Using Machine Learning Algorithms for Housing Price Prediction: The Case of Islamabad Housing Data

  • Imran, Imran (Department of Computer Science, Bahria University Islamabad) ;
  • Zaman, Umar (Department of Computer Science, Iqra University Islamabad) ;
  • Waqar, Muhammad (Department of Computer Science, Bahria University Islamabad) ;
  • Zaman, Atif (Department of Computer Science, Bahria University Islamabad)
  • 발행 : 2021.06.01


House price prediction is a significant financial decision for individuals working in the housing market as well as for potential buyers. From investment to buying a house for residence, a person investing in the housing market is interested in the potential gain. This paper presents machine learning algorithms to develop intelligent regressions models for House price prediction. The proposed research methodology consists of four stages, namely Data Collection, Pre Processing the data collected and transforming it to the best format, developing intelligent models using machine learning algorithms, training, testing, and validating the model on house prices of the housing market in the Capital, Islamabad. The data used for model validation and testing is the asking price from online property stores, which provide a reasonable estimate of the city housing market. The prediction model can significantly assist in the prediction of future housing prices in Pakistan. The regression results are encouraging and give promising directions for future prediction work on the collected dataset.



  1. Ng, A.; Deisenroth, M. Machine learning for a London housing price prediction mobile application. Imperial College London 2015.
  2. Sangani, D.; Erickson, K.; Al Hasan, M. Predicting zillow estimation error using linear regression and gradient boosting. 2017 IEEE 14th International Conference on Mobile Ad Hoc and Sensor Systems (MASS). IEEE, 2017, pp. 530-534.
  3. Oladunni, T.; Sharma, S. Spatial dependency and hedonic housing regression model. 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE, 2016, pp. 553-558.
  4. Khalafallah, A. Neural network based model for predicting housing market performance. Tsinghua Science and Technology 2008, 13, 325-328.
  5. Lim, W.T.; Wang, L.; Wang, Y.; Chang, Q. Housing price prediction using neural networks. 2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD). IEEE, 2016, pp. 518-522.
  6. Chica-Olmo, J. Prediction of housing location price by a multivariate spatial method: Cokriging. Journal of Real Estate Research 2007, 29, 91-114.
  7. Bahia, I.S.H.; others. A Data Mining Model by Using ANN for Predicting Real Estate Market: Comparative Study. International Journal of Intelligence Science 2013, 3, 162.
  8. Stevens, D.; Wubben, S.; van Zaanen, M. Predicting real estate price using text mining. Department of Communication and Information Sciences. Tilburg University 2014.
  9. Pow, N.; Janulewicz, E. Liu (Dave) Liu. Prediction of real estate property prices in Montreal.
  10. Nghiep, N.; Al, C. Predicting housing value: A comparison of multiple regression analysis and artificial neural networks. Journal of real estate research 2001, 22, 313-336.
  11. Park, B.; Bae, J.K. Using machine learning algorithms for housing price prediction: The case of Fairfax County, Virginia housing data. Expert systems with applications 2015, 42, 2928-2934.
  12. Yadav, M.; Goyal, N. Comparison of Open Source Crawlers- A Review. International Journal of Scientific & Engineering Research September, 2015, 6.
  13. Mahoney, M. Orbitz Sued by Southwest Airlines. E-Commerce Times 2001.
  14. Facebook. Facebook, Inc v. Power Ventures. 844 F.Supp.2d 1025 (E.D. Cal. ). Facebook, Inc v. Power Ventures 2012.
  15. Gervais, D.J. The Protection of Databases. 92 CHI.-Kent L. Rev. 1109 2007.
  16. v. Hamidi, I.C. Intel Corp. v. Hamidi. 71 P.3d 296 2003.
  17. Hirschey, J. Symbiotic Relationships: Pragmatic Acceptance of Data Scraping. SSRN Electronic Journal 2014.
  18. Weisberg, S. Applied linear regression. John Wiley & Sons 2005.
  19. Panigrahi, S.; Mantri, J.K. Epsilon SVR and decision tree for stock market forecasting. Green Computing and Internet of Things Oct. 2015.
  20. Vinod, H.D. A survey of ridge regression and related techniques for improvements over ordinary least squares. The Review of Economics and Statistics 1978.
  21. Gluhovsky, I. Multinomial least angle regression. IEEE transactions on neural networks and learning systems 2011, 23, 169-174.
  22. Li, Q.; Lin, N. The Bayesian elastic net. Bayesian analysis 2010.
  23. Zemel, R.S.; Pitassi, T. A gradient-based boosting algorithm for regression problems. Advances in neural information processing systems 2001, pp. 696-702.
  24. Breiman, L. Random Forests. Machine Learning 2001, 45.
  25. Ruder, S. An overview of gradient descent optimization algorithms. arXiv 2017, 1609.04747v2.
  26. Crammer, K.; Dekel, O.; Keshet, J.; Shalev-Shwartz, S.; Singer, Y. Online passive aggressive algorithms 2006.