Decline In House Prices Research Articles

Measurement of Systemic Financial Risk Based on Real Estate Market

This paper constructs a systematic financial risk index system by selecting indicators from five aspects: macroeconomic risk, stock market risk, financial institution risk, international market risk and real estate market risk, uses the entropy value method to determine the weights of the indicators and calculates the systematic financial risk index from 2007 to 2024. The results of the study show that the systemic financial risk index has a rising trend, especially in recent years. The decline in housing prices and the slowdown in macroeconomic growth result in a significant increase in systemic financial risk. This paper provides some reference for stabilizing house prices and reducing systemic financial risk.

The effects of macroprudential policies on house prices: Evidence from non-ordinary house purchasing policy using China real transaction data

This paper contributes to the international policy debate on the effect of macroprudential policy on housing prices. Our identification relies on the fact that ordinary housing and non-ordinary housing in Beijing face different loan-to-value (LTV) restrictions and transaction tax rates. This paper uses regression discontinuity (RD) design to test whether the differentiated regulation of ordinary housing and non-ordinary housing caused a discontinuous change in housing prices with house-level transaction data. We find that housing prices have a significant discontinuous decline at the cutoff point caused by macroprudential policy, which we take 140 m2 s since it is the identification standard of non-ordinary housing. Further, we carry out RD-DID analysis to investigate the influence of loan-to-value (LTV) restrictions on housing prices. We find that around 2 % decline in house prices is caused by credit policy.

China's GDP at risk: The role of housing prices

This paper studies the impact of house prices on the distribution of GDP growth in China (the 5th, median, and 95th percentiles). We show that house price appre-ciation positively affects future GDP growth, with a more significant impact on the tail outcomes - GDP at risk. Moreover, we find that housing bust is associated with GDP growth vulnerability; a sharp decline in house prices generates severe economic downturns. Our finding is supported by the investment channel, a housing boom stim-ulates investment, which boosts GDP growth. However, the subsequent housing bust suppresses investment, leading to increased downside risks to GDP growth.

Damage versus risk perception: Why do house prices recover after hurricanes?

AbstractI study house price dynamics following Hurricane Sandy to explain the common puzzling finding of a price drop followed by a complete price recovery. Applying a quasi‐experimental difference‐in‐differences research design on Zillow parcel‐level sales data combined with Federal Emergency Management Agency data on damaged structures, I show that the extent of direct damages drives the decline in house prices. The extent of remodeling expenditures, as estimated from building permits, is found to be responsible for causing the return of prices to pre‐storm levels. Comparing flood insurance take‐up rates in the affected and non‐affected areas within floodplains and similarly for outside floodplains, I find no revision in perceived risk in the floodplain. In contrast, there has been an increase in flood insurance take‐up rates in affected areas outside of floodplains after the hurricane.

The blind spot in residential mortgages: Increasing default option value in the face of declining house prices

The interest rate hikes intended to combat inflation have not only significantly increased mortgage payments, but also significantly depressed house prices. The probability of default for mortgages is typically estimated through the debt servicing ability of the obligor. The alternative estimation based on the default option, although well studied in literature, is typically not used by practitioners. This option is normally ‘deep out of money’ and moves in the money if the loan to value (LTV) increases significantly, even creating negative equity. This is typically a remote probability due to the down payment requirements, but not as much under the current environment, where the house price depreciation has significantly increased LTVs, especially for newer mortgages underwritten when house prices were at their peak. This paper discusses the potential risk management oversights in this environment, illustrating that the increasing default risk is not adequately captured in probability of default (PD) models based on debt servicing ability alone. This is also true for the loss given default (LGD) risk if the contemporaneous LTV effects are not captured. Numerical examples are provided to demonstrate the material increase in PD, LGD and the combined expected loss risk with increasing LTV. It also discusses an alternative default option PD model, its calibration and its usage for stress testing along with LGD modelling, which together capture the contemporaneous LTV effects.

Estimating regional house price levels

While quality‐adjusted indices tracing the evolutions of regional house prices are increasingly available, this is less the case for data on regional house price levels. Yet, such information is fundamental to assess housing affordability and barriers to labour mobility. This article puts forward a method to compile regional house price levels that are consistent with the evolutions given by house price indices, representative of the underlying stock of dwellings, and based on the information on house price levels that is available at all dates rather than in a single reference period. Results for Spain show that the decline in house prices following the 2008–2009 crisis initially reduced the dispersion in house prices across Spanish regions, but this dispersion has increased again afterwards. Moreover, the relative housing affordability of Madrid deteriorated compared to all other Spanish regions in the last decade. Monitoring whether COVID‐19 will reverse this trend will be key.

