| In the context of "innovation-driven development" and vigorously building innovative cities,the city's innovation ability has attracted attention from all walks of life.Due to similar economic,social,and innovation environments between cities with close innovation capabilities,housing prices show significant spatial and temporal correlations.At the same time,housing prices with different innovation capabilities also show significant differentiation.Based on this,this article establishes a system of influencing factors on housing prices in innovative cities,and uses mathematical models to prove the spatiotemporal correlation of housing prices between cities with similar innovation capabilities.The spatial-temporal autocorrelation model of urban house price non-equilibrium panels was constructed to analyze the spatial-temporal correlations and differences of influencing factors between cities with heterogeneous innovation capabilities.The research helps the real estate market entities to understand the spatial and temporal changes of housing prices from a dynamic perspective,and provides theoretical support for the government to formulate differentiated housing price adjustment policies based on the heterogeneity of urban innovation capabilities.The main work of this article is as follows:(1)The influence mechanism of urban innovation factors,economic environment and social environment on housing prices in innovative cities is analyzed.Based on this,an innovative city house price influencing factor system including 11 factors and 16 indicators,including four factors of urban innovation input,innovation output,economic fundamentals and social infrastructure,was constructed.(2)Based on the theory of supply and demand,a spatial-temporal correlation mathematical model of urban housing prices was established,which proved the spatial-temporal correlation of housing prices between cities.Based on the spatio-temporal weight matrix,a spatial-temporal autocorrelation measurement model of the unbalanced panel of urban housing prices is established,which can not only consider the impact of historically-associated cities,contemporaneously-associated cities,and potential-associated cities on the target city at the same time,but also solve the sample cities and The numbers are all different.(3)An empirical study of housing prices in cities with heterogeneous innovation capabilities was conducted,and the spatial-temporal correlation of housing prices and the differences in influencing factors on housing prices between cities with heterogeneous innovation capabilities were compared and analyzed.The study found that,in terms of the spatial-temporal correlation of house prices,historically competing cities and competing cities have a significant positive impact on the target city's house prices,and potential competitive cities have a significant negative impact on the target city's house prices.And cities with strong innovation ability have a greater degree of correlation with housing prices than cities with weak innovation ability.In terms of influencing factors on housing prices,for cities with strong innovation ability,innovation input,innovation output and economic environment have a significant impact on urban housing prices,while only urban green space has a significant impact on housing prices in the social environment.The impact of resources and medical facilities on urban house prices is not significant.For cities with weak innovation capabilities,the economic environment,social environment,innovation funding and invention patent applications all have a significant impact on urban housing prices,while innovation talent investment and invention patent grants have no significant impact on urban housing prices. |
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