market data and methods for real estate portfolio ratings (lausberg/wiegner)
DESCRIPTION
University. Background. Introduction. Market Data. Methods. Conclusion. Market Data and Methods for Real Estate Portfolio Ratings (Lausberg/Wiegner). OUTLINE. Presentation by Carsten Lausberg - PowerPoint PPT PresentationTRANSCRIPT
© Carsten Lausberg 2009, p. 1
Market Data and Methods for Market Data and Methods for Real Estate Portfolio RatingsReal Estate Portfolio Ratings
(Lausberg/Wiegner)(Lausberg/Wiegner)
Presentation by
Carsten Lausberg
prepared for the 16th Annual Conference of the European Real Estate Society,
June 24-27, 2009 in Stockholm/Sweden
Background
Introduction
Market Data
Methods
Conclusion
University
OUTLINE
© Carsten Lausberg 2009, p. 2
Nürtingen-Geislingen University: One of the Nürtingen-Geislingen University: One of the Pioneers in Real Estate Education in GermanyPioneers in Real Estate Education in Germany
Munich
Stuttgart GEISLINGEN
Located in south-western Germany
3,500 students in 20 degree courses
Several rankings show HfWU in the top flight of Germany‘s business schools (Stern, Spiegel, ManagerMagazin, Stiftung Warentest, Wirtschaftswoche, Focus)
Major in real estate management since 1983
Degree course in real estate management since 1998 (Diplom/Master and B.S.)
Today: Appr. 360 real estate students currently enrolled, 12 real estate professors and more than 27 lecturers, 500 alumni in the real estate industry
Accredited by RICS and FIBAA
Background
Introduction
Market Data
Methods
Conclusion
University
© Carsten Lausberg 2009, p. 3
Motivation For This StudyMotivation For This Study
Motivation:
Improvement of market transparency in real estate risk management
Rating:
- Central element of modern risk management- Especially for Banks, thanks to Basel II- Huge leap of development- Not well documented in the real estate literature
Subject:
- Internal credit rating for a large group of German banks(4 years, 800 loans in the development sample, 3,000+ in the validation)
- External rating for German open-ended funds(3 years, 35 portfolios, 3,800 properties, 70,000 contracts)
- Market rating for 125 German cities
Background
Introduction
Market Data
Methods
Conclusion
University
© Carsten Lausberg 2009, p. 4
DefinitionsDefinitions
Rating:
A holistic evaluation of the future perspectives of a property, a real estate portfolio, a real estate product or another issue connected with real estate
on the basis of indicators
Purposes:
- Credit rating- Investment rating
Subjects:
- Physical assetsProperty/ProjectPortfolio
- Financial assets based on propertiesLoans, bonds, MBSStocks
- Others (market, tenant, manager…)
Background
Introduction
Market Data
Methods
Conclusion
University
© Carsten Lausberg 2009, p. 5
Input DataInput Data
ManagementMarketProperty
Rating Quality
Background
Introduction
Market Data
Methods
Conclusion
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Method Person Data Others
© Carsten Lausberg 2009, p. 6
Classification Classification of Market Data of Market Data According to the Frame of ReferenceAccording to the Frame of Reference
Background
Introduction
Market Data
Methods
Conclusion
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National levele.g., political stability, economic and socio-demographic framework, natural space conditions
Economic conditionse.g., GDP, structure of the economy, unemployment
Location levele.g., submarket data, location quality
Property markete.g., rents, yields, vacancy rates
Spatial reference Factual reference
Regional levele.g., property market, rents, yields, economic and socio-demographicconditions
Social conditions e.g., population, income, purchasing power, household sizes
© Carsten Lausberg 2009, p. 7
Classification Classification of Market Data of Market Data According to Scale Type and Objectivity According to Scale Type and Objectivity
Background
Introduction
Market Data
Methods
Conclusion
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Raw datae.g., offer rents, sales volume, number of transactions, number of building permits, number of inhabitants
Classification datae.g., type of use(office/housing/retail…), market type (A-city, prime location), tenant type (A, B, C)
Processed datae.g., market rents, marketyields, vacancy rates, rentforecasts, indices
