Time-varying Inter-Urban Housing Price Spillovers in China: Causes and Consequences

2021 ◽  
pp. 101396
Author(s):  
Yunzhi Lu ◽  
Jie Li ◽  
Haisheng Yang
Keyword(s):  
Housing Price ◽  
Urban Housing ◽  
Time Varying

Local Land-Development Policies and Urban Housing Values

1981 ◽  
Vol 13 (10) ◽  
pp. 1253-1276 ◽  
Author(s):  
Jennifer R Wolch ◽  
S A Gabriel
Keyword(s):  
Land Use ◽  
San Francisco ◽  
Social Relations ◽  
Development Policy ◽  
Local Governments ◽  
Housing Price ◽  
Land Development ◽  
Urban Housing ◽  
Local State ◽  
Development Policies

This paper evaluates the urban housing price impacts of local state land-development policy, and suggests that local governments have multiple objectives and constraints which shape their policy stance toward growth. Based on analyses of San Francisco Bay Area suburban cities, we find that land-use policies of the local state have important housing price effects, with restrictive policies increasing average home values by approximately 15%. Findings on local motivations for use of land-use controls suggest that such policies are adopted to protect private and public consumption levels of residents and thus insure reproduction of social relations. However, constraints on development policy, deriving from historical conditions of urbanization and configuration of the local economic base; development policies of neighboring jurisdictions; and past development policies in a locality, are important factors shaping land-development policy of the local state.

scholarly journals School Desegregation and Urban Change: Evidence from City Boundaries

2012 ◽  
Vol 4 (1) ◽  
pp. 85-108 ◽  
Author(s):  
Leah Platt Boustan
Keyword(s):  
Public Schools ◽  
Metropolitan Areas ◽  
Housing Prices ◽  
Housing Price ◽  
Urban Housing ◽  
Urban Residents ◽  
Urban Residence ◽  
Tax Base ◽  
Central Cities ◽  
The City

I examine changes in the city-suburban housing price gap in metropolitan areas with and without court-ordered desegregation plans over the 1970s, narrowing my comparison to housing units on opposite sides of district boundaries. Desegregation of public schools in central cities reduced the demand for urban residence, leading urban housing prices and rents to decline by 6 percent relative to neighboring suburbs. Aversion to integration was due both to changes in peer composition and to student reassignment to nonneighborhood schools. The associated reduction in the urban tax base imposed a fiscal externality on remaining urban residents. (JEL H75, I21, I28, J15, R23, R31)

Time‐varying impact of housing price fluctuations on banking financial risk

2021 ◽  
Author(s):  
Jingbin Wang ◽  
Beibei Xia ◽  
Huiling Qiao
Keyword(s):  
Financial Risk ◽  
Housing Price ◽  
Time Varying ◽  
Price Fluctuations

Study on Commodity Residential House Price Volatility of the Guanzhong Urban Agglomeration Using Spatial Econometric Analysis

2014 ◽  
Vol 638-640 ◽  
pp. 2436-2441
Author(s):  
Feng Lan ◽  
Ying Tian
Keyword(s):  
Housing Prices ◽  
House Price ◽  
Great Influence ◽  
Housing Price ◽  
Price Volatility ◽  
Urban Housing ◽  
Urban Agglomeration ◽  
Spatial Econometric ◽  
Residential House ◽  
House Price Volatility

This paper was based on the theory of spatial econometric model. It selected the panel datas of the guanzhong urban agglomeration of five core cities from 1998 to 2012, and inspected the commodity residential house price if there is a space dependency relationship between the two cities. On the basis to analyze the main factors influencing the commodity residential house price volatility and research on the influence of the housing price direction. Results show that the sample is significant spatial correlation between urban housing prices. Xi 'an have great influence on regional cities housing price. The urban population, household disposable income, land acquisition costs, sales area are the main influence factors affecting housing price volatility.

