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.