Housing Price Bubble Research Articles

House price bubbles and its predictive capacity on inflation and economic growth: Some Indian evidence

House price bubbles and its predictive capacity on inflation and economic growth: Some Indian evidence

Housing price bubbles in Greater Sydney: evidence from a submarket analysis

Recognising the rapid increase in housing prices and the presence of socio-economic and demographic disparities that often characterise a metropolitan city, we adopted a sub-city approach and deployed an array of methods to detect bubbles in the different regions of Greater Sydney – western, inner-west, southern, eastern and northern – over 1991 to 2016, using Westerlund error correction-based panel cointegration, backward supremum augmented Dickey–Fuller (BSADF) procedure, and dynamic ordinary least square (DOLS) tests. Our cointegration results show no evidence of cointegration between real house price and rent in the western region. However, there is strong evidence of cointegration in the eastern and northern regions. This confirms the existence of housing submarkets in Greater Sydney, and an indication of housing price bubbles in Western Sydney. Further, the formal BSADF bubble tests reveal strong evidence of explosive price bubbles in Western Sydney, while there is no comparable evidence for the other regions of Sydney, which further highlights the importance of submarket analysis. Importantly, the DOLS results suggest that housing investment plays a major role in the build-up of housing bubbles in Western Sydney, supporting Shiller's Psychological Theory of bubbles which posits that bubbles occur via the speculative behaviour of investors. The implications of the findings are also discussed.

An Innovative GIS-Based Territorial Information Tool for the Evaluation of Corporate Properties: An Application to the Italian Context

The financial transmission of the USA’s housing price bubble has highlighted the inadequacy of the valuation methods adopted by the credit institutions, due to their static nature and inability to understand complex socio-economic dynamics and their related effects on the real estate market. The present research deals with the current issue of using Automated Valuation Methods for expeditious assessments in order to monitor and forecast market evolutions in the short and medium term. The paper aims to propose an evaluative model for the corporate market segment, in order to support the investors’, the credit institutions’ and the public entities’ decision processes. The application of the proposed model to the corporate real estate segment market of the cities of Rome and Milan (Italy) outlines the potentialities of this approach in property big data management. The elaboration of input and output data in the GIS (Geographic Information System) environment allowed the development of an intuitive platform for the immediate representation of the results and their easy interpretation, even to non-expert users.

Towards a Typology of Housing Price Bubbles: A Literature Review

ABSTRACT The article presents a detailed review of the literature relating to speculative price bubbles on the real estate market, in particular the typology of speculative price bubbles. The aim of the study was to synthesize the knowledge on different types of price bubbles in the international literature. A typology of price bubbles was proposed based on functional and structural criteria that account for the structure of price bubbles and the real estate market. The research methods involved critical analysis and a review of the literature with a commentary. The study proposes a novel typology of price bubbles and defines the concept of a “conversion point” which is associated with changes in the group of factors that lead to the formation of price bubbles and changes in bubble type.

Diagnosing Housing Fever with an Econometric Thermometer

Housing fever is a popular term to describe an overheated housing market or housing price bubble. Like other financial asset bubbles, housing fever can inflict harm on the real economy, as indeed the US housing bubble did in the period following 2006 leading up to the general financial crisis and great recession. One contribution that econometricians can make to minimize the harm created by a housing bubble is to provide a quantitative ‘thermometer’ for diagnosing ongoing housing fever. Early diagnosis can enable prompt and effective policy action that reduces long term damage to the real economy. This paper provides a selective review of the relevant literature on econometric methods for identifying housing bubbles together with some new methods of research and an empirical application. We first present a technical definition of a housing bubble that facilitates empirical work and discuss significant difficulties encountered in practical work and the solutions that have been proposed in the past literature. A major challenge in all econometric identification procedures is to assess prices in relation to fundamentals, which requires measurement of fundamentals. One solution to address this challenge is to estimate the fundamental component from an underlying structural relationship involving measurable variables. A second aim of the paper is to improve the estimation accuracy of fundamentals by means of an easy-to-implement reduced-form approach. Since many of the relevant variables that determine fundamentals are nonstationary and interdependent we use the IVX (Phillips et al. (2009); Kostakis et al. (2015)) method to estimate the reduced-form model to reduce the finite sample bias which arises from highly persistent regressors and endogeneity. The recursive evolving test of Phillips, Shi, and Yu (2015a, PSY) is applied to the estimated non-fundamental component for the identification of speculative bubbles. The new bubble test developed here is referred to as PSY-IVX. An empirical application to the eight Australian capital city housing markets over the period 1999 to 2017 shows that bubble testing results are sensitive to different ways of controlling for fundamentals and highlights the importance of accurate estimation of these housing market fundamentals.

