Housing Bubble Research Articles

Housing Market Dynamics in Kazakhstan: An Estimated DSGE Model

This study investigates the drivers of housing price fluctuations in Kazakhstan by using a dynamic stochastic general equilibrium (DSGE) model with the housing market. We estimate the model by using Bayesian methods with data for the period of 2010Q1 to 2020Q4. We find that housing preference shocks play a crucial role in explaining for housing market fluctuations. These shocks account for a substantial portion of housing price and consumption behavior variations. Surprisingly, monetary policy, government spending, productivity and markup shocks show limited explanatory power for housing price fluctuations. Our findings suggest the importance of monitoring potential housing bubbles, as well as the impact of housing market fluctuations on the broader economy, thus highlighting the housing wealth effect. Additionally, our analysis indicates that the pension withdrawal policy has a minor long-term effect on business-cycle fluctuations in Kazakhstan. Overall, technology shocks are key drivers of gross domestic product (GDP) variance, while inflation rate variation is mainly explained by monetary policy and foreign demand shocks.

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.

Finance and local economic growth: New evidence from China

AbstractWe study the relationship between finance and growth using a sample of 275 Chinese cities from 2009 to 2018. We exclude bank loans to local governments through the local government financing vehicles (LGFVs) and construct a better financial development index which measures the level of loans extended by banks to enterprises and households. We find that financial development in the form of a higher loan‐to‐GDP ratio leads to lower economic growth. This negative relationship between finance and growth may be attributed to various mechanisms, including discrimination in bank lending, housing market bubbles, and an imbalance in growth between real and financial sectors.

Monetary Policy and Housing Bubble in Ireland

This paper discusses monetary policy and economic performance in Ireland in the early 21st century. Based on empirical analysis, it analyzes the causes of the emergence of the Irish housing bubble with the aim of exploring whether "monetary policy is the crucial cause of the formation of the housing bubble".

The Relationship between Real Estate Bubble and Financial Crisis Taking Shanghai as an Example

In recent years, the real estate market has become increasingly hot, developing very quickly, and has become one of the important industries to promote the development of GDP in China. However, the rapid development of many negative effects, such as high prices and rising costs of living, has caused the real estate market to bubble, causing people to worry about whether it will cause a financial crisis, resulting in an economic crash. As a result, many scholars have conducted various studies on the real estate bubble, but they have not reached a consensus on the bubble. Economic crashes caused by real estate bubbles have occurred on occasion throughout history, and many financial crises are triggered by excessive real estate bubbles. This paper visually analyzes the development of the real estate market in Shanghai in recent years using a literature review method, analyzes the transmission relationship between the real estate bubble and the financial crisis, expounds on the harm caused by the bursting of the real estate bubble, and proposes corresponding countermeasures for the benign development of the real estate market.

Analysis of the Relationship between Expansionary Monetary Policy and the Housing Bubble in the United States based on the Taylor Rule

The "subprime crisis" refers to a situation that occurred in the United States in which investment funds were compelled to close due to the bankruptcy of subprime mortgage institutions, causing stock market volatility brought on by the financial storm. This situation occurred between 2006 and 2009 and is referred to as the bubble economic crisis. It caused a liquidity crisis in the major financial markets around the world. In the spring of 2006, the "subprime mortgage crisis" in the US started to manifest. The Taylor rule argues that expansionary monetary policy is the cause. The housing bubble in the United States and expansionary monetary policy are the subjects of this essay. As a result of the analysis of related phenomena, this paper concludes that the subprime crisis is caused by expansionary monetary policy.

Anchoring Effect in Chinas Real Estate Market

Since the reform and opening up, China's real estate industry has prospered rapidly and has become an important force driving rapid economic growth and promoting urbanization. In recent years, China's real estate industry has entered an inflection point of development, and real estate bubbles and cyclical sharp fluctuations in the real estate market have occurred from time to time. Behavioral finance has provided a new research direction for how to explain this fluctuation. Based on this, this essay uses the classic anchoring effect theory in behavioral finance theory to study the existence and characteristics of anchoring effect in China's real estate market from the micro level, and explains the entire mechanism through three applications. The first application empirically tested the existence of the anchoring effect in China's real estate market, and analyzed the difference between the anchoring effect in the eastern and western regions of China. It verified that the decision of housing prices in the central region was affected by the anchoring effect of the micro subject, while the anchoring effect in the eastern and western regions was not significant. The second application analyzed Hong Kong's secondary residential market, and a statistical model including anchoring effects was also presented in order to study the dynamics of house prices. The third application provides an improved Hedonic model, which combines various literature data to study the influence of age, growth area and other characteristics of home buyers on real estate prices under the anchoring effect.

Securitization:The curse of the Financial System during the 2008 Crisis

During the 2008 financial crisis, many investment and commercial banks were hit hard. The primary reasons that caused the financial crisis were the abuse of mortgage securitization and the housing bubble. In this paper, our group mainly focused on the potential issue of the mortgage securitization structure and the impact that the abuse of securitization caused. Our group analyzed the correlation between mortgage defaults through the OLS linear regression model. It showed an underestimation of the possibility of defaulting on securitization products (MBS, CDO, etc.). Furthermore, our group analyzed the expansion of this potential risk due to the raised housing bubble. After analyzing the potential risk, our group analyzed the impacts that the securitization caused in macro and micro views. First, our group analyzed vital factors that affect bank performance using the OLS linear regression model. Moreover, based on the change of these critical factors caused by the exposed potential risk from the securitization structure, our group analyzed the banks performance during the financial crisis. Second, our group analyzed the macro impacts before, during, and after the financial crisis due to the abuse of securitization. Overall, according to the potential risk in the securitization structure and the macro and micro impacts caused by the exposed potential risk, our group concluded that the abuse of securitization was the curse for the whole financial system at that time.

Holding Onto the Past: Previous Homes, Post-Move Housing Consumption, and the Great Recession

We document that households relocated during the 2007-2009 Great Recession and its aftermath were substantially more likely to hold their previous homes for an extended period of time. We identify two contributing factors to this phenomenon. First, falling house prices pushed many homes into the “negative-equity” and “near-negative-equity” territories, and this made it challenging for owners to sell their homes. Second, we also show that falling home values had a more widespread effect that made all homeowners, regardless of their equity positions, more reluctant to sell. Additionally, we find households without mortgages are more likely to hold previous homes. Overall, we show the relationship between the loan-to-value (LTV) ratio and the likelihood of holding is U-shaped. We further examine the impact of holding previous homes on post-move housing tenure and housing consumption choices. We find that holding previous homes is associated with renting for a longer period. For households that bought new homes after relocation, holding previous homes is associated with the new residences that are less expensive and smaller. Our results suggest that, for households that moved during the housing bust, the Great Recession has a long-lasting effect on their housing consumption choices.

Owner‐occupancy fraud and mortgage performance

AbstractWe identify occupancy fraud—borrowers who misrepresent their occupancy status as owner‐occupants rather than investors—in residential mortgage originations. Unlike previous work, we show that fraud was prevalent in originations not just during the housing bubble but also persists through more recent times. We also demonstrate that fraud is broad‐based and appears in government‐sponsored enterprise and bank portfolio loans, not just in private securitization; these fraudulent borrowers make up one third of the effective investor population. Occupancy frauds obtain credit at lower interest rates, suggesting a motivation for undertaking fraud. These fraudulent borrowers perform substantially worse than similar declared investors, defaulting at a 75% higher rate. We also provide evidence consistent with fraudulent borrowers’ defaults being more “strategic,” suggesting that this population poses a risk in the face of declining house prices.

Социально-экономическое развитие Аргентины в первой половине XIX в.

