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Real Estate Modelling and Forecasting By Chris Brooks 4 pdf

Real Estate Modelling and Forecasting by Chris Brooks and Sotiris Tsolacos_2 pot

Real Estate Modelling and Forecasting by Chris Brooks and Sotiris Tsolacos_2 pot

... 5.3 7.1 3 .4 4.71998 6.2 10.1 4. 0 5 .4 1999 10 .4 9.5 4. 9 5.62000 11.1 11.7 5.3 5.72001 11.3 5 .4 5.8 7.12002 4. 0 5.6 6.2 7.32003 2.6 5.7 9.6 8.020 04 −3.7 2.1 9.9 9.52005 0.8 4. 7 10 .4 10.12006 ... variables are generated as independent random series, the statistical 64 Real Estate Modelling and Forecasting (4) The confidence interval for y is given by (y −tcrit· SE(y), y + tcrit· SE(y))where ... 16Q1 4. 3 (4th) = 0.8 4. 3 (4th) = 2.1Q312.8 (13th) = 9.9 12.8 (13th) = 9.5IQR 9.1 7 .4 µ 4. 9 5.8σ223.0 17.8σ 4. 8 4. 2CV 0.98 0.73Source: Authors’ own estimates, based on Property and Portfolio...
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Real Estate Modelling and Forecasting by Chris Brooks and Sotiris Tsolacos_3 doc

Real Estate Modelling and Forecasting by Chris Brooks and Sotiris Tsolacos_3 doc

... MacGregor and White, 2002). Employment in business and finance is a proxy for businessconditions among firms occupying office space and their demand for office88 Real Estate Modelling and Forecasting 4. 8.2 ... estimation, and is discussed in detail in this and subsequentchapters.xyFigure 4. 2Scatter plot of twovariables with a lineof best fit chosen by eye 94 Real Estate Modelling and Forecasting been. ... theintercept are given by ˆβ =xtyt− T¯x¯yx2t− T¯x2 (4. 4) ˆα =¯y −ˆβ¯x (4. 5)Equations (4. 4) and (4. 5) state that, given only the sets of observations xt and yt, it is always...
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Real Estate Modelling and Forecasting by Chris Brooks and Sotiris Tsolacos_4 ppt

Real Estate Modelling and Forecasting by Chris Brooks and Sotiris Tsolacos_4 ppt

... that in section5.9 – i.e. the true DGP is represented by yt= β1+ β2x2t+ β3x3t+ β 4 x4t+ ut(5.50)128 Real Estate Modelling and Forecasting 5.8.2 Determining the number of restrictions, ... =utgt(4A.33)From (4A.15), the intercept variance would be writtenvar( ˆα) = Eutgt2=g2tEu2t= s2g2t(4A. 34) Writing (4A. 34) out in full for g2t and expanding ... statistic. These tests can beconstructed in several ways, and the precise approach to constructing the1 14 Real Estate Modelling and Forecasting If the test isH0: βi= 0H1: βi= 0i.e....
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Real Estate Modelling and Forecasting by Chris Brooks and Sotiris Tsolacos_6 potx

Real Estate Modelling and Forecasting by Chris Brooks and Sotiris Tsolacos_6 potx

... denoted by T1(even though it maycome second). The test statistic is given by test statistic =RSS −RSS1RSS1×T1− kT2(6.61)190 Real Estate Modelling and Forecasting of data at hand, ... 1; at the second step, obser-vations 1 to k +2 are used; and so on; at the final step, observations 1 to T1 74 Real Estate Modelling and Forecasting explanatory variables as a consequence of the ... of the RESET, heteroscedasticity and autocorrelation tests.Equally, a small number of large outliers could cause non-normality and 172 Real Estate Modelling and Forecasting is no relationship...
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Real Estate Modelling and Forecasting by Chris Brooks and Sotiris Tsolacos_7 doc

Real Estate Modelling and Forecasting by Chris Brooks and Sotiris Tsolacos_7 doc

... Constant −3.53 13 .44 4. 91(0.08) (0.60) (0.03) (0 .42 ) (0.25) (0.37)VACt−1−2.19 4. 05 −1.78 VACt−0. 74 −3. 84 −0.60(0.01) (0.11) (0.01) (0.02) (0.05) (0.07)OFSgt 4. 55 4. 19 4. 13 OFSgt5.16 ... Bera–Jarque test) and the form of the equation with the RESET test.Normality test:BJ = 330.1526+(3 .42 − 3)2 24 = 0.372 14 Real Estate Modelling and Forecasting is determined by Akaike’s ... 12 and T − k = 20 at the 5 per cent level of significance is F12,20= 2.28.The212 Real Estate Modelling and Forecasting As a result, the equilibrium real rent varies through time with the real risk-free...
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Real Estate Modelling and Forecasting by Chris Brooks and Sotiris Tsolacos_9 doc

Real Estate Modelling and Forecasting by Chris Brooks and Sotiris Tsolacos_9 doc

... −39.79 1 241 .4 120 04 0.618 3 .4 −13.30 −22. 14 1 −26 .49 −35.33 935.8 12005 0.893 0.1 −3. 64 −13.19 1 −16.83 −26.38 44 4.0 12006 2.378 −0.2 4. 24 4.37 0 −17 .43 −8.82 153.7 12007 2.593 −2.3 3 .48 6.32 ... −23.12 18.88 18.88 356.39 17.98 5 34. 45 −12.37 66.102007 3 .48 −17.56 21. 04 21. 04 442 .55 12.11 308. 24 −12.37 251.22Sum of column 89 .46 89 .46 1786.12 544 .59 3236.01 42 6.21Forecast periods 5 5 5 5 ... 8.25 68.06 3 24. 36 689.59 −12.37 31.8120 04 −13.30 −21.73 8 .43 8 .43 71.06 176.89 47 2.19 −12.37 0.862005 −3. 64 −13. 24 9.60 9.60 92.16 13.25 175.30 −12.37 76.212006 4. 24 4.10 −8. 34 −8. 34 69.56 17.98...
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Real Estate Modelling and Forecasting by Chris Brooks and Sotiris Tsolacos_11 ppt

