scholarly journals Prediction of China’s Housing Price Based on a Novel Grey Seasonal Model

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Dayin Li ◽  
Lianyi Liu ◽  
Haitao Lv
Keyword(s):  
Real Estate ◽  
Prediction Models ◽  
Housing Prices ◽  
Market Price ◽  
Housing Price ◽  
Real Estate Market ◽  
Accurate Prediction ◽  
Price Changes ◽  
New Information ◽  
Seasonal Model

The fluctuation of real estate prices has an important impact on China's economic development. Accurate prediction of real estate market price changes has become the focus of scholars. The existing prediction methods not only have great limitations on the input variables but also have many deficiencies in the nonlinear prediction. In the process of real estate market price forecasting, the priority of data and the seasonal fluctuation of housing price are important influencing factors, which are not taken into account in the traditional model. In order to overcome these problems, a novel grey seasonal model is proposed to predict housing prices in China. The main method is to introduce seasonal factor decomposition into the new information priority grey prediction model. Two practical examples are used to test the performance of the new information priority grey seasonal model. The results show that compared with the existing prediction models, this method has better applicability and provides more accurate prediction results. Therefore, the proposed model can be a simple and effective tool for housing price prediction. At the same time, according to the prediction results, this paper analyzes the causes of housing price changes and puts forward targeted suggestions.

scholarly journals Did the COVID-19 Pandemic Crisis Affect Housing Prices Evenly in the U.S.?

2021 ◽  
Vol 13 (21) ◽  
pp. 12277
Author(s):  
Xinba Li ◽  
Chuanrong Zhang
Keyword(s):  
Real Estate ◽  
Spatial Patterns ◽  
Price Change ◽  
Housing Prices ◽  
Housing Price ◽  
Real Estate Market ◽  
Moran’S I ◽  
Moran's I ◽  
Price Changes ◽  
The U.S

While it is well-known that housing prices generally increased in the United States (U.S.) during the COVID-19 pandemic crisis, to the best of our knowledge, there has been no research conducted to understand the spatial patterns and heterogeneity of housing price changes in the U.S. real estate market during the crisis. There has been less attention on the consequences of this pandemic, in terms of the spatial distribution of housing price changes in the U.S. The objective of this study was to explore the spatial patterns and heterogeneous distribution of housing price change rates across different areas of the U.S. real estate market during the COVID-19 pandemic. We calculated the global Moran’s I, Anselin’s local Moran’s I, and Getis-Ord’s statistics of the housing price change rates in 2856 U.S. counties. The following two major findings were obtained: (1) The influence of the COVID-19 pandemic crisis on housing price change varied across space in the U.S. The patterns not only differed from metropolitan areas to rural areas, but also varied from one metropolitan area to another. (2) It seems that COVID-19 made Americans more cautious about buying property in densely populated urban downtowns that had higher levels of virus infection; therefore, it was found that during the COVID-19 pandemic year of 2020–2021, the housing price hot spots were typically located in more affordable suburbs, smaller cities, and areas away from high-cost, high-density urban downtowns. This study may be helpful for understanding the relationship between the COVID-19 pandemic and the real estate market, as well as human behaviors in response to the pandemic.

scholarly journals Valuation methods for the housing market: Evidence from Budapest

2016 ◽  
Vol 66 (3) ◽  
pp. 527-546 ◽  
Author(s):  
Dávid Kutasi ◽  
Milán Csaba Badics
Keyword(s):  
Neural Network ◽  
Real Estate ◽  
Large Scale ◽  
Housing Prices ◽  
Market Price ◽  
Housing Price ◽  
Real Estate Market ◽  
Hedonic Regression ◽  
Valuation Methods ◽  
Valuation Method

Different valuation methods and determinants of housing prices in Budapest, Hungary are examined in this paper in order to describe price drivers by using an asking price dataset. The hedonic regression analysis and the valuation method of the artificial neural network are utilised and compared using both technical and spatial variables. In our analyses, we conclude that according to our sample from the Budapest real estate market, the Multi-Layer Preceptron (MLP) neural network is a better alternative for market price prediction than hedonic regression in all observed cases. To our knowledge, the estimation of housing price drivers based on a large-scale sample has never been explored before in Budapest or any other city in Hungary in detail; moreover, it is one of the first papers in this topic in the CEE region. The results of this paper lead to promising directions for the development of Hungarian real estate price statistics.

