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

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

Site Redevelopment in a City YearsSin-Yi Housing price index (Q4) New Floor Space as % of total stock Household Units % 894, % 906, % 914, % 923, % 933, % 941, % 947, % 958, ,418 3 Housing price, new floor spaces, and household units over time

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

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

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

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

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

Variable Selection and Visual Inspection  Analytical unit- neighborhood 9

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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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

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

Regression Results 14 Dependent var Independent var Rate of net floor space increase β Standard- ized β T valueP value Constant0.209* Rate of changes in population Rate of changes in household number 0.531** Income level: medium 0.042** Income level: high 0.089** Average building ages ** Variation of building ages Building vacancy rate Metro station: yes 0.042** R2R ** Unit-Li number449 * : α<0.05 **: α<0.01

Spatial Regression 15 Spatial Reg Type Independent Var Spatial Lag ModelSpatial Error Model βZ valueP valueβZ valueP value Ρ Constant 0.183** ** Rate of changes in population Rate of changes in household number 0.527** ** Income level: medium 0.037* * Income level: high 0.082** ** Average building ages ** ** Variation of building ages Building vacancy rate Metro station: yes 0.041** ** Λ R2R Diagnose OLSSpatial LagSpatial Error Test type Test valueP valueTest valueP valueTest valueP value LR test LM-lag LM-error * : α<0.05 **: α<0.01

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

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

Thank you for your listening! 18