Predicting Housing Price in Beijing Using ARIMA Models Público

Abstract

With the rapid increase in housing demand, more and more homebuyers in Beijing tend to purchase housing properties without collecting enough information. To provide a solution for information asymmetry and high information acquiring cost in the housing market, this study focusses on the housing price forecasting. Reliable forecasts could provide valuable information for homebuyers and sellers and help them better understand the local housing market. This paper seeks to predict the housing price in Beijing using ARIMA models due to the model’s demonstrated outperformance in predicting time series data accuracy. In addition, district-by-district housing price prediction is also performed.

 

Table of Contents

1. Introduction................................................................................................................................. 1

2. Research Methodology ............................................................................................................... 3

2.1. Background: ARIMA Model Approach.............................................................................................3

2.2. Workflow Chart..................................................................................................................................5

2.3. Evaluation Criteria of ARIMA models ..............................................................................................6

3. Data and Data Pre-Processing..................................................................................................... 7

3.1. Data ....................................................................................................................................................7

3.2. Data Pre-Processing ...........................................................................................................................8

4. Result ........................................................................................................................................ 14

4.1. RQ1: What is the performance of ARIMA in predicting housing price in Beijing?........................14

4.2. RQ2: Will the forecasting performance differ from district to district?...........................................18

5. Discussion ................................................................................................................................. 20

5.1. Limitations of Study .........................................................................................................................20

5.2. ARIMA Model Performance............................................................................................................21

6. Conclusion ................................................................................................................................ 24

References..................................................................................................................................... 25 Appendix....................................................................................................................................... 27 

About this Honors Thesis

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School	Emory College
Department	Quantitative Science
Degree	B.S.
Submission	Honors Thesis
Language	English
Research Field	Economics, General Statistics
Palavra-chave	Housing Price Data ARIMA Models Time Series Analysis
Committee Chair / Thesis Advisor	Xiong, Ruoxuan, Emory University
Committee Members	Jacobson, Jeremy, Emory University Luo, Mi, Emory University

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