Exploring nonlinear and interaction effects of TOD on housing rents using XGBoost

Understanding the relationship between transit-oriented development (TOD) and housing rents is crucial for formulating effective TOD strategies and optimizing housing market management. These strategies contribute to a healthy housing market and sustainable urban development. Traditional regression...

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Vydané v:Cities Ročník 158; s. 105728
Hlavní autori: Peng, Chen, Yang, Shengfu, Zhang, Peng, Hu, Shougeng
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier Ltd 01.03.2025
Predmet:
Housing rent
Machine learning
Nonlinear effect
SHAP
Transit-oriented development
XGBoost
SHAP
Nonlinear effect
Transit-oriented development
XGBoost
Housing rent
Machine learning
ISSN:0264-2751
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Abstract Understanding the relationship between transit-oriented development (TOD) and housing rents is crucial for formulating effective TOD strategies and optimizing housing market management. These strategies contribute to a healthy housing market and sustainable urban development. Traditional regression models used in existing studies often fail to capture the nonlinear, and interaction effects of TOD on housing rents. This study addresses these limitations by applying the eXtreme Gradient Boosting (XGBoost) algorithm combined with Shapley Additive Explanations (SHAP) analysis to evaluate the effects of TOD on housing rents within Wuhan's Third Ring Road. Our approach not only identifies key TOD factors such as overall walkability, parking lot density, and commercial density but also uncovers significant nonlinear and threshold effects on housing rents. Moreover, we reveal the intricate interaction effects among key TOD variables, demonstrating how the local impact of one factor can be amplified or diminished by changes in another. This study provides novel insights into the complex mechanisms of TOD impacts on housing rents and offers actionable guidance for crafting targeted urban development strategies that promote urban equity and foster a sustainable housing market. •Application of the XGBoost algorithm and SHAP analysis to examine the effects of TOD on housing rents•Overall walkability, parking lots density, and commercial density are crucial contributors affecting housing rents in TOD.•Demonstrates the nonlinear relationship between TOD and housing rent•Highlights intricate interaction effects among TOD variables•Provides practical implications for more effective urban development strategies
AbstractList Understanding the relationship between transit-oriented development (TOD) and housing rents is crucial for formulating effective TOD strategies and optimizing housing market management. These strategies contribute to a healthy housing market and sustainable urban development. Traditional regression models used in existing studies often fail to capture the nonlinear, and interaction effects of TOD on housing rents. This study addresses these limitations by applying the eXtreme Gradient Boosting (XGBoost) algorithm combined with Shapley Additive Explanations (SHAP) analysis to evaluate the effects of TOD on housing rents within Wuhan's Third Ring Road. Our approach not only identifies key TOD factors such as overall walkability, parking lot density, and commercial density but also uncovers significant nonlinear and threshold effects on housing rents. Moreover, we reveal the intricate interaction effects among key TOD variables, demonstrating how the local impact of one factor can be amplified or diminished by changes in another. This study provides novel insights into the complex mechanisms of TOD impacts on housing rents and offers actionable guidance for crafting targeted urban development strategies that promote urban equity and foster a sustainable housing market. •Application of the XGBoost algorithm and SHAP analysis to examine the effects of TOD on housing rents•Overall walkability, parking lots density, and commercial density are crucial contributors affecting housing rents in TOD.•Demonstrates the nonlinear relationship between TOD and housing rent•Highlights intricate interaction effects among TOD variables•Provides practical implications for more effective urban development strategies
ArticleNumber 105728
Author Zhang, Peng
Yang, Shengfu
Hu, Shougeng
Peng, Chen
Author_xml – sequence: 1
  givenname: Chen
  surname: Peng
  fullname: Peng, Chen
  organization: School of Public Administration, China University of Geosciences, Wuhan 430074, PR China
– sequence: 2
  givenname: Shengfu
  surname: Yang
  fullname: Yang, Shengfu
  email: yangshf@cug.edu.cn
  organization: School of Public Administration, China University of Geosciences, Wuhan 430074, PR China
– sequence: 3
  givenname: Peng
  surname: Zhang
  fullname: Zhang, Peng
  organization: School of Land Engineering, Chang'an University, Xi'an 710054, PR China
– sequence: 4
  givenname: Shougeng
  surname: Hu
  fullname: Hu, Shougeng
  organization: School of Public Administration, China University of Geosciences, Wuhan 430074, PR China
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Keywords SHAP
Nonlinear effect
Transit-oriented development
XGBoost
Housing rent
Machine learning
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SSID ssj0004640
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Snippet Understanding the relationship between transit-oriented development (TOD) and housing rents is crucial for formulating effective TOD strategies and optimizing...
SourceID crossref
elsevier
SourceType Enrichment Source
Index Database
Publisher
StartPage 105728
SubjectTerms Housing rent
Machine learning
Nonlinear effect
SHAP
Transit-oriented development
XGBoost
Title Exploring nonlinear and interaction effects of TOD on housing rents using XGBoost
URI https://dx.doi.org/10.1016/j.cities.2025.105728
Volume 158
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