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題名 半參數地理加權邏輯斯迴歸在實價登錄資料分析的應用
An application of semi-parametric geographically weighted logistic regression in real estate transaction data analysis
作者 鄭貫廷
Zheng, Guan-Ting
貢獻者 陳怡如
Chen, Yi-Ju
鄭貫廷
Zheng, Guan-Ting
關鍵詞 實價登錄
新冠肺炎疫情
中古屋市場分析
空間異質性
半參數地理加權邏輯斯迴歸
Actual price registration
COVID-19 epidemic
Pre-owned home market analysis
Spatial heterogeneity
Semi-parametric Geographically Weighted Logistic Regression
日期 2024
上傳時間 4-九月-2024 14:56:34 (UTC+8)
摘要 自101年8月1日起，實價登錄制度的實施使房價資訊更加透明，有助於深入分析影響房市的因素。自全球疫情爆發以來，原物料價格上漲推高了新建房屋的成本，使得價格相對穩定且地理位置優越的中古屋更受青睞。為了探討不同地區房市交易狀況與各因素之間的關係，近年來許多學者開始重視空間分析。在空間統計分析中，空間異質性 (spatial heterogeneity) 強調變數間的關係會因地區不同而有所變化；而地理加權迴歸 (Geographically Weighted Regression；GWR) 是探討此類空間異質性時備受歡迎的方法之一。然而，由於技術和軟體的限制，目前的房市分析中對於空間異質性的討論較少，特別是針對部分迴歸關係允許隨空間變動或不隨空間變動的分析更是相對缺乏。本研究認為，半參數地理加權迴歸建模技術能更準確地捕捉房市中的空間異質性。此外，儘管現有軟體已提供了半參數地理加權迴歸建模的工具，但在處理二項分佈的比例型資料時，特別是使用半參數地理加權邏輯斯迴歸模型 (Semi-parametric Geographically Weighted Logistic Regression；S-GWLR) 進行分析，仍然存在一定的侷限性，而且其在房市分析中的應用相對較少見。為了填補這一空白，本研究聚焦於中古屋成交比例資料的分析，利用新冠疫情第三級緊戒發布前後一年內新北市1032個村里的實價登錄資料，運用半參數地理加權邏輯斯迴歸技術，探討各項特徵因子對中古屋成交狀況的影響是否存在空間異質性，並比較疫情前後的變化。 本研究採用兩階段估計方法來估計半參數地理加權邏輯斯迴歸的模型參數，並透過拔靴法來估計標準誤及進行變數的空間平穩性分析。本研究利用模擬實驗進一步證實，隨著樣本量的增加，兩階段估計方法在半參數地理加權邏輯斯迴歸模型中的精確度顯著提高。在實價登錄之中古屋資料分析中，本研究將模型結果與廣義線性迴歸模型及地理加權廣義線性迴歸模型進行比較，結果顯示，半參數地理加權邏輯斯模型在表現上優於非空間廣義線性迴歸模型與地理加權廣義線性迴歸模型，顯示出其在房地產市場分析中的重要應用價值。
Since August 1, 2012, the introduction of the Actual Price Registration System has made housing price information more transparent, facilitating in-depth analysis of the real estate market. Since the outbreak of the global COVID-19 pandemic, rising raw material costs have increased the cost of newly constructed homes, making preowned home more desirable due to their relatively stable prices and advantageous locations. To explore the relationships between housing market transactions and various factors in different regions, many scholars have increasingly focused on spatial analysis in recent years. In spatial statistical analysis, spatial heterogeneity emphasizes that the relationships between variables may vary across regions, and geographically weighted regression (GWR) is a popular method for exploring such spatial heterogeneity. However, due to technical and software limitations, the discussion of spatial heterogeneity in current real estate market analysis is limited, particularly in analyses that allow some regression relationships to vary spatially while others do not. In this study, we argue that semi-parametric geographically weighted regression modeling techniques can more effectively capture spatial heterogeneity in the housing market. In addition, while existing software provides tools for semi-parametric geographically weighted regression analysis, they have limitations when handling proportion data with a binomial distribution as the response variable, and the application of semi-parametric geographically weighted logistic regression (S-GWLR) models to real estate market analysis remains relatively rare. To address this gap, this study focuses on analyzing the proportion of transactions in preowned home using data from the Actual Price Registration System of 1032 villages in New Taipei City within one year before and after the COVID-19 pandemic. By applying S-GWLR techniques, we aim to examine the spatial heterogeneity in the regression relationships between various factors and the proportion of transactions in preowned home. We employ a two-stage geographically weighted maximum likelihood method to estimate the parameters of the S-GWLR model. In addition, we utilize the bootstrapping method to compute the standard errors of the estimators and to conduct tests for spatial stationarity. Simulation experiments further verify that the two-stage estimation method achieves high accuracy in S-GWLR models with proportion outcomes as the sample size increases. In our empirical analysis of real estate housing data in New Taipei City, we compare the results of the S-GWLR model with those of the classical non-spatial logistic regression (LR) model and the geographically weighted generalized linear regression (GWLR) model. The findings show that the S-GWLR model outperforms both the non-spatial LR and the GWLR models, demonstrating its significant application value in real estate market analysis.