This article examines formation of socioeconomic structure of Argenti-na after the War of independence. The author focuses on the main fea-tures of a new social order: land boom, estancia expansion, ranching export, expansion of foreign capital. After the War of Independence, large land ownership, latifundists became dominant in the country. Creole merchants, while maintaining their positions in the export-import trade, began to create large estancias. The high profitability of cattle breeding, in comparison with agriculture, led to the transfor-mation of cattle breeding into the main sphere of capital application. Along with the oligarchy, foreign capital became the most important component of the emerging structure. English capital appeared in Ar-gentina as a trading capital, but began to invest in estancias and saladeros – cattle processing enterprises. Argentine industry – manufactories and craft workshops in Buenos Aires and rural industry in the interior provinces – could not compete with foreign goods. Argentina has become an economic colony: the export of cattle products in exchange for the import of manufactured goods.

Efficient Multi-Change Point Analysis to Decode Economic Crisis Information from the S&P500 Mean Market Correlation.

Identifying macroeconomic events that are responsible for dramatic changes of economy is of particular relevance to understanding the overall economic dynamics. We introduce an open-source available efficient Python implementation of a Bayesian multi-trend change point analysis, which solves significant memory and computing time limitations to extract crisis information from a correlation metric. Therefore, we focus on the recently investigated S&P500 mean market correlation in a period of roughly 20 years that includes the dot-com bubble, the global financial crisis, and the Euro crisis. The analysis is performed two-fold: first, in retrospect on the whole dataset and second, in an online adaptive manner in pre-crisis segments. The online sensitivity horizon is roughly determined to be 80 up to 100 trading days after a crisis onset. A detailed comparison to global economic events supports the interpretation of the mean market correlation as an informative macroeconomic measure by a rather good agreement of change point distributions and major crisis events. Furthermore, the results hint at the importance of the U.S. housing bubble as a trigger of the global financial crisis, provide new evidence for the general reasoning of locally (meta)stable economic states, and could work as a comparative impact rating of specific economic events.

A Systematic Bibliometric Analysis of the Real Estate Bubble Phenomenon: A Comprehensive Review of the Literature from 2007 to 2022

This article presents the results of a bibliometric review of the study of real estate bubbles in the scientific literature indexed in Web of Science and Scopus, from 2007 to 2022. The analysis was developed using a sample of 2276 documents, which were reviewed in R software and analyzed with the assistance of the Bibliometrix package of the same software. The results indicate that there has been considerable productivity on the topic of real estate bubbles since 2007, with an emphasis on housing price formation processes and the social effects when bubbles burst. The authors found that there were not many case studies located in Latin America or Africa, nor were there approaches with advanced predictive modeling techniques using machine learning or artificial intelligence. The article provides an understanding of the state of the art in real estate bubble research and situates new research in front of the influential literature previously published.

Macroprudential policy and the real estate market: Effectiveness and repercussions

Macroprudential policies to counter the housing bubble has been widely implemented to maintain financial stability. However, few attempts have been made to address the (un)intended externalities, namely, the “bubble-thy-neighbor” spillovers of Forbes et al. (2016). A loan-to-value (LTV) policy adopted in a local rather than the overall housing market in Taiwan provides an opportunity to investigate the transmission from the bubbles to spillovers under time-varying econometric frameworks. The empirical results show that the LTV policy has been very effective in eliminating local housing bubbles, while the information regarding exorbitant housing prices in regulated areas had increasing ripple effects on other unregulated areas.

What Influenced Hanoi’s Apartment Price Bubble between 2010 and 2021?

This study focused on testing the existence of an apartment price bubble in Hanoi (Vietnam) and on determining the factors that affected it in the period between 2010 and 2021. Using the fundamental factor approach, the authors applied VAR regression using time series data. Specifically, we used the ADF unit test to test the stationarity of the variables based on the following criteria: AIC (Akaike information criterion); LR (likelihood ratio); FPE (final prediction error); HQ (Hanan–Quinn information criterion); and Schwarz (SC) to find the optimal lag (Lag) for the model. We also applied the Granger causality test to determine the correlation between the economic variables that appeared in the model with the PR index. We present the results of the research model through the push–response function and the variance decomposition to consider and evaluate the impact of the PR index shock on itself and the other variables. The literature in this field includes many studies that are similar to this one; however, no research has been conducted that has focused on analysing whether variables, such as per capita income and the urbanisation rate, influence the formation of real estate bubbles. This focus is especially relevant in Hanoi, which is an important part of the Vietnamese real estate market. Through this study, we aimed to fill this gap and to contribute to the references on the Hanoi real estate market and its influencing factors.

