Graduation Presentation Delft, University of Technology 1st Mentor: Philip Koppels 2nd Mentor: Hilde Remøy Commissoner: Remon Rooij Lab coordinator: Theo van der Voordt Student number: Date: In-transparency of the Amsterdam office market: The underlying incentive and effective rental price development A quantitative research into the market dynamics and spatial segmentation of the Amsterdam office market over the period /50

I. Problem introduction II. Problem definition, Approach & Methods III. Theoretical framework IV. Empirical research V. Conclusions VI. Reflection on outcomes Content Content | I – Problem introduction | II – Problem definition | III -Theoretical framework | IV - Empirical research | V – Conclusions | VI – Reflection 2 / 50

I.Problem introduction II.Problem definition III.Theoretical framework and conclusions IV.Empirical research V.Conclusions VI.Reflection on outcomes 3 / 50

Rental adjustment equation (Hendershott, 1994) Schematically illustrated by Koppels & Keeris (2006) Face rental price Incentive Effective rental price 2. In-transparency/Asymmetric information availability 1. Segmentation / Sub-market behaviour 2A 2B Problem introduction I – Problem introduction | II – Problem definition | III -Theoretical framework | IV - Empirical research | V – Conclusions | VI – Reflection 4 / 50 Explanation ?

1. Self-sustaining system Other consequences of limited/in-transparency I – Problem introduction | II – Problem definition | III -Theoretical framework | IV - Empirical research | V – Conclusions | VI – Reflection 5 / Research implications 2. Barrier third parties & Competitive functioning market

I.Problem introduction II.Problem definition III.Theoretical framework and conclusions IV.Empirical research V.Conclusions VI.Reflection on outcomes 6 / 50

1. To what extend does a price index based on face rents, provide an accurate reflection of the market dynamics in the Amsterdam Office market over the period 2002 – 2012? 2. Do spatial market segments differentiate in market dynamics in the Amsterdam office market over the period ? Main research questions I – Problem introduction | II – Problem definition | III -Theoretical framework | IV - Empirical research | V – Conclusions | VI – Reflection 7 / 50

Approach I – Problem introduction | II – Problem definition | III -Theoretical framework | IV - Empirical research | V – Conclusions | VI – Reflection 8 / 50

Supply data Transaction dataProperty databaseVacancy dataSupply data Approach | Datamining process I – Problem introduction | II – Problem definition | III -Theoretical framework | IV - Empirical research | V – Conclusions | VI – Reflection 9 / 50 GIS Data BAG Database Office Stock database Own Research Data

Approach I – Problem introduction | II – Problem definition | III -Theoretical framework | IV - Empirical research | V – Conclusions | VI – Reflection 10 / 50

Approach | Data analyses (1/3) I – Problem introduction | II – Problem definition | III -Theoretical framework | IV - Empirical research | V – Conclusions | VI – Reflection 11 / 50 Study 1 Study 2 Average development Incentives & effective rents + market comparison Face rental price Incentive Effective rental price Testing relations between variables - by development |Method | Analyzing development |Method | (Lagged) Correlation Vacancy Incentives Rental prices Eco. indicators

Approach | Data analyses (2/3) I – Problem introduction | II – Problem definition | III -Theoretical framework | IV - Empirical research | V – Conclusions | VI – Reflection 12 / 50 Study 3 Study 4 Rental Price Indices Spatial Segmentation Analysis |Method | Average vs. Hedonic |Methods | Post-Hoc-Procedure & Correlation analyses City- Districts City- Districts Sub-office markets Business Districts

Approach | Data analyses (3/3) I – Problem introduction | II – Problem definition | III -Theoretical framework | IV - Empirical research | V – Conclusions | VI – Reflection 13 / 50 SPSS Statistics (v. 20) Study 5 Transparency analysis Difference Face & Effective rents |Method | Individual transaction analysis Face rental price Supply Face rental price Supply Nominal effective rental price Transaction Nominal effective rental price Transaction % Diff.

