| 姓名 |
鄭雅欣(Ya-Hsin Cheng)
查詢紙本館藏 |
畢業系所 |
資訊管理學系在職專班 |
| 論文名稱 |
不動產仲介業銷售住宅類別之成交預測模型—以不動產仲介S公司為例
(A transaction forecast model for residential sales in the real estate agency industry - Real estate agency S company for example)
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| 相關論文 | |
| 檔案 |
 [Endnote RIS 格式]
[Bibtex 格式]
 [相關文章]  [文章引用]  [完整記錄]  [館藏目錄]  至系統瀏覽論文 ( 永不開放)
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| 摘要(中) |
自 2020 年以來，整個社會掀起一波房地產討論熱潮，房屋、土地買賣移轉等相關話題此起彼落。在房地產關注度如此高之際，相關產業亦需加速數位轉型的腳步，透過持續優化內部作業流程、以演算法及大數據，建構客戶關係管理系統等，以新思維帶領企業邁向瞬變萬千的挑戰，「房地產科技」成為新的發展重點。其中，待銷售的房屋，對於房地產業者來說即為公司主要之獲利來源，然而，目前受委託的物件並無一個客觀用以衡量其競爭力的依據，若能開發相關工具或指標來衡量物件狀態，將有助於提升物件之銷售效率。經由過往文獻回顧發現較無客觀評斷房屋競爭力，關於銷售天數的研究與探討。因此，本研究之目的如下：
1. 運用不動產仲介業承接之房屋成交資料來建構銷售天數模型，並評估準確率。
2. 找出能有效預測房屋銷售天數之顯著影響因子與變數。
本研究基於房地產產業的應用背景，使用房地產委託之原始物件資料整理後，以演算法篩選重要特徵、運用各種方法進行建模並比較優劣，並以十摺交叉法作為驗證評估各種模型效益。經模型效益評估後，隨機森林及梯度提昇機的表現較線性回歸為佳，在評估回歸指標MSE與MAE 的結果上亦同。在重要的影響變數探討上，在自變數與銷售天數預測模型之間的關係，以「大門朝向」、「行政區」、「賣屋原因」為最主要影響因素的三大變數。 |
| 摘要(英) |
Since 2020, there has been a wave of real estate discussion in the society, and there are many topics related to housing and land sales and transfers. With such a high level of interest in real estate, the industry needs to accelerate the pace of digital transformation by continuously optimizing internal workflows, building customer relationship management systems with algorithms and big data, etc., and leading companies to the ever-changing challenges with new thinking. However, there is currently no objective basis for measuring the competitiveness of the objects commissioned. If relevant tools or indicators can be developed to measure the status of the objects, it will help to improve the efficiency of the sales of the objects. A review of past literature reveals that there is no objective assessment of housing competitiveness, and studies and research on days of sales. Therefore, the objectives of this study are as follows.
1. to construct a days-to-sale model and evaluate the accuracy rate by using the data of housing transactions undertaken by the real estate brokerage industry.
2. to identify the significant factors and variables that can effectively predict the days of sales.
Based on the application background of real estate industry, this study uses the original object data commissioned by the real estate industry and then uses an algorithm to filter the important features, apply various methods to model and compare the advantages and disadvantages, and use the cross-fold method as a validation to evaluate the effectiveness of various models. After the evaluation of model effectiveness, the random forest and gradient lifter performed better than linear regression, and the same results were obtained for the regression indicators MSE and MAE. In terms of important variables, the relationship between the independent variables and the day-of-sale prediction model, the three most important variables were "gate orientation", "administrative area", and "reason for sale". |
| 關鍵字(中) |
★ 房地產
★ 預測
★ 銷售天數
★ 多項變數
★ 永續 |
關鍵字(英) |
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| 論文目次 |
圖目錄 v
表目錄 vi
第一章 緒論 1
1.1 研究背景 1
1.2 研究動機 3
1.3 研究目的 4
1.4 研究架構與流程 5
第二章 文獻探討 7
2.1 房地產仲介業的經營概述 7
2.2 對房地產銷售預測的探討 8
第三章 研究方法 16
3.1 研究方法及架構 16
3.2 資料來源與前處理 17
3.3 研究變數說明 21
3.4 研究工具說明 23
3.5 實驗設計與評估 24
第四章 實證結果分析 26
4.1 實驗資料敘述性分析 26
4.2 實驗結果 32
4.3 變項重要性排序 34
第五章 結論與建議 37
5.1 研究結論與貢獻 37
5.2 研究限制與未來方向建議 38
參考文獻 39
中文部份 39
英文部份 40 |
