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Modelling banking-hall yield for property investment

Modelling banking-hall yield for property investment Purpose – This paper aims to build a predictive model for the investment yield of British banking-halls. Design/methodology/approach – Empirical data of similar lots sold at previous auctions are subjected to statistical analyses utilizing a cross-sectional research design. The independent variables analysed are taken from a previous study using the same cases. Models are built using logistic regression and ANCOVA. Findings – Logistic regression generally generates better models than ANCOVA. A division of Britain on a north/south divide produces the best results. Rent is as good as lot size and price in modelling, but has greater utility, because it is known prior to auctions. Research limitations/implications – Cases analysed were restricted to lots let entirely as banking-halls. Other lots comprising premises only partially used as banking-halls might produce different results. Freehold was the only tenure tested. Practical implications – The study provides a form of predictive modelling for investors and their advisors using rent which is known in advance of any sale. Originality/value – The study makes an original contribution to the field, because it builds a predictive model for investment yields for this class of property. Further research may indicate if similar predictive models can be built for other classes of investment property. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Corporate Real Estate Emerald Publishing

Modelling banking-hall yield for property investment

Tipping, Malvern; Newton, Roger
Journal of Corporate Real Estate , Volume 17 (1): 22 – Apr 7, 2015
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References (26)

Publisher
Emerald Publishing
Copyright
Copyright © Emerald Group Publishing Limited
ISSN
1463-001X
DOI
10.1108/JCRE-04-2014-0009
Publisher site
See Article on Publisher Site

Abstract

Purpose – This paper aims to build a predictive model for the investment yield of British banking-halls. Design/methodology/approach – Empirical data of similar lots sold at previous auctions are subjected to statistical analyses utilizing a cross-sectional research design. The independent variables analysed are taken from a previous study using the same cases. Models are built using logistic regression and ANCOVA. Findings – Logistic regression generally generates better models than ANCOVA. A division of Britain on a north/south divide produces the best results. Rent is as good as lot size and price in modelling, but has greater utility, because it is known prior to auctions. Research limitations/implications – Cases analysed were restricted to lots let entirely as banking-halls. Other lots comprising premises only partially used as banking-halls might produce different results. Freehold was the only tenure tested. Practical implications – The study provides a form of predictive modelling for investors and their advisors using rent which is known in advance of any sale. Originality/value – The study makes an original contribution to the field, because it builds a predictive model for investment yields for this class of property. Further research may indicate if similar predictive models can be built for other classes of investment property.

Journal

Journal of Corporate Real Estate – Emerald Publishing

Published: Apr 7, 2015

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