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 & 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)


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


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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