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描述 碩士
國立政治大學
統計學系
111354019
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0111354019
資料類型 thesis
dc.contributor.advisor 陳怡如zh_TW
dc.contributor.advisor Chen, Yi-Juen_US
dc.contributor.author (作者) 鄭貫廷zh_TW
dc.contributor.author (作者) Zheng, Guan-Tingen_US
dc.creator (作者) 鄭貫廷zh_TW
dc.creator (作者) Zheng, Guan-Tingen_US
dc.date (日期) 2024en_US
dc.date.accessioned 4-九月-2024 14:56:34 (UTC+8)-
dc.date.available 4-九月-2024 14:56:34 (UTC+8)-
dc.date.issued (上傳時間) 4-九月-2024 14:56:34 (UTC+8)-
dc.identifier (其他 識別碼) G0111354019en_US
dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/153365-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 統計學系zh_TW
dc.description (描述) 111354019zh_TW
dc.description.abstract (摘要) 自101年8月1日起，實價登錄制度的實施使房價資訊更加透明，有助於深入分析影響房市的因素。自全球疫情爆發以來，原物料價格上漲推高了新建房屋的成本，使得價格相對穩定且地理位置優越的中古屋更受青睞。為了探討不同地區房市交易狀況與各因素之間的關係，近年來許多學者開始重視空間分析。在空間統計分析中，空間異質性 (spatial heterogeneity) 強調變數間的關係會因地區不同而有所變化；而地理加權迴歸 (Geographically Weighted Regression；GWR) 是探討此類空間異質性時備受歡迎的方法之一。然而，由於技術和軟體的限制，目前的房市分析中對於空間異質性的討論較少，特別是針對部分迴歸關係允許隨空間變動或不隨空間變動的分析更是相對缺乏。本研究認為，半參數地理加權迴歸建模技術能更準確地捕捉房市中的空間異質性。此外，儘管現有軟體已提供了半參數地理加權迴歸建模的工具，但在處理二項分佈的比例型資料時，特別是使用半參數地理加權邏輯斯迴歸模型 (Semi-parametric Geographically Weighted Logistic Regression；S-GWLR) 進行分析，仍然存在一定的侷限性，而且其在房市分析中的應用相對較少見。為了填補這一空白，本研究聚焦於中古屋成交比例資料的分析，利用新冠疫情第三級緊戒發布前後一年內新北市1032個村里的實價登錄資料，運用半參數地理加權邏輯斯迴歸技術，探討各項特徵因子對中古屋成交狀況的影響是否存在空間異質性，並比較疫情前後的變化。 本研究採用兩階段估計方法來估計半參數地理加權邏輯斯迴歸的模型參數，並透過拔靴法來估計標準誤及進行變數的空間平穩性分析。本研究利用模擬實驗進一步證實，隨著樣本量的增加，兩階段估計方法在半參數地理加權邏輯斯迴歸模型中的精確度顯著提高。在實價登錄之中古屋資料分析中，本研究將模型結果與廣義線性迴歸模型及地理加權廣義線性迴歸模型進行比較，結果顯示，半參數地理加權邏輯斯模型在表現上優於非空間廣義線性迴歸模型與地理加權廣義線性迴歸模型，顯示出其在房地產市場分析中的重要應用價值。zh_TW
dc.description.abstract (摘要) Since August 1, 2012, the introduction of the Actual Price Registration System has made housing price information more transparent, facilitating in-depth analysis of the real estate market. Since the outbreak of the global COVID-19 pandemic, rising raw material costs have increased the cost of newly constructed homes, making preowned home more desirable due to their relatively stable prices and advantageous locations. To explore the relationships between housing market transactions and various factors in different regions, many scholars have increasingly focused on spatial analysis in recent years. In spatial statistical analysis, spatial heterogeneity emphasizes that the relationships between variables may vary across regions, and geographically weighted regression (GWR) is a popular method for exploring such spatial heterogeneity. However, due to technical and software limitations, the discussion of spatial heterogeneity in current real estate market analysis is limited, particularly in analyses that allow some regression relationships to vary spatially while others do not. In this study, we argue that semi-parametric geographically weighted regression modeling techniques can more effectively capture spatial heterogeneity in the housing market. In addition, while existing software provides tools for semi-parametric geographically weighted regression analysis, they have limitations when handling proportion data with a binomial distribution as the response variable, and the application of semi-parametric geographically