Efficient Approaches for House Pricing Prediction by Using Hybrid Machine Learning Algorithms

  • Sruthi ChiramelEmail author
  • Doina LogofătuEmail author
  • Jyoti RawatEmail author
  • Christina AnderssonEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1178)


To own a house is dream of many. However, in this age of inflation and sky rocketing housing prices, its not always easy to find dream home within the constrained budget. Also, in addition to budget, there are several other factors that contributes towards finding the right home-location, ease of access, transportation etc. In such a scenario, a house price predicting system will be helpful for both buyers and sellers. This research aims to predict house prices in IOWA state, USA using regression analysis. The prediction is arrived at by help of various explanatory variables such as area of the property, location of the house, material used for construction, age of the property, number of bedrooms and garages and so on. This paper elaborates on the performance of Linear regression and Ridge regularization for model prediction. It also details the machine learning techniques used and its significance pertaining to the results.


Linear regression Ridge regularization Machine learning 


  1. 1.
    Ioannides, Y.M.: Interactive property valuations. J. Urban Econ. 53, 145–170 (2003)CrossRefGoogle Scholar
  2. 2.
    French, N.: Valuation of specialized property - a review of valuation methods. J. Prop. Invest. Financ. 22(6), 533–541(9) (2004)Google Scholar
  3. 3.
    Ghodsi, R.: Estimation of housing prices by fuzzy regression and artificial neural network. In: Fourth Asia International Conference on Mathematical/Analytical Modelling and Computer Simulation, no. 1 (2010)Google Scholar
  4. 4.
    Rahadi, R.A., Wiryono, S.K., Koesrindartotoor, D.P., Syamwil, I.B.: Factors influencing the price of housing in Indonesia. Int. J. Hous. Mark. Anal. 8(2), 169–188 (2015)CrossRefGoogle Scholar
  5. 5.
    Limsombunchai, V.: House price prediction: hedonic price model vs. artificial neural network. Am. J. Appl. Sci. 1(3), 193–201 (2004)CrossRefGoogle Scholar
  6. 6.
    Król, A.: Application of hedonic methods in modelling real estate prices in Poland. In: Lausen, B., Krolak-Schwerdt, S., Böhmer, M. (eds.) Data Science, Learning by Latent Structures, and Knowledge Discovery. SCDAKO, pp. 501–511. Springer, Heidelberg (2015). Scholar
  7. 7.
    Kryvobokov, M., Wilhelmsson, M.: Analysing location attributes with a hedonic model for apartment prices in Donetsk, Ukraine. Int. J. Strat. Prop. Manag. 11(3), 157–178 (2007)CrossRefGoogle Scholar
  8. 8.
    Ottensmann, J.R., Payton, S., Man, J.: Urban location and housing prices within a hedonic model. J. Reg. Anal. Policy 38(1), 19–35 (2008)Google Scholar
  9. 9.
    Khurana, U., Samulowitz, H., Turaga, D.: Feature engineering for predictive modeling using reinforcement learning. In: The Thirty-Second AAAI Conference on Artificial Intelligence (AAAI 2018) (2018)Google Scholar
  10. 10.
    Doreswamy, Vastrad, C.M.: Performance analysis of regularized linear regression models FOR oxazolines and oxazoles derivates descriptor dataset. Int. J. Comput. Sci. Inf. Technol. (IJCSITY) 1(4) (2013) Google Scholar
  11. 11.
    Kumari, K., Yadav, S.: Curric. Cardiol. Stat. 4, 33–36 (2018)Google Scholar
  12. 12.
  13. 13.
    Hoerl, A.E., Kennard, R.W.: Ridge regression biased estimation for nonorthogonal problems. Technometrics Am. Soc. Qual. 12, 55–67 (1970)zbMATHGoogle Scholar
  14. 14.
    an de Meulen, P., Micheli, M., Schmidt, T.: Forecasting House Prices in Germany. Ruhr Economic Papers, vol. 294 (2011)Google Scholar

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© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringFrankfurt University of Applied SciencesFrankfurt a.M.Germany

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