Peer-Dependence Valuation Model for Real Estate Appraisal

  • Junchi Bin
  • Bryan Gardiner
  • Eric Li
  • Zheng LiuEmail author


Deep learning recently attracts considerable attention thanks to its powerful computational capacities in image processing and natural language processing. More and more real estate brokers provide online “Deep” expert systems to help clients with their inquiry of targeted properties before deciding on the transaction of properties. The real estate appraisal is one of the most significant concerns for the clients. In the appraisal process, the estimation of house price depends not only on its attributes but also their neighbors. The influence from neighbors is known as peer-dependence, which is not directly measurable. Thus, real estate appraisal can be improved if the valuation includes the peer-dependence measurement. In this paper, we propose a peer-dependence valuation model (PDVM), which is capable of converting the peer-dependence-based valuation problem into a sequence prediction problem. In the proposed model, we first develop a method, K-nearest similar house sampling (KNSHS), to generate sequences from the to-be-value house and nearby houses. Secondly, the bidirectional long short-term memory (B-LSTM) layers extract the features of sequences. Finally, the fully connected (FC) layer estimates the house price based on the features. The experimental results indicate that our model outperforms the other state-of-the-art machine learning models being used for real estate appraisal.


Real estate appraisal Peer-dependence Recurrent neural network 



The authors express their sincere gratitude to Kaiqi Zhang (AECOM), Fang Shi (University of British Columbia, Kelowna, Canada), and Dr. Huan Liu (China University of Geosciences, Wuhan, China) for the helpful discussions when this work was being carried out.

Funding Information

The study was supported by Mitacs Accelerate Program (Application Reference Number: IT08399).


