Chris Brooks Real Estate Modelling and Forecasting 9 pptx
Chris Brooks Real Estate Modelling and Forecasting_1 ppt
... rejected the null hypothesis in favour of
170 Real Estate Modelling and Forecasting
25
20
15
10
(Regression residuals)
5
198 4
198 6
198 8
199 0
199 2
199 4
199 6
199 8
2000
2002
2004
2006
0
−5
−10
−15
Figure ... in which
164 Real Estate Modelling and Forecasting
one: the empirical model containing many lags or differenced terms is
difficult to interpret and may n...
Chris Brooks Real Estate Modelling and Forecasting_5 docx
... forecasts
with the realised values for only two years: 199 6 and 199 7. The regression
model over-predicted by 3.6 percentage points in 199 6 and by three percent-
age points in 199 7. The naive methods, ... United
States, the United Kingdom and Australia and assess the improvement over
284 Real Estate Modelling and Forecasting
Table 9. 8 Data on real rent growth for fore...
Chris Brooks Real Estate Modelling and Forecasting_6 pdf
... predeterminedness and strict
exogeneity
320 Real Estate Modelling and Forecasting
7,000
6,000
5,000
Actual
Simulated
4,000
3,000
2,000
1,000
197 8
198 0
198 2
198 4
198 6
198 8
199 0
199 2
199 4
199 6
199 8
Figure ... 1 ,90 9 476 .9
2Q04 −1.43 78 ,90 4 90 ,403 6.5 20 ,95 8 118 19, 592 147 1 ,90 8 686.0
3Q04 −0 .97 78,138 90 ,4 59 6.4 21,074 116 19, 721 1 29 1 ,90 7...
Chris Brooks Real Estate Modelling and Forecasting_7 docx
... 79, 111 90 ,403 5.7 21,010 130 19, 818 294 1 .96
3Q04 0.07 79, 168 90 ,4 59 4.8 21,128 118 20,111 293 1 .95
4Q04 1.17 80, 091 90 ,477 4.0 21,212 83 20,3 59 247 1.47
1Q05 2.02 81,705 90 ,563 3.6 21,302 90 ... 0. 59
2Q06 2. 89 92,873 90 , 496 1.4 21,620 90 21,314 1 69 0.64
3Q06 2.84 95 ,510 90 , 396 1.2 21,741 122 21,484 170 0.65
4Q06 2.64 98 ,027 90 ,481 1.1
21, 897 155 21,65...
Chris Brooks Real Estate Modelling and Forecasting_9 pptx
... are taken
from MacKinnon, Haug and Michelis ( 199 9).
70
60
50
40
30
20
10
0
−10
−20
−30
Jan. 90
Jan. 91
Jan. 92
Jan. 93
Jan. 94
Jan. 95
Jan. 96
Jan. 97
Jan. 98
Jan. 99
Jan. 00
Jan. 01
Jan. 02
Jan. ... 0.71
4Q08 91 .5 162.4 −3.02 0.8 0.625 0. 69
1Q 09 91.6 163.2 −3. 39 0.8 0.118 0.13
2Q 09 91.8 164.0 −3.72 0.8 0.151 0.16
3Q 09 92.0 164 .9 −4.02 0 .9 0.180 0.20
4Q...
Chris Brooks Real Estate Modelling and Forecasting_10 pdf
... ( 199 2) The markets for real estate assets and space:
a conceptual framework, Journal of the American Real Estate and Urban Economics
Association 20(2), 181 98 .
( 199 6) Urban Economics and Real Estate ... 251–7
securitised real estate 258 9, 282, 344–5, 385
shocks 2 39, 352–6, 361, 3 69 73, 378
sign predictions 288 9, 299 , 301
significance level 59 64, 95 –101, 124
s...
Real Estate Modelling and Forecasting Hardcover_14 pptx
... the
Cointegration in real estate markets 405
130
Jan. 90 =100
Asia
Europe
United States
120
110
100
90
80
Jan. 90
Jan. 91
Jan. 92
Jan. 93
Jan. 94
Jan. 95
Jan. 96
Jan. 97
Jan. 98
Jan. 99
Jan. 00
Jan. ... 0.71
4Q08 91 .5 162.4 −3.02 0.8 0.625 0. 69
1Q 09 91.6 163.2 −3. 39 0.8 0.118 0.13
2Q 09 91.8 164.0 −3.72 0.8 0.151 0.16
3Q 09 92.0 164 .9 −4.02 0 .9 0.180 0...
Real Estate Modelling and Forecasting by Chris Brooks and Sotiris Tsolacos_2 pot
... Tokyo
200
120
100
80
60
60
40
20
0
−20
−40
−60
40
20
0
1Q93
197 1
197 4
197 7
198 0
198 3
198 6
198 9
199 2
199 5
199 8
2001
2004
2007
1Q96
1Q 99
1Q02
3Q03
3Q06
1Q05
3Q94
3Q97
3Q00
0
1Q78
1Q80
1Q82
1Q84
1Q86
1Q88
1Q90
1Q92
1Q94
1Q96
1Q98
1Q00
1Q02
1Q04
1Q06
Figure ... 7.3 2.6 2.6
199 7 5.3 7.1 3.4 4.7
199 8 6.2 10.1 4.0 5.4
199 9 10.4 9. 5 4 .9 5.6
2000 11.1 11.7 5.3 5.7
2001...
Real Estate Modelling and Forecasting by Chris Brooks and Sotiris Tsolacos_3 doc
... (yoy%)
20
15
10
5
−5
−10
−1
−2
−3
−15
−20
−25
197 9
198 1
198 3
198 5
198 7
198 9
199 1
199 3
199 5
199 7
199 9
2001
2003
2005
197 9
198 1
198 3
198 5
198 7
198 9
199 1
199 3
199 5
199 7
199 9
2001
2003
2005
0
(a) Real office rents
(b) Employment ... and adding ˆα will
84 Real Estate Modelling and Forecasting
20
(yoy %)
Actual
Fitted
(%)
15
10
5
0
197 9
1...
Real Estate Modelling and Forecasting by Chris Brooks and Sotiris Tsolacos_4 ppt
... by
s
2
(X
X)
−1
= 0 .91 (X
X)
−1
=
⎡
⎣
1.82 3. 19 −0 .91
3. 19 0 .91 5 .92
−0 .91 5 .92 3 .91
⎤
⎦
(5.15)
The coefficient variances are on the diagonals, and the standard errors
are found by taking the square ... (5.16)
var(
ˆ
β
2
) = 0 .91 ⇔ SE(
ˆ
β
2
) = 0 .95 (5.17)
var(
ˆ
β
3
) = 3 .91 SE(
ˆ
β
3
) = 1 .98 (5.18)
The estimated equation would be written
ˆ
y = 1.10 − 4.40x
2
+ 19. 8...