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A new toolkit for land value analysis and scenario planning

Author

Listed:
  • Chris Pettit
  • Y Shi
  • H Han

    (University of New South Wales, Australia)

  • M Rittenbruch
  • M Foth

    (1969Queensland University of Technology, Australia)

  • S Lieske

    (The 1974University of Queensland, Australia)

  • R van den Nouwelant

    (6489Western Sydney University, Australia)

  • P Mitchell

    (1969Queensland University of Technology, Australia)

  • S Leao

    (University of New South Wales, Australia)

  • B Christensen

    (1969Queensland University of Technology, Australia)

  • M Jamal

Abstract

In the digital era of big data, data analytics and smart cities, a new generation of planning support systems is emerging. The Rapid Analytics Interactive Scenario Explorer is a novel planning support system developed to help planners and policy-makers determine the likely land value uplift associated with the provision of new city infrastructure. The Rapid Analytics Interactive Scenario Explorer toolkit was developed following a user-centred research approach including iterative design, prototyping and evaluation. Tool development was informed by user inputs obtained through a series of co-design workshops with two end-user groups: land valuers and urban planners. The paper outlines the underlying technical architecture of the toolkit, which has the ability to perform rapid calculations and visualise the results, for the end-users, through an online mapping interface. The toolkit incorporates an ensemble of hedonic pricing models to calculate and visualise value uplift and so enable the user to explore what if? scenarios. The toolkit has been validated through an iterative case study approach. Use cases were related to two policy areas: property and land valuation processes (for land taxation purposes) and value uplift scenarios (for value capture purposes). The cases tested were in Western Sydney, Australia. The paper reports on the results of the ordinary least square linear regressions – used to explore the impacts of hedonic attributes on property value at the global level – and geographically weighted regressions – developed to provide local estimates and explore the varying spatial relationships between attributes and house price across the study area. Building upon the hedonic modelling, the paper also reports the value uplift functionality of the Rapid Analytics Interactive Scenario Explorer toolkit that enables users to drag and drop new train stations and rapidly calculate expected property prices under a range of future transport scenarios. The Rapid Analytics Interactive Scenario Explorer toolkit is believed to be the first of its kind to provide this specific functionality. As it is problem and policy specific, it can be considered an example of the next generation of data-driven planning support system.

Suggested Citation

  • Chris Pettit & Y Shi & H Han & M Rittenbruch & M Foth & S Lieske & R van den Nouwelant & P Mitchell & S Leao & B Christensen & M Jamal, 2020. "A new toolkit for land value analysis and scenario planning," Environment and Planning B, , vol. 47(8), pages 1490-1507, October.
    • Handle: RePEc:sae:envirb:v:47:y:2020:i:8:p:1490-1507
    DOI: 10.1177/2399808320924678
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    References listed on IDEAS

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    Cited by:

    1. Haozhi Pan & Stan Geertman & Brian Deal, 2020. "What does urban informatics add to planning support technology?," Environment and Planning B, , vol. 47(8), pages 1317-1325, October.
    2. Qiwei Song & Yifeng Liu & Waishan Qiu & Ruijun Liu & Meikang Li, 2022. "Investigating the Impact of Perceived Micro-Level Neighborhood Characteristics on Housing Prices in Shanghai," Land, MDPI, vol. 11(11), pages 1-21, November.
    3. Shlomit Flint Ashery & Carl Steinitz, 2022. "Issue-Based Complexity: Digitally Supported Negotiation in Geodesign Linking Planning and Implementation," Sustainability, MDPI, vol. 14(15), pages 1-19, July.
    4. Ng, Matthew Kok Ming & Roper, Josephine & Pettit, Christopher & Lee, Chyi Lin, 2021. "The Reflection of Income Segregation and Accessibility Cleavages in Sydney’s House Prices," SocArXiv 2psk5, Center for Open Science.
    5. Keaton Jenner & Peter Tulip, 2020. "The Apartment Shortage," RBA Research Discussion Papers rdp2020-04, Reserve Bank of Australia.
    6. Gao, Qishuo & Shi, Vivien & Pettit, Christopher & Han, Hoon, 2022. "Property valuation using machine learning algorithms on statistical areas in Greater Sydney, Australia," Land Use Policy, Elsevier, vol. 123(C).
    7. Dina Margrethe Aspen & Andreas Amundsen, 2021. "Developing a Participatory Planning Support System for Sustainable Regional Planning—A Problem Structuring Case Study," Sustainability, MDPI, vol. 13(10), pages 1-13, May.
    8. Oliver Lock & Michael Bain & Christopher Pettit, 2021. "Towards the collaborative development of machine learning techniques in planning support systems – a Sydney example," Environment and Planning B, , vol. 48(3), pages 484-502, March.
    9. Ying Li & Yani Lai & Yanliu Lin, 2024. "The Role of Diversified Geo-Information Technologies in Urban Governance: A Literature Review," Land, MDPI, vol. 13(9), pages 1-27, September.

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