With the weight matrix based on geographical distance and population mobility，it used the dynamic spatial autoregressive model and the spatial Durbin model to estimate the inhibiting effect of housing prices on regional innovation， and decomposed the spatial effects into multi-dimensions， and identified the spatial differences of the inhibiting effects. It finds that the local housing price rise not only significantly inhibits the emergence of local innovation， but also indirectly drags down the innovation of surrounding areas through population migration， inter-regional imitation and so on. With the weight matrix based on geographical distance， the inhibiting effect of the housing price rise on innovation presents the most significant “gradient differentiation” in space. In the “diamond” area with Dezhou， Luoyang， Changsha and Suzhou as the four endpoints， the regional housing prices rise has the biggest drag on the innovation in China. With the weight matrix based on population mobility， the inhibiting effect of housing prices on regional innovation presents “decentralization” and “hierarchy”. The cities which occupy “traffic” hub， including Guangzhou， Beijing， Shenzhen， Shanghai， Chengdu， Dongguan， Suzhou， Chongqing， Zhengzhou， Foshan， Xi’an， Hangzhou， drag on the overall regional innovation. In the future， the policy of housing price regulation for the “traffic hub” cities are more effective in promoting the emergence of overall innovation in China.