Environmentally Impaired Property Transaction Analysis Combining Decision Trees and Monte Carlo Simulation Timothy Havranek and Poh Boon Ung October 2007

Agenda Background Information Modeling Process Decision Tree Results

Project Background and Objective Estimate probable remediation costs Work in support of purchase negotiations of an environmentally impaired property Client is a property development company: – Would acquire liability for historical environmental impacts – Seeking strategic plan for least cost remediation in light of uncertainties – Planned to use estimate in support of lowering purchase offer Current property owner had filed Chapter 11 bankruptcy procedures which complicated the negotiation process

Site Background Property was former metals processing plant Soil, groundwater, surface water, and wetlands impacted by lead, arsenic and chromium Groundwater also impacted by petroleum hydrocarbons Property is over 150 acres in size and located near a major river Property could be redeveloped for industrial, commercial, or municipal use Current owner under a consent order to perform environmental cleanup

Decision Analysis Process Decisi on Proces s Framin g Modelin g Evaluation & Sensitivity Analysis Decisio n Action Plan

Framing Meeting for QDA Shared Vision of Path Forward Site Background Stakeholder Analysis Multi-Criteria Analysis Current Policies Decisions & Choices Chance Events StrategiesAction Items

Potential Remedial Alternatives

Model Structure Capital and Operations & Maintenance (O&M) costs were estimated using standard engineering costs forms – Pert Distributions used to estimate uncertainties in quantities, unit prices, installation year, and O&M durations Engineering costs sheets were linked to 30-year cash flow model for each remedial technique Net Present Value for each remedial technique linked decision tree and Monte Carlo simulation model

Benefits of Decision Tree Visual representation of available choices Valuable communication tool Helps organize alternatives Provides a working map of the project strategy

Estimating Individual Cost Components

Root of Decision Tree: Groundwater and Soil Components

Middle Portion of Tree: Soil and Surface Water Remediation

Tree Terminal Nodes: Wetland Issues

PrecisionTree Settings

Net Present Value: Frequency Histogram * Distribution based on values of one sampled path per iteration and decisions follow current optimal path

Net Present Value: Risk Profile * Distribution based on values of one sampled path per iteration and decisions follow current optimal path

Output Statistics (cost in millions) * Results based on values of one sampled path per iteration and decisions follow current optimal path

Net Present Value: Frequency Histogram * Distribution based on values of one sampled path per iteration and decisions may change (based on expected values)

Output Statistics (cost in millions) * Results based on values of one sampled path per iteration and decisions may change (based on expected values)

Net Present Value: Frequency Histogram * Distribution based on expected value of model per iteration and decisions follow current optimal path

Output Statistics (cost in millions) * Results based on expected value of model per iteration and decisions follow current optimal path

Sensitivity Analysis on Impermeable Wall Failure Probability

Two Way Sensitivity Analysis on Probabilities

Results and Conclusions Important to apply appropriate settings in modeling process – Internal management and understanding – Negotiation process Sensitivity analysis useful to identify decision break-points Results are being used in negotiations process; client is actively using the results in negotiations to buy the site Decision tree analysis assisted in: – Identifying optimum strategy – Communicating path forward – Determining the response and potential effects to chance outcomes Combination of Monte Carlo simulation and decision tree analysis provides additional insights into range of potential outcomes