2015 12 * 35 3 4 5 JEL R21 J28 J61 F293. 3 A 1000-6249 2015 12-090 - 14 2003 16. 1% 2003 6 / 2013 6 / 10 9 2013 2003 2002 133. 73 2001 30 * E - mail kaikudan@ 126. com 510050 E - mail lcyw41020118@ 163. com 8 510600 E - mail 27089910@ qq. com 6 541001 90
2015 12 2003 187. 7 53. 97 2007 18% 2013 638. 72 2003 2013 10 5 2013 52. 6% 33. 4% 11. 2% 2. 2% 0. 5% 2009 2013 2014 2014 2014 2012 2005 2011 M2 28. 5% 1996 5 2009 70 5 1 1 91
2007 2010 2014 50% 80% 1. 26% 2000 10 2005 8. 33% 2014 1990 2003 1998 1999 2003 2004 2011 2011 10% 1998 2013 2000 2010 35 2004 1998 9 2 2011 2013 2007 18 2012 2001 2013 16% 2015 2015 Saiz A. 2007 92
2015 12 1% 1% Degen K. and Fischer A. M. 2010 85 2001 2006 1% 2. 7% Gonzalez L. and Ortega F. 2013 2000 2010 1. 5% 2% 35 2010 90 1 2013 93
2000 80 90 2015 1998 11 12 24 21 2010 15% 15% 15% - 50% 2011 2010 1998 8. 5% 1999 2004 17% 1999 2000 42. 86% 42. 46% 2005 2002 15% 2010 26. 5% 2012 30% 2001 2013 516. 37% 638. 72 2000 2010 27 3 16 94
2015 12 3 40 2011 2012 2011 25 2011 62. 5% 2003 2004 2004 10 2013 1 35 35 2001 2011 1 2014 7 70 64 4 2014 2014 95
1. hpri 35 2001 2011 / 35 2. coll 2003 2013 6 21 30 9 11 6 7 6 12 985 5 211 4 9 7 2 3 500-1000 15060 22. 4% 68. 0% 9. 2% 0. 4% 52. 7% 47. 3% 985 19. 9% 211 9. 4% 28. 9% 28. 1% 7. 7% 6. 0% 2013 31 52. 6% 35 30 5 5 GDP 31 GDP 21% 35 63. 65% 63. 65% 35 GDP 35 GDP 35 i = 63. 65% * i GDP /35 GDP 2013 13. 9% 12. 8% 2011 25. 69 2011 2012 25 35 t i = * 63. 65% * t i GDP / t 35 GDP 3. 1 incom 96
2015 12 2 cred M2 2012 32. 2% 2011 3 peop 2013 / Lnhpri i t = c +! 1 Lncoll i t +! 2 Lnincom i t +! 3 Lncred i t +! 4 Lnpeop i t + ε t c Lnhpri i t Lncoll i t Lnincom i t Lncred i t Lnpeop i t! 1! 2! 3! 4 ε t 1 1 1 1 Lncoll Lnhpri Lncoll 2 6 Lncoll 1 5 1 Lncoll 1 5 1% 1 2 6 Lncoll 1 5 1 3 4 5 3 0. 373 1 0. 125 3 4 1 1 1 2 3 Lncoll Lncoll Lncoll Lnhpri 97
52% 31 21 26 10 5 42 35 2010 Lnincom 1 7 Lncoll Lnincom 1% 1 6 Lncoll Lnincom Lncoll 4 Lncoll 0. 373 Lnincom 7 0. 539 0. 073 Lncred 1 1 7 Lncred 1% Lnhpri Lnpeop Lncoll 7 Lnpeop 1% Lnhpri Lncoll 1 2 3 5 6 Lnpeop 1% Lncoll 4 Lnpeop 4 hprice i t - 1 2010 hprice i t - 1 2 1 7 J sargan GMM hprice i t - 1 1 7 hprice i t - 1 1% Lnhpri i t Lncoll Lncoll 1 5 1% 98
2015 12 1 3 4 5 3 Lnincom Lncred Lnpeop Lncoll 4 Lnpeop 1 1 2 3 4 5 6 7 C - 0. 524 *** - 2. 68-0. 480 ** - 2. 36-0. 067 0. 316 0. 364 * 1. 78 0. 301 1. 55 0. 075 0. 39-0. 483 ** - 2. 45 0. 002 Lncoll i t 0. 03 Lncoll i t - 1 0. 125 *** 3. 04 Lncoll i t - 2 0. 227 *** 4. 65 Lncoll i t - 3 0. 373 *** 8. 56 Lncoll i t - 4 0. 338 *** 9. 48 Lncoll i t - 5 0. 263 *** 8. 36 Lnincom i t 0. 652 *** 9. 91 0. 537 *** 7. 30 0. 264 *** 3. 34 0. 073 * 1. 55 0. 143 ** 2. 25 0. 289 *** 5. 02 0. 539 *** 10. 01 Lncred i t 0. 290 *** 7. 80 0. 250 *** 6. 68 0. 203 *** 5. 70 0. 191 *** 5. 86 0. 210 *** 6. 65 0. 215 *** 6. 67 0. 251 *** 7. 15 Lnpeop i t 0. 667 *** 5. 35 0. 620 *** 4. 81 0. 288 ** 2. 19 0. 200 1. 61 0. 242 ** 2. 04 0. 353 *** 2. 98 0. 621 *** 4. 98 Adj_ R2 0. 938 0. 936 0. 940 0. 949 0. 949 0. 947 0. 936 F 153. 49 149. 56 159. 48 187. 247 190. 74 181. 61 154. 05 t * ** *** 10% 5% 1% Hausman Test Fixed model Lncoll i t 2001 2011 Lncoll i t - 1 2000 2010 2001 2011 99