The Impact of Commitment on Pending Home Sales in Entrapping Situations

This paper examines the decision-making processes of home buyers during the Global Financial Crisis, with a specific focus on passive coping mechanisms and the role of risk aversion. The study investigates how risk aversion influences the behavior of home buyers and highlights the disparities between high-income and low-income buyers. The findings reveal that high-income home buyers exhibit a risk aversion parameter that ranges from 1.74 to 1.99, while low-income home buyers have a parameter that ranges from 0.60 to 0.62, thus indicating different levels of risk tolerance and decision-making patterns among the income groups. These findings suggest that low-income home buyers are more likely to endure unfavorable situations or avoid making changes in periods of declining house prices than high-income home buyers. This behavior aligns with the concept of passive coping, where low-income home buyers choose to endure unfavorable situations rather than making changes because they fear the negative outcomes that may arise from taking a different course of action. They might worry that if they abandon the contract, they will miss out on the opportunity to own a home, live in a desirable neighborhood, or secure a specific property. This fear of potential losses leads them to choose passive coping, where they endure the current situation rather than making changes that could involve risks or uncertainties.

The Impact of Commitment on Pending Home Sales in Entrapping Situations

This paper examines the decision-making processes of home buyers during the Global Financial Crisis, with a specific focus on passive coping mechanisms and the role of risk aversion. The study investigates how risk aversion influences the behavior of home buyers and highlights the disparities between high-income and low-income buyers. The findings reveal that high-income home buyers exhibit a risk aversion parameter that ranges from 1.74 to 1.99, while low-income home buyers have a parameter that ranges from 0.60 to 0.62, thus indicating different levels of risk tolerance and decision-making patterns among the income groups. These findings suggest that low-income home buyers are more likely to endure unfavorable situations or avoid making changes in periods of declining house prices than high-income home buyers. This behavior aligns with the concept of passive coping, where low-income home buyers choose to endure unfavorable situations rather than making changes because they fear the negative outcomes that may arise from taking a different course of action. They might worry that if they abandon the contract, they will miss out on the opportunity to own a home, live in a desirable neighborhood, or secure a specific property. This fear of potential losses leads them to choose passive coping, where they endure the current situation rather than making changes that could involve risks or uncertainties.

Global Financial Crisis and Risk Management

This paper explores the 2008 financial crisis. It looks at its causes, consequences, and the changes that were effected to prevent the global economy from a similar crisis in the future. The crisis began in the United States, spread quickly, and ended up affecting the global populations jobs, homes, and savings. The paper identifies the Federal Reserves financial policies, subprime mortgages, and credit default swaps (CDS) as the main causes of the 2008 financial crisis each of these factors played a significant part in leading to the crisis. The combination of these factors made the effects of the 2008 financial crisis even more severe, leading to a broader economic downturn. The consequences of the crisis included increased regulation of the financial industry, government intervention through bailouts, a decline in housing prices, higher unemployment rates, and the breakdown of significant several major banks and financial institutions. These consequences were severe and governments had to come up with solutions to prevent a similar happening in the future through measures like the Dodd-Frank Act and the Basel III framework.

Application of Machine Learning in Boston House Price Prediction

House prices are an important economic indicator for a country or region, rising house prices are often associated with economic growth and increased employment opportunities, while a decline in house prices may indicate an economic slowdown or other unfavorable factors. Governments and relevant agencies need to understand the dynamics of housing prices to formulate appropriate housing policies and plan urban development. As one of the major economic centers in the United States, the fluctuations in Boston's housing prices can reflect the local economic conditions and development trends. This paper selects data from Boston in 1970. Each record in this database describes a Boston suburb or town. The proposed method was evaluated using 5 metrics. By comparison, the paper ends up with the result that XGBoost works best out of the four regression models.

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.

Analysis of House Price Determination in 13 G20 Countries: Random Effect Model

A house or a place to live is a primary need for all beings, but the lack of affordable housing to buy or rent can trigger a global housing crisis. After the collapse of the housing market in the US or the subprime crisis of 2008-2009 there was a revival of focus on the housing market. Empirical research has been conducted to look at the variables that affect house prices. However, it is still rare for researchers to examine the influence of macroeconomic variables in countries that have a strong role in the world economy. Therefore this study aims to determine the effect of the Consumer Price Index, Construction GDP, Unemployment Rate, Population Density, Exchange Rate and Power Purchasing Parity on the House Price Index as an indicator that reflects house prices. So, the contribution of this research is to provide novelty in the use of the house price index to determine the determinants that influence it. This research is a quantitative research using secondary data. The regression model used in this study is the Random Effects Model. The result of this study is the Consumer Price Index and Purchasing Power Parity have a significant positive impact on house prices. Therefore, the government needs to maintain the stability of these two indicators so that house prices remain stable. The Unemployment Rate and Exchange Rate show a significant negative effect on house prices. Therefore, the government needs to monitor and maintain the stability of the exchange rate and reduce the unemployment rate so that there is no significant decline in house prices. Meanwhile, construction GDP and population density show no effect on house prices. Additional studies are urgently needed to identify factors and housing price movements contributing to global and regional levels.

