Housing Price Bubble Research Articles

Detecting house price bubbles in G7 countries: New evidence and heterogeneous determinants

In recent years, the high growth of house prices in G7 countries has attracted some attention from all sectors due to the role of the real estate bubble in the global financial crisis of 2008. In this research, we analyzed the house price bubbles and their determinants in G7 countries. Firstly, we detected multiple house price bubbles in G7 countries between 1970 and 2022 and located when the house price bubbles emerged and burst. Second, we investigated the determinants of house price bubbles in each country and in the G7 countries as a whole. The results show that the level of house price bubbles and their determinants is significantly diversified over countries. However, total population, size of private debt, GDP per capita, and urbanization are among the common drivers of the whole G7 countries. Based on these results, we provide some suggestions on the critical points for a stable real estate market development.

The supply elasticity and housing price bubbles in New South Wales, Australia

AbstractIn this study, we use a supply and demand model to estimate housing supply elasticity in New South Wales Australia based on yearly data for the 126 local government areas from 1991 to 2019, and investigate effects of supply elasticity on responses of housing prices and new constructions to housing price bubbles from 2002 to 2004. In particular, when conducting supply elasticity estimation, we use multi‐factor error structures to address cross‐sectional dependence issues, which is ignored by previous studies. We find that the supply elasticity estimates are statistically significant at the state level (0.36), in the regional coastal area (0.97) and in the regional inland area (0.41 but can be inconsistent and inefficient due to cross‐sectional dependence), while perfectly inelastic in the metropolitan area. Effects of supply elasticity on the market responses to bubbles are largely affected by market heterogeneity such as overall supply elasticity levels and demand degrees. The findings may compensate for the limitations of the theory in Glaeser et al. (2008, J Urban Econ, 64:198–217) and offer potential avenues for future development of the theory. Our results implies that bubbles promote development in elastic areas of the metropolitan market and inelastic areas of the regional coastal market, and that balancing urban and regional development is important for dealing with bubble‐related affordability issues.

Detection and forecasting of housing price bubbles in Indonesia, Malaysia, and Singapore

Detection and forecasting of housing price bubbles in Indonesia, Malaysia, and Singapore

House price bubble and analysis of the factors affecting house price: Turkey case

House price bubble and analysis of the factors affecting house price: Turkey case

A Measure of China's Housing Price Bubble from the Perspective of Dynamic House-price-to-income Ratio

The objective of this study is to improve the current model for determining the upper limit of the static price-to-income ratio by considering multiple elements that impact the growth of residents' income throughout the duration of their mortgage. The technique being offered presents a model that incorporates a dynamic house-price-to-income ratio. The underlying principle of this concept is derived from the theoretical framework of the equitable maximum correlation between property prices and income. The range of the measured dynamic ratio between house prices and income spans from 2.0755 to 4.3662. The calculation of this ratio should be based on the average interest rate of loans for commercial real estate, the ratio of mortgage repayment to income, and the average rate of growth in per capita disposable income for Chinese banks. The study employed the dynamic house-price-to-income ratio approach to calculate the dynamic house-price-to-income ratio for 31 provinces in China. This was done by considering the provinces with the lowest per capita living area and those with a moderately livable area. The findings of the comparison demonstrate that, once the essential criteria for a reasonably adequate per capita living area are met, the housing prices in all provinces, with the exception of Inner Mongolia, Shandong, and Hunan, exceed the acceptable threshold of the dynamic ratio between income and house prices.

Analysis Of House Price Bubbles and Macroeconomic Policy Regulation

House prices are a reflection of a country's economic growth and a city's overall competitiveness, and are determined by supply and demand after integrating various factors. Real estate is an important pillar industry all over the world, and almost everyone sees property as an important reflection of economic property. Real estate includes residential and commercial property. In 2023 real estate will remain an important and large industry. House prices are something that is closely watched in people's lives. When there is a change in house prices it can have an impact on the whole society and economy. This study focuses on the impact of macroeconomic policies on the house price bubble economy and the causes of the house price bubble economy. The study will adopt an objective perspective and a formulaic approach to analyze the house price bubble economy. This study will use the example of Japan during the bubble economy to explain this. It is found that different macroeconomic policies will lead to different economic effects that will trigger the strength of the house price bubble, such as macroeconomic policies that lead to inflation and then lead to the strengthening of the house price bubble.