  1. 1.
    E.A. Antipov, E.B. Pokryshevskaya, Mass appraisal of residential apartments: an application of random forest for valuation and a CART-based approach for model diagnostics. Expert Syst. Appl. 39(2), 1772–1778 (2012)CrossRefGoogle Scholar
  2. 2.
    C.G. Atkeson, A.W. Moore, S. Schaal, Locally weighted learning. Artif. Intell. Rev. 11(1/5), 11–73 (1997)CrossRefGoogle Scholar
  3. 3.
    A. Bellotti, Reliable region predictions for automated valuation models. Ann. Math. Artif. Intell. 81(1), 71–84 (2017)MathSciNetCrossRefGoogle Scholar
  4. 4.
    J. Bin, S. Tang, Y. Liu, G. Wang, B. Gardiner, Z. Liu, E. Li, Regression model for appraisal of real estate using recurrent neural network and boosting tree. In: 2017 2nd IEEE International Conference on Computational Intelligence and Applications (ICCIA), Beijing, China, pp. 209–213 (2017)Google Scholar
  5. 5.
    H. Chen, Z. Zeng, H. Tang, Landslide deformation prediction based on recurrent neural network. Neural. Process. Lett. 41(2), 169–178 (2013)CrossRefGoogle Scholar
  6. 6.
    K. Chen, Y. Zhou, F. Dai, in A LSTM-based method for stock returns prediction: a case study of China stock market. 2015 IEEE International Conference on Big Data (Big Data) (IEEE, Santa Clara, 2015), pp. 2823–2824Google Scholar
  7. 7.
    T. Chen, C. Guestrin, Xgboost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD’16, ACM Press (2016)Google Scholar
  8. 8.
    F. Chollet, et al., Keras. (2015)
  9. 9.
    S. Chopra, T. Thampy, J. Leahy, A. Caplin, Y. LeCun, in Discovering the hidden structure of house prices with a non-parametric latent manifold model. KDD ’07 Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD’07 (ACM Press, San Jose, 2007)Google Scholar
  10. 10.
    D.D. Cock, House prices: advanced regression techniques. Accessed June 01 2017 (2017)
  11. 11.
    Estated, Property reports., Accessed 10 September 2017 (2017)
  12. 12.
    G.Z. Fan, S.E. Ong, H.C. Koh, Determinants of house price: a decision tree approach. Urban Stud. 43 (12), 2301–2315 (2006)CrossRefGoogle Scholar
  13. 13.
    J.H. Friedman, Greedy function approximation: a gradient boosting machine. Ann. Stat. 29(5), 1189–1232 (2001)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Y. Fu, H. Xiong, Y. Ge, Z. Yao, Y. Zheng, Z.H. Zhou, in Exploiting geographic dependencies for real estate appraisal: a mutual perspective of ranking and clustering. KDD ’14 Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (ACM, New York, 2014), pp. 1047–1056Google Scholar
  15. 15.
    A. Gensler, J. Henze, B. Sick, N. Raabe, in Deep learning for solar power forecasting - an approach using autoencoder and LSTM neural networks. 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (IEEE, Budapest, 2016)Google Scholar
  16. 16.
    I. Goodfellow, Y. Bengio, A. Courville. Deep learning (MIT Press, Cambridge, 2016)zbMATHGoogle Scholar
  17. 17.
    M. Graczyk, T. Lasota, B. Trawiṅski, in Comparative analysis of premises valuation models using KEEL, RapidMiner, and WEKA. Computational Collective Intelligence. Semantic Web, Social Networks and Multiagent Systems (Springer, Berlin, 2009), pp. 800–812Google Scholar
  18. 18.
    M. Graczyk, T. Lasota, B Trawiński, K Trawiński, Comparison of bagging, boosting and stacking ensembles applied to real estate appraisal. In: Proceedings of the Second International Conference on Intelligent Information and Database Systems: Part II, Hue City, Vietnam, pp. 340–350 (2010)Google Scholar
  19. 19.
    A. Graves, Generating sequences with recurrent neural networks. arXiv:1308.0850v5 (2014)
  20. 20.
    A. Graves, A. Rahman Mohamed, G. Hinton, Speech recognition with deep recurrent neural networks. arXiv:1303.5778v1 (2013)
  21. 21.
    K. He, J. Sun, Convolutional neural networks at constrained time cost. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5353–5360 (2015)Google Scholar
  22. 22.
    K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition. arXiv:1512.03385 (2015)
  23. 23.
    T.J. Kauko. Modelling the Locational Determinants of House Prices: Neural Network and Value Tree Approaches (PhD thesis Universiteit Utrecht, Utrecht, 2002)Google Scholar
  24. 24.
    V. Kontrimas, A. Verikas, The mass appraisal of the real estate by computational intelligence. Appl. Soft Comput. 11(1), 443–448 (2011)CrossRefGoogle Scholar
  25. 25.
    M.B. Kursa, A. Jankowski, W.R. Rudnicki, Boruta – a system for feature selection. Fund. Inform. 101 (4), 271–285 (2010)MathSciNetGoogle Scholar
  26. 26.
    T. Lasota, P. Sachnowski, B. Trawiṅski, in Comparative analysis of regression tree models for premises valuation using statistica data miner. Computational Collective Intelligence. Semantic Web, Social Networks and Multiagent Systems (Springer, Berlin, 2009), pp. 776–787Google Scholar
  27. 27.
    Y. LeCun, Y. Bengio, G. Hinton, Deep learning. Nature. 521(7553), 436–444 (2015)CrossRefGoogle Scholar
  28. 28.
    S. McGreal, A. Adair, D. McBurney, D. Patterson, Neural networks: the prediction of residential values. J. Prop. Valuat. Invest. 16(1), 57–70 (1998)CrossRefGoogle Scholar
  29. 29.
    V. Moosavi, Urban data streams and machine learning: a case of swiss real estate market. arXiv:1704.04979 (2017)
  30. 30.
    N. Nguyen, A. Cripps, Predicting housing value: a comparison of multiple regression analysis and artificial neural networks. The Journal of Real Estate Research. 22(3), 313–336 (2001)Google Scholar
  31. 31.
    B. Park, J.K. Bae, Using machine learning algorithms for housing price prediction: the case of Fairfax County, Virginia housing data. Expert Syst. Appl. 42(6), 2928–2934 (2015)CrossRefGoogle Scholar
  32. 32.
    RF/MAX, Home estimates., Accessed November 20 2017 (2017)
  33. 33.
    D. Sangani, K. Erickson, M.A. Hasan, Predicting Zillow estimation error using linear regression and gradient boosting. In: 2017 IEEE 14th International Conference on Mobile Ad Hoc and Sensor Systems (MASS), pp. 530–534 (2017)Google Scholar
  34. 34.
    H. Selim, Determinants of house prices in turkey: hedonic regression versus artificial neural network. Expert Syst. Appl. 36(2), 2843–2852 (2009)CrossRefGoogle Scholar
  35. 35.
    T. Tieleman, G. Hinton, Lecture 6.5 - RMSProp. COURSERA: neural networks for machine learning (2012)Google Scholar
  36. 36.
    B Trawiński, Z. Telec, J. Krasnoborski, M. Piwowarczyk, M. Talaga, T. Lasota, E. Sawiłow, Comparison of expert algorithms with machine learning models for real estate appraisal. In: 2017 IEEE International Conference on Innovations in Intelligent Systems and Applications (INISTA), Gdynia, Poland, pp. 51–54 (2017)Google Scholar
  37. 37.
    V.N. Vapnik, Statistical Learning Theory. Wiley-Interscience (1998)Google Scholar
  38. 38.
    N. Vo. A New Conceptual Automated Property Valuation Model for Residential Housing Market (PhD thesis Victoria University, Victoria, 2014)Google Scholar
  39. 39.
    K. Vrijdag. Auction Price Prediction: an Instance-Transfer Learning Approach (PhD thesis Eindhoven University of Technology, Eindhoven, 2016)Google Scholar
  40. 40.
    I. Wilson, S. Paris, J. Ware, D. Jenkins, Residential property price time series forecasting with neural networks. Knowl.-Based Syst. 15(5), 335–341 (2002)CrossRefGoogle Scholar
  41. 41.
    E. Worzala, M. Lenk, A. Silva, An exploration of neural networks and its application to real estate valuation. Journal of Real Estate Research. 10(2), 185–201 (1995)Google Scholar
  42. 42.
    Q. You, R. Pang, J. Luo, Image based appraisal of real estate properties. IEEE Trans. Multimed. 19 (12), 2751–2759 (2017)CrossRefGoogle Scholar
  43. 43.
    K. Zhang, W.L. Chao, F. Sha, K. Grauman, Video summarization with long short-term memory. In: Computer Vision – ECCV 2016, IEEE, pp. 766–782 (2016)Google Scholar
  44. 44.
    Zillow, Home for sales., Accessed 16 December 2017 (2017)
  45. 45.
    Zillow, Zillow prize: Zillow’s home value prediction., Accessed Dec 02 2018 (2017)

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Junchi Bin
    • 1
  • Bryan Gardiner
    • 2
  • Eric Li
    • 3
  • Zheng Liu
    • 1
    Email author
  1. 1.Faculty of Applied ScienceUniversity of British ColumbiaKelownaCanada
  2. 2.Data NerdsKelownaCanada
  3. 3.Faculty of ManagementUniversity of British ColumbiaKelownaCanada

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