A Proposed Statistical Model for Real Estate Appraisal Applied to the Mixed-Use Historical Maspiro District of Cairo

A Proposed Statistical Model for Real Estate Appraisal Applied to the Mixed-Use Historical Maspiro District of Cairo

A. Abdel Aty Mohamed 

Department of Architecture, Faculty of Engineering, Cairo University, Egypt

Page: 
302-316
|
DOI: 
https://doi.org/10.2495/SDP-V7-N3-302-316
Received: 
N/A
| |
Accepted: 
N/A
| | Citation

OPEN ACCESS

Abstract: 

This paper introduces a new statistical methodological approach for the real estate appraisal based on the consideration of the changing purchasing power of money, by deducing an equation based mainly on all the affecting urban context variables other than the market, cost, and income approach that are currently used for that purpose. This is achieved through testing the proposed statistical model using these urban variables on one of the most important districts in downtown Cairo, Maspiro (next to Tahrir Square incorporating 1130 land lots), together with comparing its predicted values with a sample evaluated by professional real estate appraisers to ensure its validity. Maspiro district confronts the Nile River, and faces the Egyptian Union of Radio and Television Building, Ministry of Foreign Affairs, Embassy of Brazil, Embassy of Italy, and others. Accordingly, the paper fi nally illustrates that all theoretical approaches dealing with the real estate appraisal are subject to some defects ignoring the changing circumstances of each district and the urban planning variables that constitute its real value. They mainly depend on factors that are subject to change from time to time in accordance with the surrounding political, social, and economic circumstances. Over or underestimations may lead to economic loss and mislead the proposed developmental plans for the regions. The urban variables, on the other side, once measured for each real estate are not subject to these changes. Therefore, the research tests the validity of fi nding strong correlation between these variables and their real value, in the form of an equation by using statistical methods.

Keywords: 

 mixed-use development, real estate appraisal, urban context variables, urban development, urban economics.

  References

[1] Smullyan, C., Kicking the Dirt at the Speed of Light. Symposium. Into the public markets: Real Estate and the New Financial Era. MIT Center for Real Estate: Cambridge. MA., 1994. 

Retrieved: May 28. 2003. from http://www.arctos.com/ teleres1.html.

[2] Hendershott, P. & Kane, E., Causes and consequences of the 1980’s commercial construc-tion boom. Journal of Applied Corporate Finance, 5(1), pp. 61–70, 1994. doi: http://dx.doi. org/10.1111/j.1745-6622.1992.tb00482.x

[3] Kummerow, M.F., A system dynamics model of cyclical offi ce oversupply. Journal of Real Estate Research, 18(1), pp. 233–255, 1999.

[4] Sterman, J. D., Business Dynamics: Systems Thinking and Modeling for a Complex World, McGraw-Hill: USA, 2000.

[5] Hanipah, B.M., Ph.D. Dissertation on the real estate’s role in the Asian fi nancial crisis. Presentation made at the Pacifi c Rim Real Estate Society annual meetings. Brisbane, 2003.

[6] Janssen, C. & Yang, Z., Estimating the Market Value of a Proposed Townhouse Develop-ment. Journal of Property Investment & Finance, 17, pp. 501–516, 1999. doi: http://dx.doi.

org/10.1108/14635789910294912

[7] Hoesli, M., Jani, E. & Bender, A., Monte Carlo simulations for real estate valuation. 

Journal of Property Investment & Finance, 24, pp. 102–122, 2006. doi: http://dx.doi. org/10.1108/14635780610655076

[8] Galaty, F.W., Allaway, W.J. & Kyle, R.C., Modern Real Estate practice, 16th edn. Dearborn Financial Publishing: U.S.A., ch. 18, pp. 300–316, 2006.

[9] Jacobus, C.J. & Gillett, T.E., Georgia Real Estate, An Introduction to the Profession, 7th edition. Thomson Learning, Inc.: U.S.A., pp. 368–398, 2008.

[10] Fisher, D., The Commercial Real Estate Investor’s Handbook: A Step-by-Step Road Map to Financial Wealth, Atlatic Publishing Group: U.S.A., pp. 63–68, 2007.

[11] Mark, J. & Goldberg, M.A., Multiple regression analysis and mass assessment: a review of the issues. The Appraisal Journal, pp. 89–109, 1988.

[12] Murphy, L.T III, Determining the appropriate equation in multiple regression analysis. The Appraisal Journal, pp. 498–513, 1989.

[13] Ambrose, B.W., An analysis of the factors affecting light industrial property valuation. Journal of Real Estate Research, 5(3), pp. 355–370, 1990.

[14] Fehribach, F.A., Rutherford, R.C. & Eakin, M.E., An analysis of the determinants of industrial property valuation. The Journal of Real Estate Research, 8(3), pp. 365–376, 1993.

[15] Lasen, J.E. & Peterson, M.L., Correcting for errors in statistical appraisal equations. The Real Estate Appraiser and Analyst, pp. 45–49, 1988.

[16] Coleman, J.W. & Larsen, J.E., Alternative estimation techniques for linear appraisal models. The Real Estate Appraiser and Analyst, pp. 45–49, 1989.

[17] Newsome, B.A. & Zeitz, J., Adjusting comparable sales using multiple regression analysis, the need for segmentation. The Appraisal Journal, pp. 526–532, 1992.