Modelling banking-hall yield for property investment

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.

Citation

Tipping, M. and Newton, R. (2015), "Modelling banking-hall yield for property investment", Journal of Corporate Real Estate, Vol. 17 No. 1, pp. 4-25. https://doi.org/10.1108/JCRE-04-2014-0009