Valuations of building plots using the AHP method

    Fatma Bunyan Unel Affiliation
    ; Sukran Yalpir Affiliation


Predicting the value of real estate is a complex endeavor due to the abundance of subjective criteria. Objective consideration of the value-affecting criteria in real estate and regulation of decision support systems will enable the acquisition of more accurate results. In this study, analytic hierarchy process (AHP), a type of multi-criteria decision analysis (MCDA), is used to reproduce coefficients that serve as the basis for real estate valuation. A region in the Selcuklu district of Konya, Turkey was used to test the model created by AHP. Weighted criteria describing areas subjected to purchase/sale were generated by the AHP method and then validated. Additionally, a valuation model was created by the multiple regression analysis (MRA) method for comparison and performance analyses. Weighted values were transformed from AHP points and acquired from the MRA method and then joined with geographic information systems (GIS). Value maps of the study area and purchase/sale values were generated according to these newly created models. The performance comparison and value maps revealed that the AHP method is more successful than the MRA method. This study addressed the complexity of criteria issue by using the original hierarchical structure of AHP and thus contributes to the world economy by enabling the generation of more accurate estimations.

Keyword : Real estate valuation, MCDA, decision making, multiple regression analysis (MRA), analytic hierarchy process (AHP), geographic information systems (GIS)

How to Cite
Bunyan Unel, F., & Yalpir, S. (2019). Valuations of building plots using the AHP method. International Journal of Strategic Property Management, 23(3), 197-212.
Published in Issue
Feb 18, 2019
Abstract Views
PDF Downloads
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.


Abidoye, R. B., & Chan, A. P. C. (2017). Valuers’ receptiveness to the application of artificial intelligence in property valuation. Pacific Rim Property Research Journal, 23(2), 175-193.

Absar, N., Pathak, A., & Uddin, M. A. (2016). Multi-Criteria analysis for the best location selection in Chittagong city area, Bangladesh. International Journal of Computer Applications, 143(8), 10-18.

Albayrak, A. S. (2008). Weighted regression analysis alternative to the least squares technique in the presence of none constant variance and an application. Afyon Kocatepe University, Journal of Economics and Administrative Sciences, X(II), 111-134.

Alonso, J. A., & Lamata, M. T. (2006). Consistency in the analytic hierarchy process: a new approach. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 14(4), 445-459.

Antipov, E. A., & Pokryshevskaya, E. B. (2012). Mass appraisal of residential apartments: an application of random forest for valuation and a CART-based approach for model diagnostics. Expert Systems with Applications, 39, 1772-1778.

Aragonés-Beltrán, P., García-Melón, M., Aznar, J., & Guijarro, F. (2009). Asset appraisal method based on the analytic network process. In XIII International Conference on Engineering Projects. Badajoz.

ArcGIS. (2016). Environmental Systems Research Institute (ESRI) Inc. (version 10.4.1). Trademark, Redlands, California, USA.

Arribas, I., García, F., Guijarro, F., Oliver, J., & Tamošiūnienė, R. (2016). Mass appraisal of residential real estate using multilevel modelling. International Journal of Strategic Property Management, 20(1), 77-87.

Aznar, J., Guijarro, F., & Moreno-Jiménez, J. M. (2011). Mixed valuation methods: a combined AHP-GP procedure for individual and group multicriteria agricultural valuation. Annals of Operations Research, 190, 221-238.

Ball, J., & Srinivasan, V. C. (1994). Using the analytic hierarchy process in house selection. Journal of Real Estate Finance and Economics, 9, 69-85.

Barańska, A. (2013). Real estate mass appraisal in selected countries–functioning systems and proposed solutions. Real Estate Management and Valuation, 21(3), 35-42.

Bellver, J. A., & Mellado, V. C. (2005). An application of the analytic hierarchy process method in farmland appraisal. Spanish Journal of Agricultural Research, 3(1), 17-24.

Bender, A., Din, A., Favarger, P., Hoesli, M., & Laakso, J. (1997). An analysis of perceptions concerning the environmental quality of housing in Geneva. Urban Studies, 34(3), 503-513.

Bender, A., Din, A., Hoesli, M., & Laakso, J. (1999). Environmental quality perceptions of urban commercial real estate. Journal of Property Investment & Finance, 17(3), 280-296.

Bender, A., Din, A., Hoesli, M., & Brocher, S. (2000). Environmental preferences of home-owners: further evidence using the AHP method. Journal of Property Investment and Finance, 18(4), 445-455.

