SANDAG Inaccuracies November 2017 Proprietary Confidential Using Civita

SANDAG Inaccuracies November 2017 Proprietary & Confidential

Using Civita to Estimate Soccer. City Traffic – SANDAG DATA ONLY Soccer. City is More Mixed Use… Trips Using SANDAG Data Only The SANDAG estimated 170% increase is provably incorrect 57% …and 40% bigger Can perfectly grow Civita to calculate Soccer. City trips – before transit and mixed use benefits. Proves inaccuracy of SANDAG model 2 Proprietary & Confidential

Why is the model so wrong? Almost every dial turned against us: • 13% higher population of employed individuals than multi-family average and directly comparable property • IMPACT: More daily trips • 44% higher job density for our site per SQF than surrounding comparable land uses • IMPACT: More daily trips • 2/3 less walking from our site than surrounding areas, despite next door retail center and walking trail connecting the river • IMPACT: More daily trips • 1/5 of the trolley use predicted by surrounding sites (and 1/3 of sites with a 10 minute walk to transit) notwithstanding 3 trolley stop access from our site • IMPACT: More daily trips • 13% more single car usage at our site compared to SANDAG estimates at most comparable mixed use site – all coming out of 3 passenger car trips • IMPACT: More daily trips These changes amountallowed to a substantial erroranalytical in trip rates estimated by the –model. How are politics to outweigh integrity at SANDAG $200 m stadium with premier viewing experience and flexible seating from 21, 000 – 32, 000 Again – this is SANDAG data vs. SANDAG estimates. particularly now? Soccer. City treated differently 3 Proprietary & Confidential

Populations Are Assumed to Be Different – With What Basis? Employment Density in Multi-Family Residents Only 1. 5 miles 61% of Residents Employed 54% of Residents Employed Population unlikely to be so different over such a short distance in Mission Valley. Again – this is SANDAG estimates vs. SANDAG estimates. Soccer. City treated differently 4 Proprietary & Confidential

Job Density Per SQF in SC Much Higher Than Surrounding Sites Job Density Buildup – SANDAG Data Land Use MGRA Job Density Comparison Metric Amount Jobs SC # Implied Jobs 773 2, 400, 000 4, 757 Office 6104 SQF 390, 000 MF 6109 Units 190 0 4, 800 0 Retail 6166 SQF 375, 000 1191 740, 000 2, 350 Hotel 5856 Rooms 961 325 450 152 Park 2752 Acres 87 14 61. 4 10 Total SANDAG output is 44% higher than expected from nearby sites 7, 269 SANDAG Assigned to Soccer. City 10, 480 Parks – 45 x Higher than Other Parks MGRA 2752 2756 2765 22551 3576 3630 3629 Average Soccer. City vs. Average Park Morley Field Balboa Park NTC Park Robb Field Mission Bay GC MGRA Acres 87 101 84 43 63 99 71 78 61 Jobs 14 0 1 0 3 41 7 9 290 Jobs/Acre 0. 16 0. 00 0. 01 0. 00 0. 05 0. 41 0. 10 4. 72 45. 1 x 5 SANDAG assigned 44% more job density overall. SANDAG assigned 45 x the job density to Soccer. City Parks vs. their own data. SANDAG assigned Soccer. City 32% of the number of employees in the entire City of San Diego Parks & Rec Department. Proprietary & Confidential

Trolley Usage Significantly Low For Soccer. City has Trolley Station Onsite Trolley Use – SANDAG & MTS Data SANDAG output is 78% lower than expected from nearby sites and MTS data 10 Min Walk 5 Min Walk MTS Ridership Survey Top 3 trolley use sites average a 9 minute walk from a station and 9% trolley usage. Despite 100% of Soccer. City being within a 10 min walk of a station and 45% within a 5 min walk of a station, SANDAG assigned 3. 6% trolley usage to Soccer. City, lower than sites on the other side of Friars Road. 6 Proprietary & Confidential

SANDAG Estimates Higher Usage at Other MV Sites Mission Valley Office Mission Valley Residential Soccer. City – Much Closer to Trolley SANDAG Assumed Trolley Use 10 Min Walk 5 Min Walk 7 MV Office 9. 9% MV Resi 8. 2% Soccer. City 3. 6% Proprietary & Confidential

Walk Allocation for Soccer. City Well Below Mission Valley Sites Nearby Sites – Much Higher Walk Splits Walk % Comp Avg = 14. 5% SC 1 3 2 4 MGRA Site Soccer. City was assigned a walk split of 4. 6%, 69% lower than the average of a comparable set of sites in Mission Valley. Even the storage tanks just north of Soccer. City have a higher walk split. Walk % 6168 River Run Resi 15. 0% 6180 Rio San Diego Plaza Office 16. 0% 6200 Rio Vista Retail 12. 7% 6184 Hazard Center Mixed Use 14. 5% Average Soccer. City 8 Use 14. 5% 4. 6% Proprietary & Confidential

Use Of Cars Assumed To Be Different – Again, With What Basis? Auto Trips by Passenger Load – Soccer. City vs. Mixed Use Comp 1 Passenger (SOV) 2 Passenger (HOV 2) 3+ Passenger (HOV 3) SANDAG output allocates 12% more trips to least efficient mode at the expense of most efficient mode Land Use Soccer. City Hazard Center Redevelopment Housing YES Office YES Retail YES Hotel YES Park YES NO When compared to a mixed-use development with similar land uses in the same area, SANDAG assigned 12% more trips to single passenger rides and 12% less trips to 3+ passenger rides, drastically increasing ADT. 9 Proprietary & Confidential

Why Not Just Fix The Model? – Apparently Politics… • We have been trying. We have requested a significant amount of information to help inform SANDAG of errors in our model vs surrounding • Politics is now getting in the way – PER SANDAG’S OWN MODELLER • When asked why there will not be a separate technical meeting (to ensure accuracy), he responded “Politics have forced our hand” How are politics allowed to outweigh analytical integrity at SANDAG – particularly now? $200 m stadium with premier viewing experience and flexible seating from 21, 000 – 32, 000 10 Proprietary & Confidential