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

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COMPARISON OF RENT PREDICTION MODELS: THE CASE OF ISTANBUL OFFICE M ARKET. Dilek PEKDEMİR DTZ Pamir &amp; 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 MARKET

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
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)
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
• 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
• 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
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, [email protected]

Hakkı Yeten C., No:12/7, 34365, Şişli/İstanbul, TURKEY

Phone: +90 (212) 231 5530 ext.126