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COMPARISON OF RENT PREDICTION MODELS: THE CASE OF ISTANBUL OFFICE M ARKET

COMPARISON OF RENT PREDICTION MODELS: THE CASE OF ISTANBUL OFFICE M ARKET. Dilek PEKDEMİR DTZ Pamir & Soyuer. Background. Hedonic office rent prediction models based on multiple regression Difficult to incorporate large number of variables in to a simple mathematical model

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COMPARISON OF RENT PREDICTION MODELS: THE CASE OF ISTANBUL OFFICE M ARKET

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  1. COMPARISON OF RENT PREDICTION MODELS: THE CASE OF ISTANBUL OFFICE MARKET Dilek PEKDEMİR DTZ Pamir & Soyuer

  2. Background • Hedonic office rent prediction models based on multiple regression • Difficult to incorporate large number of variables in to a simple mathematical model • Multicollinearity between independent variables • Selection of dependent variable; asking or contract rent data • Aim; to examine the problem with construction of an office rent prediction models and development of a viable prediction models for Istanbul

  3. Methodology • Selection of dependent variables • Asking, gross and net contract rent • Multicollinearity and reduce number of variables • Backward method in the standard regression analysis • Factor analysis to grouped related variables • Model selection • R-squared, t-statisctics • Akaike Information Criteria (AIC) and Shwartz’s Bayesian Criteria (SBC)

  4. Data • In the light of the literature 34 variables are obtained between 1996 – 2006 • Four office submarkets in the CBD • Three different rental value • 59 observations • 155 contract data is obtained, but only 59 contract data is available for the same office units • Gross and net contract rent is calculated

  5. Prediction Models - Standard Regression • Asking rent • Outlier observation • High explanatory powers (R2=0.85, adj.R2=0.64) • Multicollinearity between locational and building variables • Reduced model with backward (R2=0.77, adj.R2=0.70) • Gross and net contract rent • No outliers • High explanatory powers (R2=0.84, adj.R2=0.63) • Reduced model with backward (R2=0.79, adj.R2=0.72) • No multicollinearity in reduced model

  6. Prediction Models – Factor Analysis • Factor values resulting from factor analysis are substituted into regression model • 5 factors with eigenvalues explaining 78% of total variance is obtained • Rent equation is constructed with; • 5 factors representing the influence of 21 variables, • 7 independent variables not related to any of predetermined factors and • 6 dummy variables

  7. Prediction Models – Factor Analysis • The attributed meanings of factor groups: • Factor 1; attractiveness for new office investments • Factor 2; building characteristics • Factor 3; economic and market conditions • Factor 4; quality of region • Factor 5; lease conditions

  8. Prediction Models – Factor Analysis • Asking rent • Lower explanatory powers (R2=0.48, adj.R2=0.34) • No multicollinearity • Gross contract rent • Lower explanatory powers (R2=0.46, adj.R2=0.21) • Net contract rent • Improvement in explanatory powers (R2=0.51, adj.R2=0.29) • Factor 3 (economic and market condition), office supply and new office investments are found most significant variables.

  9. Results • No distinctive difference in the explanatory powers (R2) of models with different rental values • But, the adjusted R2 are improved in the reduced models • Outlier data in asking rental values while no outliers in gross or net contract rental value • Multicollinearity between locational and contract variables in standard model, but solved in reduced models • The explanatory power of models with factor analysis is lower than standard model

  10. Comparison

  11. Conclusion • Gross contract rent is more reliable data to produce better rental predictions, it also includes tax effect • In general, building and locational variables are found significant • Distance to CBD, transportation nodes, prestigious areas and accessibility are the most important rental determinants • Secondary centres gain importance • Quality and prestige of the office buildings; building age, no of elevators, no of floors, parking ratio are found significant

  12. THANK YOU !!!! For further information; Dilek Pekdemir, pekdemird@dtz.com.tr Hakkı Yeten C., No:12/7, 34365, Şişli/İstanbul, TURKEY Phone: +90 (212) 231 5530 ext.126

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