AEW Global Focused Real Estate Fund Class C · NASDAQ:NRCFX
AEW Global Focused Real Estate Fund Class C — AEW — AEW New Data in Real Estate Lab, Frankfurt 2019
68 slides from AEW Global Focused Real Estate Fund Class C's conference presentation, Feb 12, 2019
Slide 1: New Data in Real Estate Lab
NEW DATA IN REAL ESTATE LAB FRANKFURT 12th FEBRUARY 2019 Focused on the Future of Real Estate 1 FOCUSED ON THE FUTURE OF THE REAL ESTATE
Slide 2: AEW New Data in Real Estate Lab
AEW NEW DATA IN REAL ESTATE LAB 12TH FEBRUARY 2019, FRANKFURT 10:30 Registration & Welcome 11:00 Peter Papadakos of Green Street Advisors: “Granular asking rent and quality driven valuation modelling for listed German multi-family residential companies” 11:40 Ralf Koschny of BulwienGESA and Norman M
Slide 3: German Residential Listed PropCos: Valuation Deduction
Definitive Leaders in Real Estate Research for Over 30 Years Definitive Leaders in Real Estate Research for over 30 years German Residential Listed PropCos: Valuation Deduction AEW New Data in Real Estate Lab | February 12, 2019 Peter Papadakos, Managing Director – Research Dave Bragg, Managing Dire
Slide 4: About Green Street Advisors
About Green Street Advisors Founded in 1985, Green Street Advisors is a leading independent research and advisory firm concentrating on the commercial real estate industry in North America and Europe. Additional information on the firm is available at www.greenstreetadvisors.com, or sign up for Gree
Slide 5: The Green Street Difference
The Green Street Difference Time-tested Approach to Valuation RESEARCH PRODUCTS How Real estate NAV-BASED METHODOLOGY and REITS are priced versus Underlying value of real estate capital market alternatives Green Street’s NOI, cap rates, and NAV estimates are the industry standard Requires seri
Slide 6: Mining Real-Time Residential Market Rental Data
Mining Real-Time Residential Market Rental Data… Green Street’s Methodology ...of Collecting, Packaging, Assessing & Presenting German Residential Rental Data What is Web Aggregation? Our programmed bot runs through the data sources, i.e. websites, and gathers the data it has been asked for, then it
Slide 7: Berlin Case Study: Mapping City-Wide Asking Rents
Berlin Case Study: Mapping City-Wide Asking Rents… Berlin City-Wide Asking Rents… … Versus PropCos’ Exposure to Berlin Postcodes Source: Tableau, Green Street Advisors ©2019, Green Street Advisors, LLC. Use of this report is subject to the Terms of Use listed at the end of the report.
Slide 8: Berlin Case Study: Asking Rents as Proxies of Postcode Location
Berlin Case Study (cont’d): …As Proxies of Postcode Location Example: Green Street Attribution of Spot Multiples Based on Asking Rents per Postcode Berlin City-Wide Asking Rents & In-Place Rent Multiples With thousands of data mined we can plot a Utilising Rents as Proxy of Postcode Quality 1 distri
Slide 9: Berlin Case Study: Spot Valuation
Berlin Case Study (cont’d): …And Spot Valuation Average Implied … Portfolio Market Asking Rent (1) (per Green Street’s … Portfolio … Valuation Analysis) Quality Multiple € 10.6/m2 B 29.3x € 11.1/m2 B 29.7x (1) Weighted by number of units. Source: Tableau, Green Street Advisors ©2019, Green Street Ad
Slide 10: Residential Asking Rents: Today vs. 1 Yr Ago
Residential Asking Rents: Today vs. 1 Yr Ago Top-3 vs. Bottom-3 Cities: Change in Asking Rents vs. 1 Yr Ago Asking Residential Rents as of End-18 (delta vs. 1 Yr Ago) Rent/sqm '18 Rent/sqm '17 € 16.3 € 5.8 +10% € 14.6 +1% +9% € 5.8 € 14.9 € 13.3 € 5.4 +1% € 5.4 € 5.1 € 6.1 +1% +11% € 5.0 € 5.5 Rosto
Slide 11: Green Street's Disclosure Information
Green Street’s Disclosure Information Management of Conflicts of Interest: Conflicts of interest can seriously impinge the ability of analysts to do their job, and investors should demand unbiased research. In that spirit, Green Street adheres to the following policies regarding conflicts of interes
Slide 12: Asset Check
ASSET CHECK AEW DATA LAB Frankfurt, 12.02.2019
Slide 13: Asset Check Corporate Cooperation
Ad ’ UNTERNE#MENSKOOPERATION DREES & . Spoulwiengesa | SB panies “vy ASSET CHECK g Digital zu schatzen wissen ~ Der Asset Check bietet Ihnen ein vollstandig digitales objekt- und marktdatenbasiertes Benchmarking Ihrer Immobilie.
