中國大陸1998年正式全面推行住房貨幣化改革，但在16年的發展歷程中遇到很多問題。近年來，住宅問題在大陸已成為關注的熱點。臺灣地區與大陸有著相似的如重視住房、土地的文化背景，又在制度政策和發展階段上存在諸多不同，可作為大陸的參照。
過往研究中，房價所得比相關研究主要著眼于尋找一個指標數據上的區間來判斷一個地區房價的合理性，在就家庭的異質性來探尋不同家戶事實上承擔的住房負擔壓力區別方面的研究有所欠缺。
本研究基於中國人民大學主持之「全國綜合社會調查」，和臺灣中研院主持之「華人家庭動態資料庫」資料，通過將受訪家庭樣本當前住宅價格與家戶年所得的比值（壓力指標），與其家戶因素比較，探究不同類型、不同地域的家戶在住房選擇行為中壓力承擔的不同，並比較兩岸差異。方法上通過敘述統計分類，再建立多元迴歸模型，檢定各因素顯著性並計算彈性。實證顯示，在大陸模型中，同住人數、面積、家戶區位相關剩餘所得，以及虛擬變數是否在直轄市或者省會城市、是否為少數民族存在顯著影響；而台灣模型中，除面積和所得同樣顯著外，虛擬變數是否在台北市、是否未婚亦影響顯著。此外，面積彈性兩岸較為接近，但剩餘所得彈性存在較大差距。兩岸重點地區模型在調整后顯著變數略有減少，如大陸模型中少數民族不再顯著，台灣模型中面積因素不再顯著，但剩餘的變數仍與整體模型較為類似。

Relationship Research between House Price-to-income Ratio and Family Factors: Comparison of Mainland China and Taiwan
Xu Chenwei
Chen Yen-Jong
Department of Urban Planning, College of Planning and Design
SUMMARY
In recent years, high housing price has become a hot topic in the Mainland China. Taiwan and Mainland China have similar cultural background pay attention to housing and land, but different political systems and stages of development.
Through reviewing the past studies, we found that papers about House Price-to-Income Ratio mainly focused on finding a suitable range to judge the house price of an area. But research to find the family heterogeneity’s influence on this ratio is very rare.
This study, based on 2006 data of China General Social Survey (CGSS) in Mainland China and Panel Study of Family Dynamics (PSFD) in Taiwan, compare the different influence on house price to income ratio by family factors in Mainland China and Taiwan. It construct multivariate regression model which dependent variable is the ratio of current house price to family annual income (as pressure index) and independent variables are family factors. Results are the tested regression models, causes analysis of the differences and some policy recommendations. In the Mainland China Model, we found family size, housing area, household annual location-related residual income, and household location, minority have significant impact; while in the Taiwan Model, area and surplus income, household location were also significant, but dummy variable marriage was significant too. The elasticity of area is near and household annual location-related residual income is far more different. The key regions models decreased some significant variables but still relatively similar to the overall models.
Key words: Housing Pressure, Family Factors, Multiple Regression, Mainland China, Taiwan
INTRODUCTION
In recent years, high housing price has become a hot topic in the Mainland China. Taiwan and Mainland China have similar cultural background pay attention to housing and land, but different political systems and stages of development. So in this research we compare them in the overall level and key regional level.
Through reviewing the past studies, we found that papers about housing pressure mainly focused on finding a suitable range to judge the house price of an area. But research to find the family heterogeneity’s influence on this ratio is very rare. So we choose families’ house Price-to-Income Ratio as the index to research the heterogeneity.
MATERIALS AND METHODS
This study, based on 2006 data of China General Social Survey (CGSS) in Mainland China and Panel Study of Family Dynamics (PSFD) in Taiwan, compare the different influence on house price to income ratio by family factors in Mainland China and Taiwan. In order to find the housing pressure difference between families, we use several family factors to distinguish the different kinds, regions or social levels. At the same time, we also compare data between the Mainland China and Taiwan.
In research methods, this study use descriptive statistics to classify the data first. Then create multiple regression models for comparison, and for depth study. It construct multivariate regression model which dependent variable is the ratio of current house price to family annual income (as housing pressure index) and independent variables are family factors, such as housing area, family size, family annual location-related residual income, family annual medical costs, family annual cost on education, head of household’s gender, age, education, and several dummy variables, for example, the household location (whether in the important regions). We use the Goldfeld-Quandt test to test whether the residuals exist heteroskedasticity. And adjust the heteroskedasticity by Weighted Least Square. Then we use the Variance Inflation Factor and the Tolerance to test the models’ multicollinearity and finally use Kolmogorov-Smirnov test to test whether the residuals comply the Normal distribution.
RESULTS AND DISCUSSION
Results are the tested regression models, causes analysis of the differences and some policy recommendations. In the Mainland China Model, we found family size, housing area, household annual location-related residual income, and dummy variables household location (whether in the municipalities or provincial capital cities), whether head of household was minority have significant impact on the families’ PIR; While in the Taiwan Model, housing area and household annual location-related residual income, household location (whether in the Taipei City) were also significant, but dummy variable whether head of household was not married was significant too. The elasticity of housing area in the Mainland China model was 1.104, and elasticity of household annual location-related residual income was -0.107; The elasticity of housing area in the Taiwan model was 1.199, and elasticity of household annual location-related residual income was -0.203. Compare the two models, the elasticity of housing area were near and sensitive, but household annual location-related residual income were far more different, by the way, they were all insensitive. R square of the two models were 0.509 and 0.567, not so high.
Then for the key regions model, in Mainland China key regions model (in three municipalities, Beijing, Tianjin and Shanghai), only housing area and household annual location-related residual income were significant, the elasticity of housing area was 1.113 and elasticity of household annual location-related residual income was -0.125; in Taiwan key regions model (in the big Taipei area), household annual location-related residual income and dummy variable whether head of household was not married were significant, elasticity of household annual location-related residual income was -0.208. Although in the key regions models, several variables become not significant, the models were generally close to the overall models.
For housing policies, to guide to build the suitable area houses and control the large area houses’ proportion in the housing market will be effective, at the same time, increasing household income to make its growth rate higher than the price and basic expenditure, was long-term measure but effective for a variety of situations. Because both in the models of Mainland China and Taiwan, housing area acted significant for the household PIR, and its elasticity were sensitive, more than 1%. On the other hand, in all model types of this study, the variable household annual location-related residual income had significant negative impact on the household PIR, while its elasticity was insensitive.

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