Paper by: Hilde Remøy Philip Koppels Hans de Jonge
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Transcript of 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
April 19, 2023 2/19 Real Estate & Housing – Faculty of Architecture – Delft University of Technology
April 19, 2023 3/19 Real Estate & Housing – Faculty of Architecture – Delft University of Technology
Supply and absorption
Total supply
Total absorption
Net absorption
April 19, 2023 4/19 Real Estate & Housing – Faculty of Architecture – Delft University of Technology
Leegstand in Amsterdam
April 19, 2023 5/19 Real Estate & Housing – Faculty of Architecture – Delft University of Technology
•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
April 19, 2023 6/19 Real Estate & Housing – Faculty of Architecture – Delft University of Technology
Structural vacancy
April 19, 2023 7/19 Real Estate & Housing – Faculty of Architecture – Delft University of Technology
April 19, 2023 8/19 Real Estate & Housing – Faculty of Architecture – Delft University of Technology
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
April 19, 2023 9/19 Real Estate & Housing – Faculty of Architecture – Delft University of Technology
•Year of construction•Configuration • Internal specification•External appearance
Additional location variables
•Plot and location quality•Accessibility•Amenities•Functional mix
Variables
April 19, 2023 10/19 Real Estate & Housing – Faculty of Architecture – Delft University of Technology
Logistic regression,
Dependent variable >=30% structural vacancy
Model developed starting with basic factors, adding obsolescence factors and finally plot- and location factors
Model
April 19, 2023 11/19 Real Estate & Housing – Faculty of Architecture – Delft University of Technology
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
April 19, 2023 12/19 Real Estate & Housing – Faculty of Architecture – Delft University of Technology
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
9 208.682a .160 .234a. Estimation terminated at iteration number 5 because parameter estimates changed by less than .001.
April 19, 2023 13/19 Real Estate & Housing – Faculty of Architecture – Delft University of Technology
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
April 19, 2023 14/19 Real Estate & Housing – Faculty of Architecture – Delft University of Technology
ModelObsolescence and Year of Construction
Model Summary
Step-2 Log
likelihoodCox & Snell R Square
Nagelkerke R Square
1 187.049a .226 .330
8 195.859a .192 .281a. Estimation terminated at iteration number 6 because parameter estimates changed by less than .001.
April 19, 2023 15/19 Real Estate & Housing – Faculty of Architecture – Delft University of Technology
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
April 19, 2023 16/19 Real Estate & Housing – Faculty of Architecture – Delft University of Technology
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.
April 19, 2023 17/19 Real Estate & Housing – Faculty of Architecture – Delft University of Technology
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
April 19, 2023 18/19 Real Estate & Housing – Faculty of Architecture – Delft University of Technology
• 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