The Role of Moving Shocks, Unemployment, and Policy in Understanding Housing Bust

I use panel data from the Survey of Consumer Finances to study the recent U.S. housing bust in a quantitative lifecycle model. In the model, households face two types of idiosyncratic shocks: income shocks and moving shocks. The income shocks produce the large and long-lasting impact of unemployment on future earnings documented in recent empirical work. The moving shocks are estimated from survey data on reasons for moving to match the characteristics of marginal buyers. Movers are younger, have lower wealth and less secure jobs, making them more sensitive to unemployment and credit conditions. Moving shocks amplify the quantitative importance of labor and credit channels that explain the observed decline in house prices and rise in foreclosures. The Home Affordable Modification Program helped stabilize prices and reduce foreclosures at a relatively small cost, working mainly as insurance against bad shocks to the most vulnerable households.

The Impact of Property Tax on House Prices--Theoretical analysis based on the supply and demand model and the four-quadrant rule

This paper analyzes the impact of the real estate tax on house prices from theoretical models such as the long- and short-term supply and demand models and the four-quadrant analysis model, followed by a real estate tax country analysis. It is found that for most small and medium-sized cities, a real estate tax that imposes a tax on both the transfer and retention segments will lead to a significant decline in house prices in the medium to long term, but overall, house prices will still fluctuate up and down from the original trend. For cities with high house prices and large house price increases, a tax on only the transfer link will only drive The conclusion has some predictive value for the effect produced by the pilot real estate tax reform work, and at the same time, it has some guiding significance for the proposal of real estate tax reform in the paper.

특수학교 설립이 주변 아파트 매매가격에 미치는 영향

Special schools are essential facilities to guarantee the right to education for students with disabilities. Still, it is not easy to establish those schools due to strong opposition from local residents. The main reason for the residents' resistance is the negative effect of the community image and the resultant decline in housing prices. By studying the case of Seojin School, this study attempts to examine the special school's external economic impact and explore strategies to respond to the NIMBY phenomenon of local residents effectively.
 This study analyzes the sale price of apartments in nearby areas, focusing on Seojin School, which opened in 2020. 2017 was the year when the “Second Resident Hearing” was held, and the residents' NIMBY demonstration was covered in the national news. Before and after the debate, the sale price of apartments around Seojin School for five years(2015~2019) is analyzed using a hedonic price function model. The polygonal model was set in consideration of the spatial research area based on our judgment of the impact zone of Seojin school.
 First, the location of Seojin School has a negative effect on the apartment sale price.
 Second, these negative externalities still exist despite the passage of time. However, the negative external effect was very small, less than 10,000 won.

Has the Newly Imposed Property Tax Controlled Housing Prices? An Analysis of China’s 2009–2020 Interprovincial Panel Data

The stability of the real-estate market is crucial to China’s economic development and, in times of crisis, the economy will experience systemic adverse reactions that require appropriate regulation by the state using tax policy tools. Therefore, we analyzed the impact of real-property tax on house prices using panel data for 31 provinces in China from 2009 to 2020 using an empirical method, i.e., the instrumental variables approach. The empirical results show that each of the previous property-related taxes actually contributed to the increase in house prices and did not have a dampening effect. The newly introduced property tax will lead to a decline in house prices, which will help to alleviate the overheating of real-estate investment and mitigate the real-estate bubble crisis. A rational view of the impact of a property tax on housing prices needs to be taken in the context of factors such as income levels, consumer price levels, loan rates, and Chinese consumer culture. In order to achieve the goal of “no speculation in housing”, we also need to pay attention to the regulating effect of a property tax in combination with many other factors. This study is important for promoting property tax reform, curbing overheated real-estate investment, and promoting healthy economic development.

Macroeconomic determinants of households' indebtedness in Portugal: What really matters in the era of financialisation?