Approach I – Problem introduction | II – Problem definition | III -Theoretical framework | IV - Empirical research | V – Conclusions | VI – Reflection 14 / 50

Societal perspective Academic perspective Real Estate Market Relevance I – Problem introduction | II – Problem definition | III -Theoretical framework | IV - Empirical research | V – Conclusions | VI – Reflection 15 / 50

I.Problem introduction II.Problem definition III.Theoretical framework and conclusions IV.Empirical research V.Conclusions VI.Reflection on outcomes 16 / 50

Office markets | Cyclical I – Problem introduction | II – Problem definition | III -Theoretical framework | IV - Empirical research | V – Conclusions | VI – Reflection 17 / 50 Vacancy Property Cycle: Rental prices, Vacancy, supply/demand vary around a long-term trend Vacancy indicator of specific cycle position

1. Segmented structure ignored; inter-related office markets in London (Stevenson, 2007) 1. Sub-urban level most appropriate level for analyzing office market dynamics (Jones, 1995) 2. Sub-markets different behavior in the short term, but follow a similar trend in the long run (Mueller, 1995) 3. Clustering of offices  higher rents - Amsterdam office market (Brounen en Jennen, 2009) Office markets | Segmented / sub-market behavior I – Problem introduction | II – Problem definition | III -Theoretical framework | IV - Empirical research | V – Conclusions | VI – Reflection 18 / 50

Koppels & Keeris (2006): 1. Vacancy and rents  2 year time-lag Landlords reluctant to adjust their rental rates when there are fluctuations in the vacancy rate 2. Incentives and Vacancy  No time-lag Incentives used as short-time price adjustments 3. Corr. Vacancy and Real rents Stronger when structural components of vacancy are left out of the equation Office markets |Vacancy from theory I – Problem introduction | II – Problem definition | III -Theoretical framework | IV - Empirical research | V – Conclusions | VI – Reflection 19 / 50

Rental price indices | Office market I – Problem introduction | II – Problem definition | III -Theoretical framework | IV - Empirical research | V – Conclusions | VI – Reflection 20 / 50

I.Problem introduction II.Problem definition III.Theoretical framework and conclusions IV.Empirical research V.Conclusions VI.Reflection on outcomes 21 / 50

Data overview I – Problem introduction | II – Problem definition | III -Theoretical framework | IV - Empirical research | V – Conclusions | VI – Reflection 22 / 50 Transactions per year

Study 1 | Average Incentive and Rental price development & market comparison I – Problem introduction | II – Problem definition | III -Theoretical framework | IV - Empirical research | V – Conclusions | VI – Reflection 23/ 50

Calculating effective rents | DCF method I – Problem introduction | II – Problem definition | III -Theoretical framework | IV - Empirical research | V – Conclusions | VI – Reflection 24 / 50 % Incentives NPV check % Incentives - % Incentives NPV Check

Incentives in the Amsterdam office market I – Problem introduction | II – Problem definition | III -Theoretical framework | IV - Empirical research | V – Conclusions | VI – Reflection 25 / 50 ICT-CRISIS ECONOMIC RECOVERY ECO NOMIC RECESSION

Contract vs. Effective rental price development I – Problem introduction | II – Problem definition | III -Theoretical framework | IV - Empirical research | V – Conclusions | VI – Reflection 26 / 50 ECONOMIC RECOVERY ICT-CRISIS ECONOMIC RECESSION

Price development explained by Market I – Problem introduction | II – Problem definition | III -Theoretical framework | IV - Empirical research | V – Conclusions | VI – Reflection 27 / 50

Face rental price comparison I – Problem introduction | II – Problem definition | III -Theoretical framework | IV - Empirical research | V – Conclusions | VI – Reflection 28 / 50 Pearson Correlations Average difference: 15-23%

