IDEAS home Printed from https://ideas.repec.org/a/spr/compst/v38y2023i4d10.1007_s00180-023-01391-z.html
   My bibliography  Save this article

House quality index construction and rent prediction in New York City with interactive visualization and product design

Author

Listed:
  • Xiang Shen

    (George Washington University)

  • Shunyan Luo

    (George Washington University)

  • Mingze Zhang

    (George Washington University)

Abstract

Housing is of primary importance for immigrants in New York City. This study analyzed the housing conditions of and price changes for residents in New York City from the NYCHVS survey over the past 30 years. First, a house condition index is defined through dimension reduction approach with a supervised framework. In addition, spatio-temporal information is leveraged to build a two-stage model to predict rent. Data visualization is utilized to show immigrant preferences interactively and provide information for both researchers and new residents to the city.

Suggested Citation

  • Xiang Shen & Shunyan Luo & Mingze Zhang, 2023. "House quality index construction and rent prediction in New York City with interactive visualization and product design," Computational Statistics, Springer, vol. 38(4), pages 1629-1641, December.
    • Handle: RePEc:spr:compst:v:38:y:2023:i:4:d:10.1007_s00180-023-01391-z
    DOI: 10.1007/s00180-023-01391-z
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s00180-023-01391-z
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s00180-023-01391-z?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:compst:v:38:y:2023:i:4:d:10.1007_s00180-023-01391-z. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.