数据特征驱动的房地产市场集成预测研究

管理评论 ›› 2020, Vol. 32 ›› Issue (7): 89-101.

数据特征驱动的房地产市场集成预测研究

崔明明¹, 刘晓亭¹, 李秀婷^1,2, 董纪昌^1,2

1. 中国科学院大学经济与管理学院, 北京 100190;
2. 中国科学院大数据挖掘与知识管理重点实验室, 北京 100190

收稿日期:2019-05-23 出版日期:2020-07-28 发布日期:2020-08-08
通讯作者: 李秀婷(通讯作者),中国科学院大学经济与管理学院、中国科学院大数据挖掘与知识管理重点实验室副教授,硕士生导师,博士
作者简介:崔明明,中国科学院大学经济与管理学院博士研究生;刘晓亭,中国科学院大学经济与管理学院博士研究生;董纪昌,中国科学院大学经济与管理学院、中国科学院大数据挖掘与知识管理重点实验室教授,博士生导师,博士。
基金资助:
国家自然科学基金面上项目（71573244；71974180）；国家自然科学基金重点项目（71850014）。

Integrated Data Characteristic Driven Forecasting Research on Real Estate Market

Cui Mingming¹, Liu Xiaoting¹, Li Xiuting^1,2, Dong Jichang^1,2

1. School of Economics and Management, University of Chinese Academy and Sciences, Beijing 100190;
2. Key Laboratory of Big Data Mining and Knowledge Management, Chinese Academy of Sciences, Beijing 100190

Received:2019-05-23 Online:2020-07-28 Published:2020-08-08

摘要/Abstract

摘要： 房地产市场是一个复杂的系统，房价是多种因素驱动下的综合表现结果。传统单一的预测方法预测精度不能较好地对经济决策起到支持作用。本文基于TEI@I思想，采用综合集成预测方法，对房地产市场变化的方向和水平进行预测。基于房地产市场动态循环系统筛选关键预测指标，采用景气分析法对房地产市场的变动方向进行了初步预判，之后采用分而治之的思想，建立了数据特征驱动的房地产市场集成预测模型对房地产市场进行定量预测。基于数据特征选择合适的基准模型进行建模预测，比单一预测模型的预测精度更高，可以准确预测房地产市场投资、需求和价格。本研究有助于丰富房地产市场预测理论与方法，科学预测房地产市场走势，为政府制定政策、开发商投资以及居民购房提供决策依据。

关键词: 复杂系统, 数据特征驱动, 房地产市场, 集成预测, TEI@I

Abstract: Housing market is a complex system, and housing prices are results of various factors. The prediction accuracy of traditional single forecasting model is not enough to well support economic decision-making. Based on the TEI@I idea, an integrated prediction model is created to predict the direction and level of change in the real estate market, using key predictive indicators based on the dynamic circulation system of the real estate market. Firstly, the changing direction of the real estate market is predicted by boom analysis. Then, based on the divide-and-rule idea, integrated data characteristic driven forecasting model of the real estate market is established. The model quantitatively predicts the real estate market and demonstrates the effectiveness of different forecasting methods by comparing data decomposition and data feature-driven basic model selection. This paper concludes that applying the appropriate base model according to the data feature can predict the investment, demand and price of the real estate market more accurately than the single prediction model. This research enriches the theory and method of real estate market forecasting and predicts the trend of the real estate market more accurately. Moreover, it provides recommendations for the government to design policies and make decisions, for real estate developers to invest and for residents to purchase houses.

Key words: complex system, data characteristic driven, real estate market, integrated forecasting, TEI@I

崔明明, 刘晓亭, 李秀婷, 董纪昌. 数据特征驱动的房地产市场集成预测研究[J]. 管理评论, 2020, 32(7): 89-101.

Cui Mingming, Liu Xiaoting, Li Xiuting, Dong Jichang. Integrated Data Characteristic Driven Forecasting Research on Real Estate Market[J]. Management Review, 2020, 32(7): 89-101.

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