Study 2 | Rental price indices I – Problem introduction | II – Problem definition | III -Theoretical framework | IV - Empirical research | V – Conclusions | VI – Reflection 29 / 50

Rental price indices | Average vs. Hedonic I – Problem introduction | II – Problem definition | III -Theoretical framework | IV - Empirical research | V – Conclusions | VI – Reflection 30 / 50

Rental price index | Average vs. Hedonic I – Problem introduction | II – Problem definition | III -Theoretical framework | IV - Empirical research | V – Conclusions | VI – Reflection 31 / 50

Rental price index comparison market reports I – Problem introduction | II – Problem definition | III -Theoretical framework | IV - Empirical research | V – Conclusions | VI – Reflection 32 / 50

Study 3| Testing relations between variables I – Problem introduction | II – Problem definition | III -Theoretical framework | IV - Empirical research | V – Conclusions | VI – Reflection 33/ 63

Vacancy rates in the Amsterdam office market I – Problem introduction | II – Problem definition | III -Theoretical framework | IV - Empirical research | V – Conclusions | VI – Reflection 34 / 50

Vacancy & Rental price I – Problem introduction | II – Problem definition | III -Theoretical framework | IV - Empirical research | V – Conclusions | VI – Reflection 35 / 50 Pearson (Lagged) Correlations Real FaceReal ContractReal Effective

Vacancy & Incentives I – Problem introduction | II – Problem definition | III -Theoretical framework | IV - Empirical research | V – Conclusions | VI – Reflection 36 / 50 0,523 0,678* 0,714* Pearson (Lagged) Correlations Percentage incentives

Study 4: Spatial Segmentation analysis I – Problem introduction | II – Problem definition | III -Theoretical framework | IV - Empirical research | V – Conclusions | VI – Reflection 37 / 50

Sample 1| Sub-office markets Sample 2| Business Districts Spatial segmentation analysis | Samples I – Problem introduction | II – Problem definition | III -Theoretical framework | IV - Empirical research | V – Conclusions | VI – Reflection 38 / 50

Spatial segmentation analysis | Incentives I – Problem introduction | II – Problem definition | III -Theoretical framework | IV - Empirical research | V – Conclusions | VI – Reflection 39 / 50 Sample 1| Sub-office markets Sample 2| Business Districts

Spatial Segmentation Analysis | Incentive conclusions I – Problem introduction | II – Problem definition | III -Theoretical framework | IV - Empirical research | V – Conclusions | VI – Reflection 40 / 50 Post-Hoc test & Development analysis Correlation outcomes

Spatial segmentation analysis | Effective rental price I – Problem introduction | II – Problem definition | III -Theoretical framework | IV - Empirical research | V – Conclusions | VI – Reflection 41 / 50 Sample 1| Sub-office markets Sample 2| Business Districts

Spatial Segmentation Analysis | Rental price conclusions I – Problem introduction | II – Problem definition | III -Theoretical framework | IV - Empirical research | V – Conclusions | VI – Reflection 42 / 50 Post-Hoc test & Development analysis Correlation outcomes

Study 5: “Transparency” analysis I – Problem introduction | II – Problem definition | III -Theoretical framework | IV - Empirical research | V – Conclusions | VI – Reflection 43 / 50

Transaction analysis I – Problem introduction | II – Problem definition | III -Theoretical framework | IV - Empirical research | V – Conclusions | VI – Reflection 44 / 50 Connected 238 of 454 transactions Deleted: transactions > 106 final accurate transactions Average difference 20%

I.Problem introduction II.Problem definition III.Theoretical framework and conclusions IV.Empirical research V.Conclusions VI.Reflection on outcomes 45 / 50

