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Key performance indicators to predict the future performance of office nodes
The property sector is globally regarded as one of the best asset classes to invest in. There is substantial data available in respect of the historical performance of the property sectors and geographical locations. The challenge today however, is to be able to predict which office nodes will, i...
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| Format: | Thesis |
| Language: | English |
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University of Pretoria
2018
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| _version_ | 1867613709522698240 |
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| access_status_str | Open Access |
| author2 | Boshoff, Douw G.B. |
| author_browse | Boshoff, Douw G.B. |
| author_facet | Boshoff, Douw G.B. |
| collection | Thesis |
| dc_rights_str_mv | © 2018 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. |
| description | The property sector is globally regarded as one of the best asset classes to invest in. There is
substantial data available in respect of the historical performance of the property sectors and
geographical locations. The challenge today however, is to be able to predict which office
nodes will, in this fast changing environment, be the best performing nodes in the future. This
research project endeavours to answer this burning research question. Interviews were
conducted with 18 commercial property experts specialising in the different nodes of the main
metropolitan regions of South Africa, namely Pretoria, Johannesburg, Cape Town and
Durban. Through the interviews, it became evident which key performance indicators (KPIs)
are regarded by the property specialist as the most important KPIs to consider when
investigating office nodes’ performance. In the model formulated, total return was used as the
measure of the performance of the different nodes. The most relevant KPIs mentioned by the
specialists were used in a multiple regression model as the independent variables and total
return as the dependent variable. Twenty years of data from MSCI was examined in the
multiple regression model. The regression models were used to further determine which of
the KPIs contributed the most towards explaining total return as the measurement of
performance. The purpose of the different regression models were to determine a model with
the highest adjusted R-square, F-value, as well as the highest significance of all the KPIs used
in the model, to enable the researcher to use the Beta values to determine the total return of the different nodes in the future. The model formulated enables the investor to identify the best
performing office nodes in the future. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/67871 |
| institution | University of Pretoria (South Africa) |
| language | English |
| last_indexed | 2026-06-10T12:40:27.509Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository |
| publishDate | 2018 |
| publishDateRange | 2018 |
| publishDateSort | 2018 |
| publisher | University of Pretoria |
| publisherStr | University of Pretoria |
| record_format | dspace |
| source_str | UPSpace — University of Pretoria Institutional Repository |
| spelling | oai:repository.up.ac.za:2263/67871 Key performance indicators to predict the future performance of office nodes Boshoff, Douw G.B. u12028429@tuks.co.za Pienaar, Mareli Magdalena Unrestricted UCTD Key Performance Indicators Decision-making models Future performance Office node The property sector is globally regarded as one of the best asset classes to invest in. There is substantial data available in respect of the historical performance of the property sectors and geographical locations. The challenge today however, is to be able to predict which office nodes will, in this fast changing environment, be the best performing nodes in the future. This research project endeavours to answer this burning research question. Interviews were conducted with 18 commercial property experts specialising in the different nodes of the main metropolitan regions of South Africa, namely Pretoria, Johannesburg, Cape Town and Durban. Through the interviews, it became evident which key performance indicators (KPIs) are regarded by the property specialist as the most important KPIs to consider when investigating office nodes’ performance. In the model formulated, total return was used as the measure of the performance of the different nodes. The most relevant KPIs mentioned by the specialists were used in a multiple regression model as the independent variables and total return as the dependent variable. Twenty years of data from MSCI was examined in the multiple regression model. The regression models were used to further determine which of the KPIs contributed the most towards explaining total return as the measurement of performance. The purpose of the different regression models were to determine a model with the highest adjusted R-square, F-value, as well as the highest significance of all the KPIs used in the model, to enable the researcher to use the Beta values to determine the total return of the different nodes in the future. The model formulated enables the investor to identify the best performing office nodes in the future. Construction Economics MSc (Real Estate) Unrestricted 2018-12-05T08:05:44Z 2018-12-05T08:05:44Z 2009/06/18 2018 Dissertation Pienaar, MM 2018, Key performance indicators to predict the future performance of office nodes, MSc (Real Estate) Dissertation, University of Pretoria, Pretoria, viewed yymmdd <http://hdl.handle.net/2263/67871> S2018 http://hdl.handle.net/2263/67871 en © 2018 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. application/pdf University of Pretoria |
| spellingShingle | Unrestricted UCTD Key Performance Indicators Decision-making models Future performance Office node Key performance indicators to predict the future performance of office nodes |
| title | Key performance indicators to predict the future performance of office nodes |
| title_full | Key performance indicators to predict the future performance of office nodes |
| title_fullStr | Key performance indicators to predict the future performance of office nodes |
| title_full_unstemmed | Key performance indicators to predict the future performance of office nodes |
| title_short | Key performance indicators to predict the future performance of office nodes |
| title_sort | key performance indicators to predict the future performance of office nodes |
| topic | Unrestricted UCTD Key Performance Indicators Decision-making models Future performance Office node |
| url | http://hdl.handle.net/2263/67871 |