2 1 2 3 4 5 6 7 Lnhpri i t - 1 0. 394 *** 11. 94 0. 323 *** 11. 185 0. 276 *** 17. 22 0. 232 *** 7. 32 0. 170 *** 7. 46 0. 339 *** 11. 93 0. 353 *** 14. 60 0. 013 Lncoll i t 0. 685 Lncoll i t - 1 0. 161 *** 6. 87 Lncoll i t - 2 0. 274 *** 8. 76 Lncoll i t - 3 0. 312 *** 7. 11 Lncoll i t - 4 0. 232 *** 9. 55 Lncoll i t - 5 0. 101 *** 8. 13 Lnincom i t 0. 531 *** 18. 33 0. 277 *** 14. 92 0. 251 *** 22. 39 0. 016 ** 2. 15 0. 184 *** 3. 78 0. 416 *** 18. 38 0. 318 *** 7. 31 0. 024 * Lncredit i t 1. 71 0. 127 *** 6. 10 0. 101 *** 8. 95 0. 105 *** 4. 72 0. 080 *** 4. 74 0. 074 ** 2. 44 0. 183 *** 6. 95 Lnpeop i t 0. 736 *** 4. 02 0. 468 ** 2. 50 0. 252 *** 4. 46 0. 216 1. 55 0. 173 *** 6. 14 0. 852 *** 6. 17 0. 576 *** 8. 43 J - statistic 30. 347 31. 349 33. 257 30. 806 30. 276 29. 295 35. 827 P 0. 448 0. 518 0. 311 0. 425 0. 452 0. 554 0. 252 35 1 3 4 5 100
2015 12 2009 M2 28. 5% 1996 5 70 5 2009 3 5 1 2 2004 1990 2003 1998 1999 2003 2003 2004 Degen K. Fischer A. M. 2010 Immigration and Swiss House Prices CEPR Discussion Paper NO. 7583. Gonzalez L. Ortega F. 2013 Immigration and Housing Booms Evidence from Spain Journal of Regional 101
Science 53 1 pp. 37-59. Saiz A. 2007 Immigration and Housing Rents in American Cities Journal of Urban Economics 61 2 pp. 345-371. 28 2014 2013 1 2012 11 83-90 2013 03 43 10 7 19 2007 7 2007 12 49-50 2013 6 100-112 2014 8 31 2010 9 67-78 2010 7 30 2013 2013 1 7 2013 2 82-85 2015 1 116-123 2011 6 18-32 2014 9 12 2011 2011 2012 11 30 2014 1 30-54 2011 6 22 2013 8 19 2015 1 22 http / / www. gov. cn /guowuyuan /2015-01 /22 /content_ 2807852. htm 2011 11 8 2014 5 88-92 2012 6 1-12 2010 6 21 2010 4 2 2014 50% 1 17 2010 N 3 31 http / /money. 163. com /10 /0331 /15 / 6347GLM700253G87. html 102
2015 12 2011 4 29 2011 35 1 122-129 2013 1 10 16 College Enrollment Expansion Population Immigration and House Price Rising Zhang Chao Li Chao Tang Xin Abstract This paper has analyzed the mechanism of china's house price rising by a new perspective of population immigration based on the background of college enrollment expansion and tested the proposition by using the panel data of the national 35 large and medium - sized cities. The results shows that the main driving force of china's house price rising comes from new immigrants housing demand and the university graduates have become an immigrant group with the highest purchase capacity the largest population the strongest free migration ability and motive force among all the immigration population w hile the college enrollment expansion w as implemented. They come from all over the country but has focused on some large and medium cities after graduation from the university which has a significant positive impact on the local housing prices. Due to factors such as purchasing pow er limit the graduates immigration had made a lagging positive impact on cities' house prices and the third year after graduation had the largest impact w hile the fourth and fifth years had the reduced impact. This paper also confirmed that the variables such as the income the credit amount reflecting currency factors the population density reflecting population grow th and the last house price reflecting investment demand have a positive impact on cities' house prices. Keywords College Enrollment Expansion Population Immigration Graduates House Price Rising Rigid Demand. 103