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This chapter presents several approaches for identifying and dating the speculative bubble on real estate market. Using the real estate price index (IPAI), statistical and structural approaches were combined in order to detect the existence of a bubble on the Moroccan real estate market. The results obtained affirm that the Moroccan real estate market experienced a speculative bubble during the period 2006–2008 explained mainly by the boom of credit during the same period. The use of the Markov switching model affirmed that the speculative bubble on Morocco is cyclic and consequently corroborates the critic formulated by Evans (1991) concerning the traditional approaches for the detection of financial bubbles. Thus, the analysis of the series of the bubble, extracted using the Kalman filter, affirms the existence of two regimes, namely an explosive regime and a normal regime. The first regime describes the periods of explosion of the bubble and lasts for about 9 quarters, while the second, lasting for 14 quarters, describes the periods of return to the average cycle.

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.

Speculative bubbles have been occurring periodically in local or global real-estate markets and are considered a potential cause of economic crises. In this context, the detection of explosive behaviors in the financial market and the implementation of early warning diagnosis tests are of critical importance. The recent increase in Brazilian housing prices has risen concerns that the Brazilian economy may have a speculative housing bubble. In the present chapter, we employ a recently proposed recursive unit root test in order to identify possible speculative bubbles in data from the Brazilian residential real-estate market. The empirical results show evidence for speculative price bubbles both in Rio de Janeiro and São Paulo, the two main Brazilian cities.

The purpose of this paper is to calculate the bank efficiency of removing potential risks that are hidden from the extreme portfolio of bank’s assets and further compare the differences and causes of bank’s efficiency and potential risk level between China’s representative banks and OECD representative banks in 2011-2015.

Based on the weight-limited DEA model, this paper calculates the bank’s efficiency and further compares the differences between China’s representative banks and OECD representative banks by using commercial banks’ transnational data.

By analyzing US representative banks’ data, the authors find that the excessive expansion of the scale of banks’ investment for the non-real economy shrinks after the bubble burst and would not improve the efficiency of banks immediately. The OECD representative banks rather prefer to extreme asset portfolio so that the potential risks gradually increase, while there is a diminishing effect on investments in non-real economies to improve bank efficiency. On the other hand, China’s representative banks have the signs of reducing investment in the real estate market, but the existence of the bubble in the market led to a lagged effect on the impact of adjustment of bank asset portfolio on efficiency.

This paper has practical significance for commercial banks to improve efficiency and reduce credit risks. This is conducive to the implementation of targeted supervision by the banking supervision department.

Based on the lesson that the financial crisis created by the real estate bubble burst in the USA in 2008 and the financial market active guidance of the developed economies, faced with the reality of Chinese real estate market bubble rising and the continuous improvement of Chinese financial market, this paper compares the differences between representative banks in China and OECD, and explores the causes by using the cross-country data of commercial banks.

By adjusting the weight of the input variables in the efficiency measurement, quantifying the risk is often overlooked by the changes in bank efficiency. This potential risk is caused by the bank’s investment preferences in the non-real economy represented by real estate and tradable financial assets.

Greece, Ireland, Portugal and Spain have been in the spotlight of the recent economic crisis in Europe. With their economy strongly reliant on the construction industry, these countries have become widely exposed to the downturn in the property sector. This paper aims to examine residential property bubble dynamics in the period from 2003 to 2014 and investigate the role of financing conditions in the formation of these bubbles.

Building on the present value model in conjunction with the rational bubble assumption, the study applies the discounted cash flow (DCF) approach and applies weighted average cost of capital (WACC) to capture real estate bubble dynamics in the four countries. Reduced form vector autoregression models are used to examine the relationship between financing conditions and the bubble indicator.

The bubble indicator suggests that Spain and Ireland experienced a large rise in the bubble relative to moderate increases in Portugal and Greece in the period from 2003 up to the collapse in 2008. Our findings from the empirical analysis indicate that central bank policy shifts that impact interest rates and lending volumes on the domestic level have a significant and leading effect on the formation of residential property bubbles.

Only little research on real estate bubbles takes financial leverage into account. This paper bridges this gap by applying the WACC in the DCF model to identify real estate bubbles. While using a distinct bubble indicator, this analysis provides new insights into the linkage between financing conditions and real estate bubbles.

This paper analyses the construction of value under the context of radical uncertainty (Keynes, 1936; Orléan, 1987) in the financialised real estate sector in France. It is based on a participant observation of valuation practices in an international real estate consulting firm and 26 in-depth interviews with professionals of the sector. We show that these practices rely on an institutional architecture that participates in the consolidation and legitimisation of the accumulation activity of asset managers and thus in the feeding of real estate bubbles in the hearts of large metropolises. Completing the conventionalist approach of value (Orléan, 2011) by focussing on the functioning of the organisations involved in the valuation process, I show that the determination of value is less the result of the emergence and autonomisation of a collective belief through market relationships than the product of power relationships between agents integrated in hierarchical professional organisations and in a specific legal framework.

The purpose of this paper is the development for a conceptual framework with regard to the risk management of real estate positions as foundation for transaction decisions. In this context, the current market environment and legal obligations are the main drivers for market participants to improve their risk management practices. Based on this environment, a practical but science backed model is outlined.

The paper uses a conceptual approach based on the existing literature to develop a practical decision support system. In addition, the current risk management best practices are outlined to illustrate the corporate and methodological foundation for the decision support system.

The conceptual model development reveals a clear necessity for the supplementation of price to value measures. Additional measures are derived from theoretic considerations based on Monte Carlo Simulation approaches to the risk management of property investments. These additional risk metrics support investors in order make risk-appropriate decisions.

The resulting decision support system can be applied to the risk management of transaction decisions. Here, the model can be applied in any investment decision to support portfolio management considerations from a comprehensive risk management perspective. Investors can implement the system as part of their transaction procedure.

The existing body of literature mainly focuses on macroeconomic ratios in the context of decision support. In contrast, the present paper reveals a corporate decision support system, which is supposed to foster decisions of market agents especially with regard to potential price and value divergences and tightening legal obligations.

The purpose is to review what is known about property bubbles and their causes.

The method has been to review the literature on bubbles in the property and other asset markets to examine their likely causes and whether there are specific aspects of the property market that make it more prone to bubbles.

The property market has features that make it susceptible to bubbles, particularly inelasticity in supply and the absence of short selling. Bubbles can develop where there are heterogeneous beliefs. The way in which property tends to be financed helps to facilitate bubbles and transmit their effects onto the wider economy.

The collapse in property prices after the financial crisis of 2008, like previous bubble collapses, has inflicted serious damage on the wider economy through losses of banks' capital, reductions in lending, and increased risk aversion. Understanding why bubbles exist offers the potential to devise policies to limit the impact of their collapse.

Much of the literature on asset bubbles is based on securities markets. It is important to recognise the differences between the property market and securities markets, particularly how investment is financed.

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.