The Determinants of Redeveloping Sites in a City- the Taipei ExperienceTzuchin Lin, Yu-Hsiang TsaiDept. of Land EconomicsNational Chengchi UniversityTAIWAN

1ERES 13 June-16 June 2012, Edinburgh

Outlines

Site Redevelopment in a City

Variable Selection and Visual Inspection

Regression Model and Results

Conclusions

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Site Redevelopment in a City

Years Sin-Yi Housing price index (Q4)

New Floor Space as % of total stock

Household Units

2001 105.93 2.12% 894,7632002 107.91 2.11% 906,9882003 115.95 1.91% 914,7162004 132.08 1.94% 923,3252005 145.45 1.57% 933,1102006 166.64 1.46% 941,3172007 181.53 1.67% 947,7452008 182.47 1.93% 958,4332009 220.03 969,418

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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

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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

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Site Redevelopment in a City

Taipei– 9,593 inhabitants per km2

– housing price more than doubles within last 10 years

– an ideal place for investigation

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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

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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

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Variable Selection and Visual Inspection Analytical unit- neighborhood

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Variable Selection and Visual Inspection

Percentage of net floor space

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Net floor spacesMax. allowable floor spaces over 9 years

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Variable Selection and Visual Inspection

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A high-high a new metro line

B high-high

financial hub(Taipei 101)

C low-low old and run-down neighborhoods

A

BC

unit-Lihigh-highlow-lowlow-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)

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Net floor spacesMax. allowable floor spaces over 9 years

Regression Results14

Dependent varIndependent var

Rate of net floor space increase β Standard-ized β T value P value

Constant 0.209* -- 2.544 0.011Rate of changes in population

-0.022 -0.017 -0.195 0.846Rate of changes in household number

0.531** 0.446 4.684 0.000Income level: medium 0.042** 0.117 2.718 0.007Income level: high 0.089** 0.178 3.308 0.001Average building ages -

0.004** -0.188 -2.549 0.010Variation of building ages -0.106 -0.110 -1.542 0.124Building vacancy rate -0.119 -0.042 -1.152 0.250Metro station: yes 0.042** 0.119 2.619 0.009R2 0.276**Unit-Li number 449

* : α<0.05**: α<0.01

Spatial Regression15

* : 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

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Site redevelopment has not produced spillover effects

New floor supply are more likely to appear in wealthy neighborhoods than in run-down areas

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Thank you for your listening!

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