Slide 14: Why – How – What?
WHY – HOW – WHAT? WHAT WHAT IS WHAT you do THE USER‘S HOW HOW you do it ›WHY‹ ? WHY WHY you do it Most People never answer this question
Slide 15: Valid Data for Markets and Assets
ESSENTIAL INFORMATION ON POINT increased Quickly decision- accessible making information valid data profound for markets initial assessment and assets Bundled Benchmarks know-how on yields, in one app markets and buildings
Slide 16: Our Story
..OUR STORY:
Slide 17: Our Story
..OUR STORY:
Slide 18: © bunditinay – gettyimages.com
© bunditinay – gettyimages.com
Slide 19: Have a Look Inside
HAVE A LOOK INSIDE: @ ssx208 96 % Bayenthal Palais g fitz Reuter srate 04 tice Buleing & Optinize 2008 8 174 27154 medium very good ZOOM 1 LOCATION = REVENUE € Se MARKET A 1,075,935 107 costs CCAPEX- benchmark 263,000 € Maintenance 15,815 Operating cost 72,230 Object management 9.625
Slide 20: Have a Look Inside
HAVE A LOOK INSIDE: ms Ones 96% Bayenthal Palais g Fite Ree Suto 94 50968 Kain Otte Buiing © Optimize 2004 8 174 27154 medium very good ceartaintype yes 52 2 1 ~ REVENUE Return depending on type of use per sam - market standard 320,833 Development of metket rents depending on sage and location - mar
Slide 21: Have a Look Inside
HAVE A LOOK INSIDE: ge MARKET 2004 8 «174 27154 medium very good 1,075,935 76153 €
Slide 22: Have a Look Inside
HAVE A LOOK INSIDE: mS Seer = I costs Bayenthal Palais @ fitz Reuter Suate 84 50968 Kain 263,000 € Object management all
Slide 23: Your Added Value
YOUR ADDED VALUE Are you looking for… − reliable benchmarks for your property regarding yields, market performance, demographic and cost situation? − a valid database from 80 years of combined market and construction engineering experience of two independent market leaders? − the crucial knowledge a
Slide 24: The Asset Check: Your Advantages
the Asset Check: your 4 advantages Focused Information Easily Independent and accessible Reliable bulwiengesa + Drees & Sommer Fast and precise © bunditinay – gettyimages.com
Slide 25: A Universal Tool for All Investment Phases
A UNIVERSAL TOOL FOR ALL INVESTMENT PHASES REAL ESTATE
Slide 26: Portfolio Analysis
PORTFOLIO ANALYSIS the Asset Check provides... − basic information for your portfolio strategy − a broad set of relevant benchmarks − Decision Indicators − Hold − Hold & Optimize − Exit − Asset focused − For several types of use − daily actual © kantver – Fotolia.com
Slide 27: Knowledge Level at Purchase
→ KNOWLEDGE LEVEL AT PURCHASE red = without ASSET CHECK / green = with ASSET CHECK Market ASSET CHECK analysis / bidding due diligence PURCHASE Capex Opex today yesterday ∆2 ∆1 [ time ] Exposee Start Transaktion Start (T)DD
Slide 28: Knowledge Level of the Portfolio Holder and Potential Investor
→ KNOWLEDGE LEVEL OF THE PORTFOLIO HOLDER AND POTENTIAL INVESTOR red = without ASSET CHECK / green = with ASSET CHECK Market ASSET CHECK analysis / bidding due diligence Capex Opex today yesterday ∆2 ∆1 [ time ] Exposee Investment decision Start of Sale / inventory / sale Process
Slide 29: Years of Competence and Know-How Merged into a Valid Database
80 YEARS OF COMPETENCE AND KNOW-HOW MERGED INTO A VALID DATABASE bulwiengesa and Drees & Sommer Comparables CAPEX / Rents / Modernization Purchase prices OPEX / Revitalization Location, Maintenance Asset class Operating Economic and market Costs and regional analysis performance USP Property Managem
Slide 30: Digitization? We Walk the Talk!
