Real Estate Modelling and Forecasting by Chris Brooks and Sotiris Tsolacos 12 pptx
Real Estate Modelling and Forecasting by Chris Brooks and Sotiris Tsolacos_2 pot
... vacancy or measures of demand) have confined empirical investigationsto standard regression models, which make lesser demands on the data7250 Real Estate Modelling and Forecasting Table 3.1 Summary ... correction and we would divide by N rather than N − 1.68 Real Estate Modelling and Forecasting 1,2001,000800600400(a) Index of US income returns(c) All-property risk premium(b) Index of real ... 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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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 ... OLS by settingzt=1xt and regressing y on a constant and z. Clearly, then, a surprisingly var-ied array of models can be estimated using OLS by making suitable102 Real Estate Modelling and ... ˆα and ˆβ, to find the values of α and β that minimise the residual sum of squares to give the line that is closest92 Real Estate Modelling and Forecasting xy0Figure 4.11Effect on the standarderrors...
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Real Estate Modelling and Forecasting by Chris Brooks and Sotiris Tsolacos_4 ppt
... x2 and not x1.So,whereisx1? In fact, it is the constant term, usually represented by a column of onesof length T :x1=⎡⎢⎢⎢⎣11...1⎤⎥⎥⎥⎦(5.3) 112 Real Estate Modelling and ... that in section5.9 – i.e. the true DGP is represented by yt= β1+ β2x2t+ β3x3t+ β4x4t+ ut(5.50) 128 Real Estate Modelling and Forecasting 5.8.2 Determining the number of restrictions, ... inparticular by means of the tests we discuss in the next chapter and theevaluation of forecast performance in later chapters. On the basis of the118 Real Estate Modelling and Forecasting ytxtFigure...
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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, ... 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 ... considered an indicator of thedemand and supply balance in the real estate market – i.e. it reflects demand and supply conditions. As business conditions strengthen and firms needto take on more...
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Real Estate Modelling and Forecasting by Chris Brooks and Sotiris Tsolacos_7 doc
... per cent level of significance is F 12, 20= 2.28.The 212 Real Estate Modelling and Forecasting As a result, the equilibrium real rent varies through time with the real risk-free rate. The author ... office tenant demand, the ratio of government employment over the sumof the financial, insurance and real estate and service office tenants and the level of occupied stock. McGough and Tsolacos (2002), ... Bera–Jarque test) and the form of the equation with the RESET test.Normality test:BJ = 330.1526+(3.42 − 3)224= 0.37214 Real Estate Modelling and Forecasting is determined by Akaike’s...
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Real Estate Modelling and Forecasting by Chris Brooks and Sotiris Tsolacos_9 doc
... the United Kingdom and Australia and assess the improvement over292 Real Estate Modelling and Forecasting employment at lags 1 and 3), new construction lagged five periods and the longer leading ... employment and 274 Real Estate Modelling and Forecasting of UK office construction efficiently incorporates all available information,including that contained in the past values of construction and whethermulti-span ... actual, etc.; N denotes the naiveforecast of 12. 37 per cent (rent growth in the previous year, 2002).294 Real Estate Modelling and Forecasting Table 9 .12 Empirical forms of equations (9.25) to...
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Real Estate Modelling and Forecasting by Chris Brooks and Sotiris Tsolacos_11 ppt
... of tables 10.4 and 10.6, it350 Real Estate Modelling and Forecasting Running the causality tests, in our case, it is interesting to study whetherSPY, 10Y and CBY lead ARPRET and, if so, whether ... squares●two-stage least squares358 Real Estate Modelling and Forecasting reflect only the variation in real estate returns. This series, denoted PROPRES,is the real estate market return measure used ... Methodology Brooks and Tsolacos (1999) employ a reduced-form VAR, and hence eachequation can be estimated separately using OLS. We noted earlier that,340 Real Estate Modelling and Forecasting 11.3...
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Real Estate Modelling and Forecasting by Chris Brooks and Sotiris Tsolacos_12 pptx
... figure 12. 6 plots the value of an autoregressive process of order1 with different values of the autoregressive coefficient as given by (12. 4).382 Real Estate Modelling and Forecasting 12. 2 CointegrationIn ... 0.00);RESET: 4 .12 (p = 0.04).372 Real Estate Modelling and Forecasting and the trend-stationary process, so-called because it is stationary around alinear trend,yt= α + βt + ut (12. 2)where ... specification in (12. 51) has been esti-mated as a potential cointegrating regression:yt= α1+ β1xt+ u1t (12. 51)386 Real Estate Modelling and Forecasting the equation cancels. Model (12. 45) has...
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Real Estate Modelling and Forecasting By Chris Brooks_1 doc
... measurement error and revi-sions (e.g. absorption data are subject to stock and vacancy rate revisions42 Real Estate Modelling and Forecasting Box 3.1 Time series data in real estate Series FrequencyRents ... Real Estate Modelling and Forecasting including range, quartiles, variance, standard deviation, semi-standarddeviation and the coefficient of variation; higher moments – that is, skew-ness and ... comparison of real and nominal rents is given infigure 2.1. Interestingly, office rents in real terms in Singapore recovered totheir 1991 level only in 2007.18 Real Estate Modelling and Forecasting principles...
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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 ... variablesRegressand RegressorsEffect variable Causal variablesExplained variable Explanatory variablesLeft-hand side (LHS) variable Right-hand side (RHS) variables52 Real Estate Modelling and Forecasting xf(x)xf(x)Figure ... 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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