than 50% of the country's fixed capital stock (Baffoe-Bonnie, 1998). Economic theory suggests that wealth is one of the key drivers of aggregate consumption in any economy, and so therefore, a downturn in the housing market is likely to be followed by a decrease in household consumption levels, which may in turn have adverse effects on the growth rate of an economy. Empirical evidence of this is shown in Case et al. (2001), who use a panel of 14 countries in their study to present statistical evidence that a 10% rise in housing wealth would lead to a 1.1% increase in consumption. The behaviour of the residential real estate market is also important because of the impact of house price falls on the lending portfolios of commercial banks and other financial institutions. Wheelock (2006) shows that a period of large declines in house prices is very often followed by an increase in the rate of mortgage defaults, which has an adverse effect on banks' profits. The reduction in profitability may lead to failures in banks and other real estate lenders and a subsequent slowdown in economic activity. A recent high profile example of this was the collapse of Lehman Brothers which was heavily exposed to the real estate market via mortgage backed securities. Following the failure of Lehmans in September 2008, the CBOE Volatility Index (VIX), often referred to as the “fear index”, jumped 70%,1 and a global recession followed shortly thereafter. In their empirical study, Ghent and Owyang (2010) provide evidence that house price changes drive business cycles. Thus, given the potentially detrimental effects of declining housing prices on the economy, a deeper understanding of the economic drivers of the residential real estate market is required. The purpose of this paper is to investigate how changes in key macroeconomic variables could influence the growth in house prices, depending on which part of the cycle the real estate market is in. This study examines the impact of macroeconomic drivers of real estate price changes in a three-regime-switching context, thus providing information on how selected economic factors influence price changes in the residential real estate market depending on whether the housing market is a “boom”, “steady-state” or “crash” regime. The paper further contributes to the existing literature by investigating the likelihood of monetary policy tools precipitating a switch from a crash regime. Most prior studies on this topic do not allow for significant structural breaks in house prices resulting from huge upswings and collapses in prices, instead assuming that the relationship between house prices and economic variables is stable and consistent. The sensitivity of house prices to changes in these variables could, however, depend on the stage in the housing cycle. Failing to account specifically for volatile periods such as the period between 2000 and 2012 in the US housing market could produce results which may not reflect a true picture of the relationship between macro factors and growth in house prices. Xiao (2007) and Nneji et al. (forthcoming), studying the residential property market in Hong Kong and the US respectively, provide evidence that structural breaks in real estate prices caused by speculative bubbles are likely to disconnect the housing market from the cost of renting. As a result, the relationship between the housing market and its macroeconomic determinants may be regime-varying, and so accounting for the housing market cycle is critical when examining its response to external macroeconomic factors. We seek to address several policy-relevant questions in this research. First, is the housing market more sensitive to economic changes in boom or bust periods? Second, which economic factor(s) has(have) the strongest impact on the housing market in each regime or cycle? Third, is it possible for policymakers to influence a switch away from the “bust” state (typically characterised by negative growth) using monetary policy tools? Studying the US housing market between 1960 and 2011, we apply a three state Markov switching model to examine the possibility that macroeconomic drivers of house price changes are regime-specific. The regime-switching methodology implemented in this paper also enables us to identify cycles in the housing market. We can then evaluate whether policymakers are able to switch the housing market from a crash regime to a steady state simply by using monetary policy tools. To our knowledge, this is the first study that applies a three Markov switching model in the context of the relationship between the real estate market and the macroeconomy, and the first study that examines the efficacy of policy tools for causing a switch away from the “crash” regime. The layout of the rest of the paper is as follows. Section 2 is dedicated to reviewing the existing literature on the relationship between economic variables and the residential real estate market. The following section, Section 3, provides descriptive statistics for the data used in the paper. In Section 4 a more detailed explanation of the methodologies implemented is given. Section 5 interprets and discusses the results from the Markov switching regressions, and in Section 6 we examine the power of monetary policy tools in influencing a switch in the housing market from a crash regime. We conclude in Section 7.

In this paper, we employ a three-state Markov switching nonlinear econometric model to examine the relationship between the residential real estate market and key macroeconomic variables in the US. We investigate the regime-dependent effects of changes to short term interest rates, term spread, inflation and GDP on house prices — i.e., we study the effect of these macroeconomic variables on house price dynamics in times of housing booms, busts and tranquillity. The paper then proceeds to evaluate the effect of policy changes on switching regimes in the housing market. We find the following. First, there is statistical evidence to suggest there to be three distinct regimes in the housing market, namely “steady-state”, “boom” and “crash” regimes. Changes to the selected macroeconomic variables significantly affect the dynamics of house prices in the steady-state and boom regimes only. During housing busts, however, the real estate market disconnects from these macroeconomic fundamentals. We also find evidence of varying degrees of sensitivity of house prices to changes in these economic variables, with prices generally being more sensitive during housing booms. We also show that using a standard, single-state methodology inappropriately indicates that house prices are responsive to changes in the short term interest rate and GDP only, thus providing an incomplete view of the market. Our second set of key findings in this study is based on the estimated probabilities of switching from one regime to another. We find that there is a 5% chance of moving from a housing boom in one quarter to a housing bust in the next quarter, but the most persistent regime is the steady-state as there is a 98% chance of remaining within it if the housing market was previously in the steady-state. Third, we show that monetary policy could potentially be used as a tool to enforce a switch away from housing busts. Using a probit model based on the estimated filtered probabilities from the Markov switching model, we find that there is a statistically significant and positive relationship between increases in the term spread and the probability of being in the housing crash regime, thus implying that a reduction in the spread between long and short term interest rates reduces the probability of being in the crash regime. Interest rates and money supply cannot, however, be used to instigate a switch away from a housing bust. Although this study provides a detailed examination of the impact of macroeconomic fluctuations on the housing market, we do not evaluate the effect of other non-monetary policy changes on the market due to data constraints, and thus further research could be conducted in this area. If researchers were able to access unique fiscal data, they could extend this study to investigate whether it is possible to use fiscal policies to enforce a transition away from housing busts. This would allow policymakers to judiciously select the most appropriate tools to use in reinvigorating a collapsing real estate market or possibly dampening demand during times of overheating.