The Impact of

Urban Green Vegetation on

Nearby Housing Prices:

Evidence from Kaohsiung, Taiwan

Yi-Ting Lee

Chien-Yuan Sher

Han-Ching Hsieh

NSYSU 中山大學

Management College

X

Taiwan Forestry Research Institute

11^th Asian Seminar in Regional Science

Oct.8 – Oct.9, 2021

Introduction

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Research question

Estimate the benefit of urban green vegetation on nearby residents.

• Observe the increments of housing prices
• A willingness to pay to live near green amenities

YT Lee, CY Sher, and HC Hsieh

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• Most studies used hedonic price models (Waltert and Schläpfer 2010).
• Many models had spatial dependency terms (Franco and Macdonald 2018).
• Many works focused on green open spaces (Franco and Macdonald 2018).
• Many works indicated that proximity to green open spaces improves home values (Sander and Zhao 2015); some found negative or insignificant effect (Waltert and Schläpfer 2010).

Literature review

YT Lee, CY Sher, and HC Hsieh

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• Ignoring sporadic green vegetation (Franco and Macdonald 2018)
• Omitting unobservable community features

Literature review

Methodology issues of previous works:

YT Lee, CY Sher, and HC Hsieh

Methodology

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Data

• 130436 transactions of residential properties from the 3^rd season of 2012 to the end of 2019 in old Kaohsiung City, the urban area of Kaohsiung Special Municipality (the blue part in the map).

YT Lee, CY Sher, and HC Hsieh

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Data

YT Lee, CY Sher, and HC Hsieh

• Green coverage ratios for 4548 1^st-level dissemination areas and 702 2^nd-level dissemination areas

Satellite imagery taken by FORMOSAT-2 and SPOT 6

NDVI of each pixel

Green coverage ratio for each area

Source: Taiwan Forestry Research Institute

Satellite imagery of old Kaohsiung City

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Empirical methodology

• A hedonic model with a spatial dependency term

• Pi,j,t: Total price of property i in the dissemination area j sold in season t
• PSi,t-1: Average unit price around property i happened in season t-1
• SGVj,t: Surrounding green vegetation cover of the dissemination area j
• PGVj,t: Periphery green vegetation cover of the dissemination area j

YT Lee, CY Sher, and HC Hsieh

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Empirical methodology

YT Lee, CY Sher, and HC Hsieh

• A hedonic model with a spatial dependency term

• SGVj,t (GC for dark green part)
• PGVj,t (GC for light green part)

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Empirical methodology

• A hedonic model considering unobservable community features

• Take the average over transactions in the same 2^nd-leve dissemination area

YT Lee, CY Sher, and HC Hsieh

• Deduct the average from the value of each observation, we get our empirical model as follows:

Results

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Data Description

YT Lee, CY Sher, and HC Hsieh

• The Average of total price of each transaction: 7515K NT dollars
• The Average of SGVj,t: 0.13
• The Average of PGVj,t: 0.16

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D: densely populated area

​

Lush: SGVj,t > 0.5

Medium: 0.5 > SGVj,t >0.1

Low: 0.1 > SGVj,t > 0

No vegetation: SGVj,t = 0

Data Description

YT Lee, CY Sher, and HC Hsieh

• The distributions of transactions among categories

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Estimation Results

YT Lee, CY Sher, and HC Hsieh

• Estimation without a consideration of unobservable community features

• Estimated coefficients on SGVj,t: significant but negative
• Estimated coefficients on PGVj,t: significant but negative

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Estimation Results

YT Lee, CY Sher, and HC Hsieh

• Estimation considering unobservable community features

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Conclusion

YT Lee, CY Sher, and HC Hsieh

• If the SGV of a property is higher than the mean SGV of the properties in the same community by 0.18, its price will be higher than the mean property price in the same community by 0.6%, ceteris paribus.
• Buyers may tend to care for PGV, but this tendency is not significant.

Thanks for

Your Attention

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