1. The Determinants of Redeveloping Sites in a City- the Taipei Experience Tzuchin Lin, Yu-Hsiang Tsai Dept. of Land Economics National Chengchi University TAIWAN 1 ERES 13 June-16 June 2012, Edinburgh

2. Outlines  Site Redevelopment in a City  Variable Selection and Visual Inspection  Regression Model and Results  Conclusions 2

3. Site Redevelopment in a City YearsSin-Yi Housing price index (Q4) New Floor Space as % of total stock Household Units 2001105.932.12% 894,763 2002107.912.11% 906,988 2003115.951.91% 914,716 2004132.081.94% 923,325 2005145.451.57% 933,110 2006166.641.46% 941,317 2007181.531.67% 947,745 2008182.471.93% 958,433 2009220.03 969,418 3 Housing price, new floor spaces, and household units over time

4. Site Redevelopment in a City  Built sites need to be redeveloped – buildings are short of supply – buildings have been significantly deteriorated  A city – where land is scarce – site redevelopment is commonly observed – Lin (2012): half of demolished buildings only reached half of their physical life 4

5. Site Redevelopment in a City  When a building was torn down – benefits from a new building exceed the benefits of continuing use of an old building (demolition costs considered) – previous studies focused on what determines the timing of demolishing a building – what determines the redevelopment pace of a city as a whole through replacement of buildings 5

6. Site Redevelopment in a City  Taipei – 9,593 inhabitants per km 2 – housing price more than doubles within last 10 years – an ideal place for investigation 6

7. Site Redevelopment in a City  Factors that affect site redevelopment – macro factors: income level, housing stock, population changes – site and building: zoning, building age, land acquisition – location: distance to city centre, accessibility to facilities 7

8. Variable Selection and Visual Inspection  Data Sources – government records -- permits to demolish and construct a building (2001-2009) -- public buildings and public facilities are excluded – government statistics -- population, household number, income, building age, building vacancy rate – city zoning map -- metro stations 8

9. Variable Selection and Visual Inspection  Analytical unit- neighborhood 9

10. Variable Selection and Visual Inspection Percentage of net floor space 10 Net floor spaces Max. allowable floor spaces over 9 years

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12. Variable Selection and Visual Inspection 12 Ahigh-higha new metro line Bhigh-high financial hub (Taipei 101) Clow-low old and run-down neighborhoods A B C unit-L i high-high low-low low-high

13. Regression Results  Dependent variable – percentage of net floor space  Independent variable – rate of changes in population – rate of changes in household number – income level (low, medium, high) – average building ages – variation of building ages – building vacancy rate – metro station (yes, no) 13 Net floor spaces Max. allowable floor spaces over 9 years Macro factors Site and building Location

14. Regression Results 14 Dependent var Independent var Rate of net floor space increase β Standard- ized β T valueP value Constant0.209*--2.5440.011 Rate of changes in population -0.022-0.017-0.1950.846 Rate of changes in household number 0.531**0.4464.6840.000 Income level: medium 0.042**0.1172.7180.007 Income level: high 0.089**0.1783.3080.001 Average building ages - 0.004** -0.188-2.5490.010 Variation of building ages -0.106-0.110-1.5420.124 Building vacancy rate -0.119-0.042-1.1520.250 Metro station: yes 0.042**0.1192.6190.009 R2R2 0.276** Unit-Li number449 * : α<0.05 **: α<0.01

15. Spatial Regression 15 Spatial Reg Type Independent Var Spatial Lag ModelSpatial Error Model βZ valueP valueβZ valueP value Ρ0.1081.5440.123 Constant 0.183**3.1660.0020.200**3.4350.001 Rate of changes in population -0.022-0.1920.848-0.019-0.1650.869 Rate of changes in household number 0.527**5.2850.0000.525**5.2310.000 Income level: medium 0.037*2.0110.0440.040*2.1210.034 Income level: high 0.082**3.2040.0010.086**3.2850.001 Average building ages -0.004**-3.0680.002-0.004**-3.2080.001 Variation of building ages -0.095-1.9050.057-0.095-1.8770.061 Building vacancy rate -0.109-0.8870.375-0.114-0.9080.364 Metro station: yes 0.041**2.8140.0050.041**2.8160.005 Λ 0.0560.7060.480 R2R2 0.2810.277 Diagnose OLSSpatial LagSpatial Error Test type Test valueP valueTest valueP valueTest valueP value LR test 2.566 0.1090.477 0.490 LM-lag 2.9470.086 LM-error 0.4700.493 * : α<0.05 **: α<0.01

16. Conclusions  Taipei has continued growing through replacement of old buildings – increase: 386, decrease: 22, unchanged: 41 (mostly in building-restricted areas)  Household number matters more than population: demographical changes  New floor supply was unable to press down the rising price 16

17.  Site redevelopment has not produced spillover effects  New floor supply are more likely to appear in wealthy neighborhoods than in run-down areas 17

18. Thank you for your listening! 18