Paper by: Hilde Remøy Philip Koppels Hans de Jonge

April 19, 2023 1/19 Real Estate & Housing – Faculty of Architecture – Delft University of Technology

The beauty or the beast?

Obsolescence and structural vacancy in the case of Amsterdam

- work in progress -

Paper by: Hilde RemøyPhilip KoppelsHans de Jonge


Supply and absorption

Total supply

Total absorption

Net absorption


Leegstand in Amsterdam


•200 office buildings in Amsterdam•105 buildings with structural vacancy• 55 buildings with more than 30% structural vacancy

•Structural vacancy: The data were collected in 2007, office space that was vacant for 3 or more years was considered structurally vacant

•Logistic regression: Building and location characteristics relationship to structural vacancy

Office scan


Structural vacancy


Baum (1991,1997), recognises building quality, defined by obsolescence related factors (configuration, internal specification and external appearance) and physical deterioration, to be a better explanation of rental value and depreciation than simple age.

Variables


•Year of construction•Configuration • Internal specification•External appearance

Additional location variables

•Plot and location quality•Accessibility•Amenities•Functional mix

Variables


Logistic regression,

Dependent variable >=30% structural vacancy

Model developed starting with basic factors, adding obsolescence factors and finally plot- and location factors

Model


Model

Year of Construction

Model Summary

Step-2 Log

likelihood

Cox & Snell R Square

Nagelkerke R Square

1 220.942a .089 .130a. Estimation terminated at iteration number 6 because parameter estimates changed by less than .001.

Variables in the equation B S.E. Exp(B)95% C.I. for

EXP(B)

Lower Upper

Step 1 year of Construction

***

1950-1964 -1.562 1.067 .210 .026 1.699

1965-1979 .325 .557 1.385 .465 4.125

1980-1994 1.153***

.377 3.167 1.514 6.625

constant -1.529 .306 .217The reference category is 1995-2009


Model

Obsolescence factors

The reference category for quality is excellent, for material natural stone

Model Summary

Step

-2 Log likelihood

Cox & Snell R Square

Nagelkerke R Square

1 203.728a .179 .262



The reference category for for entrance spatiality is >15, for structural grid <=5.4, quality is excellent, for material natural stone

Variables in the Equation

B S.E. Exp(B)

95% C.I.for EXP(B)

Lower Upper

Step 9a

Entrance spatiality

**

<8 .488 .511 1.629 .599 4.4358-15 -1.111** .484 .329 .127 .850Structural grid *>8 metres -.099 .841 .906 .174 4.705>5.4 metres <= 8 metres

.978* .563 2.660 .882 8.019

quality ***poor 2.565*** .629 13.004 3.788 44.641adequate 1.618*** .588 5.045 1.593 15.979material **concrete -2.047*** .770 .129 .029 .583metal -1.263* .696 .283 .072 1.106bricks -2.168*** .711 .114 .028 .461glass -.565 .685 .568 .148 2.176Constant -1.791 .667 .167


ModelObsolescence and Year of Construction

Model Summary

Step-2 Log

likelihoodCox & Snell R Square

Nagelkerke R Square

1 187.049a .226 .330



Variables in the Equation B S.E. Exp(B)95% C.I.for EXP(B)Lower Upper

Step 8

year of Construction

*

1950-1964 -2.699** 1.188

.067 .007 .690

1965-1979 -.241 .637 .786 .226 2.7391980-1994 .283 .465 1.328 .534 3.303entrance spatiality **<8 .608 .531 1.837 .649 5.1988-15 -1.015** .492 .362 .138 .951quality ***poor 2.677*** .717 14.539 3.569 59.232adequate 1.804*** .637 6.072 1.743 21.156material **concrete -2.105** .829 .122 .024 .619metal -1.282* .733 .278 .066 1.168bricks -

2.266***.816 .104 .021 .513

glass -.419 .717 .658 .162 2.679constant -1.087 .518 .337

The reference category for Construction year is 1995-2009, for entrance spatiality >15, for quality excellent, for material natural stone


Model

Obsolescence, basic and location factors

Model Summary

Step-2 Log

likelihoodCox & Snell R Square

Nagelkerke R Square

1 142.544a .371 .536

31 171.399c .273 .395a. Estimation terminated at iteration number 20 because maximum iterations has been reached. Final solution cannot be found.

b. Estimation terminated at iteration number 7 because parameter estimates changed by less than .001.

c. Estimation terminated at iteration number 6 because parameter estimates changed by less than .001.


The reference category for Construction year is 1995-2009, for entrance spatiality >15, for quality excellent, for material natural stone

Variables in the Equation

B S.E. Exp(B)

95% C.I.for EXP(B)

Lower UpperStep 31a

year of Construction *1950-1964 -2.273* 1.174 .103 .010 1.0281965-1979 .325 .712 1.384 .343 5.5911980-1994 .458 .519 1.581 .572 4.368

material **concrete -1.963* .918 .140 .023 .849metal -.838 .836 .433 .084 2.226bricks -2.055** .927 .128 .021 .787glass -.092 .825 .912 .181 4.591

quality **poor 2.124*** .812 8.362 1.701 41.100adequate 1.493** .732 4.450 1.061 18.675

entrance spatiality **<8 .600 .562 1.823 .606 5.4808-15 -1.270** .545 .281 .097 .817

travel time to highway exit

**

Reis_opaf_cat(1) .232 .730 1.261 .301 5.275Reis_opaf_cat(2) 1.264* .704 3.541 .892 14.059Reis_opaf_cat(3) -.359 .859 .699 .130 3.765

square within 50 metres -2.340** 1.178 .096 .010 .970trees within 50 metres .085** .039 1.089 1.009 1.175constant -1.795 .892 .166


• Model improves for each block of factors added.

• The model including Year of construction AND obsolescence factors is a stable basic model: obsolescence and YoC stay important for the model also when location characteristics are added.

• Buildings with poor facade quality have a higher risk for structural vacancy!

Conclusion: Beauty

Paper by: Hilde Remøy Philip Koppels Hans de Jonge

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