weighted logistic regression (S-GWLR) models to real estate market analysis remains relatively rare. To address this gap, this study focuses on analyzing the proportion of transactions in preowned home using data from the Actual Price Registration System of 1032 villages in New Taipei City within one year before and after the COVID-19 pandemic. By applying S-GWLR techniques, we aim to examine the spatial heterogeneity in the regression relationships between various factors and the proportion of transactions in preowned home. We employ a two-stage geographically weighted maximum likelihood method to estimate the parameters of the S-GWLR model. In addition, we utilize the bootstrapping method to compute the standard errors of the estimators and to conduct tests for spatial stationarity. Simulation experiments further verify that the two-stage estimation method achieves high accuracy in S-GWLR models with proportion outcomes as the sample size increases. In our empirical analysis of real estate housing data in New Taipei City, we compare the results of the S-GWLR model with those of the classical non-spatial logistic regression (LR) model and the geographically weighted generalized linear regression (GWLR) model. The findings show that the S-GWLR model outperforms both the non-spatial LR and the GWLR models, demonstrating its significant application value in real estate market analysis.en_US
dc.description.tableofcontents 目錄 誌謝 i 摘要 ii Abstract iv 目錄 vi 圖目錄 ix 表目錄 xi 第一章緒論 p1 1.1 研究動機 p1 1.2 研究目的 p4 1.3 研究架構 p4 第二章文獻探討 p6 2.1 影響房地產成交狀況之因素 p6 2.1.1 物件特徵因子 p6 2.1.2 外部環境因子 p7 2.1.3 總體因子 p7 2.1.4 新冠肺炎疫情與中古屋市場 p8 2.2 地理加權迴歸 p9 2.2.1 係數估計 p10 2.2.2 帶寬與核函數 p10 第三章研究方法 p13 3.1 邏輯斯迴歸模型 p13 3.1.1 係數估計 p13 3.2 地理加權邏輯斯迴歸模型(GWLR) p14 3.2.1 係數估計 p14 3.3 半參數地理加權邏輯斯迴歸模型(S-GWLR) p16 3.3.1 兩階段估計方法(TSGWML) p16 3.3.2 拔靴法檢定空間平穩性 p18 3.3.3 拔靴法計算標準誤 p19 3.4 拓展地理加權邏輯斯迴歸以適應比例資料型態 p20 3.5 空間自相關檢定 p21 3.6 模型評估指標 p22 3.6.1 偏差(Deviance) p22 3.6.2 赤池資訊量準則(AIC) p22 3.6.3 修正後赤池資訊量準則(AICc) p23 3.6.4 貝葉斯資訊量準則(BIC) p23 第四章實價登錄資料介紹與前處理 p24 4.1 資料來源 p24 4.2 資料前處理 p26 4.3 反應變數 p27 4.4 解釋變數 p27 4.4.1 房屋特徵 p27 4.4.2 社會經濟特徵 p28 4.4.3 鄰近設施特徵 p29 4.5 變數篩選 p30 第五章模擬實證 p34 5.1 模擬資料生成與參數設定 p34 5.2 模型評估指標 p35 5.3 模擬結果與分析 p38 第六章研究結果 p40 6.1 敘述性資料分析 p40 6.1.1 反應變數 p40 6.1.2 解釋變數 p41 6.2 傳統非空間邏輯斯迴歸模型分析 p47 6.3 空間平穩性分析 p48 6.4 空間自相關分析 p49 6.5 模型比較 p50 6.6 變數顯著性 p51 6.6.1 常數變數顯著性 p51 6.6.2 空間變化變數顯著性 p52 第七章結論 p57 7.1 總結與討論 p57 7.2 未來研究方向 p59 參考文獻 p61 附錄 A p66zh_TW
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dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0111354019en_US
dc.subject (關鍵詞) 實價登錄zh_TW
dc.subject (關鍵詞) 新冠肺炎疫情zh_TW
dc.subject (關鍵詞) 中古屋市場分析zh_TW
dc.subject (關鍵詞) 空間異質性zh_TW
dc.subject (關鍵詞) 半參數地理加權邏輯斯迴歸zh_TW
dc.subject (關鍵詞) Actual price registrationen_US
dc.subject (關鍵詞) COVID-19 epidemicen_US
dc.subject (關鍵詞) Pre-owned home market analysisen_US
dc.subject (關鍵詞) Spatial heterogeneityen_US
dc.subject (關鍵詞) Semi-parametric Geographically Weighted Logistic Regressionen_US
dc.title (題名) 半參數地理加權邏輯斯迴歸在實價登錄資料分析的應用zh_TW
dc.title (題名) An application of semi-parametric geographically weighted logistic regression in real estate transaction data analysisen_US
dc.type (資料類型) thesisen_US
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