DIGITIZATION? WE WALK THE TALK!
Slide 31: Become an Asset
© kalafoto – Fotolia.com BECOME AN ASSET
Slide 32: Data Driven Risk-Adjusted Returns Framework
DATA DRIVEN RISK-ADJUSTED RETURNS FRAMEWORK GRANULAR DATA QUANTIFIES REQUIRED RATE OF RETURNS 12 FEBRUARY 2019 RESEARCH & STRATEGY Hans Vrensen CFA, CRE Dennis Schoenmaker Ph.D. MANAGING DIRECTOR ASSOCIATE Tel +44 (0)20 7016 4753 Tel +44 (0)20 7016 4860 hans.vrensen@eu.aew.com dennis.schoenmaker@eu.
Slide 33: Table of Contents
TABLE OF CONTENTS I. METHODOLOGY II. RISK ADJUSTED RETURN FRAMEWORK RESULTS Focused on the Future of Real Estate 2
Slide 34: Methodology
METHODOLOGY Focused on the Future of Real Estate
Slide 35: Risk-Adjusted Return Methodology
RISK-ADJUSTED RETURN METHODOLOGY FOCUS ON MINIMUM ACCEPTABLE LEVEL OF RETURN GIVEN EACH SEGMENT’S RISK PREMIA EXPECTED RATE OF RETURN (ERR) REQUIRED RATE OF RETURN (RRR) Volatility Capital Return Premium > Attractive Liquidity Premium Or Gross < Less Attractive Depreciation Income Return Risk-Free R
Slide 36: Property Value Ratio
NET DEPRECIATION LOWER IN GATEWAY CITIES VERSUS NON-GATEWAY CITIES LAND VALUES SUBSTANTIAL PROPORTION 1.0 0.8 0.6 Property Value Ratio 0.4 0.2 0.0 0 10 20 30 40 50 60 70 80 90 100 Building Age London Paris Warsaw Source: RCA & AEW Focused on the Future of Real Estate 5
Slide 37: Net Depreciation Follows the J-Curve
NET DEPRECIATION FOLLOWS THE J-CURVE NEW BUILDINGS TEND TO DEPRECIATE FASTER THAN OLDER BUILDINGS -1.4% -1.2% -1.0% % Decline in Value - Net Depreciation -0.8% -0.6% -0.4% -0.2% 0.0% 1 10 20 30 40 50 Building Age Source: RCA & AEW Focused on the Future of Real Estate 6
Slide 38: Gross Depreciation Rates Lower in Low Yielding Office Markets
GROSS DEPRECIATION RATES TEND TO BE LOWER IN LOW YIELDING OFFICE MARKETS CORRELATION INDICATES RELATIONSHIP BETWEEN YIELDS AND DEPRECIATION 6 R² = 0.4407 5 Prime Yield (%) 4 3 2 1 1.5 2 2.5 3 Depreciation Rate (% per annum of Capital Value) Source: RCA, MSCI & AEW Focused on the Future of Real Estat
Slide 39: Data Driven Liquidity Premium Indicates Wide Range in Premia
DATA DRIVEN LIQUIDITY PREMIUM INDICATES WIDE RANGE IN PREMIA LONDON CITY OFFICE MARKET MOST LIQUID MARKET 300 250 200 150 100 50 0 2007 2009 2011 2013 2014 2016 2018 Max Min Average Source: RCA & AEW Focused on the Future of Real Estate 8
Slide 40: Average Liquidity Premium Is Lower at Time of Low Yields
AVERAGE LIQUIDITY PREMIUM IS LOWER AT TIME OF LOW YIELDS RELATIONSHIP BETWEEN YIELD COMPRESSION AND LIQUIDITY 7.0 R² = 0.883 6.5 6.0 Prime Yield (%) 5.5 5.0 4.5 4.0 100 110 120 130 140 150 160 170 Liquidity Premium (bps) Source: RCA & AEW Focused on the Future of Real Estate 9
Slide 41: Maximum Volatility Premium Continues to Decline
MAXIMUM VOLATILITY PREMIUM CONTINUES TO DECLINE AFTER 2012-15 PAUSE LOWER VOLATILITY AMID STABLE CAPITAL RETURNS 100 90 80 70 60 50 40 2007 2009 2011 2013 2014 2016 2018 Source: RCA & AEW Focused on the Future of Real Estate 10
Slide 42: Risk Adjusted Return Framework Results
RISK ADJUSTED RETURN FRAMEWORK RESULTS Focused on the Future of Real Estate
Slide 43: Expected Office Returns Most Attractive in Berlin and Glasgow
