Linear Programming in a Multi-Criteria Model for Real Estate Appraisal

  • Benedetto Manganelli
  • Pierfrancesco De Paola
  • Vincenzo Del Giudice
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9786)


In real estate appraisal, research has long been addressed to the experimentation of multi-parametric models able to reduce the margin of error of the estimate and to overcome or to limit, as far as possible, the problems and difficulties that the use of these models often involves. On the one hand, researchers are trying to overcome the essentially deductive approach that has characterized the traditional discipline, and on the other, to minimize the problems arising from a merely inductive approach. The real estate market is characterized by an inelastic supply and by properties whose complexity and differentiation often involve, also and especially on the demand side, subjective and psychological elements that could distort the results of an inductive investigation. This problem can be overcome by increasing the size of the survey sample, and by using statistical analysis. Statistical analyses, however, are often based on very strong assumptions. A multi-criteria valuation model that uses linear programming is applied to the real estate market. The model, integrated with the inductive and deductive approach, exceeds many of the assumptions of the best known statistical approaches.


Linear programming Multi-criteria valuation model Real estate market 


  1. 1.
    Isakson, H.R.: Using multiple regression analysis in real estate appraisal. Appraisal J. 69(4), 424–430 (2001)Google Scholar
  2. 2.
    Manganelli, B., Pontrandolfi, P., Azzato, A., Murgante, B.: Using geographically weighted regression for housing market segmentation. Int. J. Bus. Intell. Data Min. 9(2), 161–177 (2014)CrossRefGoogle Scholar
  3. 3.
    Manganelli, B., Pontrandolfi, P., Azzato, A., Murgante, B.: Urban residential land value analysis: the case of Potenza. In: Murgante, B., Misra, S., Carlini, M., Torre, C.M., Nguyen, H.-Q., Taniar, D., Apduhan, B.O., Gervasi, O. (eds.) ICCSA 2013, Part IV. LNCS, vol. 7974, pp. 304–314. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  4. 4.
    Newsome, B.A., Zietz, J.: Adjusting comparable sales using multiple regression analysis - the need for segmentation. Appraisal J. 60, 129–135 (1992)Google Scholar
  5. 5.
    Simonotti, M., Salvo, F.: Ciuna, M,: Multilevel methodology approach for the construction of real estate monthly index numbers. J. Real Estate Lit. 22(2), 281–302 (2015)Google Scholar
  6. 6.
    Do, Q., Grudnitski, G.: A neural network approach to residential property appraisal. The Real Estate Appraiser, pp. 38–45, December 1992Google Scholar
  7. 7.
    Evans, A., James, H., Collins, A.: Artificial neural networks: an application to residential valuation in the UK. J. Property Valuation Investment 11(2), 195–203 (1992)Google Scholar
  8. 8.
    Tay, D., Ho, D.: Artificial intelligence and the mass appraisal of residential apartments. J. Property Valuation Investment 10(2), 525–540 (1993)CrossRefGoogle Scholar
  9. 9.
    Worzala, E.M., Lenk, M.M., Silva, A.: An exploration of neural networks and its application to real estate valuation. J. Real Estate Res. 10(2), 185–201 (1995)Google Scholar
  10. 10.
    Peterson, S., Flanagan, A.: Neural network hedonic pricing models in mass real estate appraisal. J. Real Estate Res. 31(2), 147–164 (2009)Google Scholar
  11. 11.
    Tajani, F., Morano, P., Locurcio, M., D’Addabbo, N.: Property valuations in times of crisis: artificial neural networks and evolutionary algorithms in comparison. In: Gervasi, O., Murgante, B., Misra, S., Gavrilova, M.L., Rocha, A.M.A.C., Torre, C., Taniar, D., Apduhan, B.O. (eds.) ICCSA 2015. LNCS, vol. 9157, pp. 194–209. Springer, Heidelberg (2015)CrossRefGoogle Scholar
  12. 12.
    Morano, P., Tajani, F., Locurcio, M.: Land use, economic welfare and property values: an analysis of the interdependencies of the real estate market with zonal and socio-economic variables in the municipalities of the Region of Puglia. Int. J. Agric. Environ. Inf. Syst. 6(4), 16–39 (2015)CrossRefGoogle Scholar
  13. 13.
    Manganelli, B., De Mare, G., Nesticò, A.: Using genetic algorithms in the housing market analysis. In: Gervasi, O., Murgante, B., Misra, S., Gavrilova, M.L., Rocha, A.M.A.C., Torre, C., Taniar, D., Apduhan, B.O. (eds.) ICCSA 2015. LNCS, vol. 9157, pp. 36–45. Springer, Heidelberg (2015)CrossRefGoogle Scholar
  14. 14.
    Manganelli, B.: Un sistema di equazioni strutturali per la stima di masse di immobili. Genio Rurale, 2 (2001)Google Scholar
  15. 15.
    Bravi, M., Fregonara, E.: Structural equations models in real estate appraisal. In: International Real Estate Conference - American Real Estate and Urban Economics Association, Orlando, 23–25 May 1996Google Scholar
  16. 16.
    D’Amato, M.: Comparing rough set theory with multiple regression analysis as automated valuation methodologies. Int. Real Estate Rev. 10(2), 42–65 (2007)Google Scholar
  17. 17.
    Jacquet-Lagreze, E., Siskos, Y.: Assessing a set of additive utility functions for multicriteria decision-making, the UTA Method. Eur. J. Oper. Res. 10(2), 151–164 (1982)CrossRefMATHGoogle Scholar
  18. 18.
    Siskos, Y., Yannacopoulos, D.: UTASTAR: an ordinal regression method for building additive value functions. Investigacao Operacional 5(1), 39–53 (1985)Google Scholar
  19. 19.
    Kettani, O., Oral, M., Siskos, Y.: A multiple criteria analysis model for real estate evaluation. J. Global Optim. 12(2), 197–214 (1998)MathSciNetCrossRefMATHGoogle Scholar
  20. 20.
    Oral, M., Kettani, O.: A mathematical programming model for market share prediction. Int. J. Forecast. 5(1), 59–68 (1989)CrossRefGoogle Scholar
  21. 21.
    Pekelman, D., Subrata, K.S.: Mathematical programming models for the determination of the attribute weights. Manage. Sci. 20, 1217–1229 (1974)CrossRefMATHGoogle Scholar
  22. 22.
    Sivrinisan, V., Shocker, A.D.: Linear programming techniques for multidimensional analysis of preferences. Psychometrica 38(3), 337–369 (1973)MathSciNetCrossRefMATHGoogle Scholar
  23. 23.
    Oral, M., Kettani, O.: Modeling the process of multiattribute choice. J. Oper. Res. Soc. 40(3), 281–291 (1989)CrossRefMATHGoogle Scholar
  24. 24.
    Pace, K.: Appraisal using generalized additive models. J. Real Estate Res. 15(1), 77–99 (1998)Google Scholar
  25. 25.
    Del Giudice, V., Manganelli, B., De Paola, P.: Spline smoothing for estimating hedonic housing price models. In: Gervasi, O., Murgante, B., Misra, S., Gavrilova, M.L., Rocha, A.M.A.C., Torre, C., Taniar, D., Apduhan, B.O. (eds.) ICCSA 2015. LNCS, vol. 9157, pp. 210–219. Springer, Heidelberg (2015)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Benedetto Manganelli
    • 1
  • Pierfrancesco De Paola
    • 2
  • Vincenzo Del Giudice
    • 2
  1. 1.University of BasilicataPotenzaItaly
  2. 2.University of Naples “Federico II”NaplesItaly

Personalised recommendations