logging in or signing up Designated Forestland Presentation - UW GIS Certificate Program trevorwong Download Post to : URL : Related Presentations : Let's Connect Share Add to Flag Embed Email Send to Blogs and Networks Add to Channel Copy embed code: Embed: Flash iPad Dynamic Copy Does not support media & animations Automatically changes to Flash or non-Flash embed WordPress Embed Customize Embed URL: Copy Thumbnail: Copy The presentation is successfully added In Your Favorites. Views: 87 Category: Education License: All Rights Reserved Like it (0) Dislike it (0) Added: June 26, 2011 This Presentation is Public Favorites: 0 Presentation Description Student Project for University of Washington GIS Certificate Program -- Students: Robbie Andrus, Trevor Wong, UW GIS Certificate Program -- Project Sponsor: Luke Rogers, UW College of the Environment -- 2nd Place - Dick Thomas Student Presentation Competition, 2011 Washington GIS Conference (WAURISA) -- Final Presentation to Certificate Class 6 June 2011 Comments Posting comment... Premium member Presentation Transcript Estimating Market Value of Designated Forestland: Estimating Market Value of Designated Forestland Sponsor: Luke Rogers, UW College of the Environment Students: Robbie Andrus, Trevor Wong University of Washington GIS Certificate Program Spring 2011Project Description: Project Description Designated Forestland (DFL) – Created to maintain forested lands for timber and ecological benefit – DFL = 20+ acres of timber + no improvements – Average = Operability + Land Grade Problem: "Designated forestland” (DFL) is currently valued using a table of statewide averages from the Department of Revenue. – No individual assessment – Only forest lands not required to be appraised by county assessor Goal: Develop model to estimate market sales price of DFL lands. – Model should be based on land’s “highest and best use” 2 | 15Risks & Deliverables: Risks & Deliverables Risks Experiment - An educated trial and error process Is there any relationship at all between the value of one assessed forest land parcel vs. another? Assessor’s true market value Compare to assessor’s true market value for another source Deliverables Model to assess market value of parcels in DFL program (R 2 > .75) Cartographic examples of model and model process A final presentation Documentation of model and project workflow 2 | 15Current DFL Assessment: Current DFL Assessment 3 | 15 Parcel comparison DFL non-DFL ID P42244 P42236 Zone IF IF Site Class 4 3 UGA Dist 3 2 Rds Dist 2 2 Slope 12 13 Acres 160 158.49 TMV $24,000 $319,300 TMV/Acre $150 $2,021Slide 5: 4 | 15Skagit Zoning + DFL: Skagit Zoning + DFL Parcel Stats: Total Parcels in Skagit Co.: 78,925 DFL Parcels: 1372 - only 137 w/o package deals Skagit Zoning Federal Lands UGA’s DFL Timberlands 5 | 15Slide 7: Model 1: Attribute Model Skagit Zoning Federal Lands UGA’s DFL Timberlands 6 | 15Slide 8: 7 | 15Slide 9: Model 1.5 Forest Cover Attribute Model Forest Cover Model 8 | 15 Source: National Land Cover Dataset Restrictions: Covered by forest No public lands Near DFL ParcelsSlide 10: Model 2 An investigation of neighboring parcels 9 | 15 Restrictions: New study area No public lands No DFL Parcels No UGASlide 11: Model 2: Total Market Value 10 | 15Slide 12: Euclidean Allocation 11 | 15Slide 13: Focal Mean (1 mi) 12 | 15Slide 14: Model 2: Total Market Value 10 | 15Slide 15: Interpolation: IDW and Krige 13 | 15Slide 16: 14 | 15Best Effort in R: Best Effort in R Summary of the Linear Regression model (built using lm): Call: lm(formula = RLG_Mod_Wgt_Ac ~ ., data = crs$dataset[crs$train, c(crs$input, crs$target)]) Residuals: Min 1Q Median 3Q Max -2.16862 -0.48981 -0.03274 0.56682 2.40408 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 9.53120805 0.44381397 21.476 < 2e-16 *** IDW3 0.00018021 0.00004139 4.354 3.03e-05 *** RLG_TOTAL_AREA -0.60002975 0.08794881 -6.822 5.23e-10 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.9183 on 109 degrees of freedom Multiple R-squared: 0.3757, Adjusted R-squared: 0.3642 F-statistic: 32.79 on 2 and 109 DF, p-value: 7.086e-12 2 | 15Slide 18: 18Slide 19: Model 2.1 Forest Cover Neighbors Model 8 | 15 Source: National Land Cover Dataset Restrictions: Revised study Area 50% Forest Cover No public lands Near DFL Parcels 131 Forested ParcelsSlide 20: 20Slide 21: Model 3: Revised Study Area TMV vs. TMV - Focal 10 | 15 Restrictions: Smaller Study Area ¼ mi UGA 1 mi DFL TMV $1,000-$18,000Slide 22: Model 3: Revised Study Area TMV vs. TMV - Interpolation 10 | 15 Restrictions: Smaller Study Area ¼ mi UGA 1 mi DFL TMV $2,000-$15,000 Land use restrictionSlide 23: 23Slide 24: The Data Dilemma Large Sample Size: Include many parcels Model 2.0 / 3.0 Parcels included may not be applicable e.g. Trailer Park Small Sample Size: Exclude many parcels Model 2.1 / 3.1 Significantly reduced sample size Lots of statistical noise High margin of error 15 | 15 County DFL Parcels Forest Cover Parcels (Model 1.5, 2.1) Privately Held Parcels (Model 2.0, 3.0, 3.1 ) Skagit 137 157 1800 Kitsap 231 N/A N/A Okanogan 108 N/A N/ASlide 25: 25 Project DocumentationSlide 26: Deliverable Comparison Original vs. Reality Model to assess market value of parcels in DFL program (R 2 > .75) 1.0 - Attribute Model (R 2 = .02) 1.5 - Forest Cover Model (R 2 = .03) 2.0 - Nearest Neighbor Model (R 2 = .04) 2.1 – Forest Cover Nearest Neighbor Model (R 2 = .05) 3.0 – Revised Nearest Neighbor Model (R 2 = .41) Cartographic examples of model and model process Successfully completed A final presentation Here it is!! Documentation of model and project workflow Check!! 15 | 15Slide 27: Summary Conclusions: Currently accept Null Hypothesis No relationship Why? Deficiencies in data Few comparable sales Forestland-to-Forestland Transactions do not generate usable sales data Perhaps we could know more about Statistics Goals Achieved: Data: Acquisition, Import, & Manipulation ModelBuilder Build Troubleshoot Output Cartographic Representation 15 | 15Slide 28: Methodology Difference Robbie : Using DFL Sales Prices: Historical Sales Data + DFL Attributes (size, location) = Predicted Market Value Trevor : Using TMV Assessed Value: Annually Assessed TMV Market Values + TMV Attributes (size, location) = Predicted Market Value Best R 2 = 0.108 Best R 2 = 0.112Slide 29: Thank You Sponsor: Luke Rogers, Our Instructors Suggestions ? Questions? You do not have the permission to view this presentation. In order to view it, please contact the author of the presentation.