IDEAS home Printed from https://ideas.repec.org/a/vrs/remava/v23y2015i4p95-104n10.html
   My bibliography  Save this article

Predictive Modeling Of Office Rent In Selected Districts Of Abuja, Nigeria

Author

Listed:
  • Udoekanem Namnso

    (Department of Estate Management and Valuation, Federal University of Technology, Minna, Niger State, Nigeria)

  • Ighalo James

    (Department of Estate Management, Bells University of Technology, Ota, Nigeria)

  • Sanusi Yekeen

    (Department of Urban and Regional Planning, Federal University of Technology, Minna, Niger State, Nigeria)

Abstract

This study examined the drivers of office rents in selected districts of Abuja, Nigeria. These districts are Asokoro, Maitama and Utako. Primary and secondary data were utilized for the study. Primary data include office rental levels and office space data in the study areas for the period 2001-2012, and were obtained through structured questionnaires administered to real estate surveying and valuation firms which are active in the commercial property markets in the study areas. Secondary data for the study were obtained from the National Bureau of Statistics (NBS) and the Central Bank of Nigeria (CBN), and consist mainly of macroeconomic variables in Nigeria during the study period. Using single-equation regression analysis, the developed office rent model accounted for 76%, 72% and 75% of the variation in office property rents in the commercial property market of the Asokoro, Maitama and Utako districts respectively. The study also revealed that real GDP growth and vacancy rate are the major determinants of rental growth in the office property market in the districts of Asokoro and Maitama, while real GDP growth is the major driver of office rents in the Utako district. The socioeconomic implication of the findings is that the government can generate substantial revenue from property rate through sustained commercial property rental performance in the study areas. Such revenue can be deployed to provide and maintain public infrastructure, thereby improving the wellbeing of the citizenry.

Suggested Citation

  • Udoekanem Namnso & Ighalo James & Sanusi Yekeen, 2015. "Predictive Modeling Of Office Rent In Selected Districts Of Abuja, Nigeria," Real Estate Management and Valuation, Sciendo, vol. 23(4), pages 95-104, December.
  • Handle: RePEc:vrs:remava:v:23:y:2015:i:4:p:95-104:n:10
    DOI: 10.1515/remav-2015-0040
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/remav-2015-0040
    Download Restriction: no

    File URL: https://libkey.io/10.1515/remav-2015-0040?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
    ---><---

    More about this item

    Statistics

    Access and download statistics

    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:vrs:remava:v:23:y:2015:i:4:p:95-104:n:10. 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: Peter Golla (email available below). General contact details of provider: https://www.sciendo.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.