COMPARISON OF RENT PREDICTION MODELS: THE CASE OF ISTANBUL OFFICE MARKET 16th ERES Conference 24 – 27 June 09, Stockholm 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 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

Selection of dependent variables 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)

Four office submarkets in the CBD Three different rental value 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

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

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

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

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.

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

Comparison

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

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