Did monetary policy cause a housing bubble in theUS in the first half of the 2000s?

Loose monetary policy is one of the causes of the housing bubble’s burst. The relationship between monetary policy andthe housing bubble is the main topic of this essay. Through analyzing economists’ opinions, collecting data and makingregression, and evaluating the cause of the housing bubble on my own, this essay concludes that monetary policy isnot the main cause. In this essay, I consider the relationship comprehensively. I first select and evaluate two works ofliterature with different views about the relationship between the housing bubble and monetary. Then in the discussionpart, I draw on both ideas and find other arguments to draw my conclusion. This essay announces that the housingbubble is affected by various factors, and monetary policy is just one of them.

Transmission of housing bubbles among industrial sectors

We find that housing bubbles are accompanied by sluggish demand in non-housing-related sectors both currently in China and previously in Japan. To explain this, we construct a theory to model the differential impact of housing bubbles on housing-related and non-housing-related industrial sectors by combining the neoclassical framework of general equilibrium with the input–output table. We also find that the housing bubble increases investments and output prices in housing-related sectors (crowding-in effect) while simultaneously reducing investments and output prices in non-housing-related sectors (crowding-out effect). Our theoretical findings could explain the negatively significant correlation between housing or land prices and the Consumer Price Index (CPI) during China and Japan’s bubble economy eras. Hence, this paper provides new insights into why and how the housing bubble distorts the economy differentially, for different industrial sectors.

Housing Fever in Australia 2020–23: Insights from an Econometric Thermometer

AbstractAustralian housing markets experienced widespread and, in some cases, extraordinary growth in prices between 2020 and 2023. Using recently developed methodology that accounts for fundamental economic drivers, we assess the existence and degree of speculative behaviour, as well as the timing of exuberance and downturns in these markets. Our findings indicate that speculative behaviour was indeed present in six of the eight capital cities at some time over the period studied. The sequence of events in this nation‐wide housing bubble began in the Brisbane market and concluded in Melbourne, Canberra, and Hobart following the interest rate rise implemented by the Reserve Bank of Australia in May 2022. As of March 2023, the housing markets in Sydney, Canberra and Hobart had broadly regained stability, while Melbourne's return to its normal state is more gradual. In addition, over‐corrections against fundamentals are evident in the housing markets of Brisbane, Adelaide, Darwin and Perth. For regular updates on the housing markets, readers may visit the authors' website at https://www.housing-fever.com.

House Bubbles, global imbalances and monetary policy in the US

This paper examines the factors driving housing price exuberance in the United States, specifically the influence of expansionary monetary policies and the global saving glut. We employ medium scale Bayesian VAR and time-varying VAR models to estimate the effects of monetary policy and global saving glut shocks on US housing bubbles. We find that, prior to the Global Financial Crisis, the impact of the saving glut shock is more enduring, powerful, and rapid in generating housing bubbles compared to monetary policy shocks. However, the recent housing boom that commenced in 2019 demonstrates a different pattern. Our results suggest that both monetary policy and the global saving glut contribute to the increase in house prices. Counterfactual policy experiments validate this conclusion.

Monetary policy and housing bubble

This research paper examines the relationship between monetary policy and housing prices in macroeconomics, with a focus on the United States. Housing prices play a substantial role in the consumer price index, and therefore, ensuring price stability for goods and services. The paper reviews the literature on two different perspectives: John Taylor's argument that loose monetary policy contributed to a surge in house prices, and Ben Bernanke's perspective that other factors caused the housing bubble. This paper contains the discussion on the literature of monetary policy and house prices, using the linear regression model and the idea of Taylor rule from different perspectives to identify the relationship between monetary policy and the housing prices. The analysis reveals differences in purposes of the models, which provides a basis for further discussion on the rule of monetary policy.