1. “ To what extend does a price index based on face rents, provides an accurate reflection of the market dynamics in the Amsterdam Office market over the period 2002 – 2012? Face rental prices – effective rental price (development): diff: % (Study 1); diff: 20% (Study 5) Underlying development; similar correlation | Prime rent development not representative for total market (Study 1) Effective rents vs. face rental indices  more cyclical and volatile (Study 2) Sig. correlation face rents & vacancy and real eff. rents & vacancy  similar rental adjustment / market dynamics (Study 3)  Overall conclusion: index based on (real) effective rental price more accurate reflection of market dynamics Conclusions I – Problem introduction | II – Problem definition | III -Theoretical framework | IV - Empirical research | V – Conclusions | VI – Reflection 46 / 50

2. “Do spatial market segments differentiate in market dynamics in the Amsterdam office market over the period ?” -Incentives : -Significant different in height -No market segmentation in development; similar correlation in incentive development -Surrounding business districts correlated; Centre / Sloterdijk -Effective rental price: -Significant differences in height sub-office and business districts (nominal) -Yes, partial market segmentation in development (Stevenson, 2007) -West, North and South-East correlated -Surrounding business districts in City-district South-East correlated Conclusions I – Problem introduction | II – Problem definition | III -Theoretical framework | IV - Empirical research | V – Conclusions | VI – Reflection 47 / 50

I.Problem introduction II.Problem definition III.Theoretical framework and conclusions IV.Empirical research V.Conclusions VI. Reflection on outcomes 48 /50

Overall: Database limitations – Only Accepted – 1/5th transactions: 450 ; LFA > 500 m2 Study 1: % Incentives and effective rental price – Higher incentives? – Market comparison; few transactions Study 2: Hedonic price index – Low R Square (max. 0,33) in research – Cyclicality and market realistic reflection might change in more accurate model Study 3: Vacancy & Rents Other vacancy rates; different outcomes No difference from natural vacancy used Reflection on outcomes I – Problem introduction | II – Problem definition | III -Theoretical framework | IV - Empirical research | V – Conclusions | VI – Reflection49 / 50 Rental adjustment equation: stronger correlation with real effective rents; possible explanations: Small amount of transactions LFA > 500 m2 Vacancy existing offices instead of entire market Current scale level not most appropriate Study 4: Spatial segmentation No diff. above and below 500 m2 Post-Hoc only diff. between some variables Study 5: Face and effective rental price - Only 106 transactions; unsure whether transactions are well-connected - Only indication of total difference

Questions & Answers? I – Problem introduction | II – Problem definition | III -Theoretical framework | IV - Empirical research | V – Conclusions | VI – Reflection50 / 50

Data analysis & Reliability check – Transaction data Municipal Tax Office Rental questionaire; incentives, rental prices – to tenant instead of landlord Only “accepted” transactions used in this research Goal: test its market conformity 5 step Screening method – Step 1: Controlling/Checking input – Step 2: Consistency analysis – Step 3: Screening of the rental value – Step 4: Reliability check – Step 5: Assigning a particular status/condition to the transaction Main rejecting reasons: Improbable sale or rental price - forced auction sales, (anti-) squatters, income requirement, sale-leaseback, rental price extension, temporary lease obligation with end-date, lower rent due rental defects of property, short rental contract, large investments in object (Possible) Family transaction Multiple disciplines in rent Objects which are out of use (Only a parking lot is rented) Approach | Data Validity I – Problem introduction | II – Problem definition | III -Theoretical framework | IV - Empirical research | V – Conclusions | VI – Reflection 51 / 63 Data reliability check

Furter research: The relation between the (real) effective rental price and vacancy in the Amsterdam sub-office markets Similar research for other market segments – retail - incentives and effective rental price development Determinants in an (real) effective rental price index compared to a (real) contract rental price index Correlation between variables in different moments of the cycle Adding non-accepted transactions to the research, in order to have a larger database, especially for transactions LFA > 500 m2 Analyzing each transaction individually in order to calculate the ‘true’ incentive percentage in the Amsterdam office market Recommendations for further research I – Problem introduction | II – Problem definition | III -Theoretical framework | IV - Empirical research | V – Conclusions | VI – Reflection52 / 63