ABSTRACT
Purpose: The COVID-19 pandemic has significantly raised economic risk and uncertainty worldwide. How does COVID-19 affect urban housing markets? Is there any difference when different areas encounter COVID-19? This study aims to investigate the impacts of the pandemic on housing prices by using Beijing's housing markets data in 2020. Design/methodology/approach: The authors use transaction-level data from April to September in 2020 to conduct a hedonic price analysis of the housing markets in Beijing. The data included 70,843 transactions scraped from a real estate agent's website. The authors use the difference-in-differences approach to evaluate the impacts of the COVID-19 outbreak from the Beijing Xinfadi market (the largest and most important food wholesale market in Beijing) in 2020. Findings: This outbreak of COVID-19 caused a 6.3% drop in housing prices in Beijing from April to September in 2020. However, the impacts of COVID-19 on housing prices in different urban neighbourhoods were spatially heterogeneous. Housing prices in neighbourhoods with industries that rely on face-to-face communication were more affected by the pandemic, while those that can work remotely were less affected. Originality/value: By investigating the impacts of COVID-19 on housing prices in Beijing, this study illustrates that urban housing prices would be impacted by the pandemic, at least in the short term. While the rise and fall of housing prices were found spatially heterogeneous in Beijing, it suggests that urban neighbourhoods with specific socioeconomic characteristics and geographic locations would unfold different resilience when encountering pandemic. By using data scraping and rigorous statistical tools, the study is probably one of the first ones examining the consequences of COVID-19 in intra-urban housing markets. © 2023, Emerald Publishing Limited.
ABSTRACT
PurposeThis paper aims to identify the economic stimulus measures that ensure stability of the Lithuanian housing market in the event of an economic shock.Design/methodology/approachThe econometric analysis includes stationarity test, Granger causality test, correlation analysis, autoregressive distributed lag models and cointegration analysis using ARDL bounds testing.FindingsThe econometric modelling reveals that the housing price in Lithuania correlates with quarterly changes in the gross domestic product and approves that the cycles of the real estate market are related to the economic cycles. Economic stimulus measures should mainly focus on stabilizing the economics, preserving the cash and deposits of households, as well as consumer spending in the case of economic shock.Originality ValueThis study is beneficial for policy makers to make decisions to maintain stability in the housing market in the event of any economic shock.
ABSTRACT
Using data on monthly community-level confirmed COVID-19 cases and housing price in China, we investigate the impact of COVID-19 on housing price. With the difference-in-difference method, we find that the housing price of the communities with confirmed COVID-19 cases would reduce by 2.47%. The impact persists three months and the extent of the impact basically becomes greater as time goes on. The results are robust after the parallel pre-trend test and the placebo test. Moreover, the impact of COVID-19 on housing price only exists in regions with a higher infection level of COVID-19 or worse medical treatment conditions.
ABSTRACT
This paper investigates the effect of COVID-19 on both housing prices and housing price gradients in China using transaction level data from 60 Chinese cities. After using a difference-in-differences (DID) specification to disentangle the confounding effects of China's annual Spring Festival, we find that housing prices decreased by two percent immediately after the COVID-19 outbreak but gradually recovered by September 2020. Moreover, our findings suggest that COVID-19 flattens the horizontal housing price gradient, reduces the price premium for living in tall buildings, and changes the vertical gradient within residential buildings. This is likely explained by the changing household preferences towards low-density areas associated with lower infection risk.
ABSTRACT
While many studies have explored green buildings, there remain many unsolved questions about how to expand this section of the housing market, such as how the capitalization effect of green buildings varies across time and location. This study investigates the price premiums of green buildings in Taipei City, the largest metropolis in Taiwan, to examine how they have changed over time, and assesses the magnitude of the capitalization effects under varying housing price levels. The empirical results indicate that Taipei residents are willing to pay higher prices for houses with green-label certification. The higher the certification level, the higher the price that residents are willing to pay. This study additionally identifies two characteristics of the capitalization effects in this market. The first is that the price premiums vary with the market condition. For example, during housing market downturns (e.g., 2014-2015), building green premiums may be unremarkable or non-existent. The second effect identified is that green buildings tend to exhibit an 'aristocratic trend.' This means that high-priced houses (i.e., houses priced above the 80% quantile) in the high-end label category attract the highest premiums, implying that the willingness to pay for green features tends to only exist among wealthy buyers. The result of this paper provides evidence to explain the heterogeneity of green build premiums. Regarding the specificity of these premiums, this paper provides recommendations for builders and managers.
ABSTRACT
The metro (or underground railways) has become a backbone in the transit systems of many cities. It has numerous externalities, such as ameliorating traffic congestion and enhancing nearby property prices. Previous studies extensively focused on the relationship between metro accessibility and property prices and obtained various interesting findings and enriched practical implications. However, this relationship in the era of the coronavirus disease 2019 (COVID-19) and other epidemic shocks has not been investigated. Based on a unique property transaction dataset (including tens of thousands of transactions stretching from 2018 to 2020) in Chengdu, China, this study develops a battery of hedonic pricing models and difference-in-differences models to decipher the time-varying relationship between metro accessibility and residential property prices. The results show that the implicit price of metro accessibility modestly decreases in COVID-19, which can be explained by the declining role of the metro. Specifically, the price elasticity of distance to the metro is −0.024 before COVID-19, but it turns to −0.018 during the pandemic. The relative price of properties within 500 m from metro stations to those farther away (500 m − 3 km) decreases by 15.4% during the pandemic. Additionally, COVID-19 does not jeopardize property prices in Chengdu. The plausibility and robustness of the core findings have been confirmed through alternative treatment groups, alternative model specifications, and placebo tests.
ABSTRACT
Mortgage is an important part in the housing market, it cause a clear and strong relationship between monetary policies and housing market. The release of growing commodity causes the diversification of housing investment. Actually, the Financial Tsunami was cause by subprime-mortgage crisis in U.S. in 2007. A lot of models have been proposed to predict housing price over the course of decades of research, but some new models should be proposed to suitable the situation in the post era of financial tsunami and COVID-19. The subprime-mortgage crisis damaged the housing market of San Francisco, directly. The housing market in a port is different from in a city, shipping is an important factor to urban development, the paper build the system dynamic model of Kaohsiung housing market, which is a port the same as San Francisco, to predict Kaohsiung housing price. This study aims to extract the related factors to the housing prices of Kaohsiung by correlation analysis, find out the critical factors of the housing prices of Kaohsiung by regression analysis. © 2021 IEEE.
ABSTRACT
The COVID-19 pandemic caused a number of challenges worldwide regarding not only the human health perspective, but also the economic situation. Quarantine, imposed in many countries, forced a substantial part of businesses to close or narrow down their activities, thus leaving corporations and employees without any or with lower income. If national governments had not undertaken any actions to save national economies, the consequences could have been even more devastating. The real estate market is an important part of economy. Instability in the real estate market can cause financial problems, vulnerability of population's welfare and other negative effects. This research aims to assess the impact of the economic stimulus measures on the real estate market under the conditions of the COVID-19 pandemic in Lithuania. The research methods include comparative analysis, correlation analysis, stationarity test, regression analysis and the ARDL models. The results indicate that the economic stimulus measures only partially contribute to stabilization of the real estate market in Lithuania. The drop in housing prices was 2.9 percent lower because of the economic stimulus in the second quarter of 2020. Maintenance of household cash and deposits as well as lending to business enterprises are the measures that allow to stabilize the real estate market in the shortest time under the conditions of the economic shock. The other governmental support measures are also important, especially if they are aimed at preserving jobs.
ABSTRACT
This paper evaluates the impact of the COVID-19 epidemic on the real estate market using a community-level panel dataset of 34 major cities in China. We find that the average housing price in communities with COVID-19 infections decreases by approximately 1.3% compared to communities with no confirmed cases. The economic implication is that homebuyers are willing to pay a premium equivalent to approximately 1.3% of the average housing price to avoid health risks. The response of real estate markets to epidemic shocks is heterogeneous in community and city characteristics. Dynamic analysis shows that the declines in home prices, transaction volumes, and rents are all short-lived, returning to their original development paths a few months after the epidemic shock. Public interventions such as community closures and quarantines may have contributed to the rapid recovery of the housing market, reducing the volatility of housing assets in the household sector.