What Influences the Real Estate Price Volatility in Hong Kong: An ARMA-GARCH Analysis

2020 ◽  
Author(s):  
Shizhen Wang ◽  
David J. Hartzell
Keyword(s):  
Hong Kong ◽  
Real Estate ◽  
Price Volatility ◽  
The Real ◽  
The Real Estate ◽  
Real Estate Price

Data Analysis on the Influencing Factors of the Real Estate Price

2021 ◽  
pp. 52-66
Author(s):  
Huang-Mei He ◽  
Yi Chen ◽  
Jia-Ying Xiao ◽  
Xue-Qing Chen ◽  
Zne-Jung Lee
Keyword(s):  
Data Analysis ◽  
Real Estate ◽  
Influencing Factors ◽  
Real Estate Market ◽  
Training Data ◽  
Market Demand ◽  
Data Set ◽  
The Real ◽  
The Real Estate ◽  
Real Estate Price

China has carried out a large number of real estate market reforms that change the real estate market demand considerably. At the same time, the real estate price has soared in some cities and has surpassed the spending power of many ordinary people. As the real estate price has received widespread attention from society, it is important to understand what factors affect the real estate price. Therefore, we propose a data analysis method for finding out the influencing factors of real estate prices. The method performs data cleaning and conversion on the used data first. To discretize the real estate price, we use the mean ± standard deviation (SD), mean ± 0.5 SD, and mean ± 2 SD of the price and divide it into three categories as the output variable. Then, we establish the decision tree and random forest model for six different situations for comparison. When the data set is divided into training data (70%) and testing data (30%), it has the highest testing accuracy. In addition, by observing the importance of each input variable, it is found that the main influencing factors of real estate price are cost, interior decoration, location, and status. The results suggest that both the real estate industry and buyers should pay attention to these factors to adjust or purchase real estate.

scholarly journals Artificial Intelligence for Modeling Real Estate Price Using Call Detail Records and Hybrid Machine Learning Approach

Entropy ◽  
2020 ◽  
Vol 22 (12) ◽  
pp. 1421
Author(s):  
Gergo Pinter ◽  
Amir Mosavi ◽  
Imre Felde
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Real Estate ◽  
Model Performance ◽  
Machine Learning Method ◽  
Learning Method ◽  
The Real ◽  
The Real Estate ◽  
Real Estate Price ◽  
Call Detail Records

Advancement of accurate models for predicting real estate price is of utmost importance for urban development and several critical economic functions. Due to the significant uncertainties and dynamic variables, modeling real estate has been studied as complex systems. In this study, a novel machine learning method is proposed to tackle real estate modeling complexity. Call detail records (CDR) provides excellent opportunities for in-depth investigation of the mobility characterization. This study explores the CDR potential for predicting the real estate price with the aid of artificial intelligence (AI). Several essential mobility entropy factors, including dweller entropy, dweller gyration, workers’ entropy, worker gyration, dwellers’ work distance, and workers’ home distance, are used as input variables. The prediction model is developed using the machine learning method of multi-layered perceptron (MLP) trained with the evolutionary algorithm of particle swarm optimization (PSO). Model performance is evaluated using mean square error (MSE), sustainability index (SI), and Willmott’s index (WI). The proposed model showed promising results revealing that the workers’ entropy and the dwellers’ work distances directly influence the real estate price. However, the dweller gyration, dweller entropy, workers’ gyration, and the workers’ home had a minimum effect on the price. Furthermore, it is shown that the flow of activities and entropy of mobility are often associated with the regions with lower real estate prices.

scholarly journals Capital Gain Expectations And Efficiency In The Real Estate Markets

2011 ◽  
Vol 6 (4) ◽  
Author(s):  
Shady Kholdy ◽  
Ahmad Sohrabian
Keyword(s):  
Real Estate ◽  
Housing Market ◽  
Housing Prices ◽  
Housing Price ◽  
Capital Gain ◽  
Real Estate Markets ◽  
The Real ◽  
The Real Estate ◽  
Real Estate Price ◽  
Economic Fundamentals

Capital gain expectation is known to be an important determinant of housing price hikes during the real estate booms. Empirically, however, specifying the way expectations about current and future economic variables are formed is a dilemma. Although it is reasonable to assume that economic fundamentals have a significant effect on the investors’ expectation about future gains, a number of housing market analysts claim that expectations of housing prices are extrapolative. This study attempts to investigate the mechanism by which investors’ capital gain expectations and psychology are shaped. The results suggest that housing prices are predictable with respect to capital gain expectations only when these expectations are formed by extrapolation of past price appreciations. Considering the large number of empirical evidence on housing market anomaly with respect to capital gain expectations, the results suggest that the extrapolative expectations can better explain the real estate price behavior than expectations that are formed by economic fundamentals.

