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研究生： 黃柏崴
Huang, Bo-Wei
論文名稱： 以XGBoost演算法分析台北市交易層級房價
Transaction Level Housing Prices in Taipei with XGBoost Algorithm
指導教授： 盧姝璇
Lu, Shu-Shiuan
口試委員: 唐震宏
TANG, JENN-HONG
林常青
Lin, Chang-Ching
學位類別： 碩士
Master
系所名稱： 科技管理學院 - 經濟學系
Department of Economics
論文出版年： 2024
畢業學年度： 112
語文別： 英文
論文頁數： 85
中文關鍵詞： XGBoost 、房價 、使用者成本
外文關鍵詞： XGBoost, housing price, user cost
相關次數： 點閱：41  下載：6 
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    • 本研究檢視台灣的房地產交易層級價格，針對高房價及其對財富不平等的影響所引發的關注進行探討。研究旨在建立台北房價的基準模型，判斷市場是否存在房價高估或低估的情況。我們提出的核心問題是：「如何判斷一棟房子是否定價過高或過低？」及其應用問題：「如何採用財政政策來解決房價失衡的問題？」我們採用使用者成本模型來估算年度房屋擁有成本，並使用XGBoost演算法來估算租金價格作為基準模型，比較擁有與租賃的成本。主要結果顯示，將資產稅率提高2.4\%，表明將有效資產稅率調整到與可比市場相當的水平，可以大幅減少房屋不平等。這項經濟分析延伸了先前的研究，提供了稅收政策對房地產市場影響的可量化見解，為台灣持續進行的住房可負擔性和財富分配討論做出貢獻。

    This study examines transaction-level housing prices in Taiwan, addressing concerns over high housing costs and their effects on wealth inequality.
    The central question we asked is: "How do we determine if a house is overpriced or underpriced?" with a follow-up question: "How could fiscal policy be adopted to address housing price imbalances?" We employs a user cost model to proxy annual house ownership costs and uses the XGBoost algorithm to estimate imputed renting prices as benchmark model, comparing the cost of owning versus renting. Key findings suggest that increasing asset tax rates by 2.4%, indicating that adjusting the effective asset tax rate to a level comparable to markets could substantially reduce housing inequality. This economic analysis extends previous research by offering quantifiable insights into the effects of tax policy on housing markets, contributing to the ongoing dialogue on housing affordability and wealth distribution in Taiwan.

    Contents Abstract (Chinese)................................................. I Abstract.......................................................... II Acknowledgements................................................... III Contents........................................................... V List of Figures.................................................... VII List of Tables..................................................... VIII 1 Introduction....................................................... 1 2 Literature Review.................................................. 6 2.1 Literature Discussion towards The Cause of Housing Inequality..... 6 2.2 Theoretical Framework of Imputed Rent in Homeownership versus Renting Decision....................................................... 9 2.3 Empirical Studies on Imputed Rent and Homeownership versus Renting Decision..................................................... 11 2.4 Summary........................................................... 13 3 Methodology and User Cost Pin-Down................................. 16 3.1 Choices of estimation method: Rental Price........................ 19 3.1.1 Choice of estimation method - rental price.................... 19 3.1.2 Comparison of other widely used ML methods - Random Forest and Support Vector Machines (SVM)....................................... 22 3.2 User cost pin-down............................................... 25 3.2.1 Real risk-free rate (𝑟𝑓).................................. 25 3.2.2 Asset tax accrued from owning a house (𝜔𝑡)........... 26 3.2.3 The depreciation rate of holding a house (𝛿).......... 26 3.2.4 The expected real growth rate of housing price for a period (Δℎ𝑝𝑒/ℎ𝑝)................................................. 27 3.2.5 The risk premium for owning rather than renting a house (𝛾)..................................................... 27 4 Parameter and data source......................................... 29 4.1 Parameter Tuning.................................................. 32 5 Empirical Results.................................................. 33 5.1 Feature Importance Analysis....................................... 33 5.2 𝑑 Ratio Analysis............................................... 35 6 Conclusion........................................................ 44 A Assets Structure for Households Sector............................ 49 B Source code........................................................ 51 B.1 Housing Data Scrawling........................................... 51 B.2 XGBoost.......................................................... 71 B.3 Boxplot.......................................................... 80 B.4 Heat map......................................................... 82

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