| 參考文獻 |
中文部份
王翠瑛(2013)，房地產購屋動機、訊息來源與考量因素 以大台北地區為例，國立臺北大學企業管理學系碩士論文。連結：https://hdl.handle.net/11296/mz67y9，擷取日期：2022/06。
李維平、賴錦慧、劉學維、林昱彤、廖玟柔(2017)，整合市場比較法與資料探勘技術之房價預測模型，先進工程學刊；12卷3期 (2017 / 10 / 01)，P141 - 149
岳蕙蘭(2009)，台灣房仲業服務接觸品質研究，國立交通大學管理學院經營管理學程碩士論文。連結：http://140.113.39.130/cgi-bin/gs32/gsweb.cgi/ccd=xeAzzm/record?r1=1&h1=1，擷取日期：2022/04。
林佐裕(2019)，應用網路搜尋行為預測房地產市場，應用經濟論叢；105期(2019/06)，P221 – 257
彭建文(2003)，不動產仲介業經營型態差異對經營績效之影響，台灣土地研究；6卷2期 (2003 / 11)，P61 – 78
黃名義、許乃文(2012)，電子商務應用對房仲業績效之影響，國立屏東商業技術學院學報；14期 (2012 / 08)，P97 – 123
劉敏(2019)，以網路搜尋點擊次數分析臺北市租屋市場之租金，國立政治大學地政學系碩士論文。連結：https://hdl.handle.net/11296/dvqtz3，擷取日期：2022/06。
蔡幸芬(2016)，運用線性關係模式探討房屋本身特徵對房屋價格變動之影響，東吳大學經濟系碩士論文。連結：https://hdl.handle.net/11296/x4ru7d，擷取日期：2022/06。
戴孟宗、林素淳、林倢伃、謝彥如(2012)，房屋仲介企業辨識系統之易辨性研究，中華印刷科技年報；(2012 / 06 / 01)，P648 - 657
簡士鈞(2019)，臺灣房價與總體因素及政策之關聯性分析，國立台灣大學國際企業學研究所碩士論文。連結：https://hdl.handle.net/11296/yah4rb，擷取日期：2022/03。
英文部份
Ben-David, Itzhak (2011). Financial Constraints and Inflated Home Prices during the Real Estate Boom. AMERICAN ECONOMIC JOURNAL: APPLIED ECONOMICS, 3(3), 55-87. DOI: 10.1257/app.3.3.55
Bo Huang, Bo Wu & Michael Barry (2008). Geographically and temporally weighted regression for modeling spatio-temporal variation in house prices.
Daniel Friesen, Mike Patterson and Bob Harmel (2011). A Comparison of Multiple Regression and Neural Networks for Forecasting Real Estate Values. 119-136
EricGhysels, AlbertoPlazzi, RossenValkanov and WalterTorousSabina (2013). Forecasting Real Estate Prices. Science Direct: Handbook of Economic Forecasting. 2(A), 509-580. doi:10.1016/B978-0-444-53683-9.00009-8
Jun Kang, Hyun Jun Lee, Seung Hwan Jeong, Hee Soo Lee and Kyong Joo Oh (2020). Developing a Forecasting Model for Real Estate Auction Prices Using Artificial Intelligence. Sustainability 2020, 12, 2899; doi:10.3390/su12072899
MałgorzataRenigier-Biłozor, SabinaŹróbek, MarekWalacik, RichardBorst, RichardGrover and Mauriziod’Amato (2022). International acceptance of automated modern tools use must-have for sustainable real estate market development. Science Direct: Land Use Policy. 113. doi:10.1016/j.landusepol.2021.105876
Marian Alexander Dietzel, Nicole Braun and Wolfgang Scha¨fers. (2014). Sentiment-based commercial real estate forecasting with Google search volume data.
Nikolai Siniak, Tom Kauko, Sergey Shavrov and Ninoslav Marina (2020). The impact of proptech on real estate industry growth. IOP Conf. Ser.: Mater. Sci. Eng. 869. DOI: 10.1088/1757-899X/869/6/062041
Peter Rossini (2000). Using Expert Systems and Artificial Intelligence for Real Estate Forecasting. Sixth Annual Pacific-Rim Real Estate Society Conference Sydney, Australia, 24-27
Rangan Gupta, Chi Keung Marco Lau, Vasilios Plakandaras & Wing-Keung Wong (2019). The role of housing sentiment in forecasting U.S. home sales growth: evidence from a Bayesian compressed vector autoregressive model. Economic Research-Ekonomska Istraživanja, 32:1, 2554-2567, DOI: 10.1080/1331677X.2019.1650657
Sanam Samadani and Carlos J. Costa (2021). Forecasting real estate prices in Portugal. 2021 16th Iberian Conference on Information Systems and Technologies (CISTI) 23 – 26 June 2021, Chaves, Portugal
Stephen J. Andriole (2018). Skills and Competencies for Digital Transformation. IT Professional, 20(6), 78-81. DOI: 10.1109/MITP.2018.2876926 |
| 指導教授 |
胡雅涵
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審核日期 |
2022-9-23 |
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