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This paper aims to investigate the presence of a housing bubble using Swedish data from 1986Q1-2016Q4 by using various methods.

First, the authors use affordability indicators and asset-pricing approaches, including the price-to-income ratio, price-to-rent ratio and user cost, supplemented by a qualitative discussion of other factors affecting house prices. Second, the authors use cointegration techniques to compute the fundamental (or long-run) price, which is then compared with the actual price to test the degree of Sweden’s housing price bubble during the studied period. Third, they apply the univariate right-tailed unit root test procedure to capture bursting bubbles and to date-stamp bubbles.

The authors find evidence for rational housing bubbles with explosive behavioral components beginning in 2004. These bubbles do not continuously diverge but instead periodically revert to their fundamental value. However, the deviation is persistent, and without any policy correction, it takes decades for real house prices to return to equilibrium.

The policy implication is that monetary policy designed to contain mortgage demand and thereby prevent burst episodes in the housing market must address external imbalances, as revealed in real exchange rate undervaluation. It is unlikely that current policies will stop the rise of house prices, as the growth of mortgage credit, improvement in Sweden’s international competitiveness and the path of interest rates are much more important factors.

Housing prices in Brazil have displayed an impressive growth in recent years, raising some concerns about the existence of a bubble in housing markets. In this paper, the authors implement an empirical methodology to identify whether or not there is a bubble in housing markets in Brazil.

Based on a theoretical model that establish that, in the absence of a bubble, a long-run equilibrium relationship should be observed between the market price of an asset and its dividends. The authors implement two methodologies. First, the authors assess whether there is a cointegration relationship between housing prices and housing rental prices. Second, the authors test whether the price-to-rent ratio is stationary.

The authors’ results show that there is evidence of a bubble in housing prices in Brazil. However, given the short span of the data, the authors perform a Monte Carlo simulation and show that the cointegration tests may be biased in small samples. Therefore, the authors should be caution when assessing the results.

The results obtained from the cointegration analysis can be biased for small samples.

The information on the excessive increase of the prices of the properties in relation to their fundamental value can help in the decision-making on investment of the economic agents.

These results corroborate the hypothesis that Brazil has an excessive appreciation in housing prices, and, as Silva and Besarria (2018) have suggested, this behavior explains, in part, the fact that the central bank has taken this issue into account when deciding about the stance of monetary policy of Brazil.

The originality is linked to the use of the Gregory-Hansen method of cointegration in the identification of bubbles and discussion of the limitations of the research through Monte Carlo simulation.

This paper aims to explore principal drivers affecting prices in the Australian housing market, aiming to detect the presence of housing bubbles within it. The data set analyzed covers the past two decades, thereby including the period of the most recent housing boom between 2012 and 2015.

The paper describes the application of combined enhanced rigorous econometric frameworks, such as ordinary least square (OLS), Granger causality and the Vector Error Correction Model (VECM) framework, to provide an in-depth understanding of house price dynamics and bubbles in Australia.

The empirical results presented reveal that Australian house prices are driven primarily by four key factors: mortgage interest rates, consumer sentiment, the Australian S&P/ASX 200 stock market index and unemployment rates. It finds that these four key drivers have long-term equilibrium in relation to house prices, and any short-term disequilibrium always self-corrects over the long term because of economic forces. The existence of long-term equilibrium in the housing market suggests it is unlikely to be in a bubble (Diba and Grossman, 1988; Flood and Hodrick, 1986).

The foremost contribution of this paper is that it is the first rigorous study of housing bubbles in Australia at the national level. Additionally, the data set renders the study of particular interest because it incorporates an analysis of the most recent housing boom (2012-2015). The policy implications from the study arise from the discussion of how best to balance monetary policy, fiscal policy and macroeconomic policy to optimize the steady and stable growth of the Australian housing market, and from its reconsideration of affordability schemes and related policies designed to incentivize construction and the involvement of complementary industries associated with property.

Beijing and Shanghai have been the leading housing markets in urban China. In the late half of the 2000s, both metropolises experienced a pronounced process of housing price appreciation. The purpose of this paper is to examine whether there exist housing price bubbles in the two largest cities in China.

The study is based on a combination of different quantitative indicators: a comparison of housing market prices with the rational expectation price, mortgage loans, and the ratios of price to income and to rent. Moreover, the statistical tool of control chart is introduced to quantify housing bubbles.

The study shows that Beijing appears to have been on the way of forming a housing price bubble between 2005 and 2008, and that there perhaps existed a housing bubble in Shanghai from 2003 to 2004. It appears that the housing market cycle in Beijing may be divided into three stages: the cycle peak stage (1991‐1997), the cycle trough stage (1998‐2003) and the second cycle peak stage (2004‐2008).

In an attempt to explain the possible existence of housing bubbles in Beijing and Shanghai, this paper uses an integrated strategy involved with such fundamentals as interest rates, rent, income and GDP. In particular, the control chart, based on per capita GDP, is introduced to identify a housing bubble.

The purpose of this paper is to provide a decision support model for the early diagnosis of housing bubbles in the UK during the maturity process of the phenomenon.

The development process of the model is divided into four stages. These stages are driven by the normal distribution theorem coupled with the case study approach. The application of normal distribution theory is allowed through the usage of several parametric tools. The case studies tested in this research include the last two UK housing bubbles, 1986 to 1989 and 2001/2002 to 2007. The central hypothesis of the model is that during housing bubbles, all speculative activities of market participants follow an approximate synchronisation, and therefore, an irrational, synchronous and periodic increase on a wide range of relevant variables must occur to anticipate the bubble component. An empirical application of the model is conducted on UK housing market data over the period of 1983-2011.