AbstractThe objective of this paper is to perform a time series econometric analysis in order to empirically assess the macroeconomic determinants and the corresponding drivers of Portuguese households' indebtedness in the period 1988 to 2016. During that period, the Portuguese economy experienced a process of financialisation that contributed to an increase in Portuguese households' indebtedness to unprecedented levels. The Portuguese households' indebtedness played a crucial role in the recent sovereign debt crisis. Based on the existing literature, we hypothesise that Portuguese households' indebtedness was due to seven macroeconomic determinants, notably housing prices, financial asset prices, the degree of personal income inequality, households' labour income, the importance of welfare state expenditures, the fraction of the working‐age population and the level of interest rates. Our findings reveal that the housing prices busts, financial asset prices, the degree of personal income inequality, households' labour income and the fraction of the working‐age population positively impact Portuguese households' indebtedness. Our findings also show that the increase in financial asset prices and the decline in housing prices were the main drivers of Portuguese households' indebtedness in the last few decades.

House prices and COVID-19 pandemic shocks in India: a nonlinear ARDL analysis

PurposeIndia is one of those countries that are severely affected by the COVID-19 pandemic. With the upsurge in the cases, the country recorded high unemployment rates, economic uncertainties and slugging growth rates. This adversely affected the real estate sector in India. As the relation of the housing market with the gross domestic product is quite lasting thus, the decline in housing prices has severely impacted the economic growth of the nation. Hence, the purpose of this paper is to gauge the asymmetric impact of COVID-19 shocks on housing prices in India.Design/methodology/approachStudies revealed the symmetric impact of macroeconomic variables, and contingencies on housing prices dominate the literature. However, the assumption of linearity fails to apprehend the asymmetric dynamics of the housing sector. Thus, the author uses a nonlinear autoregressive distributed lag model to address this limitation and test the existence of short- and long-run asymmetry.FindingsThe findings revealed the long- and short-run asymmetric impact of the COVID-19 outbreak and the peak of the COVID-19 on housing prices. The results indicate that the peak of COVID-19 had a greater impact on housing prices in comparison to the outbreak of COVID-19. This can be explained as prices will revert to normal at a speed of 0.978% with the decline in the number of COVID-19 cases. Whereas the housing prices rise at a rate of 0.714 as a result of government intervention to deal with the ill effects of the COVID-19 outbreak. Moreover, it can be inferred that both the outbreak and peak of COVID-19 will lead to a minimal decline in housing prices, while with the decline in the number of cases and reduction in the impact of the outbreak of COVID, the housing prices will rise at an increasing rate.Originality/valueTo the best of the authors’ knowledge, this is the first study to understand the impact of the outbreak and peak of COVID-19 on the housing prices separately.

The effect of gasoline prices on suburban housing values in China

By raising road transportation costs, an increase in gasoline prices should be expected to reduce housing demand in locations further from the central business district (CBD) relative to inner-city locations. This study uses a monthly real estate area dataset for 19 large cities in China over 2010–2018 to investigate the impact of gasoline prices on intra-city spatial differentials in housing prices. The findings suggest that higher gasoline prices on average lead to a relative decline in housing prices in outer suburbs, with a 1% increase in gasoline prices on average leading to a 0.004% relative reduction in home values for every additional kilometer from the CBD. The effect is larger in cities that have higher automobile ownership rates and that are less densely populated. The results are consistent with a conclusion that the rise of electric vehicles, autonomous vehicles, and working from home is likely to contribute to a lowering of geographical price differentials within Chinese cities over time.

Assessing the Impact of Aviation Noise on Housing Prices Using New Estimated Noise Value: The Case of Taiwan Taoyuan International Airport

Aviation noise at airports has a significant impact on nearby residents’ quality of life and residential property values. This study evaluated the impact of aviation noise based on house prices by using three different hedonic price models. Two novel independent noise variables, the estimated aviation noise value and noise reward fund are proposed for constructing effective hedonic price models. The real data of real estate transactions from the region defined by the Taoyuan International Airport’s 60–64 dB day-night average sound level (Ldn) and ≥65 dB Ldn noise contours are adopted as empirical data. Empirical results showed that the double-log hedonic price model with the proposed estimated aviation noise variables is the most suitable model for this study. Based on the double-log model, this study found that aviation noise has a significant negative impact on house prices in both noise contour areas of 60–64 dB Ldn and ≥65 dB Ldn. The rate of decline in house prices is approximately USD 2356.02/dB and USD 3622.78/dB in the 60–64 dB Ldn and ≥65 dB Ldn contour areas, respectively. Our results also showed that the noise reward fund had no significant impact on the house prices which implies that the current subsidy method has been maintained at an appropriate level for Taoyuan International Airport.