Real-Time Warning Signs of Emerging and Collapsing Chinese House Price Bubbles

The recent increase in Chinese house prices has led to concerns that China is vulnerable to asset price shocks. In this paper, we apply recently developed recursive unit root tests to spot the beginning and the end of potential speculative bubbles in Chinese house price cycles. Overall, we find that except for 2009–2010 actual house prices are not significantly disconnected from fundamentals. Thus, the evidence for speculative house price bubbles in China is in general weak.

DO HIGHER HOUSE PRICES INDICATE HIGHER SAFETY? PRICE VOLATILITY RISK IN MAJOR CITIES IN TAIWAN

This study investigates the housing market in Taiwan, an emerging market with relatively severe housing price inflation. Using data from the first quarter of 1991 to the second quarter of 2017 for four cities in Taiwan, this study compares the risk transmission and sources of their housing prices. The results reveal that Taipei−Taiwan’s main financial hub−has the highest house prices among the four cities but maintains the lowest risk. Thus, in terms of price volatility risk, Taipei has the safest housing market among the studied cities. Other studies have discussed the potential housing price bubbles in regions with high housing prices but have been unable to explain the continual overheating of the housing markets. The findings of this study reveal that despite having the highest housing prices and the greatest potential bubble, the Taipei housing market has the lowest fluctuation risk, making it the safest market in terms of housing investment. The results of this study imply that Taiwan’s economic development is excessively concentrated in Taipei, causing people to bear low returns and high risk when purchasing real estate in other areas, in turn increasing the continual imbalance between regional housing markets.

Is a Bubble Inflating on Poland’s Housing Market? (Czy na polskim rynku mieszkaniowym narasta bańka cenowa?)

English Abstract: The year 2019 brought double-digit growth in Polish housing prices, for both new and existing homes. In some cities, real prices for residential real estate have reached the highest levels in history, even higher than at the peak of the boom in 2008. As a result, some are saying that there is a growing price bubble. But thus far no research has been produced that would comprehensively verify this hypothesis on the basis of data from 2006-2019. This work aims to fill that gap. This is exceptionally important, because assessing the likelihood that a housing price bubble is emerging is key for the conduct of monetary and macroprudential policy in Poland. Because if we’re really dealing with growth in macroeconomic imbalances, then taking pre-emptive action to limit further price growth and prepare the economy (including the financial sector) for a potential collapse in housing prices is essential for limiting fluctuations in growth. The first problem that must be addressed in any analysis is the question of defining a price bubble, because in the literature there are various interpretations. Some say this phenomenon never happens, and others assume that price bubbles can inflate even when investors are behaving completely rationally. There are also some interpretations that are based in behavioral psychology and assume that a speculative bubble is the result of irrational, excessively optimistic expectations by buyers about future prices. Still other researchers of imbalances on the asset market don’t even get into the nature of price bubbles, defining them as excessive growth in prices to a level that can’t be maintained over the long term. For the needs of this research, I’ll adopt a hybrid definition, which brings together the conclusions of various streams in research: “A price bubble is a three-phase phenomenon that reflects growing imbalances on the asset market. While it lasts, there is relatively quick growth of prices to a level that can’t be maintained in later periods. In the first phase, the price growth is the result of changes in fundamental factors. In the second, the growth results from irrational exuberance sustained by a feedback loop. In the third, as a result of changes in fundamental factors, a relatively fast drop in asset prices occurs.” (Czerniak and Witkowski, 2016). The second problem related to determining whether we’re dealing with a price bubble or not is choosing the right tools for measurement. In the literature, many indicators have already been proposed as allowing the identification of price bubbles – from basic statistical analysis through complicated econometric and non-parametric models (ECB, 2010), including so-called early warning systems (EWS). But all of them are to a certain degree unreliable, as a statement by Visco (2005) remains valid: a price bubble can be recognized only once it bursts. That’s why it’s impossible to state with complete certainty that we’re facing this phenomenon in Poland at the moment (Gurkaynak, 2008). However, we can examine the likelihood that a bubble will develop, and assess whether the current macroeconomic and institutional situation in Poland encourage this, or the opposite. Thus, in this research I present a range of metrics of whether housing price bubbles are occurring. In the first section, I will discuss the results of applying purely statistical tools for identifying bubbles on the Polish housing market; in the second I present metrics of this phenomenon based on data concerning fundamental factors shaping housing prices in Poland. Separately, in the fourth part I will discuss access to cheap money for investment purposes. The article closes with a summary in which I juxtapose the conclusions arising from calculating various metrics of the occurrence of price bubbles on the Polish housing market. On this basis I formulate recommendations for economic policymakers, and I also propose directions for further research on this phenomenon in Poland. Polish Abstract: Rok 2019 przyniosl w Polsce dwucyfrowe wzrosty cen mieszkan i to zarowno na rynku pierwotnym, jak i na rynku wtornym. W niektorych miastach realne ceny nieruchomości mieszkaniowych osiągnely najwyzszy poziom w historii, nawet powyzej szczytu boomu z 2008 r. W rezultacie pojawily sie opinie, ze mamy do czynienia z narastaniem banki cenowej. Jednakze do tej pory nie zostaly opublikowane zadne badania, ktore w sposob kompleksowy, na podstawie dostepnych danych za lata 2006-2019 zweryfikowaly te hipoteze. Niniejsze opracowanie ma na celu uzupelnienie tej luki. Jest to niezmiernie wazne, poniewaz ocena prawdopodobienstwa narastania banki cenowej na rynku mieszkaniowym jest kluczowa z perspektywy prowadzenia polityki pienieznej i makroostroznościowej w Polsce. Jezeli bowiem faktycznie mamy do czynienia z narastaniem nierownowagi makroekonomicznej, to podjecie wyprzedzających dzialan ograniczających dalszy wzrost cen i przygotowujących gospodarke, w tym takze sektor finansowy, na ewentualne zalamanie cen na rynku mieszkaniowym, jest konieczne z punktu widzenia ograniczenia potencjalnych silnych wahan koniunktury.

Measurement and Analysis of Housing Price Bubbles in 35 Large and Medium-Sized Cities: An Empirical Study Based on China’s Statistical Data from 2010 to 2018

Based on the basic assumptions of the developers and the buyers in the housing market, this paper introduced the profit function of typical developers and the utility function of typical buyers as building the equilibrium price model of housing, and constructed the decision model between the basic value of housing, the bubbles of housing price and the degree of housing price bubbles. The absolute scale and relative scale of housing price bubbles in 35 large and medium-sized cities were calculated based on the above models. A comparative analysis was conducted on the bubble level of housing prices in 35 large and medium-sized cities in China from 2010 to 2018. Results indicated that the bubbles had an increasing trend in recent years, but there were obvious differences between cities. Housing market regulation policy should take account of the differences between cities.

Asset price bubbles in a monetary union: Mind the convergence gap

We estimate logit models of housing and stock price bubbles, using panel data for 15 EU countries for the time period 1995–2014, covering a wide set of determinants: monetary, macroeconomic, demographic, institutional and those arising from monetary integration. Our key finding is that higher degree of real convergence diminishes the probability of a bubble in the housing market caused by a fall in interest rates after joining the monetary union. Applying these results to three Central European countries (the Czech Republic, Hungary and Poland) we conclude that, given the existing economic distance in terms of per capita GDP between the three countries and the top eurozone performers, joining the monetary union would ceteris paribus significantly increase the probability of house price bubbles in those countries. This result suggests that the optimum currency area framework should be broadened to include the degree of real convergence between the monetary union and the acceding country.

Diagnosing Housing Fever with an Econometric Thermometer

Housing fever is a popular term to describe an overheated housing market or housing price bubble. Like other financial asset bubbles, housing fever can inflict harm on the real economy, as indeed the US housing bubble did in the period following 2006 leading up to the general financial crisis and great recession. One contribution that econometricians can make to minimize the harm created by a housing bubble is to provide a quantitative ‘thermometer’ for diagnosing ongoing housing fever. Early diagnosis can enable prompt and effective policy action that reduces long term damage to the real economy. This paper provides a selective review of the relevant literature on econometric methods for identifying housing bubbles together with some new methods of research and an empirical application. We first present a technical definition of a housing bubble that facilitates empirical work and discuss significant difficulties encountered in practical work and the solutions that have been proposed in the past literature. A major challenge in all econometric identification procedures is to assess prices in relation to fundamentals, which requires measurement of fundamentals. One solution to address this challenge is to estimate the fundamental component from an underlying structural relationship involving measurable variables. A second aim of the paper is to improve the estimation accuracy of fundamentals by means of an easy-to-implement reduced-form approach. Since many of the relevant variables that determine fundamentals are nonstationary and interdependent we use the IVX (Phillips et al. (2009); Kostakis et al. (2015)) method to estimate the reduced-form model to reduce the finite sample bias which arises from highly persistent regressors and endogeneity. The recursive evolving test of Phillips, Shi, and Yu (2015a, PSY) is applied to the estimated non-fundamental component for the identification of speculative bubbles. The new bubble test developed here is referred to as PSY-IVX. An empirical application to the eight Australian capital city housing markets over the period 1999 to 2017 shows that bubble testing results are sensitive to different ways of controlling for fundamentals and highlights the importance of accurate estimation of these housing market fundamentals.

Identifying House Price Booms, Bubbles and Busts: A Disequilibrium Analysis from Chaos Theory

This research was motivated by the excesses of public policy since 2008 in an attempt to re-inflate the housing markets. Is it even possible or desirable to utilize such a vast amount of public resources to inflate a single sector such as housing that suffered from such a spectacular bubble and collapse? The consequences suggest that, as a way to bolster real household incomes and aggregate output, these policies have disappointed. In contrast, there is a fear that the monetary stimuli will lead to unsustainable housing price inflation, if not a bubble. I address these questions in the analysis from the standpoint of determining the stable equilibrium and sustainable house price appreciation rates consistent with the growth of median household income. The problem of identifying stable house price appreciation is to first identify the major proximate determinants of household demand for housing. A second is to show empirically the movement, deviation, and variation of these factors over time compared to housing prices. I use median household income as the major demand factor for houses and median single family house prices as an indicator of the price. A third is determining the stable equilibrium of the growth of these factors and the appreciation of housing prices consistent with them. And a fourth is the adjustment process when there are small deviations from steady-state equilibrium compared to when deviations are large. It is this last distinction where the chaos theory of self-organizing systems and irreversibility of the housing market system enters to explain how the adjustment process is chaotic in this case. I conclude that, as of the beginning of June 2016, the evidence is overwhelming that housing price appreciation is in a bubble that will likely lead to significant declines in house price appreciation if not in house prices. An important policy recommendation to mitigate these declines and hasten a house price recovery follows. The continuation of expansionary monetary policies will only delay house price adjustments and lead to more severe price declines.