Bisello, A., Marella, G., & Grilli, G. (2016). SINFONIA project mass appraisal: beyond the value of energy performance in buildings. Procedia-Social and Behavioral Sciences, 223, 37-44.

Brondino, N. C. M., & Silva, A. N. R. (1999). Combining artificial neural networks and GIS for land valuation purposes. In Computers in Urban Planning and Manegement. Venice, Italy.

Cagsir, H. (2005). GIS based multiple criteria decision support system for urban planning problems in Gaziantep (Ms Thesis). Gaziantep University, Industrial Engineering, Gaziantep, Turkey.

Cay, T., & Uyan, M. (2014). Evaluation of reallocation criteria in land consolidation studies using the analytic hierarchy process (AHP). Land Use Policy, 30, 541-548.

Chasco, C., Gallo, J. L., & López, F. A. (2018). A scan test for spatial groupwise heteroscedasticity in cross-sectional models with an application on houses prices in Madrid. Regional Science and Urban Economics, 68, 226-238.

Cingoz, A. R. A. A. (2010). Analysis of residential house prices of gated communities in Istanbul. Journal of Social Sciences, 2, 129-139.

Cowen, D. J. (1988). GIS versus CAD versus DBMS: what are the differences? Photogrammetric Engineering and Remote Sensing, 54(11), 1551-1555.

D’Amato, M. (2010). A location value response surface model for mass appraising: an “Iterative” location adjustment factor in Bari, Italy. International Journal of Strategic Property Management, 14(3), 231-244.

Damigos, D., & Anyfantis, F. (2011). The value of view through the eyes of real estate experts: a fuzzy delphi approach. Landscape and Urban Planning, 101, 171-178.

Dey, P. K., & Ramcharan, E. K. (2008). Analytic hierarchy process helps select site for limestone quarry expansion in Barbados. Journal of Environmental Management, 88, 1384-1395.

Dimopoulos, T., & Moulas, A. (2016). A proposal of a mass appraisal system in Greece with CAMA system: evaluating GWR and MRA techniques in Thessaloniki Municipality. Open Geosciences, 8, 675-693.

Ferreira, F. A. F., Spahr, R. W., & Sunderman, M. A. (2016). Using multiple criteria decision analysis (MCDA) to assist in estimating residential housing values. International Journal of Strategic Property Management, 20(4), 354-370.

Fletcher, M., Gallimore, P., & Mangan, J. (2000). Heteroscedasticity in hedonic house price models. Journal of Property Research, 17(2), 93-108.

Garcia, N., Gamez, M., & Alfaro, E. (2008). ANN+GIS: an automated system for property valuation. Neurocomputing, 71, 733-742.

García-Melón, M., Ferrís-Oñate, J., Aznar-Bellver, J., Aragonés-Beltrán, P., & Poveda-Bautista, R. (2008). Farmland appraisal based on the analytic network process. Journal of Global Optimization, 42, 143-155.

Ge, X. J., & Runeson, G. (2004). Modeling property prices using neural network model for Hong Kong. International Real Estate Review, 7(1), 121-138.

Ghasemi, K., Hamzenejad, M., & Meshkini, A. (2018). The spatial analysis of the livability of 22 districts of Tehran Metropolis using multi-criteria decision making approaches. Sustainable Cities and Society, 38, 382-404.

Gomes, L. F. A. M., & Rangel, L. A. D. (2009). An application of the TODIM method to the multicriteria rental evaluation of residential properties. European Journal of Operational Research, 193(1), 204-211.

Halvorsen, R., & Palmquist, R. (1980). The interpretation of dummy variables in semilogrithmic regressions. American Economic Review, 70, 474-475.

Kaklauskas, A., Zavadskas, E. K., & Trinkunas, V. (2007). A multiple criteria decision support on-line system for construction. Engineering Applications of Artificial Intelligence, 20, 163-175.

Karimov, A. (2010). Office rent variation in CBD: An application of mamdani and TSK-type fuzzy rule based system (Ms Thesis). Middle East Technical University, Financial Mathematics, Ankara, Turkey.

Kauko, T. (2002). Modelling the locational determinants of house prices: neural network and value tree approaches (PhD Thesis). Utrecht University, Netherlands.

Kauko, T. (2003). Residential property value and locational externalities: on the complementarity and substitutability of approaches. Journal of Property Investment & Finance, 21(3), 250-270.

