An analysis of the determinants of office real estate price modelling in Nigeria: using a Delphi approach
Purpose: There are a plethora of putative influencing variables available in the literature for modelling real estate prices using AI. Their choice tends to differ from one researcher to the other, consequently leading to subjectivity in the selection process. Thus, there is a need to seek the viewp...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Emerald Publishing Limited
2022
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| Online Access: | http://eprints.utm.my/103890/1/KamalahasanAchu2022_AnAnalysisoftheDeterminantsofOfficeRealEstate.pdf |
| _version_ | 1796867549537763328 |
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| author | A. Yakub, AbdurRaheem Achu, Kamalahasan Mohd. Ali, Hishamuddin Abdul Jalil, Rohaya |
| author_facet | A. Yakub, AbdurRaheem Achu, Kamalahasan Mohd. Ali, Hishamuddin Abdul Jalil, Rohaya |
| author_sort | A. Yakub, AbdurRaheem |
| collection | ePrints |
| description | Purpose: There are a plethora of putative influencing variables available in the literature for modelling real estate prices using AI. Their choice tends to differ from one researcher to the other, consequently leading to subjectivity in the selection process. Thus, there is a need to seek the viewpoint of practitioners on the applicability and level of significance of these academically established variables. Design/methodology/approach: Using the Delphi technique, this study collated and structured the 35 underlying micro- and macroeconomic parameters derived from literature and eight variables suggested by 11 selected real estate experts. The experts ranked these variables in order of influence using a seven-point Likert scale with a reasonable consensus during the fourth round (Kendall's W = 0.7418). Findings: The study discovered that 16 variables are very influential with seven being extremely influential. These extremely influential variables include flexibility, adaptability of design, accessibility to the building, the size of office spaces, quality of construction, state of repairs, expected capital growth and proximity to volatile areas. Practical implications: The results of this study improve the quality of data available to valuers towards a fortified price prediction for investors, and thereby, restoring the valuers' credibility and integrity. Originality/value: The “volatility level of an area”, which was revealed as a distinct factor in the survey is used to add to current knowledge concerning office price. Hence, this study offers real estate practitioners and researchers valuable knowledge on the critical variables that must be considered in AI-based price modelling. |
| first_indexed | 2024-03-05T21:28:56Z |
| format | Article |
| id | utm.eprints-103890 |
| institution | Universiti Teknologi Malaysia - ePrints |
| language | English |
| last_indexed | 2024-03-05T21:28:56Z |
| publishDate | 2022 |
| publisher | Emerald Publishing Limited |
| record_format | dspace |
| spelling | utm.eprints-1038902023-12-04T06:17:34Z http://eprints.utm.my/103890/ An analysis of the determinants of office real estate price modelling in Nigeria: using a Delphi approach A. Yakub, AbdurRaheem Achu, Kamalahasan Mohd. Ali, Hishamuddin Abdul Jalil, Rohaya HD Industries. Land use. Labor Purpose: There are a plethora of putative influencing variables available in the literature for modelling real estate prices using AI. Their choice tends to differ from one researcher to the other, consequently leading to subjectivity in the selection process. Thus, there is a need to seek the viewpoint of practitioners on the applicability and level of significance of these academically established variables. Design/methodology/approach: Using the Delphi technique, this study collated and structured the 35 underlying micro- and macroeconomic parameters derived from literature and eight variables suggested by 11 selected real estate experts. The experts ranked these variables in order of influence using a seven-point Likert scale with a reasonable consensus during the fourth round (Kendall's W = 0.7418). Findings: The study discovered that 16 variables are very influential with seven being extremely influential. These extremely influential variables include flexibility, adaptability of design, accessibility to the building, the size of office spaces, quality of construction, state of repairs, expected capital growth and proximity to volatile areas. Practical implications: The results of this study improve the quality of data available to valuers towards a fortified price prediction for investors, and thereby, restoring the valuers' credibility and integrity. Originality/value: The “volatility level of an area”, which was revealed as a distinct factor in the survey is used to add to current knowledge concerning office price. Hence, this study offers real estate practitioners and researchers valuable knowledge on the critical variables that must be considered in AI-based price modelling. Emerald Publishing Limited 2022-09-29 Article PeerReviewed application/pdf en http://eprints.utm.my/103890/1/KamalahasanAchu2022_AnAnalysisoftheDeterminantsofOfficeRealEstate.pdf A. Yakub, AbdurRaheem and Achu, Kamalahasan and Mohd. Ali, Hishamuddin and Abdul Jalil, Rohaya (2022) An analysis of the determinants of office real estate price modelling in Nigeria: using a Delphi approach. Property Management, 40 (5). pp. 758-779. ISSN 0263-7472 http://dx.doi.org/10.1108/PM-08-2021-0060 DOI:10.1108/PM-08-2021-0060 |
| spellingShingle | HD Industries. Land use. Labor A. Yakub, AbdurRaheem Achu, Kamalahasan Mohd. Ali, Hishamuddin Abdul Jalil, Rohaya An analysis of the determinants of office real estate price modelling in Nigeria: using a Delphi approach |
| title | An analysis of the determinants of office real estate price modelling in Nigeria: using a Delphi approach |
| title_full | An analysis of the determinants of office real estate price modelling in Nigeria: using a Delphi approach |
| title_fullStr | An analysis of the determinants of office real estate price modelling in Nigeria: using a Delphi approach |
| title_full_unstemmed | An analysis of the determinants of office real estate price modelling in Nigeria: using a Delphi approach |
| title_short | An analysis of the determinants of office real estate price modelling in Nigeria: using a Delphi approach |
| title_sort | analysis of the determinants of office real estate price modelling in nigeria using a delphi approach |
| topic | HD Industries. Land use. Labor |
| url | http://eprints.utm.my/103890/1/KamalahasanAchu2022_AnAnalysisoftheDeterminantsofOfficeRealEstate.pdf |
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