EXPECTED OFFICE RETURNS MOST ATTRACTIVE IN BERLIN AND GLASGOW CURRENT REQUIRED RATES OF RETURN NEAR HISTORICAL LOWS DUE TO LOW BOND YIELDS 10 8 6 4 2 0 Berlin Glasgow Madrid Frankfurt London Paris Amsterdam Milan Dublin -2 Required rate of return Expected return Source: : CBRE, RCA, MSCI, Oxford Eco
Slide 44: Required Return Up from Recent Lows as Risk Free Rate Normalizes
REQUIRED RETURN UP FROM RECENT LOWS AS RISK FREE RATE NORMALIZES LONDON CITY OFFICE – EXPECTED RETURN DOWN AS YIELDS WIDEN 25 20 15 10 5 0 10/2007 10/2008 10/2009 10/2010 10/2011 10/2012 10/2013 10/2014 10/2015 10/2016 10/2017 Risk-free rate Depreciation Liquidity risk Volatility risk Expected retur
Slide 45: Risk-Adjusted Return Index Shows Proof of Concept
RISK-ADJUSTED RETURN INDEX SHOWS PROOF OF CONCEPT INDEX CONFIRMS CURRENT LATE STAGE CYCLE 1 0.75 Required return < Expected return “majority attractive markets" 0.5 Required return > Expected return 0.25 "minority attractive markets" 0 2007Q4 2008Q4 2009Q4 2010Q4 2011Q4 2012Q4 2013Q4 2014Q4 2015Q4 2
Slide 46: Despite Late Cycle, 55 of 90 Rated Attractive or Neutral
DESPITE LATE CYCLE, 55 OF 90 RATED ATTRACTIVE OR NEUTRAL RETAIL MARKETS EXPECTED TO FACE TOUGHEST CHALLENGE Less Neutral Attractive Total attractive Offices 11 15 11 37 Retail 18 12 3 33 Logistics 6 7 7 20 Total 35 34 21 90 Source: CBRE, RCA, MSCI, Oxford Economics, Natixis & AEW Focused on the Futu
Slide 47: Market Average Hides Distribution of Individual Asset Returns
MARKET AVERAGE HIDES DISTRIBUTION OF INDIVIDUAL ASSET RETURNS FAVORABLE ASSET ACQUISITIONS REMAIN POSSIBLE EVEN IN LESS ATTRACTIVE MARKETS INITIAL YIELDS OF OFFICE, RETAIL & LOGISTICS DEALS ACROSS ALL 90 MARKETS OVER THE LAST 12 MONTHS Source: RCA & AEW Focused on the Future of Real Estate 16
Slide 48: Thank You
THANK YOU BOSTON LONDON PARIS DÜSSELDORF HONG KONG AEW.COM Focused on the Future of Real Estate
Slide 49: Urban Data Science: Reliable Machine Learning Models in Real Estate
Urban Data Science Reliable Machine Learning Models in Real Estate Frankfurt 12.02.2019 - Dr. Christoph Gebele
Slide 50: Locations Matter
Locations matter Locations are essential for Office space More detail • Current and future value of a property • Development potential for different (construction) projects • Development of objects for individual target groups 12.02.2019 Geospin GmbH 2
Slide 51: City-Level Digitalisation Using Geodata
City-level digitalisation using Geodata Static Office space • 300+ Point of Interest Point of • Demography and census Interest Traffic flow • Infrastructure People density • Crime data • Number of registrated cars Dynamic • People density and traffic flows • Social Media • Weather 12.02.2019 Geospin
Slide 52: Geo-Deep-Learning to Find Hidden and Dynamic Structures
Geo-Deep-Learning to find hidden and dynamic structures to increase revenue and saving costs To enable objective and reliable forecasts • For any location without additional customer data • To boost understanding and performance identifying Geographic Key Performance Indicators (GKPIs) • To transfer
Slide 53: Geospin Turns Your Data into Dynamic Predictions
