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International Conference on Analysis of Images, Social Networks and Texts

AIST 2019: Analysis of Images, Social Networks and Texts pp 63–72Cite as

Distance in Geographic and Characteristics Space for Real Estate Price Prediction

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11832))

Abstract

The common approach to predict the price of residential property is the hedonic price model and its extension to the case of spatial autoregression. The hedonic approach models the dependence between the price and internal characteristics of an apartment, house characteristics and external characteristics. To account for the unobserved quality of the surrounding environment price model includes factors of spatial price correlation, where the distance is usually measured as the distance in geographic space. Determining the price the seller focuses not only on the observed and unobserved factors of the apartment, house and its environment but also on the prices of similar marketed objects which can be selected both by geographic proximity and by characteristics similarity. In this paper, we use ensemble clustering approach to measure objects proximity and test that the proximity of objects in the characteristics space along with spatial correlation explains the significant variation in prices that in turn leads to an improvement of predictive ability of the model.

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Author information

Authors and Affiliations

  1. Research Group for Applied Markets and Enterprises Studies, National Research University Higher School of Economics, Perm, Russia

    Evgeniy M. Ozhegov, Alina Ozhegova & Ekaterina Mitrokhina

Authors
  1. Evgeniy M. Ozhegov
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  2. Alina Ozhegova
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  3. Ekaterina Mitrokhina
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Corresponding author

Correspondence to Evgeniy M. Ozhegov .

Editor information

Editors and Affiliations

  1. RWTH Aachen University, Aachen, Germany

    Wil M. P. van der Aalst

  2. University of Ljubljana, Ljubljana, Slovenia

    Vladimir Batagelj

  3. National Research University Higher School of Economics, Moscow, Russia

    Dmitry I. Ignatov

  4. Krasovskii Institute of Mathematics and Mechanics, Yekaterinburg, Russia

    Michael Khachay

  5. National Research University Higher School of Economics, Moscow, Russia

    Valentina Kuskova

  6. University of Oslo, Oslo, Norway

    Andrey Kutuzov

  7. National Research University Higher School of Economics, Moscow, Russia

    Sergei O. Kuznetsov

  8. National Research University Higher School of Economics, Moscow, Russia

    Irina A. Lomazova

  9. Lomonosov Moscow State University, Moscow, Russia

    Natalia Loukachevitch

  10. LORIA, Vandœuvre-lès-Nancy, France

    Amedeo Napoli

  11. University of Florida, Gainesville, FL, USA

    Panos M. Pardalos

  12. Ca Foscari University of Venice, Venice, Italy

    Marcello Pelillo

  13. National Research University Higher School of Economics, Nizhny Novgorod, Russia

    Andrey V. Savchenko

  14. Kazan Federal University, Kazan, Russia

    Elena Tutubalina

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Ozhegov, E.M., Ozhegova, A., Mitrokhina, E. (2019). Distance in Geographic and Characteristics Space for Real Estate Price Prediction. In: van der Aalst, W., et al. Analysis of Images, Social Networks and Texts. AIST 2019. Lecture Notes in Computer Science(), vol 11832. Springer, Cham. https://doi.org/10.1007/978-3-030-37334-4_6

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  • DOI: https://doi.org/10.1007/978-3-030-37334-4_6

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-37333-7

  • Online ISBN: 978-3-030-37334-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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