Emerging technologies in the REIT space Facilitator: Jim Deutsch, Ernst & Young LLP

Solution Set – Session A

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Disclaimer

► EY refers to the global organization, and may refer to one or more of the member firms of Ernst & Young Global Limited, each of which is a separate legal entity. Ernst & Young LLP is a client-serving member firm of Ernst & Young Global Limited operating in the U.S.

► This presentation is © 2015 Ernst & Young LLP. All rights reserved. No part of this document may be reproduced, transmitted or otherwise distributed in any form or by any means, electronic or mechanical, including by photocopying, facsimile transmission, recording, rekeying, or using any information storage and retrieval system, without written permission from Ernst & Young LLP. Any reproduction, transmission or distribution of this form or any of the material herein is prohibited and is in violation of U.S. and international law. Ernst & Young LLP expressly disclaims any liability in connection with use of this presentation or its contents by any third party.

► Views expressed in this presentation are those of the speakers and do not necessarily represent the views of Ernst & Young LLP.

► This presentation is provided solely for the purpose of enhancing knowledge on tax matters. It does not provide tax advice to any taxpayer because it does not take into account any specific taxpayer’s facts and circumstances

► These slides are for educational purposes only and are not intended, and should not be relied upon, as accounting advice.

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► Define the business questions and capabilities – identify the key strategic questions or themes that the company wants to get answers to but has not had the capability to do historically

► Define the vision and roadmap – holistically articulate a vision, what constitutes success, and the high level roadmap to guide the journey

► Conscious focus on data management, data quality and governance – define and enable robust data management capabilities that support data quality

► Embrace an iterative delivery methodology – avoid a “boil the ocean” delivery approach and follow a proven iterative delivery methodology

► Assign the right people with the right skills with the right oversight – establish an appropriate delivery structure and then place the right people with right skills into the right roles and making it a priority

► Focus on smart technology standardization – standardize on industry relevant technologies

Successful and sustainably analytics programs are enabled through a business-led, IT-driven, joint accountability approach with the right level of executive and organizational commitment throughout the journey.

Guiding principles and success factors for reporting and analytics programs Based on our experience, the following are key items that clients must get right in order to achieve success within their reporting and analytics environment.

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Current industry scenario

► Real estate industry leaders see the competitive advantage that a leading business intelligence and analytics capability can provide. However, this operational reporting and analytics capability is not typically addressed by the leading Business Intelligence (BI) software vendors.

► Many clients still struggle to embed analytics into operational decisions in a systematic and repeatable way, often resulting in clients not realizing the full value from their business intelligence & analytics platform and capability

► Organizations spends too much time and effort delivering sourcing and structuring data instead of generating actionable insights

Current Industry Trend

► Existing investments have been largely focused on delivering historical reporting and insights driven from silo repositories of internal structured data generated from across operations.

► The industry is challenged to deal with legacy P&L structures, “walled” data environments and an extreme product-centric mindset, which has impeded the successful deployment and value realized by advanced analytics.

► Data quality and integrity issues makes it difficult to trust insights from data analysis

► Absence of skilled analytics professionals limit the growth of analytics capabilities

Root Causes

Ernst & Young LLP has deployed its core data strategy competencies and analytic capabilities to create a framework that helps clients understand their current limitations, focus on the gaps, and quickly move to be a data driven organization

How Ernst & Young LLP can help

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Real estate – sample key performance indicators (KPIs)

• Annual Income Tests - At least 75 percent of the REIT's annual gross income must be from real estate-related income such as rents from real property and interest on obligations secured by mortgages on real property

• Quarterly Asset Tests - at least 75 percent of a REIT's assets must consist of real estate assets such as real property or loans secured by real property

• Distribution Tests - at least 90 percent of REIT’s taxable income must be distributed

Real Estate Investment Trust Qualifications

• Turn Cost (Switching Cost) – Total cost incurred as a result of changing renters