A Study of Influence upon Inflation Posed by Volatility of Housing Price

2016 ◽  
Vol 20 (4) ◽  
pp. 597-606
Author(s):  
Jun Pi ◽  
◽  
Yu Song ◽  
Shenggang Yang ◽  
Fang Ju ◽  
...  
Keyword(s):  
Real Estate ◽  
Asset Price ◽  
Housing Prices ◽  
Market Price ◽  
Housing Price ◽  
Real Estate Market ◽  
Economic Market ◽  
Causality Test ◽  
The Real Estate ◽  
Effect Theory

In recent 30 years, countries from the world have attached great attention to the influence on inflation posed by asset price. Real estate market is a very important economic market for any country. Therefore, housing price has become a hot topic for discussion and research in China. Whether housing price will affect a country’s inflation or not and to what extent the effect will be are social focuses. Hence, it is feasible to theoretically apply Tobin’s Q Theory in this paper, combine the real estate market price with the assets replacement and study the inflationary impact posed by the housing price, through the application of the wealth effect theory. By using monthly statistics of China’s real estate market and inflation from the year 2005 to 2014, this paper will conduct a theoretical and empirical research on the influence that housing price has on inflation with the adoption of dynamic analysis methods including Granger Causality Test, impulse response and variance decomposition. Furthermore, this paper is featured with systematic and complete empirical thinking and methodology, comprehensive data selection and distinctive research results associated with the relationship between housing prices and inflations. According to the study result, housing price is the Granger Cause of inflation and will not drive inflation in short time. But as time passes, this effect will be gradually enhanced. This paper suggests that housing price and other price factors should be taken into consideration so as to establish a broad-sense inflation index in China.

scholarly journals Identifying Real Estate Opportunities Using Machine Learning

2018 ◽  
Vol 8 (11) ◽  
pp. 2321 ◽  
Author(s):  
Alejandro Baldominos ◽  
Iván Blanco ◽  
Antonio Moreno ◽  
Rubén Iturrarte ◽  
Óscar Bernárdez ◽  
...  
Keyword(s):  
Machine Learning ◽  
Real Estate ◽  
Housing Prices ◽  
Market Price ◽  
Real Estate Market ◽  
Support Vector ◽  
Regression Problem ◽  
The Real ◽  
The Real Estate ◽  
The Real Estate Market

The real estate market is exposed to many fluctuations in prices because of existing correlations with many variables, some of which cannot be controlled or might even be unknown. Housing prices can increase rapidly (or in some cases, also drop very fast), yet the numerous listings available online where houses are sold or rented are not likely to be updated that often. In some cases, individuals interested in selling a house (or apartment) might include it in some online listing, and forget about updating the price. In other cases, some individuals might be interested in deliberately setting a price below the market price in order to sell the home faster, for various reasons. In this paper, we aim at developing a machine learning application that identifies opportunities in the real estate market in real time, i.e., houses that are listed with a price substantially below the market price. This program can be useful for investors interested in the housing market. We have focused in a use case considering real estate assets located in the Salamanca district in Madrid (Spain) and listed in the most relevant Spanish online site for home sales and rentals. The application is formally implemented as a regression problem that tries to estimate the market price of a house given features retrieved from public online listings. For building this application, we have performed a feature engineering stage in order to discover relevant features that allows for attaining a high predictive performance. Several machine learning algorithms have been tested, including regression trees, k-nearest neighbors, support vector machines and neural networks, identifying advantages and handicaps of each of them.

FINANCIAL DEVELOPMENT AND HOUSING PRICE CHANGES UNDER THE CONDITION OF FINANCIAL CONTROL: EVIDENCE FROM CHINA

2021 ◽  
pp. 1-27
Author(s):  
XUE LI ◽  
RUO-XI ZHANG
Keyword(s):  
Real Estate ◽  
Financial Development ◽  
Housing Prices ◽  
Housing Price ◽  
Financial Control ◽  
Real Estate Industry ◽  
Price Changes ◽  
The Real ◽  
The Real Estate ◽  
Real Estate Prices

The impact of financial development on the real estate industry has increased with the degree of financial control. This research presented in this paper divides China’s area into strong and weak regions based on the degree of financial control, and then it studies the relationship between housing price changes and the level of financial development using panel data from the period 1994 to 2018. We find that the level of national sample financial development plays an important role in promoting the housing prices. In areas with strong financial control, financial resources tend to be allocated to the real estate industry, boosting real estate prices. In areas with weak financial control, the role of financial development in promoting real estate prices is not obvious. The funds accumulated by financial institutions are more marketable and independent. We have confirmed these findings by our analysis of the transformation of certain financial control and financial development variables, the application of Panel-VAR estimation methods, and other robustness tests.