Real Estate Modelling and Forecasting by Chris Brooks and Sotiris Tsolacos_11 ppt

... 21,302 90 20, 543 185 0.812Q05 2.28 83,568 90 ,49 9 3.1 21,366 64 20,6 94 151 0 .45 3Q05 2 .46 85,625 90,5 64 2.7 21 ,41 5 49 20,832 138 0.324Q05 2.59 87, 844 90 ,48 8 2.3 21 ,46 5 50 20,982 150 0 .44 1Q06 2.75 ... 2.75 90,261 90 ,44 1 1.8 21,529 64 21, 146 1 64 0.592Q06 2.89 92,873 90 ,49 6 1 .4 21,620 90 21,3 14 169 0. 64 3Q06 2. 84 95,510 90,396 1.2 21, 741 122 21 ,48 4 170 0.654Q06 2. 64 98,027 90 ,48 1 1.121,897 ... 0.808 / 0. 841 4. 23; (F 4, 4 14 ) ≈ 2.37 RejectARPRET 10Y 4 37.077 / 37 .49 3 1.16; (F 4, 4 14 ) ≈ 2.37 Do not rejectCBY ARPRET 2 0.820 / 0. 846 6.66; (F2 ,42 0) ≈ 3.00 RejectARPRET CBY 2 23.001...
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Real Estate Modelling and Forecasting by Chris Brooks and Sotiris Tsolacos_12 pptx

Real Estate Modelling and Forecasting by Chris Brooks and Sotiris Tsolacos_12 pptx

... theconditional VAR forecasting methodology outlined in box 11.1). Assuming3 94 Real Estate Modelling and Forecasting 50 40 30201002Q902Q932Q962Q992Q022Q054Q914Q 94 4Q974Q004Q034Q062Q902Q932Q962Q992Q022Q054Q914Q 94 4Q974Q004Q034Q06−10−20−30(a) ... Forecasting 50 40 30201002Q902Q932Q962Q992Q022Q054Q914Q 94 4Q974Q004Q034Q062Q902Q932Q962Q992Q022Q054Q914Q 94 4Q974Q004Q034Q06−10−20−30(a) Real rent and GDP equation (b) Real rent and employment equation 40 3020100−10−20−30Figure 12.8Residuals ... −0.1853SPYt−10.013 −0.02 64 −0. 343 1 −0.2 646 SPYt−2−0.0251 0.0 744 0 .43 75 0.259910Yt−10. 049 2 −0.0696 −0.2 545 −0.168210Yt−2−0.0072 0.0035 −0.0626 0.13 74 AAAt−1−0.0609 0.1 145 0.1208 0.0086AAAt−2−0.0019...
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Real Estate Modelling and Forecasting By Chris Brooks_1 doc

Real Estate Modelling and Forecasting By Chris Brooks_1 doc

... 3,966 4, 662 4, 248 4, 836 4, 238 4, 658 4, 5 34 3,765 4, 233 3, 347 Islington 2,516 3, 243 3, 347 3,935 3,075 3 ,40 7 3,365 2,776 2, 941 2,900Kensington and Chelsea 4, 797 5,262 4, 576 5,558 4, 707 4, 195 4, 5 14 ... 4, 498 4, 920 5 ,47 1 5,313 5,103 4, 418 3, 649 Southwark 3,223 4, 523 4, 525 5 ,43 9 5,191 5,261 4, 981 4, 441 5,012 4, 2 04 Tower Hamlets 2,537 3,851 4, 536 5,631 5,051 4, 752 4, 557 3,890 5, 143 4, 237Wandsworth ... 4, 5 14 3 ,49 7 4, 043 3 ,42 6Lambeth 4, 957 6,128 5,786 6,297 5,966 5,917 6,212 5,209 5,732 5,020Lewisham 4, 357 5,259 5,123 5, 842 5,509 5, 646 6,122 5 ,42 3 5,765 4, 679Newham 3 ,49 3 3,8 94 4,091 4, 498 4, 920...
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Real Estate Modelling and Forecasting By Chris Brooks_2 potx

Real Estate Modelling and Forecasting By Chris Brooks_2 potx

... variate can be scaled to have zero mean and unit variance by subtracting its mean and dividing by its standard deviation. 74 Real Estate Modelling and Forecasting 4. 3 Regression versus correlationAll ... Munich1992 4. 9 2.6 −3.7 −2.01993 5.8 −0.1 −2.5 −0.119 94 3 .4 2.0 −0.7 2.01995 −0.7 −2.0 0.8 2.11996 −2.5 7.3 2.6 2.61997 5.3 7.1 3 .4 4.71998 6.2 10.1 4. 0 5 .4 1999 10 .4 9.5 4. 9 5.62000 ... 180,000.On the other hand, one could envisage a situation in which there isprior information that µ<180,000 was expected. In this case, the null and 48 Real Estate Modelling and Forecasting This...
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