The impact on neighbourhood residential property valuations of a newly proposed public transport project: The Sydney Northwest Metro case study

Chen, Yuer; Yazdani, Maziar; Mojtahedi, Mohammad; Newton, Sidney

doi:10.1016/j.trip.2019.100070

Cited by 25 publications

(24 citation statements)

References 41 publications

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“…The HPM approach has been the most commonly used modelling approach in most previous studies in this field (e.g. Lieske et al , 2021; Chen et al , 2019; Wadu Mesthrige et al , 2015; Dziauddin et al , 2015; Kay et al , 2014). However, due to numerous inherent weaknesses of the HPM, fixed-effects approaches were proposed by researchers (e.g.…”

Section: Methodological Approach and Datamentioning

confidence: 99%

“…Wadu Mesthrige et al (2015), analysing the effects of an announcement of a new railway link, found that transport developments exert a positive impact on neighbouring residential nearby properties even before the completion of the development. In a recent study based on the same hedonic price modelling (HPM) approach, Chen et al (2019), investigating the effect of the construction of a major transport infrastructure development in Sydney, reported a significant price enhancement of nearby properties. The results also further suggest that the effect was negative at the announcement stage while positive during the construction stage.…”

Section: Literature Reviewmentioning

confidence: 99%

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Transport infrastructure, accessibility and residential property values: evidence from Hong Kong

Jayantha¹,

Maqsood²

2021

BEPAM

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PurposeHong Kong, like many other developed cities and countries, invests heavily in transport development. This study investigates whether the speculative benefits of future improvements in accessibility, brought about by impending transport development, will be capitalized into nearby residential property values even prior to the opening of the development.Design/methodology/approachDeviating from the standard hedonic price approach, the present study employed a fixed-effects model with a large data set of residential property transactions in the vicinity of three-stations situated along a newly proposed mass-transit-railway line in Hong Kong.FindingsThe results suggest that the values of residential properties close to stations do reflect the accessibility enhancements to be brought about by transport improvements even before the opening of the line. Results revealed a 6.5% of property value premium after the announcement of construction; and higher up to 6.7% after the operation of the line. This indicates that forthcoming new transport-infrastructure development produces changes in spatial price-gradients for neighbouring residential properties. Findings indicate that potential buyers/investors recognized the positive benefits of the planned transportation development, even before completion of the project, and are ready to pay a premium for those properties close to railway stations, representing clear evidence that residential property prices/values, near stations, reflect anticipated accessibility enhancements brought about by transport improvements.Originality/valueThis study, using a novel approach – a fixed-effects model to capture the speculative benefits of future improvements in transport infrastructure – provides a positive hypothesis that expected benefits of future improvements in accessibility are capitalized into property values.

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Section: Methodological Approach and Datamentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Transport infrastructure, accessibility and residential property values: evidence from Hong Kong

Jayantha¹,

Maqsood²

2021

BEPAM

View full text Add to dashboard Cite

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“…Although making an appropriate decision is urgent, the process is often too challenging [7] . So far, a wide range of decision-making tools and methods have been used to reach the most desirable decision 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 . However, the research in this area is scarce and has not been conducted in responding to pandemics.…”

Section: Introductionmentioning

confidence: 99%

An integrated artificial intelligence model for efficiency assessment in pharmaceutical companies during the COVID-19 pandemic

Mirmozaffari

Yazdani

Shadkam

et al. 2022

Sustainable Operations and Computers

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The spread of coronavirus disease around the world has had an immense impact on most economic sectors. Yet amid the turmoil and chaos from the worldwide pandemic, one industry is thriving noticeably. The coronavirus disease is a once in a lifetime business opportunity for pharmaceutical companies. This study presents an artificial intelligence method composed of optimization and machine learning. Data envelopment analysis (DEA) is used to measure productivities and efficiencies of pharmaceutical companies during the COVID-19 pandemic using the additive model in window analysis, the BCC (Banker-Charnes-Cooper) model, and the CCR (Charnes-Cooper-Rhodes) model. The three models are assessed using DataStream financial information with research and development (R&D) investment. The results indicated the additive model's superiority in window analysis, followed by the BCC and CCR models. In the end, some of well-known data mining algorithms, based on the suggested data, have been evaluated in various tools to find the most efficient tool and algorithm.