Time-varying spillovers among first-tier housing markets in China

Urban Studies ◽  
2019 ◽  
Vol 57 (4) ◽  
pp. 844-864 ◽  
Author(s):  
Chien-Fu Chen ◽  
Shu-hen Chiang
Keyword(s):  
National Level ◽  
Housing Price ◽  
Housing Markets ◽  
Control Policy ◽  
Sufficient Evidence ◽  
Time Varying ◽  
Ripple Effects ◽  
Chinese Characteristics ◽  
Rolling Window ◽  
Window Approach

Numerous efforts have over the last few years been devoted to studying spillovers (ripple effects) among cities as a means of evaluating overheated housing markets. What seems to be lacking, however, is the application of a rolling-window approach to further explore time-varying spillovers in a timely manner in order to look more closely at a housing market with Chinese characteristics; for example, a market with rapidly increasing prices and a sequence of policy recommendations. By focusing on total, directional and net spillovers, and using 2000–2017 monthly housing price data across six Chinese cities, this study’s results indicate that time-varying spillovers provide a better understanding of the interactions among first-tier cities. It is interesting to note that, following the downside risk faced by the economy in 2014, the spillovers among cities have been abruptly transformed into those exhibiting bilateral co-movements based on high total spillovers and low net spillovers, and these results are also confirmed by the frequency dynamics of spillovers. Based on the above, there is sufficient evidence to conclude that the housing frenzies in China, which have become a national-level issue, deserve a more explicit macro-control policy in relation to real estate assets.

scholarly journals Temporal and Spatial Effects of Urban Center on Housing Price — A Case Study on Hangzhou, China

2018 ◽  
Vol 5 (1) ◽  
pp. 89 ◽  
Author(s):  
Luhong Chu ◽  
Haizhen Wen
Keyword(s):  
Rapid Development ◽  
Housing Prices ◽  
Housing Price ◽  
Urban Land ◽  
Urban Housing ◽  
Spatial Effects ◽  
Hedonic Price ◽  
Urban Center ◽  
Center City ◽  
The Impact

<em>With the acceleration of urbanization and the rapid development of real estate, people pay more and more attention to the change of urban housing prices. Over time, the change of city center will inevitably affect the urban land or housing prices, which is reflected in the spatial distribution of urban land or housing prices. Therefore, this article attempts to explore the impact of urban center on housing prices from the perspective of multi-center city and study separately from two aspects of time and space. This paper takes the six main urban districts of Hangzhou as the research scope. At the time level, we select the residential data from 2007 to 2015 to construct models respectively based on the hedonic price theory and find that the influence of different urban center on housing price shows a certain change with time. On the spatial level, this paper choses the residential data in 2012 to construct geographic weighted regression model and the result shows that the impact of three centers on housing prices shows a certain degree of spatial heterogeneity.</em>

Forecasting house prices in Iran using GMDH

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Behrooz Nazemi ◽  
Mohsen Rafiean
Keyword(s):  
Predictive Model ◽  
House Prices ◽  
Housing Price ◽  
Urban Housing ◽  
Data Availability ◽  
Group Method ◽  
Housing Crisis ◽  
Affecting Factors ◽  
Current Position ◽  
Content Type

Purpose An accurate predictive model for forecasting urban housing price in Isfahan can be useful for sellers and owners to take more appropriate actions about housing supplying. Also, it can help urban housing planners and policymakers in managing of the housing market and preventing an urban housing crisis in Isfahan. The purpose of this paper is forecasting housing price in Isfahan city of Iran until 2022 using group method of data handling (GMDH). Design/methodology/approach This paper presents an accurate predictive model by applying the GMDH algorithm by using GMDH-Shell software for forecasting housing price in municipal boroughs of Isfahan city till the second half of 2022 based on creating time series and existing data. Alongside housing price, some other affecting factors have been also considered to control the forecasting process and make it more accurate. Furthermore, this research shows the housing price changes of boroughs on map using ArcMap. Findings Based on forecasting results, the housing price will increase at all boroughs of Isfahan till second half of the year 2022. Amongst them, Borough 15 will have the highest percentage of the price increasing (28.27%) to year 2022 and Borough 6 will have the lowest percentage of the price increasing (8.34%) to the year 2022. About ranking of the boroughs in terms of housing price, Borough number 6 and 3 will keep their current position at the top and Borough number 15 will stay at the bottom. Research limitations/implications In this research, just few factors have been selected alongside housing price to control the forecasting process owing to limitation of reliable data availability about affecting factors. Originality/value The most remarkable point of this paper is reaching to a mathematical formula that can accurately forecast housing price in Isfahan city which has been rarely investigated in former studies, especially in simplified form. The technique used in this paper to forecast housing price in Isfahan city of Iran can be useful for other cities too.

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