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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

COMPARISON OF RENT PREDICTION MODELS: THE CASE OF ISTANBUL OFFICE MARKET

Dilek PEKDEMİR

DTZ Pamir & Soyuer

background
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
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)
slide4
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
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
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 analysis1
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 analysis2
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
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
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
slide12
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

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