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Increasing overhang of serviced apartments poses a serious concern to the national property market. This study aims to examine the impacts of macroeconomic determinants, namely, gross domestic product (GDP), consumer confidence index (CF), existing stocks (ES), incoming supply (IS) and completed project (CP) on serviced apartment price changes.

To achieve more accurate, quality price changes, a serviced apartment price index (SAPI) was constructed through a self-developed hedonic price index model. This study has collected 1,567 transaction data in Kuala Lumpur, covering 2009Q1–2018Q4 for price index construction and data were analysed using the vector autoregressive model, the vector error correction model and the fully modified ordinary least squares (OLS) (FMOLS).

Results of the regression model show that only GDP, ES and IS were significantly associated with SAPI, with an R² of 0.7, where both ES and IS have inverse relationships with SAPI. More precisely, it is predicted that the price of serviced apartments will be reduced by 0.56% and 0.21% for every 1% increase in ES and IS, respectively.

Therefore, government monitoring of serviced apartments’ future supply is crucial by enforcing land use-planning regulations via stricter development approval of serviced apartments to safeguard and achieve more stable property prices.

By adopting an innovative approach to estimating the response of price change to supply and demand in a situation where there is no price indicator for serviced apartments, the study addresses the knowledge gap, especially in terms of understanding what are the key determinants of, and to what extent they influence, the SAPI.

The use of generative artificial intelligence (genAi) language models such as ChatGPT to write assignment text is well established. This paper aims to assess to what extent genAi can be used to obtain guidance on how to avoid detection when commissioning and submitting contract-written assignments and how workable the offered solutions are.

Although ChatGPT is programmed not to provide answers that are unethical or that may cause harm to people, ChatGPT’s can be prompted to answer with inverted moral valence, thereby supplying unethical answers. The authors tasked ChatGPT to generate 30 essays that discussed the benefits of submitting contract-written undergraduate assignments and outline the best ways of avoiding detection. The authors scored the likelihood that ChatGPT’s suggestions would be successful in avoiding detection by markers when submitting contract-written work.

While the majority of suggested strategies had a low chance of escaping detection, recommendations related to obscuring plagiarism and content blending as well as techniques related to distraction have a higher probability of remaining undetected. The authors conclude that ChatGPT can be used with success as a brainstorming tool to provide cheating advice, but that its success depends on the vigilance of the assignment markers and the cheating student’s ability to distinguish between genuinely viable options and those that appear to be workable but are not.

This paper is a novel application of making ChatGPT answer with inverted moral valence, simulating queries by students who may be intent on escaping detection when committing academic misconduct.