北京市居民住房需求结构分析

doi:10.11821/xb200810003

Abstract

Abstract:

A clear understanding of housing demand structure is the fundamental of making housing policies and stipulating relevant technical standards. In particular, it is critical to clarify the dividing lines of households with different demands, so as to define the target of housing policies. In addition, it is significant to estimate the appropriate housing size demand of different demand groups in order to make practical housing provision plans. In this paper, the above issues are addressed through modeling the housing demand of urban families on a micro scale. First, the influencing factors of urban households' housing demand, including family structure, income, current dwelling conditions, social status, location choice, investment tendencies, etc., are analyzed with their interactions being considered. Based on theoretical analysis, a two-level interaction model is proposed for representing the housing demand structure. Second, the model is used for analyzing the housing demand of Beijing residents, and it is empirically proved to be successful. As a result, eight family groups with significantly differentiated housing demand were identified and the thresholds for splitting the family groups, such as monthly family income of 5000 RMB and current housing status, were clarified. Furthermore, the differentiations of housing size demand across the demand groups are quantitatively clarified. It was found that 100 m², 110 m², 140 m², 170 m², and 230 m² were currently thought to be "ideal" for families with 1, 2, 3, 4, 5 or more persons, respectively. These ideal sizes are obviously unrealistic considering the restrictions of land and resources of Beijing, therefore they were adjusted based on the modeling results. The calculation implied that the appropriate housing sizes for 2-person, 3-person, 4-person and 5-or-more-person families are 70-80 m², 80-95 m², 90-110 m², and 90-155 m², respectively. These results provide many useful implications for housing policy and enrich the methodology of housing demand structure analysis through micro-scale modeling.

Key words: housing policy, housing demand, interaction model, housing consumption, housing size, Beijing

GAO Xiaolu. Modeling Housing Demand Structure with an Example of Beijing[J].Acta Geographica Sinica, 2008, 63(10): 1033-1044.

References

[1] Jin Dinghua, Zhang Shilian. Research on housing demand income flexibility in Shenzhen. Journal of Chongqing Architecture University, 2003, (3): 82-85.
[金定华, 张仕廉. 深圳市住房消费需求收入弹性探讨. 重庆建筑大学学报, 2003, (3): 82-85.]

[2] Wang Yincai, Ma Zhili. Evaluation of the reasonableness of urban residents' housing consumption in China. Economy and Management, 2005, (12): 43-45.
[王银彩, 马智利. 中国城镇居民住房消费水平合理化程度评析. 经济与管理, 2005, (12): 43-45.]

[3] Han Kefeng, Xiao Moulang. Demand elasticity of housing market. Business Economy, 2006, (3): 35-36.
[韩科峰, 肖谋琅. 住房市场需求弹性分析. 商业经济, 2006, (3): 35-36.]

[4] Xiong Shoufeng. Economic analysis of inter-period housing consumption. Journal of the Postgraduate of Zhongnan University of Economics and Law, 2007, (4): 25-29.
[熊寿峰. 跨期住房消费的经济解析. 中南财经政法大学研究生学报, 2007, (4): 25-29.]

[5] Shen Yingzi. The demand flexibility analysis about the housing consumption of Chinese town residents. Economy and Management, 2007, (3): 38-41.
[沈英姿. 中国城镇居民住房消费需求弹性分析. 经济与管理, 2007, (3): 38-41.]

[6] Zhang Hui. Analysis of urban households' housing demand and motives model. Journal of Capital University of Economics and Business, 2007, (1): 116-120.
[张辉. 居民家庭住房需求总量与动机模型分析. 首都经济贸易大学学报, 2007, (1): 116-120.]

[7] Liu Meixia. Housing Consumption of Urban Residents. Beijing: China Land Press, 2002.
[刘美霞. 城镇居民住房消费研究. 北京: 中国大地出版社, 2002.]

[8] Lavender S D. Economics for Builders and Surveyors: Principles and Applications of Economic Decision Making in Developing the Built Environment. New York: Longman, 1990.

[9] Tse R Y C, Ho C W, Ganesan S. Matching housing supply and demand: An empirical study of Hong Kong's market. Construction Management and Economics, 1999, 17(5): 625-634.

[10] Akintoye A S, Skitmore R M. Models of UK private sector quarterly construction demand. Construction Management and Economics, 1994, 12(1): 3-13.

[11] Goh B H. Residential construction demand forecasting using economic indicators: A comparative study of artificial neural networks and multiple regression. Construction Management and Economics, 1996, 14: 25-34.

[12] Goh B H. An evaluation of the accuracy of the multiple regression approach in forecasting sectoral construction demand in Singapore. Construction Management and Economics, 1999, 17(2): 231-241.

[13] Thomas Ng S, Skitmore M, Wong K F. Using genetic algorithms and linear regression analysis for private housing demand forecast. Building and Environment, 2008, 43(6): 1171-1184.

[14] Ioannides Y M, Zabel J E. Interactions, neighborhood selection and housing demand. Journal of Urban Economics, 2008, 63(1): 229-252.

[15] Haurin D R, Lee K. A structural model of the demand for owner-occupied housing. Journal of Urban Economics, 1989, 26(3): 348-360.

[16] B觟rsch-Supan A, Pollakowski H O. Estimating housing consumption adjustments from panel data. Journal of Urban Economics, 1990, 27(2): 131-150.

[17] Zhang Wenzhong, Liu Wang, Meng Bin. On location advantage value of residential environment (LAVRE) in the urban and suburban areas of Beijing. Acta Geographica Sinica, 2005, 60(1): 115-121.
[张文忠, 刘旺, 孟斌. 北京市区居住环境的区位优势度分析. 地理学报, 2005, 60(1): 115-121.]

[18] Zheng Siqi, Fu Yuming, Liu Hongyu. Urban households' location preference: The estimation of willingness-to-pay gradient model. Progress in Geography, 2005, 24(1): 97-104.
[郑思齐, 符育明, 刘洪玉. 城市居民对居住区位的偏好: 支付意愿梯度模型的估计. 地理科学进展, 2005, 24(1): 97-104.]

[19] Zheng Siqi, Fu Yuming, Liu Hongyu. An empirical study on the willingness to pay for residential location and location choice of urban residents in China. Economic Geography, 2005, 25(2): 194-198.
[郑思齐, 符育明, 刘洪玉. 城市居民对居住区位的偏好及其区位选择的实证研究. 经济地理, 2005, 25(2): 194-198.]

[20] Zheng Siqi, Liu Hongyu. Income elasticity of housing demand in China: Model, estimation and forecast. China Civil Engineering Journal, 2005, (7): 122-126.
[郑思齐, 刘洪玉. 住房需求的收入弹性: 模型、估计与预测. 土木工程学报, 2005, (7): 122-126.]

[21] Yi Xiaowen. Estimation of effective housing demand in China based on anticipated utility and income elasticity. Jiangxi Social Sciences, 2007, (4): 101-105.
[易晓文. 中国住宅有效需求测算: 基于期望效用和需求收入弹性的研究. 江西社会科学, 2007, (4): 101-105.]

[22] Geoffrey C. Income elasticity of housing demand. Review of Economics and Statistics, 1973, 55: 528-532.

[23] Liang Zongjing, Kuang Yun. Housing demand prediction with BP neural net. China Economist, 2006, (12): 9-10.
[梁宗经, 旷芸. 应用BP 神经网络进行住房需求预测分析. 经济师, 2006, (12): 9-10.]

[24] Wu Hong, He Wei. An empirical analysis upon the disequilibrium housing market of Shanghai. East China Economic Management, 2007, (11): 4-10.
[伍虹, 贺卫. 对上海市住房市场的实证非均衡分析. 华东经济管理, 2005, (11): 4-10.]

[25] Wang Shufen. Application of multivariate statistics analysis in urban resident's housing consumer in Tianjin. Journal of Tianjin Polytechnic University, 2007, (1): 81-84.
[王淑芬. 多元统计方法在天津市城市居民住房消费分析中的应用. 天津工业大学学报, 2007, (1): 81-84.]

[26] Zhang Wenzhong et al. A Study of Livable City in China: Beijing. Beijing: China Social Sciences Press, 2006.
[张文忠等. 中国宜居城市研究报告(北京篇). 北京: 中国社会科学文献出版社, 2006.]

Modeling Housing Demand Structure with an Example of Beijing

PDF (PC)

Like

Knowledge

Cited

Abstract

Cite this article

share this article

References

Related Articles 15

Metrics

Comments

[1]	ZHANG Xuebo, SONG Jinping, YU Wei, WANG Zhenbo. Formation mechanism of Beijing's jobs-housing spatial relationship based on residents' perception [J]. Acta Geographica Sinica, 2021, 76(2): 383-397.
[2]	ZHOU Kan, LI Hui, SHEN Yuming. Spatiotemporal patterns and driving factors of environmental stress in Beijing-Tianjin-Hebei region: A county-level analysis [J]. Acta Geographica Sinica, 2020, 75(9): 1934-1947.
[3]	GAO Xiaolu, WU Danxian, YAN Bingqiu. Measure and distribution of urban elderly in poverty: An empirical study in Beijing [J]. Acta Geographica Sinica, 2020, 75(8): 1557-1571.
[4]	MA Ning, HE Liye, LIANG Sujie, GUO Jun. Low-frequency characteristics of winter-time cold air activity in the Beijing-Tianjin-Hebei region and the impacts of low-frequency variation of the Siberian High [J]. Acta Geographica Sinica, 2020, 75(3): 485-496.
[5]	ZHANG Jie, SHI Peijun, YANG Jing, GONG Daoyi. The impact of the urbanization process on rainfall in Beijing:A case study of 7.21 rainstorm [J]. Acta Geographica Sinica, 2020, 75(1): 113-125.
[6]	WANG Xiangdong, LIU Xiaoqian, PEI Tao, WANG Zhenbo. Potential evaluation of urban land intensive use in Beijing-Tianjin-Hebei region based on measurement of technical efficiency [J]. Acta Geographica Sinica, 2019, 74(9): 1853-1865.
[7]	LI Bo,LI Qihang,SUN Wei. Analysis of financing costs of industrial enterprises in Beijing-Tianjin-Hebei region from geographic perspective [J]. Acta Geographica Sinica, 2019, 74(6): 1149-1162.
[8]	Zhi QIAO, Ningyu HUANG, Xinliang XU, Zongyao SUN, Chen WU, Jun YANG. Spatio-temporal pattern and evolution of the urban thermal landscape in metropolitan Beijing between 2003 and 2017 [J]. Acta Geographica Sinica, 2019, 74(3): 475-489.
[9]	ZHANG Yanji,QIN Bo,ZHU Chunwu. The impact of urban built environment on criminal behavior and residential security in Beijing [J]. Acta Geographica Sinica, 2019, 74(2): 238-252.
[10]	WANG Liang,LIU Hui. The comprehensive evaluation of regional resources and environmental carrying capacity based on PS-DR-DP theoretical model [J]. Acta Geographica Sinica, 2019, 74(2): 340-352.
[11]	SUN Si'ao, ZHENG Xiangyi, LIU Haimeng. Local and distant virtual water trades in Beijing-Tianjin-Hebei region [J]. Acta Geographica Sinica, 2019, 74(12): 2631-2645.
[12]	ZHOU Lin,TIAN Li,ZHANG Zhen,LI Wei. The street networks evolution in Beijing old city since the establishment of the Republic of China: A syntactical view [J]. Acta Geographica Sinica, 2018, 73(8): 1433-1448.
[13]	ZHAO Meifeng,QI Wei,LIU Shenghe. Spatial differentiation and formation mechanism of floating population communities in Beijing [J]. Acta Geographica Sinica, 2018, 73(8): 1494-1512.
[14]	QIN Jing,LI Langping,TANG Mingdi,SUN Yan,SONG Xinrui. Exploring the spatial characteristics of Beijing inbound tourist flow based on geotagged photos [J]. Acta Geographica Sinica, 2018, 73(8): 1556-1570.
[15]	FENG Jian,ZHONG Yichun. Restructuring of social space in Beijing from 2000 to 2010 [J]. Acta Geographica Sinica, 2018, 73(4): 711-737.