臺灣博碩士論文加值系統

購屋者常因不會估價而買到不合理的價格，雖然過去有許多學者提出不同房價預測的方法，但是預測的房價與實際價格仍有許多誤差，因此，如何減少預測房價的誤差是一個重要的議題。

本研究提出二階段的研究程序，第一階段為建構房屋估價模式，首先透過回顧過去不動產估價作業程序文獻後，建構房屋估價模式雛型，接著，本研究以機器學習理論為基礎，提出PSO-SVRXGBoost模型，以建構房價預測模型，最後將房屋估價模式雛型與房價預測模型結合，建構出嶄新的房屋估價模式。第二階段，為了讓購屋者快速獲得估價的結果，本研究透過雛型開發法，開發房屋估價系統，以輔助購屋者做使用。

本研究以台北市實價登錄的資料檢驗本研究提出的PSO-SVRXGBoost的績效，結果顯示房價預測值誤差低於SVR和XGBoost。此外，經過滿意度分析，94%的購屋者對於本研究開發的房屋估價系統相當滿意。

最後，本研究提出的二階段的研究程序，可提供後續研究人員加以沿用與擴充。本研究所使用的五步驟開發程序及滿意度評估亦可作為日後建構房屋估價系統之參考，降低開發風險與提升系統品質。對於購屋者而言，本研究開發之系統可以自動估價，解決以往不知如何委託或是尋找專業估價師的問題，並可快速瞭解房屋的價值。對於不動產業者而言，本研究之結果可以減少房價預測的誤差值，以更準確地估價。

House buyers often buy unreasonable prices due to their inability to evaluate. Although many scholars have put forward different methods for predicting house prices in the past, there are still many errors between the predicted house prices and the actual prices. Therefore, how to reduce the error of predicting house prices is an important issue.

The first stage is to construct the housing valuation model. First, the prototype of the housing valuation model is constructed after reviewing the literature of the past real estate valuation operation procedures. Then, based on the machine learning theory, the PSO-SVRXGBoost model is proposed to construct the housing price prediction model. Finally, combining the housing valuation model with the housing price forecast model, a new housing valuation model is constructed. In the second stage, in order to enable buyers to quickly obtain the appraisal results, this study developed a housing appraisal system through the prototype development method to assist buyers to use.

In this study, the performance of PSO-SVRXGBoost proposed in this study was tested with the real price registration data of Taipei City, and the results show that the error of housing price prediction value is lower than SVR and XGBoost. In addition, after satisfaction analysis, 94% of buyers are quite satisfied with the house appraisal system developed in this study.

Finally, This study proposes a two-stage research procedure that can be followed and expanded by subsequent researchers. The five-step development procedure and satisfaction evaluation used in this study can also be used as a reference for the future construction of housing appraisal system, reducing development risk and improving system quality. For home buyers, the system developed in this study can automatically evaluate, solve the problem of how to entrust or find a professional appraiser in the past, and can quickly understand the value of the house. For realtors, the results of this study can reduce the error value of housing price prediction, so as to achieve more accurate valuation.

摘　要 i
ABSTRACT iii
誌謝 v
目錄 vi
表目錄 viii
圖目錄 ix
一、 緒論 1
1.1 研究背景 1
1.2 研究動機 2
1.3 研究目的 11
1.4 論文架構 12
二、 文獻回顧 14
2.1 房屋估價方法 14
2.2 影響房價的因素 16
2.3 粒子群演算法 19
2.4 支持向量機 22
2.5 極限梯度提升 26
2.6 資訊系統成功模式 29
三、 研究方法 30
3.1 第一階段：建構房屋估價模式 31
3.1.1 建構房屋估價模式雛型 31
3.1.2 建構房價預測模型 31
3.1.3 評估模型 37
3.1.4 建構嶄新的房屋估價模式 38
3.2 第二階段：開發房屋估價系統 39
3.2.1 雛型開發法 39
3.2.2 (一)：需求分析 39
3.2.3 (二)：系統分析 41
3.2.4 (三)：系統設計 42
3.2.5 (四)：系統開發 42
3.2.6 (五)：房屋估價系統滿意度調查 42
四、 研究結果 43
4.1 第一階段：建構房屋估價模式之結果 43
4.1.1 建構房屋估價模式雛型 43
4.1.2 建構房價預測模型結果 44
4.1.3 評估模型結果 52
4.1.4 建構嶄新的房屋估價模式結果 55
4.2. 第二階段：開發房屋估價系統之結果 56
4.2.1 (一)：需求分析結果 56
4.2.2 (二)：系統分析結果 57
4.2.3 (三)：系統設計結果 57
4.2.4 (四)：系統開發結果 58
4.2.5 (五)：房屋估價系統滿意度調查結果 59
4.2.6 個案示例 61
五、 結論與建議 63
5.1 結論 63
5.2 研究貢獻 64
5.2.1 學術貢獻 64
5.2.2 實務貢獻 64
5.3 管理意涵 64
5.4 研究限制 65
5.5 未來研究建議 65
參考文獻 67
附錄一、房屋估價系統滿意度調查問卷 72

中文
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