Real Estate Modelling and Forecasting Hardcover 7 pot
Real Estate Modelling and Forecasting Hardcover_1 potx
... building in real estate 5
1.5 What do we model and forecast in real estate? 6
1.6 Model categorisation for real estate forecasting 8
1 .7 Why real estate forecasting? 9
1.8 Econometrics in real estate, ... techniques to real data and
problems in real estate.
2 Real Estate Modelling and Forecasting
1.1 Motivation for this book
The complexity of the re...
Real Estate Modelling and Forecasting Hardcover_7 pot
... 0.91 0.51
2 0. 57 0.29 0.60 0.12 0.81 0.14
30.25−0.05 0.32 −0.34 0 .73 −0.23
Sample period 1980–20 07 1981–20 07 1980–20 07 1981–20 07 1980–20 07 1981–20 07
Notes: RR is the level of real office rents; ... plausible;
●
be consistent with underlying real estate theory, including satisfying any
relevant parameter restrictions;
172 Real Estate Modelling and Forecasting
is...
Real Estate Modelling and Forecasting Hardcover_8 potx
... below.
ˆ
y = 6. 176 7 +49.458 inflation R
2
= 0. 47
ˆ
y = 4.1 572 +0.5691 long-term government bond R
2
= 0. 27
ˆ
y = 5.5034 + 0.4612 short-term interest rates R
2
= 0 .76
ˆ
y = 7. 91 97 +−0.1 176 rent R
2
= ... Bera–Jarque test)
and the form of the equation with the RESET test.
Normality test:
BJ = 33
0.15
2
6
+
(3.42 − 3)
2
24
= 0. 37
222 Real Estate Modelling and Foreca...
Real Estate Modelling and Forecasting Hardcover_13 pot
... Forecast
Dec. 06 −0.02 27 −0.0100 −0.0400 −0.0100
Jan. 07 0. 071 8 0.0200 0.2000 0.0800
Feb. 07 −0.0355 −0.00 67 0.0100 −0.0 579 −0.0400 0.0 976 −0.0100 0.0 470
Mar. 07 −0.0359 0.0030 0. 070 0 0.0186 −0.1600 ... the
non-stationarity: the random walk model with drift,
y
t
= µ + y
t−1
+ u
t
(12.1)
376 Real Estate Modelling and Forecasting
30
25
20
15
10
5
0
–5
1 40 118 1 57 2...
Real Estate Modelling and Forecasting By Chris Brooks_2 potx
... line,
y = α + βx (4.1)
Real estate analysis: statistical tools 69
25
20
15
10
5
0
–5
–10
–15
47 931 139 185 231 277 323 369 415 461 5 07 558 599 645 691 73 7 78 3 829 875 921 9 67 1013
Figure 3.9
Sample ... 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...
Real Estate Modelling and Forecasting By Chris Brooks_9 pot
... −0.06 76 ,044 90,481 6.2 22,030 101 20,662 100 1,9 47 791 .7
1Q 07 −0.01 76 ,033 90,485 6.2 22,130 100 20 ,76 2 100 1,952 75 2.9
2Q 07 0.01 76 ,043 90,4 27 6.2 22,230 100 20,863 101 1,9 57 713 .7
3Q 07 0.02 76 ,062 ... 20,840 118 19,445 174 1,909 476 .9
2Q04 −1.43 78 ,904 90,403 6.5 20,958 118 19,592 1 47 1,908 686.0
3Q04 −0. 97 78,138 90,459 6.4 21, 074 116 19 ,72 1 129 1,9 0...
Real Estate Modelling and Forecasting by Chris Brooks and Sotiris Tsolacos_2 pot
... line,
y = α + βx (4.1)
Real estate analysis: statistical tools 69
25
20
15
10
5
0
–5
–10
–15
47 931 139 185 231 277 323 369 415 461 5 07 558 599 645 691 73 7 78 3 829 875 921 9 67 1013
Figure 3.9
Sample ... 2.6
19 97 5.3 7. 1 3.4 4 .7
1998 6.2 10.1 4.0 5.4
1999 10.4 9.5 4.9 5.6
2000 11.1 11 .7 5.3 5 .7
2001 11.3 5.4 5.8 7. 1
2002 4.0 5.6 6.2 7. 3
2003 2.6 5 .7 9.6 8.0
2004 −...
Real Estate Modelling and Forecasting by Chris Brooks and Sotiris Tsolacos_6 potx
... 202 Real Estate Modelling and Forecasting
Table 7. 3 Regression models for Frankfurt rents
Model A (equation (7. 3)) Model B (equation (7. 4))
Coefficient t-ratio (p-value) ... 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 ....
Real Estate Modelling and Forecasting Hardcover_3 doc
... correction
and we would divide by N rather than N − 1.
Real estate analysis: statistical tools 69
25
20
15
10
5
0
–5
–10
–15
47 931 139 185 231 277 323 369 415 461 5 07 558 599 645 691 73 7 78 3 829 875 ... 2.0 −0 .7 2.0
1995 −0 .7 −2.0 0.8 2.1
1996 −2.5 7. 3 2.6 2.6
19 97 5.3 7. 1 3.4 4 .7
1998 6.2 10.1 4.0 5.4
1999 10.4 9.5 4.9 5.6
2000 11.1 11 .7 5.3 5 .7
2001 11.3 5.4 5...
Real Estate Modelling and Forecasting Hardcover_4 pdf
... modelled.
76 Real Estate Modelling and Forecasting
●
There are bound to be random outside influences on y that, again, cannot be
modelled. For example, natural disasters could affect real estate ... (yoy%)
20
15
10
5
−5
−10
−1
−2
−3
−15
−20
−25
1 979
1981
1983
1985
19 87
1989
1991
1993
1995
19 97
1999
2001
2003
2005
1 979
1981
1983
1985
19 87
1989
1991
1993
1995
19 97
1999...