Kauko, T. (2004). Towards infusing institutions and agency into house price analysis. Urban Studies, 41(8), 1507-1519.

Kavas, S. (2014). Multi-criteria decision support model for appraising residential real estates (Ms Thesis). Istanbul Technical University, Industrial Engineering Department, Istanbul, Turkey.

Kisilevich, S., Keim, D., & Rokach, L. (2013). A GIS-based decision support system for hotel room rate estimation and temporal price prediction: the hotel brokers’ context. Decision Support Systems, 54, 1119-1133.

Kontrimas, V., & Verikas, A. (2011). The mass appraisal of the real estate by computational intelligence. Applied Soft Computing, 11, 443-448.

Kryvobokov, M. (2005). Estimating the weights of location attributes with the Analytic Hierarchy Process in Donetsk, Ukraine. Nordic Journal of Surveying and Real Estate Research, 2(2), 7-31.

Lam, K. C., Yu, C. Y., & Lam, C. K. (2009). Support vector machine and entropy based decision support system for property valuation. Journal of Property Research, 26(3), 213-233.

Limsombunchai, V. (2004). House price prediction: hedonic price model vs. artificial neural network. In New Zealand Agricultural and Resource Economics Society (NZARES) Conference. Blenheim, New Zealand.

Lin, C. C. (2010). Critical analysis and effectiveness of key parameters in residential property valuations (PhD Thesis). State University of New York, Department of Civil, Structural, and Environmental Engineering, New York.

Liu, X., Deng, Z., & Wang, T. (2011). Real estate appraisal system based on GIS and BP neural network. Transactions of Nonferrous Metals Society of China, 21, 626-630.

Lokshina, I. V., Hammerslag, M. D., & Insinga, R. C. (2003). Applications of artificial intelligence methods for real estate valuation and decision support. In Hawaii International Conference on Business. Honolulu, Hawaii, USA.

Lughofer, E., Trawinski, B., Trawinski, K., Kempa, O., & Lasota, T. (2011). On employing fuzzy modeling algorithms for the valuation of residential premises. Information Sciences, 181, 5123-5142.

Malczewski, J. (1999). GIS and multicriteria decision analysis. New York: John Wiley and Sons.

Malczewski, J. (2006). GIS-based multicriteria decision analysis: a survey of the literature. International Journal of Geographical Information Science, 20(7), 703-726.

Maliene, V. (2011). Specialised property valuation: multiple criteria decision analysis. Journal of Retail & Leisure Property, 9, 443-450.

Masri, M. H. M., Nawawi, A. H., & Sipan, I. (2016). Review of building, locational, neighbourhood qualities affecting house prices in Malaysia. Procedia - Social and Behavioral Sciences, 234, 452-460.

Mora-Esperanza, J. G. (2004). Artificial intelligence applied to real estate valuation. CT-CATASTRO, 255-265.

Mulliner, E., Smallbone, K., & Maliene, V. (2013). An assessment of sustainable housing affordability using a multiple criteria decision making method. Omega, 41, 270-279.

Nas, B. B. (2011). Development of an approach for real-estate valuation by the methods ANN and AVM (Ms Thesis). Selcuk University, Electronic and Computer Systems Education Department, Konya, Turkey.

NetCAD. (2008). National CAD and GIS Solutions Inc. (version 5.1). Trademark, Ankara, Turkey.

Nguyen, N., & Cripps, A. (2001). Predicting housing value: a comparison of multiple regression analysis and artificial neural networks. Journal of Real Estate Research, 22(3), 313-336.

Ong, S. E., & Chew, T. I. (1996). Singapore residential market: an expert judgemental forecast incorporating the analytical hierarchy process. Journal of Property Valuation and Investment, 14(1), 50-66.

Ozer, M. (2010). Financial and numerical techniques used in real estate valuation: an application with TOPSIS and new multiple criteria models (Ms Thesis). Dokuz Eylul University, Department of Economics Money and Banking, Izmir, Turkey.

Ozkan, G., Yalpir, S., & Uygunol, O. (2007). An investigation on the price estimation of residable real-estates by using ANN and regression methods. In The 12th International Conference on Applied Stochastic Models and Data Analysis. Chania.

Ozturk, D., & Batuk, F. (2011). Technique for order preference by similarity to ideal solution (TOPSIS) for spatial decision problems. In Proceedings ISPRS. Retrieved from

Rikalovic, A., Cosic, I., & Lazarevic, D. (2014). GIS based multicriteria analysis for industrial site selection. Procedia Engineering, 69, 1054-1063.

