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Visualization-Aided Exploration of the Real Estate Data

  • Mingzhao LiEmail author
  • Zhifeng Bao
  • Timos Sellis
  • Shi Yan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9877)

Abstract

An efficient analysis of the real estate data is critical for buyers to understand the real estate market and seek appropriate properties to live in or rent. In this paper, we first collect data from different channels which are not provided in existing commercial real estate systems, and integrate them to build a location-centred comprehensive real estate dataset, including information other than the house itself, such as education profile, transportation profile and regional profile. Then we develop HouseSeeker, a visualization-aided system for buyers to explore the real estate data, find appropriate properties based on their individual requirements, and compare properties/suburbs from different aspects to discover the strengths and weaknesses of each property/suburb. We demonstrate the effectiveness of our system based on a real-world dataset in Melbourne metropolitan area: it is able to help zero-knowledge users better understand local real estate market and find preferred properties based on their individual requirements. A preliminary implementation of the system is available at http://115.146.89.158/.

Keywords

Real Estate Real Estate Market Candidate Property Commercial Real Estate Individual Requirement 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

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    Li, M., Bao, Z., Yan, S., Sellis, T.: A visual analytics framework for the housing estate data. In: IEEE PacificVis (Poster) (2016)Google Scholar
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    Sun, G., Liang, R., Qu, H., Wu, Y.: Embedding spatio-temporal information into maps by route-zooming. IEEE Trans. Vis. Comput. Graph. (2016). Early AccessGoogle Scholar
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    Turkay, C., Slingsby, A., Hauser, H., Wood, J., Dykes, J.: Attribute signatures: dynamic visual summaries for analyzing multivariate geographical data. IEEE Trans. Vis. Comput. Graph. 20(12), 2033–2042 (2014)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Mingzhao Li
    • 1
    Email author
  • Zhifeng Bao
    • 1
  • Timos Sellis
    • 2
  • Shi Yan
    • 1
  1. 1.RMIT UniversityMelbourneAustralia
  2. 2.Swinburne University of TechnologyMelbourneAustralia

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