An early warning system to identify house price bubbles

PurposeThe global financial and economic crisis resulting from the US housing crisis has shown that house prices can have far-reaching consequences for the real economy. For macroprudential supervision, it is, therefore, necessary to identify house price bubbles at an early stage to counteract speculative price developments and to ensure financial market stability. This paper aims to develop an early warning system to signal speculative price bubbles.Design/methodology/approachThe results of explosivity tests are used to identify periods of excessive price increases in 18 industrialized countries. The early warning system is then based on a logit and an ordered logit regression, in which monetary, macroeconomic, regulatory, demographic and private factors are used as explanatory variables.FindingsThe empirical results show that monetary developments have the highest explanatory power for the existence of house price bubbles. Further, the study reveals currently emerging house price bubbles in Norway, Sweden and Switzerland.Practical implicationsThe results implicate a new global housing boom, particularly in those countries that did not experience a major price correction during the global financial crisis.Originality/valueThe ordered logit model is an advanced approach that offers the advantage of being able to differentiate between different phases of a house price bubble, thereby allowing a multi-level assessment of the risk of speculative excesses in the housing market.

Asymmetric adjustment, non-linearity and housing price bubbles: New international evidence

In this paper, we detect the housing price bubbles of eighteen OECD countries under assumptions of asymmetric adjustment and non-linearity. To this end, we adopt the momentum threshold autoregressive (MTAR) and exponential smooth transition (ESTR) approaches in this study. In order to consider the possibility of asymmetry between regimes of positive and negative changes in the price-rent ratio, we modify the Kilic (2011) and Park and Shintani (2005, 2016) tests and provide their critical values, finite sample size and power via Monte Carlo simulations. Among the main results, first, it is found that the price-rent ratios of eighteen OECD nations are integrated processes of order one by using the traditional unit root tests. Second, the property of asymmetric adjustment is empirically justified for the housing markets of Belgium, Germany and the US by using the MTAR unit root test. Third, the results of the ESTR-type unit root tests point to the conclusion that the housing markets of thirteen countries are globally stationary ESTR processes, implying that the further the price-rent ratio deviates from its fundamental, the faster will be the speed of mean reversion. Finally, the results of the modified Kilic (2011) and Park and Shintani (2005, 2016) tests are in line with the ESTR unit root tests, indicating that the bubble hypothesis is not accepted.

Housing Bubbles, Economic Growth, and Institutions

Rising housing prices are accompanied by higher household consumption and firm investment that boost economic growth. However, excessive house price appreciations may distort capital allocation efficiency, for example by crowding out investments in productive sectors, which reduce long-term economic growth. Moreover, house price bubbles are typically unsustainable. Once a housing bubble bursts, credit conditions will tighten due to falling collateral value, leading to a fall in household consumption and employment that cause an economic downturn. Meanwhile, the correction of housing prices may improve investment efficiency and trigger structural reform that subsequently enhances economic growth. We look into the trade-off between the aforementioned effects at different stages of housing cycles and economic growth. Using a quarterly dataset that covers cross-country house prices over four decades, we find that house price appreciations are positively associated with economic growth, while the relationship between house price depreciations and economic growth is highly non-linear, depending on country-specific characteristics. In the absence of confounding crisis in the financial sector, short-lived large house price depreciations, rather than prolonged and modest ones, are positively associated with economic growth. The association between house price depreciations and economic growth is subject to legal systems, mortgage insurance, and personal bankruptcy law.

Detecting Imbalances in House Prices: What Goes Up Must Come Down?*

AbstractI suggest a toolkit of four bubble‐detection methods that can be used to monitor developments in house prices. These methods are applied to US, Finnish, and Norwegian data. For the United States, all measures unanimously suggest a bubble in the early to mid‐2000s, whereas current US house prices are found to be aligned with economic fundamentals. One of the measures indicates imbalances in Finland, while there are no signs of a bubble in Norway. I find that large parts of the US house price bubble can be explained by the sharp increase in capital inflows and the extension of loans to the subprime mortgage market.

