International Conference on Computational Science and Its Applications

ICCSA 2015: Computational Science and Its Applications -- ICCSA 2015 pp 36-45 | Cite as

Using Genetic Algorithms in the Housing Market Analysis

  • Benedetto Manganelli
  • Gianluigi De Mare
  • Antonio Nesticò
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9157)

Abstract

This paper tests the use of Genetic Algorithms to interpret the relationship between real estate prices and the geographic locations of the properties. Issues of choosing algorithm parameters are discussed on the basis of applying data collected in the city of Potenza to 190 houses. The aim of the study is to show the potential and the limits of genetic algorithms in this field and how they can be effectively used in the analysis of the housing market.

Keywords

Genetic algorithms Housing submarkets Mass appraisal 

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References

  1. 1.
    Del Giudice V., Torrieri F., De Paola P.: Property Value, Urban Quality and Maintenance Condition: A Hedonic Analysis in the City of Naples, Italy. Advanced Engineering Forum 11 (Economic-Estimative Dynamics and Valuation Tools), 560–565 (2014)Google Scholar
  2. 2.
    Manganelli B., Morano P.: Estimating the market value of the building sites for homogeneous areas. Advanced Materials Research, 869–870 (Sustainable Development of Industry and Economy), 14–19 (2014). doi: 10.4028/www.scientific.net/AMR.869-870.14
  3. 3.
    Salvo, F., Ciuna, M., De Ruggiero, M.: Property prices index numbers and derived indices. Property Management 32(2), 139–153 (2014)CrossRefGoogle Scholar
  4. 4.
    De Mare, G., Manganelli, B., Nesticò, A.: Dynamic analysis of the property market in the city of avellino (Italy). In: Murgante, B., Misra, S., Carlini, M., Torre, C.M., Nguyen, H.-Q., Taniar, D., Apduhan, B.O., Gervasi, O. (eds.) ICCSA 2013, Part III. LNCS, vol. 7973, pp. 509–523. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  5. 5.
    Manganelli, B.: Real Estate Investing. Springer (2015). doi: 10.1007/978-3-319-06397-3 Google Scholar
  6. 6.
    Goodman, A.C., Thibodeau, T.G.: Housing Market Segmentation. Journal of Housing Economics 7, 121–143 (1998)CrossRefGoogle Scholar
  7. 7.
    Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs, 3rd edn. Springer (1995)Google Scholar
  8. 8.
    Mitchell, M.: An Introduction to Genetic Algorithms. MIT Press Cambridge, Massachusetts London (1999). Fifth printingMATHGoogle Scholar
  9. 9.
    Nga, T., Skitmoreb, M., Wongc, K.F.: Using genetic algorithms and linear regression analysis for private housing demand forecast. Building and Environment 43(6), 1171–1184 (2008)CrossRefGoogle Scholar
  10. 10.
    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
  11. 11.
    Manganelli, B., et al.: Using geographically weighted regression for housing market segmentation. International Journal of Business Intelligence and Data Mining 9(2), 161–177 (2014)CrossRefGoogle Scholar
  12. 12.
    Del Giudice V., De Paola P.: Geoadditive Models for Property Market. Applied Mechanics and Materials, 584 – 586 (Project Management in Building and Construction Management), 2505–2509 (2014)Google Scholar
  13. 13.
    Narulaa, S.C., Wellingtonb, J.F., Lewisb, S.A.: Valuating residential real estate using parametric programming. European Journal of Operational Research 217(1), 120–128 (2012)CrossRefGoogle Scholar
  14. 14.
    Kontrimasa, V., Verikasb, A.: The mass appraisal of the real estate by computational intelligence. Applied Soft Computing 11(1), 443–448 (2011)CrossRefGoogle Scholar
  15. 15.
    Nguyen, N., Cripps, A.: Predicting Housing Value: A Comparison of Multiple Regression Analysis and Artificial Neural Networks. Journal of Real Estate Research 22(3), 313–336 (2001)Google Scholar
  16. 16.
    Worzala, E., Lenk, M., Silva, A.: An Exploration of Neural Networks and Its Application to Real Estate Valuation. Journal of Real Estate Research 10(2), 185–202 (1995)Google Scholar
  17. 17.
    Ahn, J.J., et al.: Using ridge regression with genetic algorithm to enhance real estate appraisal forecasting. Expert Systems with Applications 39(9), 8369–8379 (2012)CrossRefGoogle Scholar
  18. 18.
    De Mare, G., et al.: Economic Evaluations using Genetic Algorithms to Determine the Territorial Impact Caused by High Speed Railways. World Academy of Science, Engineering and Technology International Journal of Social, Education, Economics and Management Engineering 6(11), 672–680 (2012)Google Scholar
  19. 19.
    Miles, W.: Boom-Bust Cycles and the Forecasting Performance of Linear and Non-Linear Models of House Prices. Journal of Real Estate Finance Economy 36, 249–264 (2008)CrossRefGoogle Scholar
  20. 20.
    Choy, L.H.T., Ho, W.K.O., Mak, S.W.K.: Housing attributes and Hong Kong real estate prices: a quantile regression analysis. Construction Management and Economics 30(5), 359–366 (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Benedetto Manganelli
    • 1
  • Gianluigi De Mare
    • 2
  • Antonio Nesticò
    • 2
  1. 1.School of EngineeringUniversity of BasilicataPotenzaItaly
  2. 2.Department of Civil EngineeringUniversity of SalernoFisciano (SA)Italy

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