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題名：影響台灣房市價格關鍵因素及模式分析
作者：賴昇克
作者(外文)：LAI,SHENG-KE
校院名稱：逢甲大學
系所名稱：商學博士學位學程
指導教授：林豐智
學位類別：博士
出版日期：2022
主題關鍵詞：房產業；關鍵因素；PEST模型；AHP；critical factor；economic；social and technological model；analytic hierarchy process
原始連結：連回原系統網址new window
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摘 要
影響台灣房市價格的因素非常多而且有時也會牽連到台灣的政治、經濟、社會、科學四大結構面向，本研究希望從PEST模型中探討出能夠幫助房產業者可遵循依據的最佳模型。
一、 房產業如何應用影響房市價格關鍵因素來節省成本支出。
二、 房產業如何應用影響房市價格關鍵因素增加銷售業績。
三、 研究影響房市價格的關鍵因素所得之數據模型，並分析其對房產業表達之意義。
四、 研究影響房市價格的關鍵因素之數據模型可為房產業帶來何種指標。
由AHP研究數據中得出政治因素中之法規因子，例如：外勞移工開放、銀行增加土建融貸款成數、增值稅減半等，最能幫助房產業節省成本支出。
再由房價平均百分比數據中得出政治因素中之公共建設因子，例如：鄰近學校、市場、捷運、公園等，最能幫助房產業增加銷售業績。
本研究將AHP求得之數據排序做為X軸，房價平均百分比之排序作為Y軸，再經由迴歸分析數據，求得二次函數的曲線模型代表散落在控制成本及控制銷售業績的兩組因子排序中另有一組最佳排序，由數據模型中得出經濟因素中之經濟成長率因子，例如：經濟成長率的多寡影響到消費者的購買力，也影響到建案銷售率，進而影響到建案推出的增減等，最能幫助房產業者控制成本及銷售業績，也能減少房產業者的成本浪費及銷售盲點。
本研究從以上綜合，若房產業控制成本，首重法規因子；若控制銷售業績，首重公共建設因子，兩者皆重視由數據模型得到經濟因素中的經濟成長率因子、所得變動因子、物價波動因子為模型中排序前三名，可見經濟因素為房市關鍵因素，且房價上漲波動中若經濟成長率因子下降，由數據模型中得知本波段房市高點結束，希望本研究可提醒房產業者及消費者注意何時景氣將反轉，不要投資過度，持盈保泰。
Abstract
Numerous factors affect Taiwan’s housing prices, and they can be categorized into 4 structural aspects, namely politics, economy, society, and science. This study employs the political, economic, social and technological model to develop an optimal model that realters can follow.
I. How the real estate industry uses critical factors affecting real estate prices to reduce costs and expenditures.
II. How the real estate industry uses critical factors affecting real estate prices to increase sales.
III. Study the data model of the critical factors affecting real estate prices and analyze its significance to the real estate industry.
IV. Research which indicators the data model of the critical factors affecting real estate prices can bring to the real estate industry.
According to the research data derived from the analytic hierarchy process, this study obtains legal factors among the political factors that can help the real estate industry raise cost and expenditure efficiency the most significantly, such as opening the entry to Taiwan to migrant workers, increasing the percentage of civil construction finance loans from banks, and halving the value-added tax.
From the data of the average percentage of housing price, this study obtains the public construction factors among the political factors that can help the real estate industry increase their sales the most significantly, such as proximity to schools, markets, the MRT, and parks.
This study arranges the data obtained using the analytic hierarchy process into sequence as the X axis, and uses the sequenced average percentage of housing price as the Y axis. A regression analysis is run on the data accordingly. A curve model of quadratic function is obtained, representing that among the two-factor sequences of cost control and sales performance control, there is another set of optimal sequencing. From the model, we obtain the economic growth factors among economic factors that are most helpful in helping realters to control cost and sales performance and to reduce costs and blind spots in sales. Such factors include, for example, economic growth rate affecting consumers’ purchasing power and construction project sales rate, which further affects the number of construction projects being launched.
This study concludes that the real estate industry should prioritize legal factors for cost control and prioritize public construction factors for sales performance control. Both factors are affected by the top 3 economic factors obtained from the data model, namely the economic growth rate factor, the income change factor, and the price volatility factor. Clearly, economic factors are critical factors to the real estate market. When real estate price increases, if the economic growth rate drops, then the high point of the current real estate market has been reached. The results of this study may be used to remind realters and consumers to pay attention to the turn of economic cycles to avoid overinvestment and maintain good economic positions.
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