Do natural disasters cause an excessive fear of heights? Evidence from the Wenchuan earthquake

Housing market capitalization of environmental risk: evidence from the Tianjin explosion

Do environmental risks affect housing prices in the vicinity of industrial plants? Prior studies concentrated on the capitalization effect of infrequent natural disasters instead of industry-related environmental risks caused by production accidents in our everyday lives. In this paper, we first use the 2015 Tianjin explosion in China as an exogenous shock, which led to serious casualties and property damage with nationwide concern, to estimate the effects of environmental risk on housing prices far away from the explosion site. Based on the housing transaction units and chemical plant datasets in Beijing with the difference-in-differences hedonic model, the results indicate that first, housing prices near chemical plants decreased significantly after the explosion and lasted for at least 2 years. Second, the mitigation effects of production and consumption amenities and regional demographic features were examined. The conclusions of this paper provide a policy basis for constantly implementing public sentiment management and safety supervision, balancing industrial and residential land layouts, and improving local amenities and demographics to ensure the stability of the land market.

Housing market volatility under COVID-19: Diverging response of demand in luxury and low-end housing markets

The ongoing pandemic has led to substantial volatility in residential housing markets. However, relatively little is known about whether the volatility is dominated by housing demand or supply, and how different priced markets contribute to the volatility. This article first examines the temporal effect of COVID-19 on house prices, housing demand, and supply in Los Angeles, and second explores the effect heterogeneity in luxury and low-end housing markets within the city. For identification, the article employs a revised difference-in-differences (DID) method that controls more rigorously for unobservables and improves on the traditional DID with smaller prior trends. Using individual level data, the result first shows that, in response to the outbreak, house prices, demand, and supply all decreased in March to May 2020 and increased in July and August 2020, with demand dominating the process. Second, the heterogeneity exploration identifies diverging COVID-19 impacts in higher- and lower- priced markets. Particularly, the decline in overall price and demand before June originates mainly from the lower-priced market while the higher-priced one experienced limited changes in demand. After July, higher-priced markets led housing market's surge in price, demand, and supply, whereas the lower-priced market has not fully recovered from decreases in house prices and housing demand. Finally, a larger price decline in lower-priced markets is found to be associated with higher service shares and lower homeownership rates. The results not only facilitate market participants in their decision making but also aid local governments in formulating policies and allocating subsidies to mitigate the effects of the outbreak.

Impact of the COVID-19 pandemic on the housing market at the epicenter of the outbreak in China

The outbreak of the COVID-19 in January 2020 has had a profound impact on the global economy, so it is important to study the impact of the pandemic on the housing market. To investigate the impact of the pandemic on the housing market and the response of the housing market, this paper first uses the hedonic price model to compile the second-hand housing price index in Wuhan and its neighboring capital cities and then uses the difference-in-difference (DID) model to conduct a comprehensive study on new commercial housing and second-hand housing market. In addition, this paper also uses the VAR model to explore the housing market’s response to the epidemic situation. The results show that the negative impact of the pandemic on the housing market is mainly reflected in the volume and area of housing transactions, with little impact on housing prices. Second, the reported cases of COVID-19 have a negative impact on the housing market in the short term, which gradually weakens with time and disappears after three weeks. This paper’s findings indicate that the epidemic’s impact on the housing market is mainly due to the real estate enterprises stopping selling houses and local governments implementing home quarantine measures, which affect normal housing transactions. However, the COVID-19 pandemic did not greatly negatively impact consumers’ demand and confidence in buying houses, so the house prices remained stable overall.

The impact of COVID-19 on housing price: Evidence from China

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.

Study on Average Housing Prices in the Inland Capital Cities of China by Night-time Light Remote Sensing and Official Statistics Data

In this paper, the annually average Defense Meteorological Satellite Program-Operational Linescan System (DMSP/OLS) night-time light data is first proposed as a surrogate indicator to mine and forecast the average housing prices in the inland capital cities of China. First, based on the time-series analysis of individual cities, five regression models with gross error elimination are established between average night-time light intensity (ANLI) and average commercial residential housing price (ACRHP) adjusted by annual inflation rate or not from 2002 to 2013. Next, an optimal model is selected for predicting the ACRHPs in 2014 of these capital cities, and then verified by the interval estimation and corresponding official statistics. Finally, experimental results show that the quadratic polynomial regression is the optimal mining model for estimating the ACRHP without adjustments in most provincial capitals and the predicted ACRHP of these cities are almost in their interval estimations except for the overrated Chengdu and the underestimated Wuhan, while the adjusted ACRHP is all in prediction interval. Overall, this paper not only provides a novel insight into time-series ACRHP data mining based on time-series ANLI for capital city scale but also reveals the potentiality and mechanism of the comprehensive ANLI to characterize the complicated ACRHP. Besides, other factors influencing housing prices, such as the time-series lags of government policy, are tested and analysed in this paper.

Risk Perception and Property Value: Evidence from Tianjin Port Explosion

This paper aims to investigate the perceived risk after the Tianjin port explosion event and its impact on property value. In addition to focusing on the relationship between homebuyers’ risk perception and property sales price, this paper also considers the risk perception of property sellers via the sale-to-list ratio. By using the difference-in-differences method and the sample from the property agent, we found that the explosion event generated a long-time effect on the public and property market in the city of Tianjin. Relative to those properties far away from the explosion site, the sales price and the sale-to-list ratio of the property near the explosion site were detected to have significant drops for the first six months after the explosion event. The temporal decrease of the relative sales price and the sale-to-list ratio are the evidence of short period overreaction from the public, which decays over time. This study demonstrates an indirect method to estimate the perceived risk of the general public and provides valuable insight into sustainable port management policies.

Fear of nuclear power? Evidence from Fukushima nuclear accident and land markets in China

This paper examines whether the 2011 Fukushima Nuclear Accident (FNA) changed the Chinese public's attitude toward nuclear energy by studying transactions in land markets near nuclear power plants in China. Using a data set that matched the details of all nuclear reactors in China with information on land transactions around them before and after the FNA, we find that the accident had dynamic effects on land markets in China. Land prices within 40 km of nuclear power plants dropped by about 18% one month after the nuclear accident. However, the impact of FNA decreased over the longer term, eventually becoming statistically insignificant. Also, the impacts of the disaster varied with plant characteristics such as operating status, construction year, and size.