• Rent-to-income Ratio – (Tenant Rent– Property Expense)/Tenant Rent

• Schedule Performance Index - budgeted cost of work performed / budgeted cost of work scheduled

• Cost Performance Index - budgeted cost of work performed / actual cost of work performed

• Rent Collected Rate – received rent / total rent expected

Financial Performance

• Occupancy Rate (SQFT)- # of units occupied / # of units owned

• Leased Rate (SQFT) - # of units leased / # of units owned

• Vacancy Rate (SQFT) - # of units vacant / # of units owned

• Gross Leasing vs. Short Term Starts (SQFT) - Long term vs. short term leased SQFT

• Rollover vs. Short Term Ends (SQFT) – Long term vs. short term expiring leased SQFT

• Rent Roll - scheduled and amount of rent due from each tenant

• Renewal Conversion – percentage of existing leasers who renew leases

• Stabilization – Occupancy rate / total units

Statistical Performance

The Real Estate industry has common Key Performance Indicators leveraged to understand financial performance, statistical performance as well as REIT (Real Estate Investment Trust) qualifications. Below are some samples

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► Value is perceived in the use of dashboards and scorecards to drive business decision making

► A central access point of information ► Tools that are efficiently utilized and embraced by the end-

user ► Well defined common tools

Analytics and reporting framework

Performance measurement

Source and repository technology

Effective Business Intelligence results require that each layer of this framework be addressed.

► An integrated data warehouse within the organization ► Defined non-redundant systems throughout the

organization ► Multiple source systems efficiently integrated through the

extraction, transformation, load (ETL) process ► Non-manual process and significantly automated data

feeds

This layer will provide executives, management, analysts, and operations to effectively view and use information to make decisions

The tools and processes within this layer will be standardized and improve the overall value of the data within the organization

The most complex of layers, data feeds will go through the extract / transform / load/apply process and should be automated. Data warehouse / Data marts will provide effective decision support systems

Decision support / analytics ► Integrated tools and standardized processes ► Readily available tools with key advantages for the

organization ► Leading practice tools adequate for decision support purposes

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*All source and target applications are examples only; the sample architecture allows for various types of source applications and customized downstream reporting options

The architecture below depicts an approach Ernst & Young LLP is developing. The Ernst & Young LLP Real Estate community discovered the majority of KPI leveraged across the ERP (transaction system) and EPM (enterprise performance management) applications are standard within REIT organizations.

Source Applications

Capital Planning

Actuals

Forecast/ Budget

Data Staging Reporting Data

Mart

ETL

*JD Edwards, Yardi Systems

*Argus, RPM, Custom Apps

*JD Edwards, Custom Apps

Operational Analysis

Relational Dashboarding

ETL

Financial Management

Data Staging and Warehouse Reporting

Property Evaluations

*Realogic, Custom Apps

Metadata Management

ETL Master Data Management

Real estate representative architecture

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Key

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Manual, push information model Automated, self-service, pull information model

► Data governance guidelines are established but mostly driven by IT on a project by project basis

► Information is used as an internal tool for business unit decision making

► Business Stewards exist and confirm consistent usage/ understanding, information quality, and transparency across the organization

► BI initiatives are funded, prioritized and executed against an overall enterprise strategy

► Governance is automated and monitored throughout the enterprise to confirm enforcement

► Information is considered an enterprise asset

► Formal information governance program and organization in place and operationalized

► Policies are approved by enterprise

► Lack of information, governance principles or guidelines

► Value of information is not well understood or measured

► No dedicated governance steering mechanism is established

► Information/data quality is not meeting business needs

► Limited data quality and control exist at point of data storage/capture

► Information/data quality is generally considered proven by business

► Data quality is a principal function and major responsibility is held within the business and IT

► Information/data quality is trusted by the business

► All critical business data is flagged with quality indicators to identify known problems

► Information/data quality is considered by business as acceptable

► Major data sources and quality issues are documented for resolution

► Data quality of Information is unknown ► Data quality and control issues are

addressed on an ad hoc basis

► High level of efforts are required to compile information from inputs (process, systems, etc.)

► Standard reports, views and data are available via reporting tools

► Reporting BI tools are deployed in a strategic fashion and support Executive views, scorecards and drill downs to associated analysis

► BI applications are available based on business need

► Numerous reporting tools are in use across the organization

► Heavy reliance on ad-hoc reporting ► Reporting applications are siloed and

deployed in originating business unit

► Strategic data management processes, methodologies, and model are in place

► Data management automation strategies are starting to be deployed

► Data Management processes are fully optimized and automated

► Meta, Reference, Master data are all managed and automated enterprise wide

► Data Services are available for applications

► Standard data management processes, methodologies, and model are strategically planned for specific distribution for BI and analytical needs

► No data management processes, methodologies, models, or data movement strategies exist

► Enterprise Data can be accessed by all users in fully integrated system and web enabled BI toolsets with Mobil capabilities

► Predictive, “Big Data” and embedded analytics are leveraged

► High level of automation and integration between operational and analytical environments

► Infrastructure is optimized for enhanced performance

► Services based architecture employed to respond to changing needs efficiently

► Cloud based architectures employed where appropriate.

► “Big Data” capabilities

► Standardized environment with infrastructure tuned for specific informational/analytic needs and performance

► No Architecture or infrastructure standards exist for business intelligence and data warehousing

► Very costly to manage

► Dedicated technology infrastructure and some standardization of tools exist for supporting BI

► Data Management processes are inconsistent and require exhaustive effort by IT

► Data Management is mostly an IT effort and are ad-hoc based on project

► BI Reporting and analytical output is increasingly directed towards improving business (e.g., Enterprise standard KPIs, and associated analysis)

► Enterprise Reporting applications allow personalized reporting, analytics and visualizations

Maturity model

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Sample maturity model questions

Reporting and analytics Data governance Data quality Data Management Architecture

Def

init

ion

Reporting and Analytics is the orchestration of processes, architectures and tools to transform information and data into meaningful and insightful information in a user-friendly way

Data governance is the orchestration of an organizations resources, processes and technology to manage the organization's data to improve data quality, reduce data risks, reduce data management costs and improve transparency of data ownership across the organization

Data Quality is the measure of the enterprise’s ability to trust its data, and provides the support for making informed business decisions. It contributes to build confidence in data accuracy, availability, and reliability

The development and execution of architectures, policies, practices and procedures that properly manage the full data lifecycle needs of an enterprise

Data Architecture composes the technology, models, applications, policies, rules and standards used to collect, structure, store, arrange, integrate, analyze and disseminate information within an enterprise

Sam

ple

ques

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► How many reporting tools are in use in your organization and why?

► Is there a heavy reliance on user maintained spreadsheets, databases or other tools/systems?

► Are your reporting applications business unit specific?

► How much manual effort goes into producing the current scorecards?

► Have reporting roles been defined and profiled?

► Does your organization have a governance steering committee developed and is your data interest represented?

► Have you used the data governance committee and if so what for?

► Does your organization have data governance guidelines on an enterprise or project basis?

► Is governance lead by IT or the business?

► Does the organizational governance program identify and utilize business owners and stewards across the organization and data sets?

► How do you measure data quality (metrics, quality dashboard/ reports, other)?

► If quality metrics are in place, what is the frequency of reporting and who is the audience?

► How are data quality issues addressed?

► Who owns the data you utilize?

► How do you know quality issues are addressed?

► How are data hierarchies managed?

► Are Data Management efforts mostly managed by IT or the business?

► Is there a standardized process, method or models around data management?

► Is there a single view of master data across the organization?

► Is there a Master Data Management tool in place?

► Does the current environment support enterprise, BU, or data specific business needs?

► Are there many standalone business maintained databases/user maintain spreadsheets within the business? If so why?

► Are the BI tools standard across the organization?

