RAPID ANALYTICS INTERACTIVE SCENARIO EXPLORER (RAISE) A tool for analysing and visualising land valuation in different development scenarios

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2 RAPID ANALYTICS INTERACTIVE SCENARIO EXPLORER (RAISE) A tool for analysing and visualising land valuation in different development scenarios

3 RAISE PROJECT COLLABORATION

4 RAISE DATA DRIVEN APPROACH Rapid Analytics Interactive Scenario Explorer Analysis Collaboration Location Intelligence

5 RAISE Objectives: Develop open, cloud-based architecture to combine data, models, and visualisation. Develop an interactive scenario explorer toolkit. Explore collaborative visualisation methods. Apply toolkit to automated valuation modelling. Apply toolkit to land value uplift modelling.

6 RAISE1 to RAISE2 RAISE1 Scenario exploration: Value uplift from transportation infrastructure deploy and evaluate RAISE to assist Council planners to rapidly quantify the nominal value uplift of transportation infrastructure proposal scenarios Study area: Western Sydney

7 RAISE1 to RAISE2 RAISE2 Scenario exploration: Value uplift from transportation infrastructure redeploy RAISE for Greater Sydney area and Brisbane City Council Scenario exploration: Value uplift from changing planning controls RAISE Community Version

8 DATA IN RAISE Data Source APM AVM ABS Census NSW Department of Planning & Environment (DPE) NSW Land & Property Information (LPI) NSW Department of Education (DET) Data Derived Property transaction records (transaction time, sold price, land area size, property type, property structural attributes); APM AVM value Digital boundaries and demographics (Employment rates, Aged population, Migration) Land zoning, FSR, Maximum building heights Cadastral and digital topographic database; LPI valuation components; Property characteristics, Points of Interest, Roads, Electric Transmission Lines. School locations and catchments NSW Bureau of Crime Statistics and Research (BOCSAR) NAPLAN PSMA Australia Crime data (breaking and entering dwelling) School performance compared to national average Geoscape (building footprints, building heights, materials)

9 DATA IN RAISE Data Source QLD Open Data Brisbane City Council Data Derived Cadastral and digital topographic database, State school locations and catchments, planning datasets Current land use, Building footprints

10 PROPERTY VALUATION MODEL Data-driven knowledge of property values Using hedonic price models we assume property s value (V) is a function of some set of known attributes of the property, neighbourhood and location (A1, A2, A3...). The modelling finds the relative weighting of these attributes (W1, W2, W3...) and provide a Hedonic estimated value: V = (A 1 W 1 ) + (A 2 W 2 ) + (A 3 W 3 ) C

11 RAISE HEDONIC MODEL ATTRIBUTES Property/Land Attributes Property Type- House Property Type- Semi Property Type- Unit Number of Bedrooms Number of Bathrooms Number of Parking Swimming Pool Maximum Building Height Locational/Accessibility Attributes Proximity to City Centres (m) Proximity to Beaches (m) Proximity to Primary Schools (m) Proximity to Railway Stations Proximity to Wharfs Proximity to Universities Proximity to Swimming Pools Proximity to Commercial Zones Proximity to Industrial Zones Within 50m distance from a High Voltage Electricity Transmission Line Within 100m distance from a Main Road Within100m distance from a Railway Line (on ground) Within 20m distance from an Primary Easement Neighbourhood Attributes Family Income Crime Rates Percentage of Senior Aged People Percentage of People Born Overseas High School NAPLAN Indicator Primary School NAPLAN Indicator

12 RAISE RESIDUAL LAND VALUE MODEL Using Residual Land Value Approach This approach more directly estimates the price of land that is set by the development potential of a site. Residual Land Value Value of completed development Development cost Profit

13 RAISE RESIDUAL LAND VALUE MODEL Conceptual Framework and Objectives of the Residual Land Value Tool Predict the effects of planning controls on RLV Predict the effects of planning controls on development feasibility Identify the minimum planning controls if development is feasible Predict the effects of infrastructure projects on RLV Predict the effects of infrastructure projects on development feasibility Change planning controls Development yields Market value and development costs New Infrastructure Investment Force feasibility Residual land value Estimate change in RLV Assess feasibility Estimate change in feasibility

14 Change Planning Controls - FSR - Land zoning - Other (e.g. minimum lot size, heights and setbacks) Development Yields - Gross floor area - Sellable floor area Development Value Development costs Infrastructure Investment

15 RAISE RESIDUAL LAND VALUE MODEL S 1 f M t GS 1 m RLV = C 1 + f D 1 + t I + t GC m 1 + t D C 1 + f D 1 + t I + t D e rp 1 p e r e rp 1+t L +f L S = sale/market price C = construction costs f M = fees, marketing/sales commissions m = profit margin/developer take f D = fees, design/engineering, inc. GST t I = tax, local infrastructure contributions t GC = tax, GST charged by suppliers t D = tax, development application r = interest rate on borrowings P = development period (in years) f L = fees, legals for land transaction = tax, land tax and stamp duty t L

16 RAISE RESIDUAL LAND VALUE MODEL Development Conditions: Basic Standard Median Standard High Standard Market price /sqm floor area House Median Range Construction Cost /sqm floor area $/sqm Basic standard finish Medium standard finish High standard finish House Semi Apartment Rawlinsons Australian Construction Handbook

17 RAISE TOOLKIT FUNCTIONALITY

18 RAISE FUNCTIONALITY

19 RAISE FUNCTIONALITY BREAKDOWN OF AUTOMATED LAND VALUE WITH CERTAIN VARIABLES DISTANCE TO SELECTED POINT OF INTEREST

20 VALUE UPLIFT FUNCTIONALITY CREATE NEW TRAIN STATIONS ON MAPS SELECT A PROPERTY VALUATION MODEL TO RUN THE VALUE UPLIFT SCENARIO

21 VALUE UPLIFT FUNCTIONALITY BREAKDOWN OF AUTOMATED LAND VALUE WITH CERTAIN VARIABLES DISTANCE TO SELECTED POINT OF INTEREST

22 CASE STUDY: PARRAMATTA LIGHT RAIL STAGE 1

23 CASE STUDY: SYDNEY METRO NORTH WEST

24 RAISE RESIDUAL LAND VALUE TOOL

25 LAND PLANNING ZONES

26 FLOOR SPACE RATIO (FSR)

27 MAXIMUM BUILDING HEIGHT

28 RESIDUAL LAND VALUE WITH EXISTING LEP RLV PREMIUM STANDARD DEVELOPMENT RLV MEDIAN STANDARD DEVELOPMENT RLV BASIC STANDARD DEVELOPMENT

29 REZONING TOOL FUNCTIONALITY SELECT THE RESIDUAL LAND VALUE MODEL TO RUN THE REZONING SCENARIO SELECT THE AREA TO BE REZONED INPUT THE NEW PLANNING CONTROL PARAMETERS

30 REZONING TOOL FUNCTIONALITY TOTAL AND PERCENTAGE RLV UPLIFT BY REZONING RLV UPLIFT AND PERCENTAGE RLV UPLIFT OF EACH LAND PARCEL

31 LANDCOM WORKSHOP 28TH JUNE 2018

32 LANDCOM WORKSHOP 15TH NOV 2018

33 CITYDATA PORTAL FOR RAISE

34 CONCLUDING THOUGHTS Digital planning tools need to be part of the smart city agenda. Big data can be used to plan for the accessible city. Rapid Analytics can be use for near real-time decision support for transport and land use change scenarios Data driven tools such as RAISE, should be used to support infrastructure sequencing and future city planning Training and Education there is a need train the next generation of city planners, policy-makers with skills to harness the power of location intelligence, big data, rapid analytics, digital planning tools..

35 Thank-you! Prof. Chris Pettit