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“…Many countries are investing in developing infrastructures (Chen et al , 2019; Kabirifar and Mojtahedi, 2019), and in these projects cement is one of the most used construction materials in the world (Akbarzadeh Bengar et al , 2020; Akbarzadeh Bengar and Shahmansouri, 2020). Although PC is beneficial in many aspects, its production emits approximately 7% of the entire human-related CO 2 released into the atmosphere (Metz et al , 2005; Taylor et al , 2006).…”

Section: Introductionmentioning

confidence: 99%

A novel artificial intelligent approach: comparison of machine learning tools and algorithms based on optimization DEA Malmquist productivity index for eco-efficiency evaluation

Mirmozaffari

Shadkam

Khalili

et al. 2021

IJESM

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Purpose Cement as one of the major components of construction activities, releases a tremendous amount of carbon dioxide (CO2) into the atmosphere, resulting in adverse environmental impacts and high energy consumption. Increasing demand for CO2 consumption has urged construction companies and decision-makers to consider ecological efficiency affected by CO2 consumption. Therefore, this paper aims to develop a method capable of analyzing and assessing the eco-efficiency determining factor in Iran’s 22 local cement companies over 2015–2019. Design/methodology/approach This research uses two well-known artificial intelligence approaches, namely, optimization data envelopment analysis (DEA) and machine learning algorithms at the first and second steps, respectively, to fulfill the research aim. Meanwhile, to find the superior model, the CCR model, BBC model and additive DEA models to measure the efficiency of decision processes are used. A proportional decreasing or increasing of inputs/outputs is the main concern in measuring efficiency which neglect slacks, and hence, is a critical limitation of radial models. Thus, the additive model by considering desirable and undesirable outputs, as a well-known DEA non-proportional and non-radial model, is used to solve the problem. Additive models measure efficiency via slack variables. Considering both input-oriented and output-oriented is one of the main advantages of the additive model. Findings After applying the proposed model, the Malmquist productivity index is computed to evaluate the productivity of companies over 2015–2019. Although DEA is an appreciated method for evaluating, it fails to extract unknown information. Thus, machine learning algorithms play an important role in this step. Association rules are used to extract hidden rules and to introduce the three strongest rules. Finally, three data mining classification algorithms in three different tools have been applied to introduce the superior algorithm and tool. A new converting two-stage to single-stage model is proposed to obtain the eco-efficiency of the whole system. This model is proposed to fix the efficiency of a two-stage process and prevent the dependency on various weights. Converting undesirable outputs and desirable inputs to final desirable inputs in a single-stage model to minimize inputs, as well as turning desirable outputs to final desirable outputs in the single-stage model to maximize outputs to have a positive effect on the efficiency of the whole process. Originality/value The performance of the proposed approach provides us with a chance to recognize pattern recognition of the whole, combining DEA and data mining techniques during the selected period (five years from 2015 to 2019). Meanwhile, the cement industry is one of the foremost manufacturers of naturally harmful material using an undesirable by-product; specific stress is given to that pollution control investment or undesirable output while evaluating energy use efficiency. The significant concentration of the study is to respond to five preliminary questions.

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The impact on neighbourhood residential property valuations of a newly proposed public transport project: The Sydney Northwest Metro case study

Cited by 25 publications

References 41 publications

Transport infrastructure, accessibility and residential property values: evidence from Hong Kong

Transport infrastructure, accessibility and residential property values: evidence from Hong Kong

An integrated artificial intelligence model for efficiency assessment in pharmaceutical companies during the COVID-19 pandemic

A novel artificial intelligent approach: comparison of machine learning tools and algorithms based on optimization DEA Malmquist productivity index for eco-efficiency evaluation

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