The impact of external factors on housing prices in Lithuania

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Alina Stundziene ◽  
Vaida Pilinkienė ◽  
Andrius Grybauskas
Keyword(s):  
Real Estate ◽  
Housing Prices ◽  
Housing Price ◽  
Real Estate Market ◽  
External Factors ◽  
Content Type ◽  
The Real ◽  
The Real Estate ◽  
The Real Estate Market ◽  
The Impact

Purpose This paper aims to identify the external factors that have the greatest impact on housing prices in Lithuania. Design/methodology/approach The econometric analysis includes stationarity test, Granger causality test, correlation analysis, linear and non-linear regression modes, threshold regression and autoregressive distributed lag models. The analysis is performed based on 137 external factors that can be grouped into macroeconomic, business, financial, real estate market, labour market indicators and expectations. Findings The research reveals that housing price largely depends on macroeconomic indicators such as gross domestic product growth and consumer spending. Cash and deposits of households are the most important indicators from the group of financial indicators. The impact of financial, business and labour market indicators on housing price varies depending on the stage of the economic cycle. Practical implications Real estate market experts and policymakers can monitor the changes in external factors that have been identified as key indicators of housing prices. Based on that, they can prepare for the changes in the real estate market better and take the necessary decisions in a timely manner, if necessary. Originality/value This study considerably adds to the existing literature by providing a better understanding of external factors that affect the housing price in Lithuania and let predict the changes in the real estate market. It is beneficial for policymakers as it lets them choose reasonable decisions aiming to stabilize the real estate market.

scholarly journals Identifying Real Estate Opportunities Using Machine Learning

2018 ◽  
Author(s):  
Alejandro Baldominos ◽  
Antonio José Moreno ◽  
Rubén Iturrarte ◽  
Óscar Bernárdez ◽  
Carlos Afonso
Keyword(s):  
Machine Learning ◽  
Real Estate ◽  
Housing Prices ◽  
Market Price ◽  
Real Estate Market ◽  
Support Vector ◽  
Regression Problem ◽  
The Real ◽  
The Real Estate ◽  
The Real Estate Market

The real estate market is exposed to many fluctuations in prices, because of existing correlations with many variables, some of which cannot be controlled or might even be unknown. Housing prices can increase rapidly (or in some cases, also drop very fast), yet the numerous listings available online where houses are sold or rented are not likely to be updated that often. In some cases, individuals interested in selling a house (or apartment) might include it in some online listing, and forget about updating the price. In other cases, some individuals might be interested in deliberately setting a price below the market price in order to sell the home faster, for various reasons. In this paper we aim at developing a machine learning application that identifies opportunities in the real estate market in real time, i.e., houses that are listed with a price substantially below the market price. This program can be useful for investors interested in the housing market. We have focused in a use case considering real estate assets located in the Salamanca district in Madrid (Spain) and listed in the most relevant Spanish online site for home sales and rentals. The application is formally implemented as a regression problem, that tries to estimate the market price of a house given features retrieved from public online listings. For building this application, we have performed a feature engineering stage in order to discover relevant features that allows attaining a high predictive performance. Several machine learning algorithms have been tested, including regression trees, $k$-nearest neighbors, support vector machines and neural networks, identifying advantages and handicaps of each of them.

What drives housing prices, rent and new construction in China

2017 ◽  
Vol 10 (5) ◽  
pp. 662-686
Author(s):  
Dimitrios Staikos ◽  
Wenjun Xue
Keyword(s):  
Real Estate ◽  
Empirical Test ◽  
Housing Prices ◽  
Panel Cointegration ◽  
Real Estate Market ◽  
Estimation Methods ◽  
Content Type ◽  
New Construction ◽  
Using Data ◽  
First Time

Purpose With this paper, the authors aim to investigate the drivers behind three of the most important aspects of the Chinese real estate market, housing prices, housing rent and new construction. At the same time, the authors perform a comprehensive empirical test of the popular 4-quadrant model by Wheaton and DiPasquale. Design/methodology/approach In this paper, the authors utilize panel cointegration estimation methods and data from 35 Chinese metropolitan areas. Findings The results indicate that the 4-quadrant model is well suited to explain the determinants of housing prices. However, the same is not true regarding housing rent and new construction suggesting a more complex theoretical framework may be required for a well-rounded explanation of real estate markets. Originality/value It is the first time that panel data are used to estimate rent and new construction for China. Also, it is the first time a comprehensive test of the Wheaton and DiPasquale 4-quadrant model is performed using data from China.

International Real Estate Review

2011 ◽  
Vol 14 (3) ◽  
pp. 311-329
Author(s):  
Charles Ka Yui Leung ◽  
◽  
Jun Zhang ◽  
Keyword(s):  
Real Estate ◽  
Trading Volume ◽  
Price Dispersion ◽  
Housing Prices ◽  
Housing Price ◽  
Short Paper ◽  
Future Research ◽  
Positive Correlation ◽  
Time On The Market ◽  
Empirical Regularities

Three striking empirical regularities have been repeatedly reported: the positive correlation between housing prices and trading volume, and between housing price and time-on-the-market (TOM), and the existence of price dispersion. This short paper provides perhaps the first unifying framework which mimics these phenomena in a simple competitive search framework. In the equilibrium, sellers with heterogeneous waiting costs and buyers are endogenously segregated into different submarkets, each with distinct market tightness and prices. With endogenous search efforts, our model also reproduces the well-documented price- volume correlation. Directions for future research are also discussed.

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