JCRE-04-2014-0009.R3.pdf (881.83 kB)
Modelling banking-hall yield for property investment
journal contribution
posted on 2023-08-30, 14:22 authored by Malvern Tipping, Roger NewtonPurpose – The study seeks 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 auction.
Research limitations/implications – Cases analyzed 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.
Keywords: Banking-hall; investment; portfolio; predictive framework; rent; yield; index.
Article classification: Research paper.
History
Refereed
- Yes
Volume
17Issue number
1Page range
4-25Publication title
Journal of Corporate Real EstateISSN
1463-001XExternal DOI
Publisher
EmeraldFile version
- Accepted version
Language
- eng
Official URL
Legacy posted date
2016-08-16Legacy creation date
2016-08-05Legacy Faculty/School/Department
ARCHIVED Faculty of Science & Technology (until September 2018)Usage metrics
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