Saaty, T. L. (1980). The analytic hierarchy process. New York: McGraw-Hill.

Saaty, T. L. (2008). Decision making with the analytic hierarchy process. International Journal of Services Sciences, 1, 1.

Safian, E. E. M., Nawawi, A. H., & Sipan, I. A. (2014). Building and locational characteristics’ quality of purpose-built office and their relationship with rentals (MPRA Paper No. 64908). Retrieved from

Sarac, E. (2012). Real estate appraisal with artificial neural networks method (Ms Thesis). Istanbul Kultur University, Civil Engineering, Istanbul, Turkey.

Selim, H. (2009). Determinants of house prices in Turkey: hedonic regression versus artificial neural network. Expert Systems with Applications, 36, 2843-2852.

Shyjith, K., Ilangkumaran, M., & Kumanan, S. (2008). Multi‐criteria decision‐making approach to evaluate optimum maintenance strategy in textile industry. Journal of Quality in Maintenance Engineering, 14(4), 375-386.

SPSS. (2011). International Business Machines (IBM) Corp., Statistics (version 20). Trademark, Armonk, New York, USA.

Supciller, A. A., & Capraz, O. (2011). Application of the supplier selection based on AHP-TOPSIS methods. Econometrics and Statistics Journal, 13, 1-22.

Sykes, A. O. (1992). An introduction to regression analysis. The Inaugural Coase Lecture.

Tastan, H. (2012). Heteroscedasticity. Yıldız Technical University, Department of Economics, lesson notes from Introductory Econometrics: a Modern Approach (2nd ed.), J. Wooldridge.

Tepe, S. (2009). Examination of expropriation and property relations (Ms Thesis). Selcuk University, Department of Map Engineering, Konya, Turkey.

Tezcan, O. (2010). Using of analytic hierarchy process method for evaluating investment projects in the construction (Ms Thesis). Eskisehir Osmangazi University, Civil Engineering, Eskisehir, Turkey.

Unel, F. B., & Yalpir, S. (2013). Positional determination of real estates with analytic hierarchy process. In Proceedings of the Fourth International Conference on Mathematical and Computational Applications (pp. 326-336). Manisa, Turkey.

Vahidnia, M. H., Alesheikh, A. A., & Alimohammadi, A. (2009). Hospital site selection using fuzzy AHP and its derivatives. Journal of Environmental Management, 90, 3048-3056.

Wilkowski, W., & Budzynski, T. (2006). Application of artificial neural networks for real estate valuation. In XXIII FIG Congress. Munich, Germany.

Wong, K. W., & Wu, M. (2002). Priority setting of preferential parameters for home purchase in Chongqing-an analytic hierarchy process approach. In M. Anson, J. M. Ko, & E. S. S. Lam (Eds.), Proceedings of the International Conference on Advances in Building Technology 4–6 December. Hong Kong, China.

Worzala, E., Lenk, M., & Silva, A. (1995). An exploration of neural networks and its application to real estate valuation. Journal of Real Estate Research, 10(2), 185-201.

Yalpir, S., & Tezel, G. (2013). Comparison of SVM and MRA methods for real estate valuation. In 6th Symposium of Engineering and Technology, Çankaya University. Ankara.

Yalpir, S., & Unel, F. B. (2016). Investigation and reduction of criteria affecting the value of land plot in Turkey and International Standards by Factor Analysis. AKU Journal of Sciences and Engineering, 16(025502), 303-322.

Yilmaz, A. (2010). Real estate valuation by using multicriteria decision support system (analytic hierarchy process) and ratio study (Ms Thesis). Yildiz Tecnical University, Geomatic Engineering, Istanbul, Turkey.

Yilmaz, A., & Demir, H. (2011). Real estate valuation by using multicriteria decision support system and ratio study. In 13 Scientific and Technical Congress of Map in Turkey. Ankara, Turkey.

Yomralıoglu, T. (2002). Geographical information systems: the basic concepts and applications (2nd ed.). Turkey: Academy Bookstore.

Zhang, R., Du, Q., Geng, J., Liu, B., & Huang, Y. (2015). An improved spatial error model for the mass appraisal of commercial real estate based on spatial analysis: Shenzhen as a case study. Habitat International, 46, 196-205.

Zurada, J., Levitan, A. S., & Guan, J. (2011). A comparison of regression and artificial intelligence methods in a mass appraisal context. Journal of Real Estate Research, 33(3), 349-387.