Competition of Real Estate Brokers and Housing Prices

2020 ◽  
Author(s):  
Leiju Qiu ◽  
Dongmei Guo ◽  
Ruqi Niu
Urban Studies ◽  
1986 ◽  
Vol 23 (1) ◽  
pp. 21-31 ◽  
Author(s):  
G. Donald Jud ◽  
James Frew

1988 ◽  
Vol 17 (2) ◽  
pp. 175-187 ◽  
Author(s):  
B.J. Dunlap ◽  
Michael J. Dotson ◽  
Terry M. Chambers

2017 ◽  
Vol 10 (5) ◽  
pp. 662-686
Author(s):  
Dimitrios Staikos ◽  
Wenjun Xue

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.


2021 ◽  
pp. 1-20
Author(s):  
Chaojie Liu ◽  
Jie Lu ◽  
Wenjing Fu ◽  
Zhuoyi Zhou

How to better evaluate the value of urban real estate is a major issue in the reform of real estate tax system. So the establishment of an accurate and efficient housing batch evaluation model is crucial in evaluating the value of housing. In this paper the second-hand housing transaction data of Zhengzhou City from 2010 to 2019 was used to model housing prices and explanatory variables by using models of Ordinary Least Square (OLS), Spatial Error Model (SEM), Geographically Weighted Regression (GWR), Geographically and Temporally Weighted Regression (GTWR), and Multiscale Geographically Weighted Regression (MGWR). And a correction method of Barrier Line and Access Point (BLAAP) was constructed, and compared with three correction methods previously studied: Buffer Area (BA), Euclidean Distance (ED), and Non-Euclidean Distance, Travel Distance (ND, TT). The results showed: The fitting degree of GWR, MGWR and GTWR by BLAAP was 0.03–0.07 higher than by ND. The fitting degree of MGWR was the highest (0.883) by BLAAP but the smallest by Akaike Information Criterion (AIC), and 88.3% of second-hand housing data could be well interpreted by the model.


2011 ◽  
Vol 14 (3) ◽  
pp. 311-329
Author(s):  
Charles Ka Yui Leung ◽  
◽  
Jun Zhang ◽  

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.


2018 ◽  
Author(s):  
Radosław Trojanek

In the book, an attempt was made to catalogue knowledge concerning the importance of research into the dynamics of housing prices for social and economic development. The analysis of the experience of countries with well-developed real estate markets in the aspect of building price indexes was carried out. Based on original databases of asking and transaction prices, price indexes were built, which were then subjected to numerous resistance tests. The aims of these research tasks were as follows: 1) to examine the quality of offers for sale as a source of information about changes in the real estate market, 2) to find out whether the repeat sales method can be used for building price indexes and to critically assess this method in terms of the stability of the obtained results, 3) to analyze hedonic methods and indicate the preferred one in terms of the ratio of the quality of results to how time-consuming and cost-intensive it is to build such indexes, 4) to establish the importance of methods and sources of information for building price indexes in different time horizons, 5) to identify how important it is for the fluctuation of price indexes if the cooperative property right to a flat is not taken into account. In order to perform the research tasks and accomplish the goals scopes of the work were defined. The subject followed the aim of the study and refers to prices in the secondary housing market, encompassing both the property right and cooperative property right to a flat or house. The broad scope concerns the discussion in the general part, being narrowed down to the secondary market of flats located in multi-family and single-family buildings. The time scope covers the years 2000-2015, which is connected to the range of empirical studies carried out. They focused both on actual transactions and on offers of flats for sale. On this basis, we built databases which served as the starting point for further analyses. The study involved transactions and offers in the area of Poznan.


2021 ◽  
Vol 13 (21) ◽  
pp. 12277
Author(s):  
Xinba Li ◽  
Chuanrong Zhang

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.


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