Skip to main content
SpringerLink
Log in

Homogeneous Feature Transfer and Heterogeneous Location Fine-Tuning for Cross-City Property Appraisal Framework

  • Conference paper
  • First Online:
Data Mining (AusDM 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 996))

Included in the following conference series:

Abstract

Most existing real estate appraisal methods focus on building accuracy and reliable models from a given dataset but pay little attention to the extensibility of their trained model. As different cities usually contain a different set of location features (district names, apartment names), most existing mass appraisal methods have to train a new model from scratch for different cities or regions. As a result, these approaches require massive data collection for each city and the total training time for a multi-city property appraisal system will be extremely long. Besides, some small cities may not have enough data for training a robust appraisal model. To overcome these limitations, we develop a novel Homogeneous Feature Transfer and Heterogeneous Location Fine-tuning (HFT+HLF) cross-city property appraisal framework. By transferring partial neural network learning from a source city and fine-tuning on the small amount of location information of a target city, our semi-supervised model can achieve similar or even superior performance compared to a fully supervised Artificial neural network (ANN) method.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Antipov, E.A., Pokryshevskaya, E.B.: Mass appraisal of residential apartments: an application of random forest for valuation and a CART-based approach for model diagnostics. Expert Syst. Appl. 39, 1772–1778 (2012)

    Article  Google Scholar 

  2. Arribas, I., García, F., Guijarro, F., Oliver, J., Tamošiūnienė, R.: Mass appraisal of residential real estate using multilevel modelling. Int. J. Strateg. Prop. Manag. 20, 77–87 (2016)

    Article  Google Scholar 

  3. Bahrammirzaee, A.: A comparative survey of artificial intelligence applications in finance: artificial neural networks, expert system and hybrid intelligent systems. Neural Comput. Appl. 19, 1165–1195 (2010)

    Article  Google Scholar 

  4. Benjamin, J.D., Guttery, R.S., Sirmans, C.F.: Mass appraisal: an introduction to multiple regression analysis for real estate valuation. J. Real Estate Pract. Educ. 7, 65–77 (2004)

    Google Scholar 

  5. Born, W.L., Pyhrr, S.A.: Real estate valuation: the effect of market and property cycles. J. Real Estate Res. 9, 455–485 (1994)

    Google Scholar 

  6. Brown, K.H., Uyar, B.: A hierarchical linear model approach for assessing the effects of house and neighborhood characteristics on housing prices. J. Real Estate Educ. 7, 15–24 (2004)

    Google Scholar 

  7. Cervelló, R., García, F., Guijarro, F.: Ranking residential properties by a multicriteria single price model. J. Oper. Res. Soc. 62, 1941–1950 (2011)

    Article  Google Scholar 

  8. Chiarazzo, V., Caggiani, L., Marinelli, M., Ottomanelli, M.: A neural network based model for real estate price estimation considering environmental quality of property location. Transp. Res. Procedia 3, 810–817 (2014)

    Article  Google Scholar 

  9. Chopra, S., Thampy, T., Leahy, J., Caplin, A., LeCun, Y.: Discovering the hidden structure of house prices with a non-parametric latent manifold model. In: ACM International Conference on Knowledge Discovery and Data Mining (KDD) (2007)

    Google Scholar 

  10. D ’amato, M.: Comparing rough set theory with multiple regression analysis as automated valuation methodologies. Int. Real Estate Rev. 10, 42–65 (2007)

    Google Scholar 

  11. Del Giudice, V., De Paola, P., Forte, F.: Using genetic algorithms for real estate appraisals. Buildings 7, 31 (2017)

    Article  Google Scholar 

  12. Downes, T.A., Zabel, J.E.: The impact of school characteristics on house prices : Chicago 1987–1991. J. Urban Econ. 52, 1–25 (2002)

    Article  Google Scholar 

  13. Fan, G.Z., Ong, S.E., Koh, H.C.: Determinants of house price: a decision tree approach. Urban Stud. 43, 2301–2315 (2006)

    Article  Google Scholar 

  14. Farmer, M.C., Lipscomb, C.A.: Using quantile regression in hedonic analysis to reveal submarket competition. J. Real Estate Res. 32, 435–460 (2010)

    Google Scholar 

  15. Ferreira, E.J., Sirmans, G.S.: Ridge regression in real estate analysis. Appraisal J. 56, 311 (1988)

    Google Scholar 

  16. Fu, Y., Xiong, H., Ge, Y., Yao, Z., Zheng, Y., Zhou, Z.H.: Exploiting geographic dependencies for real estate appraisal: a mutual perspective of ranking and clustering. In: ACM International Conference on Knowledge Discovery and Data Mining (KDD) (2014)