Geospin turns your data into dynamic predictions Customer data Geo Prediction Engine We provide you reports and visualization 700+ external Geo data Probe and Apply Machine Integrate and API: Integration of our pre- sources merge data Learning publish diction in your BI software The Geospin algorith
Slide 54: Showcase: Predicting Office Rental Prices
Showcase: Predicting office rental prices Office space data Prediction rental prices whole city Prediction rental prices specific area Rental Typ Furni- Lat Lon price office shing sm 2 53.55 9.98 12,5 high 4 53.55 9.99 24,95 medium 2 53.56 9.96 16,64 none … … … … … Data supplied by Immobilien Scout
Slide 55: Analysis of People Densities Using Geo-Deep Learning
Analysis of people densities using geo-deep learning Fine-grained geographical and Area without data Using prediction model temporal predictions Geolocation (e.g.Postal adress) People density Training a model to estimate the density of people for the city of Munich. Transfer of the model to the city
Slide 56: Selected Customer References
Selected Customer References 12.02.2019 Geospin GmbH 8
Slide 57: Geospin GmbH
Geospin GmbH Geospin GmbH Kartäuserstr. 39a /geospin.de 79102 Freiburg +49 (0)761 59514614 /geospin www.geospin.de /company/geospin info@geospin.de /companies/geospin 12.02.2019 Geospin GmbH 9
Slide 58: Shopping Districts Delineation
Shopping Districts Delineation Song Zhang PhD Candidate in Real Estate Real Estate Centre, University of Groningen
Slide 59: What Is a Shopping District?
What is shopping district? Shopping districts are areas where retailers concentrate. The size or shape of a shopping district varies overtime. Defining their location and boundaries is surprisingly difficult. We propose a unique GIS technique to delineate shopping districts.
Slide 60: Data Source: Netherlands Retail Employment
Data source: from the Netherlands We use retail employment datasets to measure retail properties in the Netherlands. Here we use the data of year 2013 as an example. These datasets incorporate information about all retail properties that are registered at Dutch Chamber of Commerce. We use nu
Slide 61: Converting Points into Density Rasters
Converting points into density rasters
Slide 62: Converting Points into Density Rasters
Converting points into density rasters We use Kernel Density Estimation to convert point data into density raster surfaces. We use a granular setting with cell size being 10 meters and search radius being 100 meters. In this way, we minimize misspecifications.
Slide 63: Selecting Threshold and Converting Rasters into Polygons
Selecting threshold and converting rasters into polygons Threshold: 3,241 jobs per 𝑘𝑘𝑘𝑘2 .
Slide 64: More Examples of Shopping Districts
More examples of shopping districts Amsterdam Rotterdam Den Haag
Slide 65: Shopping Districts with Different Thresholds
Shopping districts with different thresholds 3,241 jobs per 𝑘𝑘𝑘𝑘2 6,148 jobs per 𝑘𝑘𝑘𝑘2 10,201 jobs per 𝑘𝑘𝑘𝑘2
Slide 66: Comparing Number of Jobs with Number of Properties
Comparing when using number of jobs with when using number of properties Number of Jobs Number of Properties
Slide 67: Dynamics of Shopping Districts
Dynamics of Shopping Districts
Slide 68: Thank You
Thank you! song.zhang@rug.nl
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