The measurement of financial support for real estate and house price bubbles and their dynamic relationship: An empirical study based on 31 major cities in China

<p>In recent years, China's real estate prices have continued to rise, preventing the bursting of the house price bubble, and the various risks it triggers has become an important issue that governments at all levels have to face. In this paper, the backward sup ADF (BSADF) method was used to dynamically portray the evolution of real estate price bubbles in 31 large and medium-sized cities in China over a period of 11 years, from 2013 to 2021, and the dynamic relationship between the degree of financial support for real estate (hereinafter referred to as financial support) and house price bubbles in cities was investigated by employing a panel vector autoregressive (PVAR) model with panel data. The results showed that: Multiple cyclical bubbles significantly existed in the real estate markets of the cities during the study period, and in general, house price bubbles appeared earlier in the more economically developed regions than in the less economically developed regions and have spread to the less economically developed regions. Financial support contributed to house price bubbles, supporting the theory of excessive financial support. Furthermore, the results of the sub-sample showed that financial support contributed most strongly to house price bubbles in cities in the northern region.</p>

House price bubbles under the COVID-19 pandemic

We analyze bubble formations in house prices under the COVID-19 pandemic in 382 metropolitan areas in the U.S. with a special attention to the role of population density. We use a robust sieve-bootstrap version of the right-tailed unit root test procedure to identify periods of explosiveness in price–rent ratios across metro areas when controlling for fundamentals such as mortgage debt financing costs and the wealth of households. The bubble tests reveal evidence of explosive house prices during the COVID-19 pandemic but with important cross-area differences. In contrast to earlier bubble episodes, we find that the frequency and size of bubble formations under the pandemic have been stronger in low-density metro areas compared to high-density metro areas.

Housing Price Bubbles in China: An Investigation of Six Cities

Housing Price Bubbles in China: An Investigation of Six Cities

Government-Made House Price Bubbles? Austerity, Homeownership, Rental, and Credit Liberalization Policies and the “Irrational Exuberance” on Housing Markets

Government-Made House Price Bubbles? Austerity, Homeownership, Rental, and Credit Liberalization Policies and the “Irrational Exuberance” on Housing Markets

Comparative Analysis of Housing Price Bubbles in the South African Major Cities

Comparative Analysis of Housing Price Bubbles in the South African Major Cities

Examining the Effectiveness of Housing and House Registration Policies for Raising Inflow of Talents A Case Study in Zhangjiang Science City, Shanghai

With the national economic transformation, the advance of the intelligent manufacturing industry is gradually approaching, which has a direct positive correlation with innovations and the development of high-tech industries. Sufficient technical and entrepreneurial talent supply is the key to maintaining a high level of technological progress and sustaining economic development. Therefore, as one of the most developed cities in China, Shanghai adopts several house-related policies to attract the inflow of advanced talent. This paper examines the effectiveness and feasibility of the housing and household registration policies implemented in Zhangjiang Science City, including shortening the time of transferring temporary residence permit into the registered permanent residence (TR-to-PR) and offering talents apartments to reduce rental pressure of young talents. Four simple quantitative indicators used to analyze the effectuality are the number of talents introduced annually and its growth rate; the number of TR-to-PR groups annually and its growth, respectively. As a result, this paper supports that the policies are effective in promoting talent inflow. However, the future talent retention issue is still alarming and it deserves the attention of policymakers. More policies concerning solving house price bubbles and improving the living environment should be adopted to keep talents stay and contribute to economic development in the long run.

Estimating housing price bubbles for investment and owner-occupancy

ABSTRACT This study probably marks the first attempt to differentiate between investment and owner-occupancy when measuring real estate price bubbles. The housing tenure and leverage status of each residential sale in the Auckland housing market are identified, and four groups of transactions are created: leveraged investment (LI), leveraged owner-occupancy (LO), unleveraged investment (UI) and unleveraged owner-occupancy (UO). This research applies a present value model to estimate housing price bubbles for the four groups. We discover that, in terms of the duration and size of bubbles, there are minor differences among the four groups. In addition, the overvaluation for all groups is mainly related to momentum behaviour.

Detecting housing bubble in Poland: Investigation into two housing booms

The paper investigates the potential house price bubble in 18 provincial capital cities in Poland from 2000 to 2022. This is the first comprehensive effort to examine the house price exuberance throughout two housing market cycles (2000–2013 and 2014–2022, respectively). Additionally, we evaluate the risk of the housing bubble during a volatile era when the housing market in Poland was exposed to significant external shocks connected to COVID-19.In the study, we apply the new generation of recursive housing bubble identification procedures developed by Phillips, Shi and Yu (2015) and test whether the explosive behaviour of house prices in Polish cities was linked to household incomes and rents. Using the Error Correction Model, we also checked whether house prices were linked to fundamentals.In general, we observed explosive behaviour of house prices during the 2006–2007 period, which is not justified by fundamentals in all cities examined. The length and precise timing of the bubble phases varied amongst regional cities. Despite this, the findings are consistent with earlier data from other nations, which adds credence to the idea that the 2007 housing bubble was a worldwide occurrence. Contrary to what is commonly believed and reported in the media, we have not seen any indications of irrational house price behaviour in Polish cities during the Covid-19 outbreak. Contrary to the overwhelming evidence for the house price exuberance between 2006 and 2007, we conclude that there is no solid evidence of the existence of the housing bubble in recent years.

