A new toolkit for land value analysis and scenario planning

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.

[1]  Stephen Glackin,et al.  Planning support systems for smart cities , 2017 .

[2]  S. Lieske,et al.  Modelling value uplift on future transport infrastructure , 2018, Real Estate and GIS.

[3]  Exploring property value effects of ferry terminals: Evidence from Brisbane, Australia , 2017 .

[4]  Richard E. Klosterman,et al.  The Online What if? Planning Support System: A Land Suitability Application in Western Australia , 2015 .

[5]  C. Mulley,et al.  Does residential property price benefit from light rail in Sydney , 2015 .

[6]  Marco te Brömmelstroet,et al.  From Planning Support Systems to Mediated Planning Support: A Structured Dialogue to Overcome the Implementation Gap , 2010 .

[7]  Stephen Machin,et al.  Valuing School Quality, Better Transport, and Lower Crime: Evidence from House Prices , 2008 .

[8]  Martin Tomko,et al.  The Online What if? Planning Support System , 2013 .

[9]  Richard E. Klosterman,et al.  The What If? Collaborative Planning Support System , 1999 .

[10]  Muhammad Qadeer ul Hussnain,et al.  A framework to bridge digital planning tools' utilization gap in peri-urban spatial planning; lessons from Pakistan , 2020, Comput. Environ. Urban Syst..

[11]  C. Pettit,et al.  A novel hedonic price modelling approach for estimating the impact of transportation infrastructure on property prices , 2021 .

[12]  J. Ries,et al.  School Quality and Residential Property Values: Evidence from Vancouver Rezoning , 2010, The Review of Economics and Statistics.

[13]  Computational Urban Planning and Management for Smart Cities , 2019, Lecture Notes in Geoinformation and Cartography.

[14]  Peter Newman,et al.  Tax Increment Financing Framework for Integrated Transit and Urban Renewal Projects in Car-Dependent Cities , 2015 .

[15]  H. Du,et al.  Relationship Between Transport Accessibility and Land Value: Local Model Approach with Geographically Weighted Regression , 2006 .

[16]  R. Klosterman,et al.  The What If ? Planning Support System , 2001 .

[17]  Jesper Simonsen,et al.  Participatory Design: an introduction , 2012 .

[18]  The Impact of Transmission Lines on Residential Property Values: Results of A Case Study in a Suburb of Wellington, Nz , 2000 .

[19]  J. Stillwell,et al.  Planning Support Science for Smarter Urban Futures , 2017 .

[20]  Eric R. Ziegel,et al.  Geographically Weighted Regression , 2006, Technometrics.

[21]  Maria Francesca Costabile,et al.  Towards satisfying practitioners in using Planning Support Systems , 2018, Comput. Environ. Urban Syst..

[22]  C. Mulley Accessibility and Residential Land Value Uplift: Identifying Spatial Variations in the Accessibility Impacts of a Bus Transitway , 2014 .

[24]  C. Mulley,et al.  Residential property value impacts of proximity to transport infrastructure: An investigation of bus rapid transit and heavy rail networks in Brisbane, Australia , 2016 .

[25]  Greg Costello,et al.  Towards a System of Local House Price Indices , 2002 .

[26]  Michael Batty,et al.  Big data, smart cities and city planning , 2013, Dialogues in human geography.

[27]  M L Bobrow,et al.  On planning and design. , 1976, Hospital forum.

[28]  Jennifer Pitts,et al.  Review Articles: The Effects of Electric Transmission Lines on Property Values: A Literature Review , 2010 .

[29]  Marcus Foth,et al.  Integrating ICT into the planning process: impacts, opportunities and challenges , 2013 .

[30]  John Stillwell,et al.  Planning Support Systems in Practice , 2003 .

[31]  Paul Schot,et al.  Bottlenecks Blocking Widespread Usage of Planning Support Systems , 2005 .

[32]  Stephen Glackin,et al.  A web-based 3D visualisation and assessment system for urban precinct scenario modelling , 2016 .