Skip to main content
SpringerLink
Log in
Book cover

International Conference on Genetic and Evolutionary Computing

ICGEC 2017: Genetic and Evolutionary Computing pp 121–127Cite as

A House Price Prediction for Integrated Web Service System of Taiwan Districts

  • Conference paper
  • First Online:

  • 700 Accesses

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 579))

Abstract

Buying a house is not an easy thing for the most people. If you want to buy a house, you must to consider many factors. Such as the house pattern and location. These factors directly or indirectly affect the value of the house value. The current sale of the house only to provide the price and details of house information. There is no provision of the housing prices trend. Hence, this system is a network service for combine the house price forecast and the sale of house information. House buyers make good choice by this house price prediction service. This system use analytical method and forecasting model to forecast house prices. In experimental results, we use hit rate to verification if the forecast interval is reasonable. More than half of six city’s hit rate above 75%. It is means our system can help people to buy satisfied house.

This is a preview of subscription content, 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   129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   169.99
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

Learn about institutional subscriptions

References

  1. Liang, C.P.: House price prediction system based on open government data. Master thesis, Institute of Network Engineering College of Computer Science, NCTU (2015)

    Google Scholar 

  2. Guha, S., Rastogi, R., Shim, K.: ROCK a robust clustering algorithm for categorical attributes. In: 15th International Conference on Data Engineering Proceedings, vol. 25, pp. 345–366. IEEE (1999)

    Google Scholar 

  3. Shih, M.Y., Jheng, J.W., Lai, L.F.: A two-step method for clustering mixed categroical and numeric data. Tamkang J. Sci. Eng. 13(1), 11–19 (2010)

    Google Scholar 

  4. Chiu, S.C.: Real estate price models of based on real price registration. Master thesis, Institute of Computer Science and Engineering College of Computer Science, NCTU (2014)

    Google Scholar 

  5. Apache2. https://help.ubuntu.com/lts/serverguide/httpd.html

  6. CGI. http://www.w3.org/CGI/

  7. Reddy, M.V.J., Kavitha, B.: Clustering the mixed numerical and categorical dataset using similarity weight and filter method. Int. J. Database Theory Appl. 5(1), 121–134 (2012)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computer Science, National Chiao Tung University, 1001 University Road, Hsinch, Taiwan

    Chia-Chen Fan, Shyan-Ming Yuan, Xuebai Zhang & Yu-Chuan Lin

Authors
  1. Chia-Chen Fan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shyan-Ming Yuan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xuebai Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yu-Chuan Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chia-Chen Fan .

Editor information

Editors and Affiliations

  1. School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, China

    Jerry Chun-Wei Lin

  2. Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fuzhou, Fujian, China

    Jeng-Shyang Pan

  3. School of Computer Science, Engineering and Mathematics, Flinders University, Bedford Park, South Australia, Australia

    Shu-Chuan Chu

  4. School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, China

    Chien-Ming Chen

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Singapore Pte Ltd.

About this paper

Cite this paper

Fan, CC., Yuan, SM., Zhang, X., Lin, YC. (2018). A House Price Prediction for Integrated Web Service System of Taiwan Districts. In: Lin, JW., Pan, JS., Chu, SC., Chen, CM. (eds) Genetic and Evolutionary Computing. ICGEC 2017. Advances in Intelligent Systems and Computing, vol 579. Springer, Singapore. https://doi.org/10.1007/978-981-10-6487-6_15

Download citation

  • DOI: https://doi.org/10.1007/978-981-10-6487-6_15

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-6486-9

  • Online ISBN: 978-981-10-6487-6

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation