Modelling the Influence of Customer Perception on Pricing Behaviour of Banks

Jayanthakumaran, Muhunthan

Modelling the Influence of Customer Perception on Pricing Behaviour of Banks

Jayanthakumaran, Muhunthan

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Publication Type:: Thesis
Issue Date:: 2025

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Field	Value	Language
dc.contributor.author	Jayanthakumaran, Muhunthan
dc.date.accessioned	2026-03-16T03:18:58Z
dc.date.available	2026-03-16T03:18:58Z
dc.date.issued	2025
dc.identifier.uri	http://hdl.handle.net/10453/194345
dc.description	University of Technology Sydney. Faculty of Engineering and Information Technology.	en_US.UTF-8
dc.description.abstract	Mortgage pricing exerts far-reaching influence on the economy, with changes cascading from an individual’s disposable income to overall aggregate demand. Traditional research has primarily focused on how changes in marginal costs drive overall pricing. In the Australian context, recent regulatory inquiries by the Australian Competition and Consumer Commission (ACCC) and Hayne Royal Commission (HRC) have highlighted the additional importance of public perception in pricing. This represents a research gap, as perception has been discussed qualitatively but not quantitatively modelled for this context. This study extends the traditional marginal cost-based pricing framework by incorporating perception and distinguishing between pricing strategies for new (front-book) and existing (back-book) customers. The research has three key objectives: (1) to develop a measure that captures perception, (2) to build a pricing framework integrating perception and front-/back-book differentiation, and (3) to analyse how these factors influence strategic pricing decisions. To track perception over time, this study introduces the Banking Perception Index (BPI), guided by a systematic review to inform its development. The BPI integrates factors including volume growth, customer review data, and news headlines. The resulting framework is estimated using a Nonlinear Autoregressive Distributed Lag (NARDL) approach, applied separately to front- and back-book pricing proxies to capture asymmetric and long-run dynamics. A higher BPI value corresponds to a more favourable public perception, and results show that variations in the BPI align with shifts in consumer sentiment at the organisational level. The proposed pricing framework reveals a clear distinction in how banks treat new (front-book) versus existing (back-book) clients. Empirical evidence indicates that cost-of-funds asymmetry is significant for back-book pricing relative to front-book pricing, with banks reacting more aggressively to increases in funding costs than to decreases. Moreover, customer perception significantly affects front-book pricing compared to back-book pricing, suggesting that perception does influence rates when banks target new customers. This finding supports the value of integrating behavioural dimensions into pricing models and helps to fill the identified research gap. While the approach demonstrates strong empirical performance, key limitations include reliance on proxies for measuring perception and front-book and back-book pricing data. These findings offer valuable insights for policymakers, the Reserve Bank, and commercial banks by highlighting additional factors affecting price points and the more nuanced flow-on effects of central bank policy on consumers.	en_US.UTF-8
dc.format	Thesis (PhD)
dc.language.iso	en_US	en_US.UTF-8
dc.relation	https://opus.lib.uts.edu.au/bitstream/10453/194345/1/thesis.pdf
dc.rights	info:eu-repo/semantics/openAccess
dc.rights	The author owns the copyright in this thesis including all reproduction and reuse rights for the work. The work may not be altered without the permission of the copyright owner. Attribution is essential when quoting or paraphrasing from this thesis.
dc.rights	© 2025 Muhunthan Jayanthakumaran
dc.rights	au.edu.uts.lib/cph
dc.title	Modelling the Influence of Customer Perception on Pricing Behaviour of Banks	en_US.UTF-8
dc.type	Thesis
utslib.copyright.status	open_access	*

Abstract:

Mortgage pricing exerts far-reaching influence on the economy, with changes cascading from an individual’s disposable income to overall aggregate demand. Traditional research has primarily focused on how changes in marginal costs drive overall pricing. In the Australian context, recent regulatory inquiries by the Australian Competition and Consumer Commission (ACCC) and Hayne Royal Commission (HRC) have highlighted the additional importance of public perception in pricing. This represents a research gap, as perception has been discussed qualitatively but not quantitatively modelled for this context. This study extends the traditional marginal cost-based pricing framework by incorporating perception and distinguishing between pricing strategies for new (front-book) and existing (back-book) customers. The research has three key objectives: (1) to develop a measure that captures perception, (2) to build a pricing framework integrating perception and front-/back-book differentiation, and (3) to analyse how these factors influence strategic pricing decisions. To track perception over time, this study introduces the Banking Perception Index (BPI), guided by a systematic review to inform its development. The BPI integrates factors including volume growth, customer review data, and news headlines. The resulting framework is estimated using a Nonlinear Autoregressive Distributed Lag (NARDL) approach, applied separately to front- and back-book pricing proxies to capture asymmetric and long-run dynamics. A higher BPI value corresponds to a more favourable public perception, and results show that variations in the BPI align with shifts in consumer sentiment at the organisational level. The proposed pricing framework reveals a clear distinction in how banks treat new (front-book) versus existing (back-book) clients. Empirical evidence indicates that cost-of-funds asymmetry is significant for back-book pricing relative to front-book pricing, with banks reacting more aggressively to increases in funding costs than to decreases. Moreover, customer perception significantly affects front-book pricing compared to back-book pricing, suggesting that perception does influence rates when banks target new customers. This finding supports the value of integrating behavioural dimensions into pricing models and helps to fill the identified research gap. While the approach demonstrates strong empirical performance, key limitations include reliance on proxies for measuring perception and front-book and back-book pricing data. These findings offer valuable insights for policymakers, the Reserve Bank, and commercial banks by highlighting additional factors affecting price points and the more nuanced flow-on effects of central bank policy on consumers.

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