    Google Scholar 

  17. Goldberg, D.E., Holland, J.H.: Genetic algorithms and machine learning. Mach. Learn. 3, 95–99 (1988). https://doi.org/10.1023/A:1022602019183

    Article  Google Scholar 

  18. Isakson, H.R.: Using multiple regression analysis in real estate appraisal. Appraisal J. 69, 424 (2001)

    Google Scholar 

  19. Kanojia, A., Khan, M.Y., Jadhav, U.: Valuation of residential properties by hedonic pricing method-a state of art. Int. J. Recent Adv. Eng. Technol. (IJRAET) (2016)

    Google Scholar 

  20. Kauko, T.: Residential property value and locational externalities: on the complementarity and substitutability of approaches. J. Prop. Invest. Financ. 21, 250–270 (2003)

    Article  Google Scholar 

  21. Kempa, O., Lasota, T., Telec, Z., Trawiński, B.: Investigation of bagging ensembles of genetic neural networks and fuzzy systems for real estate appraisal. In: Nguyen, N.T., Kim, C.-G., Janiak, A. (eds.) ACIIDS 2011. LNCS (LNAI), vol. 6592, pp. 323–332. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-20042-7_33

    Chapter  Google Scholar 

  22. Kontrimas, V., Verikas, A.: The mass appraisal of the real estate by computational intelligence. Appl. Soft Comput. J. 11, 443–448 (2011)

    Article  Google Scholar 

  23. Mansfield, J.R., Lorenz, D.P.: Shaping the future: the impacts of evolving international accounting standards on valuation practice in the UK and Germany. Prop. Manag. 22, 289–303 (2004)

    Google Scholar 

  24. Mccluskey, W., Anand, S.: The application of intelligent hybrid techniques for the mass appraisal of residential properties. J. Prop. Invest. Financ. 17, 218–239 (1999)

    Article  Google Scholar 

  25. Musa, A.G., Daramola, O., Owoloko, A., Olugbara, O.: A neural-CBR system for real property valuation. J. Emerg. Trends Comput. Inf. Sci. 4, 611–622 (2013)

    Google Scholar 

  26. Narula, S.C., Wellington, J.F., Lewis, S.A.: Valuating residential real estate using parametric programming. Eur. J. Oper. Res. 217, 120–128 (2012)

    Article  Google Scholar 

  27. Nghiep, N., Al, C.: Predicting housing value: a comparison of multiple regression analysis and artificial neural networks. J. Real Estate Res. 22(3), 313–336 (2001)

    Google Scholar 

  28. Pagourtzi, E., Assimakopoulos, V., Hatzichristos, T., French, N.: Real estate appraisal: a review of valuation methods. J. Prop. Invest. Financ. 21, 383–401 (2003)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of Warwick, Coventry, UK

    Yihan Guo, Shan Lin, Xiao Ma, Jay Bal & Chang-tsun Li

  2. Charles Sturt University, Wagga Wagga, Australia

    Chang-tsun Li

Authors
  1. Yihan Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shan Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xiao Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jay Bal
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Chang-tsun Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yihan Guo .

Editor information

Editors and Affiliations

  1. School of Computing and Mathematics, Charles Sturt University, Albury, NSW, Australia

    Rafiqul Islam

  2. University of Auckland, Auckland, New Zealand

    Yun Sing Koh

  3. CSIRO Scientific Computing, Canberra, Australia

    Yanchang Zhao

  4. Data Science and Engineering, Australian Taxation Office, Canberra, Australia

    Graco Warwick

  5. Department of Information Technology, University of Wollongong, Wollongong, NSW, Australia

    David Stirling

  6. School of Computing and Mathematics, Charles Sturt University, Wagga Wagga, Australia

    Chang-Tsun Li

  7. School of Computing and Mathematics, Charles Sturt University, Bathurst, Australia

    Zahidul Islam

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Guo, Y., Lin, S., Ma, X., Bal, J., Li, Ct. (2019). Homogeneous Feature Transfer and Heterogeneous Location Fine-Tuning for Cross-City Property Appraisal Framework. In: Islam, R., et al. Data Mining. AusDM 2018. Communications in Computer and Information Science, vol 996. Springer, Singapore. https://doi.org/10.1007/978-981-13-6661-1_13

Download citation

  • DOI: https://doi.org/10.1007/978-981-13-6661-1_13

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-6660-4

  • Online ISBN: 978-981-13-6661-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation