Property Valuations in Times of Crisis: Artificial Neural Networks and Evolutionary Algorithms in Comparison

  • Francesco Tajani
  • Pierluigi MoranoEmail author
  • Marco Locurcio
  • Nicola D’Addabbo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9157)


In the current economic situation, characterized by a high uncertainty in the appraisal of property values, the need of “slender” models able to operate even on limited data, to automatically capture the causal relations between explanatory variables and selling prices and to predict property values in the short term, is increasingly widespread. In addition to Artificial Neural Networks (ANN), that satisfy these prerogatives, recently, in some fields of Civil Engineering an hybrid data-driven technique has been implemented, called Evolutionary Polynomial Regression (EPR), that combines the effectiveness of Genetic Programming with the advantage of classical numerical regression. In the present paper, ANN methods and the EPR procedure are compared for the construction of estimation models of real estate market values. With reference to a sample of residential apartments recently sold in a district of the city of Bari (Italy), two estimation models of market value are implemented, one based on ANN and another using EPR, in order to test the respective performance. The analysis has highlighted the preferability of the EPR model in terms of statistical accuracy, empirical verification of results obtained and reduction of the complexity of the mathematical expression.


Property valuations Artificial neural networks Evolutionary polynomial regression Genetic algorithms Estimative analysis Market value 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Barile, S., Magna, L., Marsella, M., Miranda, S.: A marketing decision problem solved by application of neural networks. In: International Conference on Computational Intelligence and Multimedia Applications (1999)Google Scholar
  2. 2.
    Berardi, L., Kapelan, Z., Giustolisi, O., Savic, D.: Development of pipe deterioration models for water distribution systems using EPR. Journal of Hydroinformatics 10(2), 113–126 (2008)CrossRefGoogle Scholar
  3. 3.
    Brunson, A.L., Buttimer, R.J., Rutherford, R.C.: Neural Networks, Nonlinear Specification and Industrial Property Values. Working Paper Series, pp. 94–102. University of Texas at Arlington (1994)Google Scholar
  4. 4.
    Calabrò, F., Della Spina, L.: The cultural and environmental resources for sustainable development of rural areas in economically disadvantaged contexts. Economic-appraisals issues of a model of management for the valorisation of public assets. Advanced Materials Research 869–870, 43–48 (2014)Google Scholar
  5. 5.
    Calabrò, F., Della Spina, L.D.: The public-private partnerships in buildings regeneration: A model appraisal of the benefits and for land value capture. Advanced Material Research 931–932, 555–559 (2014)CrossRefGoogle Scholar
  6. 6.
    D’Alpaos, C., Canesi, R.: Risks assessment in real estate investments in times of global crisis. WSEAS Transactions on Business and Economics 11, 369–379 (2014)Google Scholar
  7. 7.
    Del Giudice, V., De Paola, P.: Geoadditive models for property market. Applied Mechanics and Materials 584, 2505–2509 (2014)CrossRefGoogle Scholar
  8. 8.
    Del Giudice, V., De Paola, P.: The effects of noise pollution produced by road traffic of Naples Beltway on residential real estate values. Applied Mechanics and Materials 587, 2176–2182 (2014)CrossRefGoogle Scholar
  9. 9.
    Do, A.Q., Grudnitski, G.: A neural network analysis of the effect of age on housing values. Journal of Real Estate Research, American Real Estate Society 8(2), 253–264 (1993)Google Scholar
  10. 10.
    Dzeng, R.J., Lee, H.Y.: Optimizing the development schedule of resort projects by integrating simulation and genetic algorithm. International Journal of Project Management 25(5), 506–516 (2007)CrossRefGoogle Scholar
  11. 11.
    Forte, F.: Costs of noise and Italian urban policies. In: 36th International Congress and Exhibition on Noise Control Engineering, vol. 6, pp. 3991–3999. Istanbul, Turkey (2007)Google Scholar
  12. 12.
    Gallego, J.: La inteligencia artificial aplicada a la valoraciòn de inmuebles. Un ejemplo para valorar Madrid. Revista CT/Catastro 50, 51–67 (2004)Google Scholar
  13. 13.
    Giles, D.E.: Interpreting Dummy Variables in Semi-logarithmic Regression Models: Exact Distributional Results. Econometrics Working Paper EWP1101, University of Victoria, Canada (2011)Google Scholar
  14. 14.
    Giustolisi, O., Laucelli, D.: Increasing generalisation of input-output artificial neural networks in rainfall-runoff modelling. Hydrological Sciences Journal 3(50), 439–457 (2005)Google Scholar
  15. 15.
    Giustolisi, O., Savic, D.: A symbolic data-driven technique based on evolutionary polynomial regression. Journal of Hydroinformatics 8(3), 207–222 (2006)Google Scholar
  16. 16.
    Giustolisi, O., Savic, D.: Advances in data-driven analyses and modelling using EPR-MOGA. Journal of Hydroinformatics 11(3–4), 225–236 (2009)CrossRefGoogle Scholar
  17. 17.
    Guarini, M.R., Battisti, F.: Social Housing and Redevelopment of Building Complexes on Brownfield Sites: The Financial Sustainability of Residential Projects for Vulnerable Social Groups. Advanced Materials Research 869–870, 3–13 (2014)Google Scholar
  18. 18.
    Guarnaccia, C., Quartieri, J., Mastorakis, N.E., Tepedino, C.: Development and Application of a Time Series Predictive Model to Acoustical Noise Levels. WSEAS Transactions on Systems 13, 745–756 (2014)Google Scholar
  19. 19.
    Guarnaccia, C.: Advanced Tools for Traffic Noise Modelling and Prediction. WSEAS Transactions on Systems 12(2), 121–130 (2013)Google Scholar
  20. 20.
    Halvorsen, R., Palmquist, R.: The interpretation of dummy variables in semilogarithmic regressions. American Economic Review 70, 474–475 (1980)Google Scholar
  21. 21.
    Islam, K.S., Asam, Y.: Housing market segmentation: a review. Review of Urban & Regional Development Studies 21(2–3), 93–109 (2009)CrossRefGoogle Scholar
  22. 22.
    Javadi, A.A., Rezania, M.: Applications of artificial intelligence and data mining techniques in soil modeling. Geomechanics and Engineering 1(1), 53–74 (2009)CrossRefGoogle Scholar
  23. 23.
    Koza, J.R.: Genetic programming: on the programming of computers by natural selection. MIT Press, Cambridge (1992)zbMATHGoogle Scholar
  24. 24.
    Król, D., Lasota, T., Trawiński, B., Trawiński, K.: Investigation of evolutionary optimization methods of TSK fuzzy model for real estate appraisal. International Journal of Hybrid Intelligent Systems 5(3), 111–128 (2008)zbMATHGoogle Scholar
  25. 25.
    Laucelli, D., Giustolisi, O.: Scour depth modelling by a multi-objective evolutionary paradigm. Environmental Modelling & Software 26(4), 498–509 (2011)CrossRefGoogle Scholar
  26. 26.
    Limsombunchai, V., Gan, C., Lee, M.: House price prediction: hedonic price model vs. artificial neural network. American Journal of Applied Sciences 1(3), 193–201 (2004)CrossRefGoogle Scholar
  27. 27.
    Liu, J.-G., Zhang, X.-L., Wu, W.-P.: Application of fuzzy neural network for real estate prediction. In: Wang, J., Yi, Z., Żurada, J.M., Lu, B.-L., Yin, H. (eds.) ISNN 2006. LNCS, vol. 3973, pp. 1187–1191. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  28. 28.
    McCluskey, W.J., Dyson, K., McFall, D., Anand, S.: The mass appraisal of residential property in Northern Ireland. In: Computer Assisted Mass Appraisal: An International review, pp. 59–77. Ashgate Publishing Limited, England (1997)Google Scholar
  29. 29.
    Morano, P., Tajani, F.: Bare ownership evaluation. Hedonic price model vs. artificial neural network. International Journal of Business Intelligence and Data Mining 8(4), 340–362 (2013)CrossRefGoogle Scholar
  30. 30.
    Morano, P., Tajani, F.: Least median of squares regression and minimum volume ellipsoid estimator for outliers detection in housing appraisal. International Journal of Business Intelligence and Data Mining 9(2), 91–111 (2014)CrossRefGoogle Scholar
  31. 31.
    Oppio, A., Corsi, S., Mattia, S., Tosini, A.: Exploring the relationship among local conflicts and territorial vulnerability: The case study of Lombardy Region. Land Use Policy 43, 239–247 (2015)CrossRefGoogle Scholar
  32. 32.
    Scorza, F., Casas, G.L., Murgante, B.: Overcoming interoperability weaknesses in e-government processes: organizing and sharing knowledge in regional development programs using ontologies. In: Lytras, M.D., Ordonez de Pablos, P., Ziderman, A., Roulstone, A., Maurer, H., Imber, J.B. (eds.) WSKS 2010. CCIS, vol. 112, pp. 243–253. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  33. 33.
    Scorza, F., Casas, G.B., Murgante, B.: That’s ReDO: ontologies and regional development planning. In: Murgante, B., Gervasi, O., Misra, S., Nedjah, N., Rocha, A.M.A., Taniar, D., Apduhan, B.O. (eds.) ICCSA 2012, Part II. LNCS, vol. 7334, pp. 640–652. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  34. 34.
    Selim, H.: Determinants of house prices in Turkey: hedonic regression versus artificial neural network. Expert Systems with Applications 36(2), 2843–2852 (2009)MathSciNetCrossRefGoogle Scholar
  35. 35.
    Torre, C.M., Mariano, C.: Analysis of fuzzyness in spatial variation of real estate market: Some Italian case studies. Smart Innovation, System and Technologies 4, 269–277 (2010)CrossRefGoogle Scholar
  36. 36.
    Wang, W.K.: A knowledge-based decision support system for measuring the performance of government real estate investment. Expert Systems with Applications 29(4), 901–912 (2005)CrossRefGoogle Scholar
  37. 37.
    Wong, K.C., Albert, P.S., Hung, Y.C.: Neural network vs. hedonic price model: appraisal of high-density condominiums. Real Estate Valuation Theory 8(2), 181–198 (2002)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Francesco Tajani
    • 1
  • Pierluigi Morano
    • 1
    Email author
  • Marco Locurcio
    • 2
  • Nicola D’Addabbo
    • 2
  1. 1.Department of Science of Civil Engineering and ArchitecturePolytechnic of BariBariItaly
  2. 2.Department of Architecture and DesignUniversity “Sapienza”RomeItaly

Personalised recommendations