Design of Real Estate Price Index System Based on Hedonic Price Method

2014 ◽  
Vol 488-489 ◽  
pp. 1463-1466
Author(s):  
Yun Du ◽  
Hui Qin Sun ◽  
Su Ying Zhang ◽  
Qiang Tian
Keyword(s):  
Real Estate ◽  
Price Index ◽  
Real Estate Market ◽  
Hedonic Price ◽  
Hedonic Price Method ◽  
The Real ◽  
The Real Estate ◽  
Real Estate Price ◽  
Price Series ◽  
Research Level

Urban real estate price index (hereinafter referred to as UREPI) is a basic data of the real estate market, its accuracy is very important for enterprises, consumers and housing management department. In view of current research level here in China and popular models, the UREPI system is compiled based on the Hedonic price method because of its advantages such as calculation simple and sample easily etc. Compiled by Eviews the system has three main stages: the data standardization, the benchmark model establishment and the application of two periods chained update method to update price series. UREPI system is combined with the real deal, so it can be used to analysis the market accurately. The results completely meet the design requirements.

Transforming China by Chinese Enterprises through Foreign Direct Investment: Experience of Hong Kong Real Estate Developers

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Mike C. W. Wong ◽  
Steven X. G. Chen ◽  
Lennon H. T. Choy
Keyword(s):  
Hong Kong ◽  
Real Estate ◽  
Investment Strategy ◽  
Economic Transformation ◽  
The Real ◽  
The Real Estate ◽  
Regulatory Frameworks ◽  
Before And After ◽  
Real Estate Developers ◽  
Investment Experience

Abstract Foreign Indirect Investment (FDI) has played a key role in China’s economic transformation. The real estate industry in China has been the second largest sector to fetch FDI for the nation since the opening up of the economy in 1978. Regarded as foreign investors both before and after the handover of sovereignty to China in 1997, Hong Kong based real estate developers (HK developers) took up a lion share of this form of FDI. This article reviews the literature and regulatory frameworks of FDI in the real estate sector in China. It investigates two major problems encountered by the HK developers, namely investment strategy and managing projects, and their solutions through the lens of institutional analysis.

The hegemony of the real estate industry: Redevelopment of ‘Government/Institution or Community’ (G/IC) land in Hong Kong

Urban Studies ◽  
2016 ◽  
Vol 54 (15) ◽  
pp. 3403-3422 ◽  
Author(s):  
Joanna Wai Ying Lee ◽  
Wing-Shing Tang
Keyword(s):  
Hong Kong ◽  
Real Estate ◽  
Capital Accumulation ◽  
Real Estate Industry ◽  
Property Development ◽  
The Real ◽  
The Real Estate ◽  
Local Literature ◽  
The Government ◽  
Government Institution

The high property price syndrome in Hong Kong has led to heightened concern about the role of landed capital in property development. Recently, the hegemony of the real estate industry has become a buzzword in local literature, but unfortunately there is neither adequate theoretical articulation nor informed understanding of the concept of hegemony. There is widespread misunderstanding of hegemony, equating it to domination by property tycoons. The local literature has overlooked the government-business collusion in constructing the common sense of society so as to dominate others. Through an empirical investigation of the redevelopment of ‘Government/Institution or Community’ (G/IC) land in Hong Kong, this article attempts to offer an alternative explanation to the land question of G/IC redevelopment by highlighting that the everyday life of the silent majority and of professionals has in fact perpetuated the hegemony of the real estate industry in Hong Kong. It is argued that the government, property developers, professionals, charitable organisations and the general public have altogether participated, in different ways and to different extents, in the capital accumulation projects of leading developer conglomerates in Hong Kong. A land (re)development regime has thus contributed to the property boom in Hong Kong.

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