The new approach successfully identifies the well-known UK historical bubble episodes over the period of 1983-2011. The study further determines that for uncovering housing bubbles in the UK, house price changes have the same weight with the debt–burden ratio when their velocity is positive. Finally, the application of this model has led us to conclude that the model’s outputs fluctuate approximately in line with phases of the UK real estate cycle.

This paper proposes a new measure for studying the presence of housing bubbles. This measure is not simply an ex post detection technique but dating algorithms that use data only up to the point of analysis for an on-going bubble assessment, giving an early warning diagnostic that can assist market participants and regulators in market monitoring.

This paper aims to assess the equilibrium house price in the city of Suva (Fiji) and to analyse the house price bubble in the Fiji housing market.

This paper adopts a time series approach to determine the presence of house price bubbles in Fiji over the period from 1988 to 2018.

The findings suggest that real income, land cost, building material price, inflation rate, volatility, household size and wealth have a positive impact on house prices, whereas user cost of capital and political disturbances have a negative impact. The findings further indicate that the Fijis’ housing market does not constitute any house price bubble.

This paper draws policy implications for a small developing state (Fiji) and other similar economies.

The price bubble in the Fiji housing market is analysed for the first time. This paper develops a comprehensive empirical approach to assess the equilibrium-housing price in Fiji.

This study investigates whether there was a housing price bubble in Beijing and Shanghai in 2003. The existence of a bubble can be interpreted from (abnormal) interactions between housing prices and market fundamentals.

With monthly data from the two cities, this paper employs standard econometric methodologies: i.e. Granger causality tests and generalized impulse response analysis, and the reduced form of housing price determinants.

Our findings suggest that there appeared a bubble in Shanghai in 2003, accounting for 22 percent of the housing price. By contrast, Beijing had no sign of a bubble in the same year. The bubble phenomenon, of course, should not be taken without caution for the constraints of data. Nonetheless, this study has laid the ground work for further investigation into abnormal housing price phenomena in Mainland China.

Our findings may help foreign investors better understand the Chinese housing markets and make better housing investment decisions in the two cities.

The purpose of this study is to determine the Turkish housing price and rent dynamics among seven big cities with a unique monthly data set over 2003–2019. The secondary purpose is to examine bubble dynamics within the price convergence framework through alternative tests.

The paper conducts two autoregressive distributed lag (ARDL) cointegration estimates for housing prices and rents and applies conditional error correction model to investigate the long-run drivers of the Turkish housing market. The authors compare ARDL cointegration in-sample forecasts and discounted cash flow (DCF) estimates with actual prices to determine the timing, magnitude and collapse period(s) of bubbles within the price convergence framework. In particular, the generalized sup augmented Dickey–Fuller (GSADF) approach time stamps multiple explosive price behaviors.

The ARDL results confirm the theory of investment value by addressing mortgage rates, the price-to-rent ratio and rents as the fundamental factors of house prices. The price-to-rent ratio offers a comparison mechanism among houses deciding to buy a new house in which rents increase monthly real estate investment returns, and mortgage rates act as the discount rate. One key finding is that these dynamics have a greater impact on house prices than mortgage rates. Furthermore, the ARDL, DCF and GSADF findings exhibit temporal overvaluations rather than bubble signals, implying that housing price appreciations, including explosive behaviors, are consistent with fundamental advances.

This paper is considered to be innovative in determining housing market dynamics through two different ARDL estimates for the Turkish housing price index and rents in real terms as dependent variables. The authors compare the boom and collapse periods of the real housing price index and its fundamentals via the GSADF test. A final key feature of this research is its extensive data set, with 11 different regressors between 2003 and 2019.

The 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.

The 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.

The 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.

The results implicate a new global housing boom, particularly in those countries that did not experience a major price correction during the global financial crisis.

The 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.

The purpose of this paper is to present a framework for the assessment of the fundamental value of house prices in the largest Ukrainian cities, as well as to identify the thresholds, the breach of which would signal a bubble.

House price bubbles are detected using two approaches: ratios and regression analysis. Two variants of each method are considered. The authors calculate the price-to-rent and price-to-income ratios that can identify a possible overvaluation or undervaluation of house prices. Then, the authors perform regression analyses by considering individual multi-factor models for each city and by using a within regression model with one-way (individual) effects on panel data.

The only pronounced and prolonged period of a house price bubble is the one that coincides with the Global Financial Crisis. The bubble signals produced by these methods are, on average, simultaneous and in accordance with economic sense.

The framework described in this paper can serve as a model for the implementation of a tool for detecting house price bubbles in other countries with emerging, small and open economies, due to adjustments for high inflation and significant dependence on reserve currencies that it incorporates.

A tool for measuring fundamental house prices and a bubble indicator for housing markets will be used to monitor the systemic risks stemming from the real estate market. Thus, it will help the National Bank of Ukraine maintain financial stability.

The framework presented in this research will contribute to the enhancement of the systemic risk analysis toolkit of the National Bank of Ukraine. Therefore, it will help to prevent or mitigate risks that might originate in the real estate market.

The authors show how to implement an instrument for detecting house price bubbles in Ukraine. This will become important in the context of the after-war reconstruction of Ukraine, with mortgages potentially becoming the main tool for the financing of the rebuilding/renovation of the residential real estate stock.