Long-Run Labor Costs of Housing Booms and Busts

Abstract We show large flows of workers into the real estate agent (REA) occupation during the early 2000s from virtually all parts of the skill, wage, and education spectrums. We find those entering REA in Metropolitan Statistical Areas (MSAs) with house price bubbles end up in occupations paying significantly less in the long-run as compared to similar REA entrants in non-bubble areas. Even in 2017, when house prices and employment return to their pre-crisis levels, REA entrants in Bubble MSAs are in occupations earning about 6% less. These results point to lasting effects of labor allocation decisions in response to distorted price signals.

Do Community Banks Stabilize Housing Prices?

This paper examines the influence that community bank residential mortgage lending has on regional housing prices and ultimately the housing price bubble that culminated with the Great Recession of 2007–09. Previous research suggests that community bank lending is influenced by financial intermediation frictions, such as difficulty attracting uninsured deposits and limited access to the bond market. Such frictions cause bank liquidity to play an important role in the ability of community banks to navigate swings in the local economy as well as real estate prices. We take these frictions as the starting point in our analysis and contribute to literature by focusing on the impact that community banks exert on housing prices. Specifically, we find that while community bank residential mortgage holdings are marginally related to housing price cycles, their mortgage lending does not contribute in an economically meaningful way to housing price bubbles. Even though bank liquidity, defined as the ratio of securities-to-assets, plays a role in the degree of community bank residential mortgage lending, it is not sufficiently strong enough to influence housing prices.

Rational housing demand bubble

We provide a unified framework with demand for housing over the life cycle and financial frictions to analyze the existence and macroeconomic effects of rational housing bubbles. We distinguish a housing price bubble, defined as the difference between the housing market price and its fundamental value, from a housing demand bubble, which corresponds to a situation where a pure speculative housing demand exists. In an overlapping generation exchange economy, we show that no housing price bubble occurs. However, a housing demand bubble may occur, generating a boom in housing prices and a drop in the interest rate, when households face a binding borrowing constraint. The multiplicity of steady states and endogenous fluctuations can occur when credit market imperfections are moderate. These fluctuations involve transitions between equilibria with and without a housing demand bubble that generate large fluctuations in housing prices consistent with observed patterns. We finally extend the basic framework to a production economy and we show that a housing demand bubble increases housing prices, which can still be characterized by large fluctuations.

House price bubble detection in Ukraine

PurposeThe 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.Design/methodology/approachHouse 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.FindingsThe 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.Research limitations/implicationsThe 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.Practical implicationsA 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.Social implicationsThe 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.Originality/valueThe 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.

서울 아파트 가격은 거품인가?*

This study analyzed whether the rise in apartment prices in Seoul could be interpreted as a housing bubble since 2017. In terms of the fact that the housing price bubble was difficult to be explained by observable changes in the market environment, this study examined the explanatory power of the endogenous relationship between the change in housing prices and economic variables in the pertinent period. To do so, this study set a model which interpreted how far the rate of change in actual housing prices deviated from the explainable change in basic market environments and applied the model to housing bubbles right before the U.S. subprime crisis (the early and mid 2000’s) and to apartment markets in Seoul after 2017. According to the result, the increase of housing prices in the U.S. markets in the mid 2000’s significantly exceeded the rise anticipated by the change in markets, while the change in apartment prices in Seoul was not considerably different from the conditions that were explained by the change in market circumstances. This suggested that the rise in housing prices in Seoul was caused not by bubbles from excessive expectations or self-realizing predictions but by factors affecting changes in the fundamental values for housing, such as government interventions or monetary policies.

Can monetary policy lean against housing bubbles?

Gali and Gambetti (2015) found protracted episodes in which stock prices rise in response to monetary policy tightening. This counter-intuitive result suggests that raising the policy rate in response to a perceived asset price deviation from fundamentals may fail to contain an emerging bubble. Since housing is often at the epicenter of deep and protracted recessions, it is essential (from a monetary policy perspective) to assess whether the result from Gali and Gambetti (2015) also holds when one considers housing instead of stock prices. Thus, we estimated a Bayesian VAR model based on an asset-pricing framework allowing for rational bubbles in the United States, the United Kingdom, and Canada. In addition, this estimation framework separates the fundamental component of housing prices from its bubble component, derived as the deviation of observed prices from the fundamental values. This allowed us to examine the responses of both components to a monetary policy shock and assess how bubbles may affect the responses of housing prices to monetary policy tightening. According to the results, we found that housing prices respond negatively to an interest rate hike, as common intuition would imply. This indicates that monetary policy may play a role in fighting housing price bubbles.