Expert estimations and opinionse.g., location quality, long-term forecasts, market scoring
quantitative(metric scale)
qualitative(nominal or ordinal scale)
hard(objective)
soft(subjective)
Scale type
Objektivität
© Carsten Lausberg 2009, p. 8
Evaluating the Suitability of Market DataEvaluating the Suitability of Market Data
Criteria:
- Objectivity, reliability, validity- Long time series - Completeness and consistency- Comparibility of definitions- Availability in a timely fashion and on a low level of aggregation
Problematic fields:
- Market segments with a general lack of data - Correlations
Conclusion:
- Still a lot of work to do- Compromises necessary- Expertise of the rating analyst important
Background
Introduction
Market Data
Methods
Conclusion
University
© Carsten Lausberg 2009, p. 9
Developing a Real Estate RatingDeveloping a Real Estate Rating
Background
Introduction
Market Data
Methods
Conclusion
UniversityMethods for Development:
- Scoring- Simulation- Combination
1) Scoring
Empirical-statistical approach
- Univariate analysis of all risk factors- Multivariate analysis - Combination of the scorecards to an overall modelEmpirical-qualitative approach
Transfer into a rating grade
© Carsten Lausberg 2009, p. 10
Developing a Real Estate RatingDeveloping a Real Estate Rating
Background
Introduction
Market Data
Methods
Conclusion
University2) Simulation
Basis: investment calculation (DCF model)
- Simplified- Extended
Partial models to reduce complexity and concentrate on the objectives of the rating
Core: modelling the rental cash flow
Transfer into a rating grade
3) Combination
© Carsten Lausberg 2009, p. 11
Modelled Contract Rent
Mo
delle
d C
on
tract R
ent
Modelled Contract Rent
I
Frictio
nal V
acancy
II III
Ince
ntives
IV
Modelling the Rental Cash FlowModelling the Rental Cash Flow
2007
Current Contract Rent
Rent [€]
2008 20102009 2011-2012
Forecasted ContractRent
Forecasted Property-Specific Market Rent
Adaptation of Contract Rent to Market Rent
Background
Introduction
Market Data
Methods
Conclusion
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© Carsten Lausberg 2009, p. 12
0 1 2 3 …
IN-OUT
=Cashflow
Forecast
Market Model
Cash Flow Model Scorecards
Actual
Market Rent
Time
Total Score
Portfolio Market
Real Estate Market• Rents• Vacancy Rates• Yields• Correlations• Sales• Estimation of lenght of
frictional vacancy• …
Input Data
Economy• Unemployment• Purchasing Power• Building PermitsSociety• Population Growth• Number of Households• Traffic situation…
OTHERDATA
Rating
€€€ €€€ €€€€€€€€€ €€€ €€€€€€
€€€ €€€ €€€€€€
€€€€€€
€€€
Properties• Address• Type• RentableSpaceEnterprise• Liquidity• Financing• Management…
Enter-prise
MARKETDATA
Background
Introduction
Market Data
Methods
Conclusion
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© Carsten Lausberg 2009, p. 13
TestingTesting
Background
Introduction
Market Data
Methods
Conclusion
UniversityAbsolutely necessary… but often neglected in practice
Key elements:
- Calibration
- Validation (out of time, out of sample, use-test)
CalibrationScore Number of
loansNumber of defaults
Probability of default
Rating grade
98-100 200 0 0,00% 1a 0,01% AAA94-97 1000 0 0,00% 1b 0,02% AA+88-93 5000 2 0,04% 1c 0,03% AA79-87 10000 5 0,05% 1d 0,04% AA-68-78 8400 3 0,04% 2a 0,05% A+… … … … … 8,00% A
… …
Internal rating model of the bank
Scoring results Probability of default
Rating grade
Master scale of Standard & Poor's
© Carsten Lausberg 2009, p. 14
Summary and ConclusionsSummary and Conclusions
Background
Introduction
Market Data
Methods
Conclusion
UniversityCombination of data from different sources and mix of methods can contribute to meaningful real estate portfolio ratings
Improvement of the data situation necessary- Official statistics, length of time series, international comparability- Role of researchers, ERES, and others to promote transparency Formalization of risk management in real estate- Standard defintions- BenchmarkingResearch and Development- Methods can be improved- Lack of knowledge and experience as well as wrong incentives
have to be taken into accountTraining- Spreading the knowledge- Necessary to reduce human errors in calculating and interpreting
rating grades
Contact:
Dr. Carsten Lausberg, M.S.Professor of Real Estate BankingNuertingen-Geislingen UniversityParkstr. 473312 [email protected]