Research on the Impact of Market Concern for Real Estate Policy on Housing Prices: Evidence from Internet Search and Hedonic Price Theory

To avoid the effects of systemic financial risks caused by extreme fluctuations in housing price,the Chinese government has been exploring the most effective policies for regulating the housing market.Measuring the effect of real estate regulation policies has been a challenge for present studies.This study innovatively employs big data technology to obtain Internet search data(ISD)and construct market concern index(MCI)of policy,and hedonic price theory to construct hedonic price index(HPI)based on building area,age,ring number,and other hedonic variables.Then,the impact of market concerns for restrictive policy,monetary policy,fiscal policy,security policy,and administrative supervision policy on housing prices is evaluated.Moreover,compared with the common housing price index,the hedonic price index considers the heterogeneity of houses and could better reflect the changes in housing prices caused by market supply and demand.The results indicate that(1)a long-term interaction relationship exists between housing prices and market concerns for policy(MCP);(2)market concerns for restrictive policy and administrative supervision policy effectively restrain rising housing prices while those for monetary and fiscal policy have the opposite effect.The results could serve as a useful reference for governments aiming to stabilize their real estate markets.

Similar physical entity matching strategy for mobile edge search

In recent years,a large number of intelligent sensing devices have been deployed in the physical world,which brings great difficulties to the existing entity search.With the increase of the number of intelligent sensing devices,the accuracy of the search system in querying the entities to match the user’s request is reduced,and the delay of entity search is increased.We use the mobile edge technology to alleviate this problem by processing user requests on the edge side and propose a similar physical entity matching strategy for the mobile edge search.First,the raw data collected by the sensor is lightly weighted and expressed to reduce the storage overhead of the observed data.Furthermore,a physical entity matching degree estimation method is proposed,in which the similarity between the sensor and the given sensor in the network is estimated,and the matching search of the user request is performed according to the similarity.Simulation results show that the proposed method can effectively reduce the data storage overhead and improve the precision of the sensor search system.

Mediation effects of online public attention on the relationship between air pollution and precautionary behavior

This study investigates the mediation effects of online public attention on the relationship between air pollution and precautionary behavior based on a merged real-world data set that includes daily air quality,Internet search and media indices,social media discussions,and product purchases.Using a Bayesian structural equation modeling approach,we show that online public attention to air pollution increases when air pollution increases,and such attention is captured by more media reports,social media discussions,and Internet searches.A comprehensive relationship involving direct and indirect effects between air pollution and precautionary behavior is established.Air pollution has a positive effect on proactive defensive behaviors,reflected in increased purchases of preventive products,and this effect is partially mediated by online media coverage and the public's Internet searches.Air pollution also motivates passive defensive behaviors,reflected in decreased purchases of outdoor sports products,and this effect is partially mediated by social media coverage.These results suggest that governments could improve the quality of policy making by considering the different roles of various forms of online public attention in the public's risk perceptions of and reactions to air pollution.