► How many resource does it take to support the current technical architecture?

► Do you have a future state BI/Data architecture strategy?

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Our methods and tools

Strategy and roadmap development

Inpu

ts a

nd a

ccel

erat

ors

Client inputs

Current state Maturity assessment

Target state Prioritization

Roadmap

BI Framework Current State

Capabilities and Models

Gaps

BI Strategy and Roadmap Finalized Architectures

Ernst & Young LLP Reference Models

Ernst & Young LLP BI Maturity Model

Stakeholder Interviews

Documentation

Stakeholder Workshops

Heat Maps and Models Assessment

Key Opportunities Opportunity Prioritization

Our structured approach to the development of BI strategies and roadmaps leverages a number of inputs, accelerators and templates.

Industry/Ernst & Young LLP Leading

Practice

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A standardized technical solution allows the freedom to make business decisions without the unknown IT implementation duration and effort.

Business Decisions

• Acquisition

• Divestitures

• Upgraded/new software selection

• Portfolio shifts

• Loan transfers

• Others…

Analytic Decisions

• Advanced analytics

• Dashboarding

• Government Regulations

• New KPIs

• Executive requests

• Others…

Repeatable “Plug & Play” Solution

Define source data required Analyze gaps Update reporting data mart (RDM) fields/tables Incorporate KPI into reports Test and deploy

Sample Repeatable Solution Processes Quick Wins – Example New KPI Request

Gather portfolio adjustments Incorporate source updates Flow data and metadata Execute impacted reports/dashboards Test and Deploy

Intermediate – Portfolio Shifts

Foundational – Acquisition

Fit/Gap on data entity changes Meta-data process and data conversion update Consolidation adjustments Reporting updates Test and Deploy

Source Applications

Capital Planning

Actuals

Forecast/Budget

DataStaging

Reporting Data Mart

ETL

*JD Edwards,Yardi Systems

*Argus, RPM, Custom Apps

*JD Edwards,Custom Apps

Operational Analysis

Relational Dashboarding

ETL

Financial Management

Data Staging and Warehouse Reporting

Property Evaluations

*Realogic,Custom Apps

Metadata Management

ETLMaster Data Management

Streamlining real estate implementations

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Real estate architecture estimation

General Questions ► What is the timeline for implementation of the repeatable architecture? ► What are the major data sources for reporting? ► Are multiple currency leveraged for your organization? ► Do end users make manual entries to complete certain reports; if yes what types? ► Are there common load issues for data? Is data generally aligned across work streams?

ERP Questions ► What current tool(s) are leveraged for transactional layer (e.g., JD Edwards, Yardi, custom)? ► What are the chart of account requirements (e.g., multiple close periods, multiple reporting charts)? ► Are there custom table Structure(s) for transactional layer?

Planning/Budgeting/Forecasting Questions ► What planning cycles are currently reported against (e.g., Monthly Forecast, Annual Budget, Long Term Business Plan)? ► Are tools in place to enable accurate forecasting (e.g., Hyperion Planning, BPC or boutique/home grown)? ► How many resources use your system for forecasting/budgeting? ► What are some of the capital expenditures you forecast? ► Is property evaluation currently included within the Forecasting process (e.g., Argus, Real Property Metrics)? ► Are variance calculations leveraged to compare budgeting consistency; if so what types?

Reporting Questions ► What are the key performance indicators leverage for reporting? ► Does the current organization have key measures outside of the standard real estate metrics reported out? ► What are the differences between the external and internal reporting requirements? ► What is the frequency in which reports are generated? ► How often is data refreshed within the various reports? ► What tools are currently leveraged for reporting (e.g., custom dashboards, financial reports)?

Data Governance Questions ► Where is metadata currently managed (e.g., ERP application, metadata management tool, manual)? ► Is there a current process for metadata additions and or updates? ► Is a structure in place for data and or metadata continuity across applications and departments?

When implementing an architecture an initial set of questions can be leveraged to properly scope the time and effort: