Presentation - UrbanSim

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Presentation - UrbanSim

2Presentation Overview• Introduc)on • Expecta)ons • Findings – Visualiza)ons • Issues Iden)fied • Possible solu)ons we are pursuing • Some methodology & lessons learned


Change in Unit Price by Land Use Type, 2001-20404• Actual change in unit price by parcel dominated by decreases ($/SQFT)


Change in Unit Price by Land Use Type, 2001-20405• Actual change in unit price by parcel dominated by decreases ($/SQFT)


Change in Unit Price by Land Use Type, 2001-20406• Actual change in unit price by parcel dominated by decreases ($/SQFT)


Change in Unit Price by Land Use Type, 2001-20408• Actual change in unit price by parcel dominated by decreases ($/SQFT)


Change in Unit Price by Land Use Type, 2001-20409• Actual change in unit price by parcel dominated by decreases ($/SQFT)


Change in Unit Price, SF Parcels w/o Development10• Without development events means only the zone-­level variables change – esp accessibility that worsens over)me with conges)on


Change in Unit Price, SF Parcels w/o Development11• Without development events means only the zone-­level variables change – esp accessibility that worsens over)me with conges)on


Change in Unit Price, SF Parcels w/o Development12• Without development events means only the zone-­level variables change – esp accessibility that worsens over)me with conges)on


Difference in Unit Price from Median SF 2001 & 2040Year 2001: $108 Year 2040: $9213


Current REPM Specs – SF (T-stats and Coeff signs)14• More variables pushing ‘down’ than ‘up’


Current REPM Specs – SF (T-stats and Coeff signs)15• More variables pushing ‘down’ than ‘up’


Unit Price and Return on Investment161221. Construc)on Costs higher than Sale Price 2. Falling Unit_Price as SQFT, DU increase 3. But does it maber?


Unit Price and Return on Investment171. Construc)on Costs higher than Sale Price 2. Falling Unit_Price as SQFT, DU increase 3. But does it maber?


Yield relative to allowable maximum density18• Year 2040, 5853 parcels • DU Averages: 33 /acre on all, 37/acre with DU added, 48 / acre with only DU added


Results not quite expectations19• Price change during simula)on is: – Overwhelmingly nega)ve • Downward influence of zone-­‐level accessibility measures worsening with conges)on over )me – Lacks connec)on between demand / vacancy rate – Somewhat spa)ally counterintui)ve • Proposal valua)on = Low or no profitability – Proposal ROI constant – Market price versus construc)on price – Development built below capacity


Findings/Directions (Model Development)• Need for local prices to reflect demand (and demand to be more sensi)ve to price) – Add an assumed term for Vacancy Rate • This would have to be an assumed coefficient, since there are no data available with which to es)mate the coefficient. – Account for endogeneity with latent demand control func)on • Computed as the sum of the loca)on choice probabili)es of agents in the HLCM and ELCM models – Replace with eventual market-­‐clearing model • Itera)ve solu)on with the HLCM and ELCM models to clear the market. Prices would increase in submarkets where the Latent Demand exceeds Supply, and decrease in those with the reverse condi)on • Could be implemented with sub-­‐markets 20


Findings/Directions (Model Refinement)21• REPM and Choice Models – Improve REPM structure/specifica)on • Including accessibility measures – Improve price variables in HLCM & ELCM • IE ‘affordability of housing choice’ – Incorporate regional macroeconomic variables? • Developer Model – Troubleshoo)ng: proposal value reference error – Review construc)on costs assump)ons


Model Diagnostic Testing at PSRCComparing & evaluating modelsExplanatory variables differ, response variable the same • Theory, Ockham’s razor • Internal model strength: RMSE (std err of es)mate), residual tests – normality, linearity, homoscedas)city • Direct comparisons: AIC, R 2 , log-­‐likelihood Response variable differs in scale/unit • Theory (+ do explanatory vbls need transforma)on?) • RMSE & residual tests s)ll important • Use predicted values to calculate results at a common scale/unit; then compare predicted vs. actual for each model Examine results in recognizable terms: $, not ln($)


Model Diagnostic Testing at PSRCComparing & evaluating modelsExample: REPM $ per bldg_sqD, prcl_sqD, or parcel? • Explanatory variables held constant • Predicted value calculated to common scale—parcel CorrelaGons Model unitExplained Variationln($/bldg_sqft) 15%ln($/prcl_sqft) 46%ln($/parcel) (64%+)• You can also model actual as func)on of predicted to get a full suite of compara)ve sta)s)cs: AIC, RMSE, log-­likelihood, etc.


Model Diagnostic Testing at PSRCUnit Price in Sea CBD (year, land use type?)24$/building_sqft: $5 to $607 within two blocks


Model Diagnostic Testing at PSRCNormalized UP = Parcel SQFT instead of Bldg25$/parcel_sqft: $91 to $724 within two blocks


Model Diagnostic Testing at PSRCComparing & evaluating modelsTechnical issues: • Need sampling weights when n differs from recordset • Adjustment for any log-­‐transformed response variable – Underes)ma)on if only exponen)ated PracGcal issues: • Unit needs to be translated for use in choice models – Build expression model (template), but needs linkages


Lessons Learned27• Es)ma)on problems versus Simula)on problems – What emerges over a range of years (unit_price, SF bias) • Predefining expecta)ons to evaluate against • Ques)on everything – Where there’s smoke there’s fire


Puget Sound Regional Council:Michael Jensen, Matthew Kitchen, PeterCaballero, Mark Simonson, Rebeccah Maskinand Hana Sevcikova

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