Identifying booms and busts in house prices under heterogeneous expectations

We introduce heterogeneous expectations in a standard housing market model linking housing rental levels to fundamental buying prices. Using quarterly data we estimate the model parameters for eight different countries, US, UK, NL, JP, CH, ES, SE and BE. We find that the data support heterogeneity in expectations, with temporary endogenous switching between fundamental mean-reverting and trend-following chartists beliefs based on their relative performance. For all countries we identify temporary house price bubbles, amplified by trend extrapolation, and crashes reinforced by fundamentalists. The qualitative predictions of such non-linear models are very different from standard linear benchmarks, with important policy implications. The fundamental price becomes unstable, e.g. when the interest rate is set too low or mortgage tax deductions too high, giving rise to multiple non-fundamental equilibria and/or global instability.

US real estate inflation prediction: Exchange rates and net foreign assets

The recent housing price inflation forecasting literature has focused on the use of domestic variables for prediction of housing price inflation. However, around 7% of all US housing sales are attributed to international buyers; a potentially important market force. Further, international finance theory finds a role for foreign savings shocks in causing the housing price bubble of the last decade. Under floating exchange rate regimes, the Dornbusch model predicts shocks to domestic or foreign economies will be reflected in exchange rates. When exchange rates are fixed, shocks are likely to affect the net foreign asset holdings. In this study, I examine the role of exchange rates returns and the net change in foreign asset holdings in improving US real estate inflation forecasts. I conduct in-sample and out-of-sample comparison of forecasting models relative to an autoregressive baseline model. I find that the inclusion of foreign sector variables can improve the US real estate inflation forecasts by up to 40%. This improvement is mostly driven by changes in the net foreign asset holdings at longer horizons. The results are robust to samples at the metropolitan level although with different gains.

Dating the start of the US house price bubble: an application of statistical process control

An exact dating of the onset of financial crises is important to learn which factors have caused or contributed to the financial turmoil. While most economists agree that the recent worldwide financial crises evolved as a consequence of a bursting bubble on the US housing market, the related literature yet failed to deliver a consensus on the question when exactly the bubble started developing. The estimates in the literature range in between 1997 and 2002, while applications of market-based procedures deliver even later dates. In this paper, we employ the methods of statistical process control to date the likely beginning of the bubble. The results support the view that the bubble on the US housing market already emerged as early as 1996/1997.

Anticipating critical transitions of the housing market: new evidence from China

ABSTRACTWe introduce a novel quantitative methodology to detect real estate bubbles and forecast their critical end time, which we apply to the housing markets of China's metropolises. Building on the Log-Periodic Power Law Singularity (LPPLS) model of self-reinforcing feedback loops, we use the quantile regression calibration approach recently introduced by two of us to build confidence intervals and explore possible distinct scenarios. We propose to consolidate the quantile regressions into the arithmetic average of the quantile-based LPPLS Confidence indicator, which accounts for the robustness of the calibration with respect to bootstrapped residuals. We make three main contributions to the literature of real estate bubbles. First, we verify the validity of the arithmetic average of the quantile-based LPPLS Confidence indicator by studying the critical times of historical housing price bubbles in the U.S., Hong Kong, U.K. and Canada. Second, the LPPLS detection methods are applied to provide early warning signals of the housing markets in some metropolises in China. Third, we determine the possible turning points of the markets in Beijing, Shanghai, Shenzhen, Guangzhou, Tianjin and Chengdu and anticipate critical transitions of China's housing markets via our multi-scales and multi-quantiles analyses. Finally, given these projections performed in February 2017, the price trajectories from March 2017 to January 2018 that became available from the time of submission to the time of revision of the present article offer quite unique genuine out-of-sample tests of the performances of our indicators.