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Big data analytics predicting real estate prices

Author

Listed:
  • Archana Singh

    (Amity University)

  • Apoorva Sharma

    (Amity University)

  • Gaurav Dubey

    (ABES Engineering College)

Abstract

The enormous data generated on daily basis amounts to big data technologies. This large amounts of data have knowledge and hidden patterns. Real estate turning out to be another biggest application in big data. The emphasis of this paper is to map the process involved in taking large amounts of data to predict the price of a house in real estate. The real estate sounds to be a long-term investment. In this paper, the housing Sale Data from Ames, Iowa is considered for the timeframe 2006–2010 with a view to construct relevant models to estimate the final sale price of a house. Due to high number of explanatory variables several models such as linear regression, random forest and gradient boosting models have been used as tools for feature selection to determine the statistically significant characteristics that influence the final sale price of a house. It has been observed that out of all the models, the gradient boosting model returned the efficient results.

Suggested Citation

  • Archana Singh & Apoorva Sharma & Gaurav Dubey, 2020. "Big data analytics predicting real estate prices," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 11(2), pages 208-219, July.
    • Handle: RePEc:spr:ijsaem:v:11:y:2020:i:2:d:10.1007_s13198-020-00946-3
    DOI: 10.1007/s13198-020-00946-3
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    References listed on IDEAS

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    1. Sonka, Steve, 2014. "Big Data and the Ag Sector: More than Lots of Numbers," International Food and Agribusiness Management Review, International Food and Agribusiness Management Association, vol. 17(1), pages 1-20, February.
    2. Peng Wang & Myounggu Kang, 2014. "An empirical analysis on the housing prices in the Pearl River Delta Economic Region of China," International Journal of Urban Sciences, Taylor & Francis Journals, vol. 18(1), pages 103-114, March.
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    Cited by:

    1. Jungsun Kim & Jaewoong Won & Hyeongsoon Kim & Joonghyeok Heo, 2021. "Machine-Learning-Based Prediction of Land Prices in Seoul, South Korea," Sustainability, MDPI, vol. 13(23), pages 1-14, November.
    2. Maral Taşcılar & Kerem Yavuz Arslanlı, 2022. "Forecasting commercial real estate indicators under COVID-19 by adopting human activity using social big data," Asia-Pacific Journal of Regional Science, Springer, vol. 6(3), pages 1111-1132, October.
    3. Cankun Wei & Meichen Fu & Li Wang & Hanbing Yang & Feng Tang & Yuqing Xiong, 2022. "The Research Development of Hedonic Price Model-Based Real Estate Appraisal in the Era of Big Data," Land, MDPI, vol. 11(3), pages 1-30, February.
    4. Marco Locurcio & Pierluigi Morano & Francesco Tajani & Felicia Di Liddo, 2020. "An Innovative GIS-Based Territorial Information Tool for the Evaluation of Corporate Properties: An Application to the Italian Context," Sustainability, MDPI, vol. 12(14), pages 1-29, July.
    5. Silva, Diego S. & Yamashita, Gabrielli Harumi & Cortimiglia, Marcelo Nogueira & Brust-Renck, Priscila G. & ten Caten, Carla Schwengber, 2022. "Are we ready to assess digital readiness? Exploring digital implications for social progress from the Network Readiness Index," Technology in Society, Elsevier, vol. 68(C).

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