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

4. 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

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,
Hakkı Yeten C., No:12/7, 34365, Şişli/İstanbul, TURKEY
Phone: +90 (212) ext.126